Hosein Gitinavard; Seyed Meisam Moosavi; Behnam Vahdani; Hamid Ghaderi
Abstract
Increasing the complexity of decision-making problems leads to utilize a group of experts instead of one expert for evaluating the contractor selection problem. This paper proposes a hesitant fuzzy preference selection index method based on risk preferences of experts. The hesitant fuzzy set is used ...
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Increasing the complexity of decision-making problems leads to utilize a group of experts instead of one expert for evaluating the contractor selection problem. This paper proposes a hesitant fuzzy preference selection index method based on risk preferences of experts. The hesitant fuzzy set is used to cope with the uncertainty in vague/hesitant situations. Also, the compromise solution is proposed to compute the weight of each expert. Moreover, the proposed approach considers the quantitative and qualitative criteria and also assists the experts to reduce margin of errors by assigning some membership degrees for each contractor versus each criterion under a set. In addition, the experts' judgments are aggregated in the last step of proposed approach in the group decision process to avoid the data loss. In the presented approach, selecting the best contractor is based on closest to positive ideal and farthest from negative ideal, simultaneously. Finally, the proposed method is applied to a case in construction industry for selecting the suitable contractor, in which the obtained results are compared with two decision methods from the recent literature to indicate the efficiency and validity of the proposed method.
Esmaeil Mehdizadeh; Vahid Rahimi
Volume 13, Issue 37 , July 2015, , Pages 123-159
Abstract
Abstract
This paper presents a mathematical model for solving dynamic cell formation problem, operator assignment and the inter-cellular and intra-cellular layouts simultaneously. The proposed model includes three objectives, the first objective seeks to minimize inter and intra-cell part movement, ...
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Abstract
This paper presents a mathematical model for solving dynamic cell formation problem, operator assignment and the inter-cellular and intra-cellular layouts simultaneously. The proposed model includes three objectives, the first objective seeks to minimize inter and intra-cell part movement, machine relocation, second objective minimize operator related cost, third objective maximize ratio of consecutive forward flows. The model is Multi-objective; therefore, the LP-metric approach is used to solve it. In order to validate the model, the proposed model has been solved by using Lingo software. Then, due to NP-hardness of the cell formation problem, for solving large scale problems, a multi-objective simulated annealing algorithm proposed. Several numerical examples solved by Lingo software and multi-objective simulated annealing algorithm. Results show that the proposed multi-objective simulated annealing algorithm solved considerably time less than the software Lingo and also none of the answers obtained by the two methods are not dominated
Amineh Hosseini; Kaveh Khalili-Damghani; Ali Emami Meibodi
Volume 14, Issue 42 , October 2016, , Pages 123-167
Abstract
In this paper, a methodology is proposed to measure the efficiency ofnational energy sector in IRAN. The technical and environmentalperformance of the oil refineries in IRAN as a major producer of energy andfuel are evaluated based on data from years 2010 to 2013. In this study, afuzzy multi-objective ...
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In this paper, a methodology is proposed to measure the efficiency ofnational energy sector in IRAN. The technical and environmentalperformance of the oil refineries in IRAN as a major producer of energy andfuel are evaluated based on data from years 2010 to 2013. In this study, afuzzy multi-objective multi-period common weight network dataenvelopment analysis approach is proposed and customized to evaluate theperformance of oil refineries. A certain scenario, called food-production inwhich a refinery is assumed as a decision making unit (DMU) consuminginputs to produce outputs, is considered to evaluate the technical andenvironmental performance in presence of undesirable outputs. The maincontribution of this study are summarized as: (1) Proposing a multiobjectivecommon weight DEA model in order to determine the weights ofinputs and outputs in a single run; (2) Calculating the long term efficiencyscores during a multiple-periods of planning incorporating dynamic natureof inputs and outputs; (3) Handling a compromise solution using fuzzymathematical programming to address multi-objective mathematicalprogramming; (4) Proposing a linear mathematical programming to achievethe global optimum solutions; (5) Enhancing the discrimination power of theDEA models; (6) Reducing the computational time of modeling and solutionprocedure; (7) incorporating effective criteria in the modeling procedure.The analysis of case study presents the efficacy and applicability ofproposed method in comparison with existing classic models.
K. Feizi; A.R. Moghaddasi
Volume 1, Issue 3 , January 2003, , Pages 125-145
Abstract
This paper serves to better introduce the concepts and some definitions of entrepreneurship, entrepreneur, and entrepreneurship training. Then some objectives, advantages and content outlines in three levels (individual, organizational and environmental) of entrepreneurship training are described. Indeed, ...
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This paper serves to better introduce the concepts and some definitions of entrepreneurship, entrepreneur, and entrepreneurship training. Then some objectives, advantages and content outlines in three levels (individual, organizational and environmental) of entrepreneurship training are described. Indeed, this article sheds light on the process, several courses, some centers and managerial issues about entrepreneurship raining.
The article concludes by discussing about experiences of some countries in implementation of entrepreneurship training.
aboosaleh mohammadsharifi; Kaveh Kahlili-Damghani; farshid abdi; soheila sardar
Abstract
Recently, Bitcoin as the most popular cryptocurrency, has attracted the attention of many investors and economic actors. The cryptocurrency market has experienced a sharp fluctuation, and one of the challenges is to predict future prices. Undoubtedly, creating methods to predict the price of bitcoin ...
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Recently, Bitcoin as the most popular cryptocurrency, has attracted the attention of many investors and economic actors. The cryptocurrency market has experienced a sharp fluctuation, and one of the challenges is to predict future prices. Undoubtedly, creating methods to predict the price of bitcoin is very exciting and has a huge impact on determining the profit and loss from its trading in the future. In this study, in order to predict the price of Bitcoin, a combination of the ARIMA model and three types of deep neural networks including RNN, LSTM, and GRU have been used. The main purpose of this study is to determine the effect of deep learning models on the performance of predicting the future price of Bitcoin. In the proposed model, first, the linear components in the data set are separated using ARIMA and the resulting residues are transferred separately to each of the neural networks. The results show that the ARIMA-GRU model has better results for RMSE and MAPE criteria than other models. Combined models also perform better than the traditional ARIMA model in forecasting.
Marzieh Karimi; Abolfazl Kazemi
Abstract
Supplier selection is the process by which firms identify, evaluate, and contract with suppliers. The supplier selection process deploys a tremendous amount of a firm's financial resources. Nowadays, supplying the required resource of organizations has become an important business approach so a competitive ...
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Supplier selection is the process by which firms identify, evaluate, and contract with suppliers. The supplier selection process deploys a tremendous amount of a firm's financial resources. Nowadays, supplying the required resource of organizations has become an important business approach so a competitive advantage may be gained by the selection of the best suppliers to provide products/services more effectively and efficiently. Many models have been developed for solving supplier selection problem. In this paper, in addition to considering the incremental discount strategies, cost of shortages is also considered so far has not been considered. In this model, two objective functions of cost minimization of the buyer, and maximize quality of product is considered. To solve the proposed supplier selection model, an improved harmony search algorithm is used. The results show that the proposed algorithm works properly in the term of both CPU time and the quality of solutions. Finally, some numerical examples for model analyses are presented.
Hossein Rezaei Dolatabadi
Volume 4, Issue 12 , March 2006, , Pages 135-161
Abstract
One of the important aspects in organizational behavior is organizational climate. While organizational climate is conceptualized as a characteristic of organizations which is reflected in the descriptions employees make of the policies, practices and conditions which exist in the environment.
Organizational ...
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One of the important aspects in organizational behavior is organizational climate. While organizational climate is conceptualized as a characteristic of organizations which is reflected in the descriptions employees make of the policies, practices and conditions which exist in the environment.
Organizational climate is reflected beliefs that employees make of the organization. The purpose of this study is to examine cross- sectional of organizational climate in steel industry>. The sample includes technical specialist, nontechnical specialist, technician and administrative people. The data collected through climate instruments is subjected. The instrument organizational climate is Litwin & Stringer questionnaire. The overall finding of this study is that agreemen people on organizational climate different. Technical specialist agreemen more than other people. The most dispersal opinion is nontechnical specialist. According to a case study in steel industry, it was found out that which organizational climate technical specialist, nontechnical specialist, technician and administrative people is different.
Parham Azimi; Farhad Hadinejad
Abstract
Reliability optimization and mean time to failure are those of the areas of interest for engineers and designers of systems and the use of redundancy components is one of the most common approaches in this field. The purpose of the optimization problem, finding the optimum number of redundancy components ...
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Reliability optimization and mean time to failure are those of the areas of interest for engineers and designers of systems and the use of redundancy components is one of the most common approaches in this field. The purpose of the optimization problem, finding the optimum number of redundancy components that must be satisfied the objectives of reliability engineering in the system. But usually the reliability improves results in increased costs and changes in parameters such as volume and weight of the system; therefore, it is necessary to establish a balance between resources. In previous researches, redundancy allocation problem is studied with non-repairable components or failure rate with exponential distribution; but in this study, repairable components and rates of failure and repair a non-exponential distribution assumed. Thus the purpose of this paper is solving reliability redundancy allocation problem with the goals of increasing the mean time to failure, reduce costs and reduce the variance of the shelf life of the system, while taking into account constraints such as volume and weight of the system. To this end, the research effort will be using mathematical and statistical techniques such as multi criteria decision making models, design of experiments, simulations, and computer software associated with them, provide a new approach for solving reliability redundancy allocation problem in series-parallel systems with repairable components
Arman Sajedinejad; Meysam Lotfi
Abstract
In this paper, a non-periodic preventive maintenance scheduling optimization model for multi-component systems is provided based on the maximum availability of system components. In addition to providing the required level of system reliability and satisfy other system constraints (maintenance activities ...
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In this paper, a non-periodic preventive maintenance scheduling optimization model for multi-component systems is provided based on the maximum availability of system components. In addition to providing the required level of system reliability and satisfy other system constraints (maintenance activities and available resources), total costs (direct and indirect) associated with minimal maintenance and, if necessary, one of the maintenance activities include in inspected and serviced simple, preventive repair and preventive replacement for each component, is proposed. Each of these activities uses various sources and regarding the position of the repairing component, effects differently on the reliability of the system. The costs considered include in direct costs (simple service, repair and replacement) as well as indirect costs (out of order and random failures). Since the proposed model has a complex structure, in order to solve the problem, the Genetic Algorithm (G.A) has been used and the results is presented. In the end, performance and use of this model, for a 10-part series - parallel is presented in the form of a case study.
Hossein Safari; Aliyeh Kazemi; Ahmad Mehrpoor Layeghi
Abstract
One of the most known subjects in management literature is performance assessment. In this paper, we have tried to provide a hybrid model in order to evaluate the performance of Iranian gas Transmission Company's operational zones. After an initial screening, due to the low number of Decision making ...
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One of the most known subjects in management literature is performance assessment. In this paper, we have tried to provide a hybrid model in order to evaluate the performance of Iranian gas Transmission Company's operational zones. After an initial screening, due to the low number of Decision making units, different combination of two input and one output are defined. 6 inputs and 4 output criteria have been chosen in initial screening. Based on the experts' judgments, weights or importance of different combinations of input and output extracted using SWARA technique. Then, based on the 22 different combinations, we calculated operational zones efficiency using DEA in the form of CCR and BCC models. In fact for each combination of two input and one output, efficiency for each operational zone has been calculated. Finally, there has been a decision matrix which its’ criteria were equal to different combinations of input and output criteria and their weights gained by SWARA and scores within the matrix were equal to taken efficiency from DEA. In order to calculate final efficiency and prioritizing each operational zone WASPAS technique is used. We use Cohen's kappa coefficient in order to prioritizing validation. The results show that zones 10, 7 and 6 with the highest efficiencies can be appropriate pattern for other zones in resource saving
Mojhgan PourAlizadeh; Alireza Amirteimoori; mohsen vaez-ghasemi
Abstract
Data envelopment analysis is auseful method to measurement unified rational and operational and environmental efficiency a supply chain. Supply chain management divisions produce two types outputs based on economic activities and inputs separate into two categories under natural and managerial disposability. ...
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Data envelopment analysis is auseful method to measurement unified rational and operational and environmental efficiency a supply chain. Supply chain management divisions produce two types outputs based on economic activities and inputs separate into two categories under natural and managerial disposability. The current paper propose a new Data Envelopment Analysis based model to efficiency assessment a supply chain under investment on certain types of inputs to new technologic innovation. In hence, dual-role factors controls cleanup costs of flaring gas and the amount electricity consumptions of power plants also dual-role indices improve expertise in transmission entities. A real case study on Iran power industry is presented to demonstrate the applicability of the proposed model. To demonstrate the capability of the proposed approach this framework is implemented for the performance evaluation of a supply chain identified by oil and gas companies, power plants, transmissions companies, dispatching companies and final consumers in Iran.
Manochehr Zamani Farizhandi; Payam Hanafizadeh; Zohreh Dehdashti Shahrokh
Abstract
Nowadays, banking industry analogous with another industries has been encountered with digital transformation. Such transformation stemmed from convergence technologies. The underpinning point of convergency in such technologies is employing multi-channel services in order to delightful and remarkable ...
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Nowadays, banking industry analogous with another industries has been encountered with digital transformation. Such transformation stemmed from convergence technologies. The underpinning point of convergency in such technologies is employing multi-channel services in order to delightful and remarkable customer experience. Based on the importance of customer experience, multi-channel services in banking industry has assumed significance attention among banking practitioners and scholars. Consequently, delivering services through different, aligned and complementary channels pointed as strategic requirement in banking industry. Accordingly, this study seeks to develop a framework for value creation for multi-channel services in banking. To accomplish such an objective, utilizing content analysis technique, 47 articles published in the realm of multi-channel services in banking in the period 2004 to 2019 considered and assessed. The findings suggest that a value creation framework for multi-channel services in banking must consider different factors such as: beliefs about intelligent financial behaviors, customer enablers, intelligent norms, intelligent tasks and intelligent technologies.
supply chain management
Shaghayegh Vaziri; Farhad Etebari; Behnam Vahdani
Abstract
In this paper, we study a novel pickup and delivery problem called carrier collaboration (CC). This problem includes a set of heterogenous (refrigerated and general type) vehicles with specific capacities for serving perishable products to several pickup and delivery nodes. Each carrier can have reserved ...
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In this paper, we study a novel pickup and delivery problem called carrier collaboration (CC). This problem includes a set of heterogenous (refrigerated and general type) vehicles with specific capacities for serving perishable products to several pickup and delivery nodes. Each carrier can have reserved and selective requests which can be delivered before the products are corrupted. The fleet of vehicles must serve reserved requests, but the selective requests can be served or not. Products are corrupted at a constant rate and a rate of corrosion in general type vehicles is greater than referigrated type veicles and the cost of using general one is less than referegireted. For the mentioned features, we develop a nonlinear mathematical model. The purpose is to find routes to maximize profits and reduce costs while at the same time, enhance customer satisfaction which is dependent on the freshness of delivered products. A Gnetic Algorithm (GA) is proposed to solve this problem due to its NP-hard nature. In this study, Variable Neighborhood Search (VNS) method is developed for improving the quality of initial solutions. Several instances are generated at different scales to evaluate the algorithm performance by comparing the results of an exact optimal solution wih that of the proposed algorithm. The obtained results demonstrate the efficiency of the proposed algorithm in providing reasonable solutions within an acceptable computational time.
saeed rayatpisha; reza ahmay; Maisam_Abbasi Abbasi
Abstract
Purpose of this research is to study criteria of sustainable supply chains in petrochemical industry. The research approach was both explorative and explanatory by using mixed methodologies for collection and analysis of data. It started with qualitative meta- synthesis of criteria of sustainable supply ...
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Purpose of this research is to study criteria of sustainable supply chains in petrochemical industry. The research approach was both explorative and explanatory by using mixed methodologies for collection and analysis of data. It started with qualitative meta- synthesis of criteria of sustainable supply chains in former publications by using MAXQDA software. Afterwards, the identified criteria were quantitatively analyzed based on Delphi-fuzzy, DEMATEL and analytic network process (ANP) methodologies. Therefore, in this step the questionnaires was completed by 23 experts of Petrochemical industry, who were selected by purposive sampling. In total, fifteen criteria of sustainable supply chains were identified and classified. In the quantitative analysis, the three criteria of "organization & corporate-centric", "environmental management" and "environmental pressures" were rated as the most critical criteria. This study highlights the importance of criteria as well as the strength of interrelationships among criteria of sustainable supply chains in petrochemical industry. The research results can be beneficial for decision makers to prioritize their resources, actions, and strategies in steering supply chains sustainable development.
Ramin Saedinia; Behnam Vahdani; Farhad Etebari; Behroz Afshar Nadjafi
Abstract
One of the most important approaches that can lead to the creation of various advantagesfor enterprises is the districting regions into the service offering locations and the demandunits, which causes the increase in level of customers’ access to get the service. On theother hand, if vehicle routing ...
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One of the most important approaches that can lead to the creation of various advantagesfor enterprises is the districting regions into the service offering locations and the demandunits, which causes the increase in level of customers’ access to get the service. On theother hand, if vehicle routing is carried out in districting regions in order to deliver productsto customers, the planning of customer service can be improved. However, in none of theresearch conducted in the area of design supply chain, vehicle routing in districting regionshas been not investigated. Therefore, in the current study, a bi-objective mathematicalmodel is presented to simultaneously focus on districting regions, facility location–allocation, service sharing, intra-district service transfer and vehicle routing. The firstobjective function minimizes the total cost of designing the CLSC network, which includescosts of opening facility and vehicle routing. The second objective function minimizes themaximum volume of surplus demand from service providers in order to achieve anappropriate balance in demand volume across all regions. Moreover, a robust optimizationapproach is used to take into account uncertainty in some parameters of the proposedmodel. In addition, the validity of the proposed mathematical model and the proposedsolution has been investigated on a real case in the oil and gas industry.
SEYYED ALI Mirnezhad; Parham Azimi; Ahmad Yousefi Hanoomarvar
Abstract
In the present study, the redundancy allocation problem (RAP) of series-parallel system has been investigated to maximize the system's availability. To achieve the research objective, budget, weight and volume constraints, and the maximum and minimum number of elements assigned to each subsystem have ...
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In the present study, the redundancy allocation problem (RAP) of series-parallel system has been investigated to maximize the system's availability. To achieve the research objective, budget, weight and volume constraints, and the maximum and minimum number of elements assigned to each subsystem have been considered. The main innovation of this research is to consider the failure and repair rates of components with non-exponential distribution function in the process of optimization. Taking into account failure and repair rate via non-exponential distribution function makes it impossible to calculate accessibility using mathematical relations. Therefore, the present study has used simulation method to calculate system availability. Since the simulation has no optimization capability On the other hand, in the redundancy allocation problem, it is necessary to evaluate the system availability recurrently in order to find the optimal solution. Further, due to the high degree of difficulty of developed mathematical function, the genetic metaheuristic algorithm was used to solve it. Finally, the efficiency of the genetic algorithm was measured against particle swarm algorithm and simulated annealing algorithm. To compare fairly, the parameters affecting the algorithms are adjusted using the Taguchi method and the algorithms are in their best practice. The computational results prove the high ability of the genetic algorithm in optimizing the concerned problem.
Samira Parsaiyan; Maghsoud Amiri; Parham Azimi; Mohammad Taghi Taghavifard
Abstract
The increasing concern about the deteriorating effects of supply chains related activities on the environment has led to the growing attention to develop green closed-loop supply chains in order to minimize greenhouse gases emission. This paper presents a green closed-loop supply chain model developed ...
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The increasing concern about the deteriorating effects of supply chains related activities on the environment has led to the growing attention to develop green closed-loop supply chains in order to minimize greenhouse gases emission. This paper presents a green closed-loop supply chain model developed under the demand uncertainty aiming at minimizing total cost and total CO2 emission across the supply chain, and maximizing the product’s market share in the presence of a competitor. In this regards, an agent-based market model is developed to estimate the demand’s parameter function then a hybrid simulation model which integrates agent-based and discrete event simulation modelling approaches is designed to simulate the closed-loop supply chain which is the novelty of this paper. Then, scenarios are created using Taguchi design of experiments (DOE) method, and are executed with the market model and the supply chain model to capture total cost, total CO2 and market share. A decision matrix is configured using scenarios and recorded results for three mentioned criteria and ELECTRE and SAW methods are used to rank scenarios and select the best one. The other contribution of this research is its comprehensiveness in considering variables related to three categories of inventory replenishment policy, marketing mix (price and advertisement) and transportation. An automotive industry case is provided to demonstrate the capabilities of the model and its applicability and effectiveness in resolving real-world problems.
Morteza Mohajer Bajgiran; Alireza Pooya; Zahra Naji Azimi; Somayeh fadaei
Abstract
Management and warehousing operations are essential parts of manufacturing and service organizations. Warehousing is a significant component of an organization's activities, which incurs high costs and deserves more attention from researchers in this field. The aim of this research is to investigate ...
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Management and warehousing operations are essential parts of manufacturing and service organizations. Warehousing is a significant component of an organization's activities, which incurs high costs and deserves more attention from researchers in this field. The aim of this research is to investigate the storage problem based on item clustering while considering all factors that affect the storage of bulky and varied products in a warehouse. The main objective of this research is to reduce transportation costs for collecting and delivering orders and to achieve more efficient use of storage space. The K-means technique is employed to solve the clustering problem, and the Generalized Allocation mathematical programming model is used to address the assignment of item categories to storage locations. This model is an integer programming model that aims to minimize transportation costs for collecting and delivering orders. This research provides a comprehensive approach to clustering and item allocation by identifying and considering effective indexes and utilizing the generalized allocation mathematical planning model to formulate and solve the problem optimally. Company managers can utilize this model to reduce their inventory costs. The innovation of this research lies in the use of clustering for the allocation of storage sites to warehouse items, followed by mathematical modeling. The proposed model was implemented at Mashhad Housebuilding Company, and several simulated problems were solved using GAMS software for validation.IntroductionThe issue of storage and warehousing is one of the main axes of industries and companies. If the warehouse is properly managed, the efficiency and productivity of the organization can be increased optimally. Warehousing is one of the main costly components in the organization's activities and deserves more attention from researchers in this field. Therefore, the main goal of this research is to reduce transportation costs during the collection and delivery of orders and more effective use of warehouse space. For this purpose, the warehouse of the Mashhad house-building factory was studied. The warehouse of the Mashhad house building factory incurs a lot of costs to collect the orders, which is the result of the improper arrangement of the warehouse. Therefore, in the current research, to achieve a suitable deployment plan and reduce storage costs, the objectives are: 1) to identify the influential criteria in the clustering of items and the model for assigning clusters to their storage location according to the studied warehouse and clustering of warehouse items, 2) to provide a model for improvement The arrangement of the group of items is followed by considering the identified criteria, parameters, and limitations.Materials and MethodsIn the present research, first, according to ABC analysis, the items are divided into three groups (A, B, and C) based on their importance in terms of storage volume and circulation. Group A items are selected for clustering, while for groups B and C, a virtual cluster is considered in the allocation problem. Influential indicators for item clustering in the warehouse were determined through content analysis. The relationship of these indicators with the problem model and their importance were identified using a questionnaire. Cummins cluster analysis was employed for item clustering. Subsequently, a generalized allocation mathematical programming model was utilized to allocate groups of items to their storage locations. This model considered limitations such as warehouse access space, interdependence between groups, demand volume, physical dimensions of items and storage locations, and crane movement during order delivery. The objective of the model was to minimize transportation costs during order collection and delivery. The problem addressed in this research is commonly known as the Warehouse Location Allocation Problem (SLAP).Discussion and ResultsIn this research, 20 clusters were obtained based on 15 indicators, resulting in a total of 154 goods items. ANOVA analysis was conducted on the obtained clusters to examine the impact of each factor on warehouse arrangement. The F statistic value indicated a significant difference among all clusters and indicators The cluster analysis results revealed that the first cluster comprised various types of footings, with the "level of activity" index scoring higher than other indices. This cluster ranked second in terms of this index. The second cluster consisted of roof products, with a higher score in the "need for quick access" index compared to other indicators. Additionally, it obtained the highest scores in the indicators of quick access requirement, demand level, consumption similarity, product activity level, and average item references in the second step, the allocation problem was formulated with two dimensions: the item cluster and its storage location. The first dimension encompassed group A items, including five clusters of frequently used items and 20 clusters resulting from the clustering process, as well as groups B and C inventory items, represented by one cluster consisting of all items from these groups and virtual items. The second dimension consisted of storage locations, with the entire storage space divided into equal areas and a total of 360 storage locations considered in the model to validate the model, the problem was solved in smaller dimensions, and the warehouse manager manually arranged the clusters in the same dimensions. The objective function value was calculated in this case and compared to the value obtained from the mathematical allocation model. The results demonstrated that the researcher's mathematical model achieved over 70% improvement compared to manual arrangement. It should be noted that the actual warehouse conditions were less efficient than the manual arrangement provided by the warehouse manager, as the items had already been clustered, and the warehouse supervisor arranged the problem manually using the data from the warehouse clustering.ConclusionsThe grouping of goods, based on the results obtained from cluster analysis, ensures that items are placed together according to important criteria such as demand, access requirements, and employee safety, among others. This arrangement creates clusters of related products, forming families of goods. This approach minimizes the search time for requested products and enables timely order fulfillment in this research, the area of each cluster was determined by considering the maximum inventory of each product. Additionally, a confidence factor was applied to account for cluster area, allowing for sufficient space in case of additional inventory. This approach ensures efficient search and timely delivery of requested products. It is recommended that if a new product is introduced to the factory's product lineup, managers should conduct cluster analysis to determine its appropriate group. If the area of that category exceeds the area considered in the present research, the allocation issue should be revisited to accommodate the new addition.
supply chain management
Mohammad ali Enayati shiraz; Seyed Abdollah Heydariyeh; Mohammad Ali Afshar kazemi
Abstract
Supply chain management can lead to a sustainable competitive advantage. The present study is in search of paper industry supply chain strategies with respect to supply chain dynamics and lean supply chain using dynamics in order to gain a competitive advantage in Iran Wood and Paper Industries Company ...
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Supply chain management can lead to a sustainable competitive advantage. The present study is in search of paper industry supply chain strategies with respect to supply chain dynamics and lean supply chain using dynamics in order to gain a competitive advantage in Iran Wood and Paper Industries Company (Chooka). For this purpose, first, using organizational data and decision makers' participation, the system dynamics model was designed and after validation, the model was simulated in a ten-year horizon. According to the behavior of target variables and model sensitivity analysis in the simulation horizon, policies in line with lean supply chain strategy and sustainable profitability strategy of Chooka business were designed and applied separately and in combination to the model. And analyzed. According to the findings of the model simulation, productivity promotion policies through the use of lean methods in internal processes, increasing the quality of paper products, increasing innovation in the production and supply of paper products, improving raw material supply management And strategic partnership with suppliers of raw materials, industrial waste management, waste and solid waste management and staff empowerment have been presented as the best combined policies of the supply chain strategy of Iran's wood and paper industries.
supply chain management
fateme khanzadi; Reza Radfar; nazanini pilevari salmasi
Abstract
Nowdays, supply chain specialists are looking for the integrated development of the supply chain model in order to increase the competitiveness, effectiveness and reduce the problems in the supply chain. They always seek to identify and develop this process so that they can cover more aspects of the ...
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Nowdays, supply chain specialists are looking for the integrated development of the supply chain model in order to increase the competitiveness, effectiveness and reduce the problems in the supply chain. They always seek to identify and develop this process so that they can cover more aspects of the chain. Adding effectiveness indicators along with LARG indicators and using the basics of the dynamic system to improve the efficiency of the supply chain is one of the innovations of this study. At first, by using research literature and studies, 12 headings of indicators were selected as LARG-E indicators. Then, with the Fuzzy Delphi method, the relationships and importance of each of these components were determined, and more important variables were modeled for further investigation. With using the concepts of dynamic systems, causal loops were drawn. Then, to check the function of the model, dynamic hypotheses were developed with the opinion of experts. In the next step, the flow diagram of the model and also the validation tests of the proposed model were done. Finally, by examining the outputs obtained from the proposed scenarios, it was found that most of variables have better behavior in LARG-E approach.IntroductionIn recent years, with the addition of various competitions in the world markets, many researches have been conducted to use new technologies and researches in order to improve the production process and increase the effectiveness of these competitions as much as possible (Mohghar et al., 2017). All the goals that work in this field increase the competitiveness of the organization. This competition is by reducing costs, being present in the market and satisfying the customer. To increase profits, protect the environment, keep the markets stable and meet the expectations of customers, organizations should be provided using the existing environments in a set of customers (Pisha et al., 2016). Use chain management requires the use of new facilities and improvements to previous findings such as lean, agile, resilience and green to increase speed and competitiveness, selection and decision-making to achieve the organization and maximum effectiveness.Today, supply chain specialists are looking for the integrated development of the supply chain model to increase the effectiveness and efficiency of the supply chain in order to increase competitiveness and reduce supply chain problems. In this case, there is a consensus among experts that there is no comprehensive model. All the mentioned cases make it inevitable to design a comprehensive and effective model for the supply chain. The verifiable issue is the conflicts and the non-alignment of all the indicators of the paradigms with each other. LARG paradigms, without considering the spirit of effectiveness in each supply chain, cannot fully protect it against continuous changes in the competitive market arena. A comprehensive model that pays particular attention to effectiveness while implementing LARG paradigms has not been examined in the literature review and the consensus of experts. Therefore, in this research, we are looking to design a comprehensive model in a LARG-effective manner so that the effect of various LARG-effective indicators on the performance of the supply chain can be investigated. The integration of LARG paradigms has been studied a lot so far, but its development is based on the concepts of innovation effectiveness of this research, and in this way, the dynamic system approach was used.Materials and MethodsTo formulate a LARG supply chain, first the framework, indicators and variables of each LARG paradigm were extracted from the research literature, then in order to develop them with effective concepts, the effective supply chain was studied. In order to implement the fuzzy Delphi approach, based on the effectiveness indicators extracted from the subject literature and LARG supply chain approaches, operational indicators were provided to the experts participating in the research in the form of a questionnaire via email and after initial coordination. After collecting the completed questionnaires, fuzzification operations, fuzzy averaging and then de-fuzzification were performed. The results were brought to the attention of the participants and they were asked to apply their desired changes according to the obtained results. This approach reached the saturation stage in the third round and there was no change in the opinions of the participants and the consensus of the panel experts was the final and trusted output of the Delphi method. Finally, according to these weights, 9 quantitative variables had the highest importance and were used for dynamic modeling. The simulation stage is done with the help of software and Nasim. According to the features of modeling based on system dynamics, this approach was chosen as the main research tool in this study because there are linear relationships between the variables and there are nested feedbacks between the variables of the subsystems, the importance of simultaneously improving the performance in different layers of the producer, supplier and distributor. Which is one of the goals of this research, with this approach, it can be a very suitable tool for decision-making by the senior managers of the organization.Discussion and ResultsOrganizations are trying to improve their competitiveness by adopting Lean, Resilient, Green and Agile strategies; But as it was said, the implementation of these paradigms, which sometimes have conflicting results, requires a new integration and index to align the goals. So far, many researches have been done by merging two or more paradigms, the combination of all 4 paradigms called LARG has greatly helped to improve the performance of supply chains, but in this research, in order to improve the conflicts between paradigms, a new concept of spiritual effectiveness was given to the supply chain. Understanding the dynamics of applying the above four strategies and their effectiveness was done using the dynamic systems approach. In this research, the indicators of the LARG supply chain were defined based on theoretical foundations and interviews with experts; then the effectiveness indicators were placed next to them. These indicators were implemented in the printing and ink industry. In this way, an effective LARG integrated system was defined; then, using a dynamic model, dynamic hypotheses were first defined and state and flow diagrams were drawn. After correctness of the model and validation of the model, two scenarios were examined for 8 important variables. After applying the scenarios, the performance of LARG and effective LARG was compared. By applying each scenario in the designed model, it was possible to check the effect of new indicators on the variables and their behavior.ConclusionsAs a result, if the components of the effective supply chain are properly integrated with the LARG concepts, they integrate the conflict that may exist between the LARG paradigms and play the role of synchronization and improvement as a ruler and standard. The effective management of the LARG supply chain may not be defined as an independent variable, but it is a result of variables and indicators that improved performance in most cases.
Hasan Rabiee; Farhad Etebari
Abstract
In this study, a location routing model has been considered for the distribution network of multiple perishable food products in a cold supply chain in which the vehicles can fuel at filling stations. Here, the fuel consumption is supposed to vary depending on the loading amount transported between the ...
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In this study, a location routing model has been considered for the distribution network of multiple perishable food products in a cold supply chain in which the vehicles can fuel at filling stations. Here, the fuel consumption is supposed to vary depending on the loading amount transported between the nodes using a fleet that uses unusual fuels. The problem has been formulated as an integer linear programming model to reduce the production of Carbon Dioxide. The model was validated using several numerical examples solved in GAMS software. Results show that in this case the fuel consumption in average decreases 14 percent. Due to the problem complexity, genetic simulated annealing algorithms were developed for solving the problems in real size and their performance has been also evaluated.
supply chain management
mona mousavie; Mahmoud Moradi; Mostafa Ebrahimpour Azbari
Abstract
In light of the continuous and rapid changes in global competition, companies face the imperative of consistently introducing new products or expanding their existing product lines to maintain their competitive edge. Recognizing that numerous factors within the supply chain influence the production, ...
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In light of the continuous and rapid changes in global competition, companies face the imperative of consistently introducing new products or expanding their existing product lines to maintain their competitive edge. Recognizing that numerous factors within the supply chain influence the production, design, distribution, and introduction of new products, understanding supply chain risks is crucial, spanning from the procurement of raw materials to the delivery of products to the market. Consequently, risk management stands as one of the most critical challenges within the supply chain, significantly impacting New Product Development (NPD) performance. This research seeks to answer the primary question: "How and to what extent do various supply chain risks affect newly developed products?" While prior research has employed various methods to evaluate and manage supply chain risks, few models have explored the interplay of these risks on each other and their influence on performance dimensions. In this study, based on a review of theoretical foundations and prior research within the clothing manufacturing sector, we identified dimensions of newly developed products and supply chain risks. We employed the Delphi technique through interviews to identify the most significant risks. Subsequently, we employed the Cross-Impact Analysis method to elucidate relationships between these factors. Finally, we utilized Bayesian networks to analyze the impact of identified risks on the performance of the selected new product, conducting sensitivity and scenario analyses. The findings indicate that environmental and supply risks are more likely to manifest than other risks, with three operational, distribution, and demand risks, influenced by environmental and supply risks, exerting the most direct impact on new product performance, particularly in the dimension of quality.IntroductionModern organizations recognize that traditional competitive strategies, such as improving quality and reducing costs, no longer suffice to remain competitive. Research has demonstrated that numerous new product development NPD projects face failure for various reasons. Effective risk identification and management, particularly concerning supply chain risks in NPD projects marked by a high degree of uncertainty, emerge as pivotal factors for NPD success. In this context, the clothing sector, characterized by a complex supply chain structure, has been extensively studied. However, prior research has predominantly examined existing risks individually, overlooking the interactions between risk components and their simultaneous effects on one or more project objectives. In this research, we not only assess the simultaneous impact of risks on product performance using the Bayesian network method, an effective approach in supply chain risk analysis, but also investigate the severity of risk impacts under different scenarios. This research addresses three primary objectives:Identification of supply chain risks in the clothing industry based on background research and case studies.Determination of interdependencies among variables using conditional modeling.Evaluation of the influence of supply chain risks on new product performance using the Bayesian network method under varying scenarios.Literature reviewNumerous researchers have investigated supply chain risks and their repercussions on product and organizational performance. Asgharenjad Nouri et al. (2021), in their article titled "The Effect of Risk Management on New Product Development in the Banking Industry," explored the impact of various risk indicators on new product development. Their results underscore the significant positive influence of managing all risk indicators, including technology, market, environment, finance, organizational resources, and commercialization, on new product development. Qazi et al. (2017), in their article titled “Supply Chain Risk Network Management,” prioritized risks and corresponding strategies through a case study involving semi-structured interviews. They initially identified organizational performance criteria and then linked them to relevant risks, using a matrix of expected profit to investigate the impact of risks on specified performance criteria. Subsequently, they employed the "weighted net evaluation" method to assess practical strategies.MethodologyIn conducting this research, we initially extracted supply chain risks and product performance dimensions from the existing literature. Subsequently, we employed the Delphi technique to select the most significant supply chain risks, providing indicators to participating experts through questionnaires with a 5-point Likert scale. We then used the Content Validity Ratio (CVR) index to confirm or reject the components derived from the questionnaires. In the next step, we used the Cross-Impact Analysis method, employing pairwise comparisons via questionnaires, to reveal relationships between the key risk criteria. Finally, we investigated the impact of identified risks on the performance of the selected new product within the supply chain of Happy Land factory using the Bayesian network method under various scenarios.Discussion and ResultsThe results from the Bayesian network analysis in this research demonstrate that environmental risk, as an external risk within Happy Land’s supply chain, exerts the most significant influence at the highest level of the Bayesian map. Subsequently, other risks, including economic risks, supplier risks, distribution risks, operational risks, and demand risks, are categorized in subsequent levels. Additionally, sensitivity analysis scenarios, depicted in the Tornado chart, reveal that supply chain risks have a substantial impact on performance criteria. According to this scenario analysis, the primary risk affecting quality and cost target nodes is operational risk, while the major risk affecting the product delivery time node is distribution risk, and the primary risk influencing profitability is demand risk. Results from both pessimistic and optimistic scenario analyses under the second scenario of the research indicate that in the pessimistic state, the presence of a high percentage of these risks significantly negatively impacts quality performance. Conversely, in optimistic scenarios, where these risk factors are not present, improvements in quality's functional dimension exhibit the most substantial impact.ConclusionWhen introducing a new product to the market, evaluating and managing supply chain uncertainties is essential due to the mutual influence of new product development and the supply chain. Supply chain risk management, which commences with the accurate identification and assessment of risks and proceeds with appropriate responses, is crucial for providing efficient and effective new products to the market. In addition to employing the Bayesian network method, a highly effective tool in supply chain risk analysis, we have endeavored to evaluate the simultaneous impact of risks on product performance and assess the severity of risk impacts under various scenarios, including optimistic, pessimistic, and sensitivity analyses. Scenario building proves to be an effective method for validating a developed model to measure the impact of risks under different conditions on target criteria.
Zahra Rafiee-Majd; Hamidreza Pasandideh
Abstract
In this paper, a three-echelon supply chain, consisting a number of suppliers, distribution centers (DCs), and retailers (customers) is modeled as an integrated bi-objective inventory- location – routing problem (ILRP) which, perishable products are delivered to the customers through DCs in a limited ...
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In this paper, a three-echelon supply chain, consisting a number of suppliers, distribution centers (DCs), and retailers (customers) is modeled as an integrated bi-objective inventory- location – routing problem (ILRP) which, perishable products are delivered to the customers through DCs in a limited time horizon, consisting of several time periods. The retailers’ demand is stochastic and is applied on the model by the concept of discrete scenario. The transportation fleet is heterogeneous, and distribution centers use a timetable, which will prevent interference of the vehicles operation and allocation of a vehicle to more than one distribution center in each time period. Three methods of calculating the distance to the ideal point are used in to solve and analysis the model. At the end, besides concluding the discussion, recommendations are made for future studies.
supply chain management
shahryar marzban; Morteza Shafiee; Mohammad Reza Mozaffari
Abstract
There is a growing concern about the social and environmental impact of the food supply chain, and the food industry faces numerous challenges. This has created significant pressure from various stakeholders to enhance the sustainable performance of the life cycle of perishable products. In this study, ...
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There is a growing concern about the social and environmental impact of the food supply chain, and the food industry faces numerous challenges. This has created significant pressure from various stakeholders to enhance the sustainable performance of the life cycle of perishable products. In this study, we aim to assess the sustainability of the supply chain for perishable products in the food industry. After examining both external and internal factors and identifying a research gap, the structure of the present study involves a four-stage supply chain. Input and output variables were selected based on perishable products and the three dimensions of sustainability. To achieve this goal, we conducted field and library studies to identify and extract relevant input, output, and intermediary indicators for evaluating the relative efficiency of supply chains in various sectors. Subsequently, we examined the supply chain's efficiency and ranked the efficient units. Given the primary focus on perishable materials, our study involved 18 dairy and meat factories in Fars province as the statistical population. We utilized WinQSB software to analyze network downtime and to model and solve the data. The results highlight that the most significant challenges faced by the companies are in the supply sector. Based on these findings, we provide recommendations for companies to enhance their performance.IntroductionIn the food industry, there are numerous inventory systems that deal with perishable items, which have a limited shelf life. These perishable items encompass a wide range of products, including food, fruits, and medicine. Given the extensive use of these products, it is crucial to model perishable products within a supply chain context. Furthermore, reviewing the contracts and regulations among the supply chain members is of great importance for decision-making in interactive conditions. This study aims to determine the most effective ordering policies at different levels of the perishable food supply chain. The goal is to maximize the overall profit of the chain while minimizing social and environmental negative impacts. Our supply chain consists of four levels, including suppliers, manufacturers, distributors, and retailers. We have thoroughly investigated the dimensions of sustainable development, ultimately leading to an assessment of the overall performance of the chain. The primary research question we seek to answer is: 'How does the performance of the perishable product supply chain align with a sustainable development approach?MethodologyIn this section, we employed the network data overlay analysis model to assess the performance of the research supply chain and determine the efficiency of the research units, with a particular focus on perishable products. Conventional Data Envelopment Analysis (DEA) models typically overlook the steps and internal processes within Decision-Making Units (DMUs). These conventional DEA models treat each company as a DMU and limit their calculations to initial inputs and final outputs. Given that DEA has been increasingly used in recent years for buyer-seller relationships, production-distribution processes, and performance evaluations in supply chains, and recognizing that a supply chain is a unique decision-making unit with not only input and output indicators but also intermediary indicators that flow from one stage to the next, traditional data envelopment analysis models may fall short in accurately and comprehensively evaluating supply chain performance due to the network or multi-stage nature of the supply chain. Hence, this study adopts the NDEA model with a fresh approach, calculating efficiency based on sustainability indicators related to perishable products in 18 manufacturing supply chains of dairy, meat, and protein products. Conducting an in-depth study to identify the significant parameters in the research field is a prerequisite for any applied research. To this end, we conducted extensive field and library research to investigate variables and indicators across various supply chain activities. This allowed us to identify and extract meaningful input, output, and intermediate indicators for evaluating supply chain performance in the supplier's sector. After reviewing existing literature, we identified 51 specific indicators that play a crucial role in the research.ResultsAccording to research findings, it is shown that the average efficiency of the supply chain for the production and distribution of perishable products in the financial year studied by the research was 0.9634% in the suppliers' sector. This average was 0.9899 in the producers' sector, 0.9903 in the distributors' sector, and 0.9707 in the retailers' sector. Therefore, the average efficiency indicates that the most significant inefficiency problems of the studied companies are related to the supplier sector. Furthermore, the overall average efficiency is 0.9950. According to the results obtained from the Anderson-Piterson Method for Employer Units Ranking, DMU3's Supply Chain demonstrates strong efficiency, and the supply chains of DMU7, DMU2, and DMU4 followed in the subsequent rankings. All supply chains were rated based on efficiency.ConclusionAmong the supply chains of the 18 companies studied in the research that deal with perishable products, the supplier process exhibits lower efficiency scores compared to the production, distribution, and sales processes. Consequently, it is recommended that inefficient companies at each stage take action to identify the factors causing inefficiency in the production, distribution, and sale processes of perishable products. This can be achieved by modeling the performance of efficient companies, with the goal of improving the efficiency at each stage and overall efficiency. Based on the model and research results, the following topics are suggested for future research: Given that most of the inefficiency is associated with the first stage of the model, it is advisable to pay greater attention to the supply of raw materials and transportation, or to select different input indicators. The supply of raw materials for factories emerged as one of the major challenges in this research, highlighting the inefficiency at the first stage. It is recommended that separate modeling be conducted to address the supply of raw materials in the food industry. The highest inefficiency in the fourth stage of the model is attributed to the limited consideration of the social dimension in sustainable development. For future research, it is suggested to focus more on social dimension indicators, such as satisfaction, motivation, empowerment, respect, mutual trust, social commitment, and the creation of suitable working conditions, as well as workers' health and safety. Regarding the inefficiency in the second stage (manufacturers), future research could explore strategies to enhance the freshness of raw materials and the shelf life of perishable products. For the inefficiency in the third stage (distributors), future research should concentrate on modeling and designing innovative distribution systems and routing for perishable products.
uncertainty
Hossein Firouzi; Javad Rezaeian; Mohammad Mehdi Movahedi; Alireza Rashidi Komijan
Abstract
This paper presents a multi-objective mathematical model for the reverse supply chain of hospital waste management in Iran during the COVID-19 pandemic, incorporating dimensions of sustainability. The objectives of the model are as follows: 1) Minimizing the costs associated with building facilities ...
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This paper presents a multi-objective mathematical model for the reverse supply chain of hospital waste management in Iran during the COVID-19 pandemic, incorporating dimensions of sustainability. The objectives of the model are as follows: 1) Minimizing the costs associated with building facilities and waste treatment centers, vehicle fuel costs, and environmental costs due to pollutant emissions; 2) Maximizing the energy generated from the waste combustion process; 3) Minimizing the risk of virus transmission resulting from inadequate waste management; and 4) Maximizing the number of job opportunities in the established centers. It is important to note that existing uncertainties are addressed through the application of fuzzy set theory. Given the multi-objective nature of the model, two multi-objective algorithms, namely the Pareto archive-based Krill Herd Algorithm and Non-dominated Sorting Genetic Algorithm II (NSGA-II), are employed to solve the defined problem. The results indicate that the proposed Krill Herd Algorithm converges to a solution with higher quality and dispersion compared to NSGA-II. Additionally, through a comparison of the spacing index and running time of the two algorithms, it is observed that NSGA-II explores the solution space with higher uniformity and solves the model in less time.IntroductionHospital waste encompasses a broad spectrum of both hazardous and non-hazardous materials. The management of hospital waste involves the development of a suitable supply chain network for handling waste generated in the healthcare sector. Improper disposal or mishandling of contaminated waste not only contributes to environmental pollution but also poses a risk of transferring viral pathogens to healthcare and recycling personnel. Research has shown that inadequate disposal of medical waste can lead to the transmission of up to 30% of hepatitis B, 1-3% of hepatitis C, and 0.3% of HIV infections from patients to healthcare workers. This paper aims to design a multi-objective mathematical model for the reverse supply chain of hospital waste management in Iran during the COVID-19 pandemic while considering the dimensions of sustainability.Literatur ReviewIn recent years, various studies have delved into the complexities of medical and hospital waste management, proposing mathematical models to address this intricate issue. The current study is built upon the work of Valizadeh et al. (2021). In their paper, a hybrid mathematical modeling approach was introduced, featuring a Bi-level programming model specifically tailored for infectious waste management during the COVID-19 pandemic. The outcomes revealed that, at the higher level of the model, governmental decisions aiming to minimize total costs associated with infectious waste management were crucial. This involved the conversion of collected infectious waste into energy, with the generated revenue being reinvested back into the system. The findings indicated that, through energy production from waste during the COVID-19 pandemic, approximately 34% of the total costs related to waste collection and transportation could be offset. The uniqueness of this study lies in its consideration of three sustainability dimensions: risk, vehicle routing, energy production, employment, and emission of polluting gases. Consequently, the novelty of this research, when compared to previous studies and the article by Valizadeh et al. (2021), is evident in several aspects. It introduces an integrated multi-objective positioning-routing model for the supply chain of waste management under pandemic conditions, taking into account sustainability dimensions, notably the economic aspect, and employs meta-heuristic algorithms for model resolution.MethdologyTo ensure the proper management of hospital waste, the waste is categorized into two groups: infectious and non-infectious waste. It is assumed that waste in hospitals and health centers is segregated and placed in infectious and non-infectious waste bins. The collected waste undergoes further processing: infectious waste is transported to incineration centers, where it is burned and converted into electrical energy, while non-infectious waste is sent to waste recycling centers, where it is reprocessed and returned to the production cycle in the industry. A multi-objective mathematical model is presented to integrate location-routing decisions in the supply chain of hospital waste management, with the following modeling assumptions:Waste segregation at the source helps prevent all waste from becoming viral, reducing the spread of viruses through waste.The risk of spreading viruses is assumed to be relatively equal for each type of waste.Two types of vehicles are considered for transporting waste: the first type carries non-infectious waste, while the second type carries infectious waste.The number of cars, waste collectors, and the capacity of waste incinerators are considered constant in this study.The mathematical model is multi-objective, with the objectives being to optimize the three dimensions of sustainability (economic, social, and environmental).The economic goal is to minimize system costs, including the cost of site location, recycling, collection, segregation of non-infectious waste, and incineration.The environmental goal is to minimize the emission of pollutants in the transportation and processing system in various facilities, as well as to maximize the production of electrical energy.The social goal is to minimize the risk of virus transmission and maximize the employment rate.Results and DiscussionThis research presents a multi-objective mathematical model for the reverse supply chain of hospital waste management during the COVID-19 pandemic in Iran and solves it. The pandemic period is considered a time of maximum utilization of health centers and waste disposal. In this context, a three-objective mathematical model was initially introduced. To solve the model, the krill herd optimization algorithm was employed. The performance of the krill herd optimization algorithm was scientifically and practically evaluated by comparing it with the well-known NSGA-II algorithm. After designing the model, both the multi-objective krill herd algorithm based on Pareto Archive and the NSGA-II algorithm were utilized to solve the model. The results of solving the model demonstrated that the proposed krill herd algorithm, designed in combination with VNS, effectively solved the model and determined the optimal solution within a boundary. Comparing the results of this algorithm with those obtained by the renowned NSGA-II algorithm revealed that the krill herd algorithm produced solutions of much higher quality.ConclusionThe comparison of the Index of dispersion between the two algorithms indicates that the krill herd optimization algorithm explores more points in the solution space, leading to a lower probability of getting stuck in local optima compared to the NSGA-II algorithm. On the other hand, the index of uniformity for the NSGA-II algorithm is lower than that of the krill herd algorithm (lower values are better), suggesting that the multi-objective genetic algorithm explores the solution space more uniformly. Considering the execution time of the two algorithms, it was observed that the NSGA-II algorithm solved the model in less time. Additionally, the increasing trend of execution time in both algorithms confirms the NP-HARD nature of the hospital waste management problem. According to the output of the MATLAB software, considering the presented model, the results affirm the capability to optimally select hospital waste recycling centers.
