project management
Roya Soltani; Ali Nobakhti
Abstract
In this paper, a recommender system based on a multilayer feedforward artificial neural network (ANN) trained by the Levenberg–Marquardt backpropagation algorithm, optimized using a genetic algorithm (GA) to fine-tune both network structure and weights, is proposed to predict competency and recommend ...
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In this paper, a recommender system based on a multilayer feedforward artificial neural network (ANN) trained by the Levenberg–Marquardt backpropagation algorithm, optimized using a genetic algorithm (GA) to fine-tune both network structure and weights, is proposed to predict competency and recommend project managers in project-oriented organizations. The system considers both hard and soft skills, which are essential for sustainable development. The performance of the proposed system was evaluated by a case study within the Iranian construction industry, utilizing the experience of 80 senior managers and experts from the Ministry of Roads and Urban Development of Iran. The results demonstrate the high accuracy of the proposed system in identifying competent project managers. To validate the system, its performance was compared with existing methods in the literature, showing superior accuracy in terms of MSE and RMSE metrics.
Introduction
In today’s dynamic business environment, projects operate within a VUCA context—characterized by volatility, uncertainty, complexity, and ambiguity—that significantly influences managerial decision-making and project outcomes. Rapid technological advancements, economic fluctuations, the complex nature of stakeholder interactions, and resource constraints have made project management an increasingly challenging undertaking. Consequently, the competence of project managers to address a wide range of human resource, technical, and economic challenges, along with their ability to build effective communication and collaboration networks, is a crucial determinant of project success (Omoush, 2020). To ensure successful project delivery, project managers must demonstrate a sound understanding of environmental dynamics and make informed, adaptive decisions that integrate both hard and soft managerial skills—skills that are now more critical than ever for achieving sustainable development. Such abilities reflect the professional competence and strategic agility required for timely and effective decision-making (Karki & Hadikusumo, 2023).
Selecting competent project managers through a data-driven recommender system that matches the desired managerial skills can substantially enhance the effectiveness of project-based organizations. Such a system can transform the manager selection process from subjective judgments to evidence-based decision-making. This approach not only improves the precision of identifying qualified managers but also contributes to better human resource allocation, reduced managerial risk, and enhanced overall project performance. Ultimately, adopting a data-driven recommender framework enables project-based organizations to strengthen their managerial capabilities and achieve a sustainable competitive advantage.
Research Questions
How can a smart recommender system be designed—by integrating ANN and GA—to accurately identify competent project managers in project-based organizations?
To what extent can optimizing the parameters of an ANN using a GA enhance the accuracy of the recommender system?
Literature review
In the literature, various data-driven methods have been developed using machine learning approaches to enhance decision-making, resolve conflicts, and improve project performance, productivity, safety, and workflow in the field of project management. A comprehensive review of the literature reveals that existing predictive models in project management have predominantly focused on forecasting various project outcomes such as quality (Najafi Zangeneh et al., 2020; Fan, 2025), infrastructure costs (Soltanian et al., 2023; Dan, 2024; Chen, 2024; Effat, 2025; Al-Gahtani et al., 2025), dispute occurrences and litigation outcomes (Ayhan et al., 2021), delays (Awada et al., 2021), and construction crew productivity (Sadatnya et al., 2023) through the application of diverse classification algorithms (see Table 1). Despite these advances, the literature lacks studies that aim to develop a predictive model capable of accurately assessing project manager competency using a hybrid framework that combines ANN with metaheuristics. Employing such an approach could provide a robust mechanism for identifying competent project managers and, consequently, enhance the likelihood of successful project delivery in complex and dynamic construction environments.
However, in the literature, the combination of ANN with metaheuristics has been employed to improve prediction accuracy across various domains. These domains include stock market forecasting (Sharma et al., 2022), electricity consumption demand prediction (Azadeh et al., 2007), and patient mortality prediction (Dybowski et al., 1996) (see Table 2).
Methodology
The steps of the proposed recommender system for identifying competent project managers are as follows:
Data preparation: First, a database comprising data related to competency is established and quantified based on the experience of 80 senior managers and specialists. Subsequently, data cleaning is performed, and records with missing values, outliers, or inconsistencies are removed from the database. Finally, 70% of the data is randomly selected for training and 30% for testing.
Neural network architecture design: A feedforward multilayer ANN is designed based on the number of hidden layers (i.e., 1, 2, or 3) and the number of neurons per layer (i.e., 2, 4, or 8). The network is trained using the Levenberg–Marquardt algorithm. After training and testing, the optimal network structure is selected based on MSE and RMSE metrics.
Optimization of neural network weights using genetic algorithm: The weights of the designed ANN are optimized using a GA to improve the network’s predictive performance.
Training and testing the ANN-GA recommender system: The ANN optimized by the GA is first trained and then tested. The performance of the proposed ANN-GA recommender system in identifying competent project managers is evaluated based on the MSE and RMSE criteria.
Results
The ANN model with a three-hidden-layer architecture and 2 neurons per layer demonstrated the best performance in terms of MSE and RMSE, with values of 0.351 and 0.593, respectively. This indicates that the designed network effectively predicts project manager competency. To further enhance prediction accuracy, the network weights were optimized using a GA. The resulting ANN-GA recommender system achieved an MSE of 0.094 and an RMSE of 0.307, showing significantly higher accuracy in identifying competent project managers compared to the non-optimized network (MSE = 0.351, RMSE = 0.593). These findings highlight the effectiveness of combining ANN with GA for data-driven competency assessment.
To validate the proposed recommender system for identifying competent project managers, its prediction error was compared with the algorithms reported by Karki and Hadikusumo (2023). As shown in Table 12, the proposed system demonstrates superior accuracy, highlighting its effectiveness over existing methods.
Discussion
Effective project management in the construction industry, a complex and high-risk sector, requires managers capable of making informed decisions under VUCA conditions. Instead of subjective judgments, experiential biases, and unstructured evaluations, the proposed recommender system can help project-based companies use data-driven, intelligent tools to identify more competent managers, improving project productivity while significantly reducing costs associated with poor managerial decisions. Additionally, the proposed system can serve as a decision-support tool for hiring new project managers or promoting existing ones by analyzing past performance and predicting their potential success in future projects.
Conclusion
The proposed recommender system integrates an ANN with a GA to identify and select competent project managers with high accuracy. Leveraging historical data and uncovering hidden patterns, the optimized ANN accurately predicts managerial competencies based on defined criteria. Validation against existing approaches demonstrates that the GA significantly enhances predictive accuracy, highlighting the system’s potential to improve managerial selection and project outcomes in practice.
quality management
Ali Ebrahimi Kordlar; Hossein Safari; Helyeh Sadat Aghamiri; Fatemeh Sharifi Tabar; Mohsen Moradi Moghaddam
Abstract
In today’s competitive environment, organizations need innovative capabilities and strategies for competitive advantage, with organizational capabilities playing a key role in success. Excellence models like EFQM help identify improvement areas and enhance performance. Since the organizational ...
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In today’s competitive environment, organizations need innovative capabilities and strategies for competitive advantage, with organizational capabilities playing a key role in success. Excellence models like EFQM help identify improvement areas and enhance performance. Since the organizational capabilities of the Mobile Communications of Iran (MCI) have not been assessed using the latest EFQM model, this study aims to identify key capabilities and develop a mathematical optimization model. Using a descriptive survey with an applied purpose, the research targeted academic experts, excellence practitioners, and MCI specialists. First, a systematic literature review identified and categorized critical capabilities. Then, expert judgment and a fuzzy inference system modeled causal links between capabilities and the EFQM framework. Mathematical equations quantified each capability, forming an integrated model. A genetic algorithm was used to optimize parameters and determine the best capability combination. The study concludes with practical implementation recommendations and suggestions for future research.IntroductionIn today’s globalized and competitive environment, organizations face increasing competition and a dynamic external landscape. To survive and lead, organizations must differentiate themselves by creating a competitive advantage through innovation. This requires management excellence models that help organizations adapt to these changing conditions. The competitive environment, characterized by geographical dispersion and organizational innovation, demands unique capabilities known as dynamic capabilities, which help organizations create, expand, and maintain their core resources. The 2020 edition of the EFQM model, based on design thinking, has evolved from an assessment tool into a vital framework for addressing the changes and disruptions organizations face daily. Its strategic focus, combined with operational performance and a results-oriented approach, makes it an ideal framework for examining the alignment of an organization's ambitions. The aim of this article is to develop a mathematical model of organizational capabilities within the EFQM 2020 excellence model, helping organizations evaluate and improve their current performance.Materials and MethodsThis research is applied in nature with a comparative approach. It follows a quantitative methodology and is based on library research. The research strategy is survey-based, and from a goal perspective, it falls under the descriptive category. Data collection is conducted through interviews and questionnaires. Organizational capabilities are first extracted from scientific sources, then matched with the sub-criteria of the model by reviewing guidelines. Based on the findings, if-then rules and a fuzzy inference system are designed using MATLAB software.Meta-heuristic methods are used to solve complex optimization problems where classical optimization and heuristic methods are ineffective. Among these, the genetic algorithm is commonly used as a function optimizer. In this model, due to the complex, non-linear, and fuzzy relationships in the fuzzy inference system within the objective function, it can be compared to a neural network. The genetic algorithm is then applied to solve the model.ResultsThe desired capabilities for the fuzzy inference system are determined by specifying the capabilities of each criterion and sub-criterion of the EFQM model. A fuzzy inference system is defined for each of the 23 sub-criteria, and at the criterion level, the systems of the sub-criteria are combined. Sensing, learning, integration, coordination, and reconfiguration routines are used to measure the capabilities of the EFQM excellence model.This research focuses on MCI. By comparing the current values with the target and the scores obtained from the genetic algorithm, it is found that, within the budget limits, the desired goal can be achieved for 38 capabilities. For capabilities such as sensing, abduction, business model development, reporting, environmental management, networking, modeling, and social responsibility, the values fall within the target range. However, for three capabilities—organizational governance development, transformation management, and improvement—the target values fall outside the selected range. These differences are minor and can likely be ignored. The transformation management capability score (25.6) is close to the minimum value of 26, indicating that improvement is not feasible within the current budget for this sub-criterion. Increasing the budget could raise the score. The organizational governance development score differs by almost 4 points, which may be due to the fuzziness in scoring and inaccuracies in the budget values assigned to each sub-criterion.ConclusionOrganizational excellence models are generally frameworks that organizations use to develop a culture of excellence, and each model attempts to provide a set of management principles that are generally employed by organizations in their geographical areas of influence. Organizational resources and capabilities are the key success factors for the organization. In this research, using the fuzzy inference system, the combination of organizational capabilities in the sub-criteria of the EFQM 2020 excellence model was designed, and the mathematical model was developed using linear programming. Finally, a genetic meta-heuristic algorithm was used to solve the model. Each sub-criterion is a fuzzy inference system composed of the organizational capabilities related to it. A set of organizational capabilities makes up each of the sub-criteria of the excellence model, and we have a point limit for each capability. The budget limit defined in this model consists of the total budget dedicated to each organizational capability constituting the relevant sub-criterion. A case study was used to check the validity of the model and its practical application in an internal organization. In this research, the studied organization is MCI.
Fatemeh Mojibian
Abstract
It’s more than one decade that industrial development based on the structure of industrial clusters as a new strategy has been planned and administrated by developed industrialized countries. Considering the importance of the role of industrial clusters in economic development programs, providing ...
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It’s more than one decade that industrial development based on the structure of industrial clusters as a new strategy has been planned and administrated by developed industrialized countries. Considering the importance of the role of industrial clusters in economic development programs, providing solutions to improve, progress and development of clusters has always been a concern for researchers and specialists.The aim of this study is to provide a mechanism for pricing process of the product in this industrial-economic phenomenon; So that the structure of the proposed model is defined based on mechanisms and activities of the components of industrial clusters. The proposed pricing process is presented based on the concept of Stackelberg game theory and tariff pricing strategy, and in order to solve the model in production level of cluster, a meta-heuristic genetic algorithm is used. Finally, the performance and efficiency of the proposed model is studied in the form of a numerical example, and using the parameter tuning Taguchi method the optimal value of the model variables are presented. Based on the obtained results, the optimal wholesale price of cluster’s products are determined and each manufacturer select the appropriate tariff based on its optimal demand.
Mohammad Nikzamir; vahid baradaran; Yunes Panahi
Abstract
Health care solid wastes include all types of waste that are produced as a result ofmedical and therapeutic activities in hospitals and health centers. About 15% to20% of these waste materials are infectious waste, which falls within the categoryof hazardous materials. Infectious waste is the one that ...
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Health care solid wastes include all types of waste that are produced as a result ofmedical and therapeutic activities in hospitals and health centers. About 15% to20% of these waste materials are infectious waste, which falls within the categoryof hazardous materials. Infectious waste is the one that must be treated beforedisposal or recycling. Hence, this paper seeks to develop a bi-objective mixedinteger programming model for the infectious waste management. In the proposedmodel, in addition to minimizing the chain costs, the reduction of risks for thepopulation exposed to the spread of contamination resulting from infectious wasteis also considered. For this purpose, a multi-echelon chain is proposed by takinginto account the green location-routing problem, which involves the location ofrecycling, disposal, and treatment centers through various treatment technologiesand routing of vehicles between treatment levels and the hospital. The routingproblem has been considered to be multi-depot wherein the criterion of reducingthe cost of fuel consumption of heterogeneous cars is used for green routing.Finally, a hybrid meta-heuristic algorithm based on ICA and GA is developedand, following its validation, its function in solving large-scale problems has beeninvestigated. Results show that the proposed algorithm is effective and efficient.
Adel Azar; Meisam Shahbazi; Ali Amiri
Abstract
The turbulent and dynamic environment of today's business world has become increasingly challenging for organizations operating in different business areas. In such a situation, in order to get rid of these conditions, moving forward and towards the perspective of the new horizons of prosperity and survival ...
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The turbulent and dynamic environment of today's business world has become increasingly challenging for organizations operating in different business areas. In such a situation, in order to get rid of these conditions, moving forward and towards the perspective of the new horizons of prosperity and survival is the dream of many of them. In the meantime, according to the specific circumstances and requirements that govern each institution or organization, and in order to achieve their desired productivity, they use certain strategies and programs that outsourcing is one of these strategies. Today, organizations are outsourcing to boost competitive ability and profit and focus on their competitive edge.In this research, a mathematical programming approach is proposed to optimize the issue of outsourcing in the supply chain. In this approach, at first the mathematical model of the problem showed and then in order to solve the problem of the theory of Markov chains described. The objective function of the problem involves minimizing the cost of purchasing, outsourcing and lost demand. In order to solve the problem, three genetic metamorphic algorithms, gray wolves and ant lion have been used. After examining the numerical expressions, Gray wolf's algorithm has the highest level of performance. In order to expand the applied dimensions of research in real-world conditions, a company (MPEICO) that manufactures insulators is considered as the case study
Mohammad Mohammadi; Kamran Forghani
Abstract
The cell formation problem and the group layout problem, both are two important problems in designing a cellular manufacturing system. The cell formation problem is consist of grouping parts into part families and machines into production cells. In addition, the group layout problem is to find the arrangement ...
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The cell formation problem and the group layout problem, both are two important problems in designing a cellular manufacturing system. The cell formation problem is consist of grouping parts into part families and machines into production cells. In addition, the group layout problem is to find the arrangement of machines within the cells as well as the layout of cells.In this paper, an integrated approach is presented to solve the cell formation, group layout and routing problems. By Considering the dimension of machines, the width of the aisles, and the maximum permissible length of the plant site, a new framework, called spiral layout, is suggested for the layout of cellular manufacturing systems. To extend the applicability of the problem, parameters such as part demands, operation sequences, processing times and machine capacities are considered in the problem formulation. The problem is formulated as a bi-objective integer programming model, in which the first objective is to minimize the total material handling cost and the second one is to maximize the total similarity between machines. As the problem is NP-hard, three metaheuristic algorithms, based on Genetic Algorithm and Simulated Annealing are proposed to solve it. To enhance the performance of the algorithms, a Dynamic Programming algorithm is embedded within them. The performance of the algorithms is evaluated by solving numerical examples from the related literature. Finally, a comparison is carried out between the proposed spiral layout and the linear multi-row layout which has recently presented in the literature
Mehdi Seifbarghy; Shima Zangeneh
Abstract
In the classic models of facility location, it is assumed that the selected facilities always work based on the schedule while, in the real world, facilities are always exposed to disruption risk and sometimes these disruptions have long-term effects on the supply chain network and cause a lot of problems. ...
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In the classic models of facility location, it is assumed that the selected facilities always work based on the schedule while, in the real world, facilities are always exposed to disruption risk and sometimes these disruptions have long-term effects on the supply chain network and cause a lot of problems. In this paper, a mixed integer programing (MIP) model presented in order to determine how to serve the customers at the time of disruption in distribution centers in a two-echelon supply chain, including distribution centers and customers. This model selects potential places that minimize traditionally supply chain costs and also the transportation cost after distribution centers disruptions. In fact, the model tries to choose the distribution centers facilities with lowest cost and highest reliability and also allocate them to customers. The problem divided into two sub-problems using Lagrangian relaxation approach. By examining sub-problems optimal conditions, a heuristic solution is used for the first sub-problem and a genetic algorithm is used for the second sub-problem to solve large-scale problems. Finally, numerical examples are presented to examine the performance and efficiency of the proposed model and approach
Mohammad saeed Company; Parham Azimi
Abstract
In this study, the use of simulation technique in bi-objective optimization of assembly line balancing problem has been studied. The aim of this paper is to determine optimal allocation of human resource and equipment to parallel workstations in order to minimize the cost of adding equipment and manpower ...
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In this study, the use of simulation technique in bi-objective optimization of assembly line balancing problem has been studied. The aim of this paper is to determine optimal allocation of human resource and equipment to parallel workstations in order to minimize the cost of adding equipment and manpower among the stations and maximize the production output. In other words, with optimal use of resources, production output is maximized and therefore productivity become maximum. To this end, with optimization via simulation, the production line process is simulated in the form of a simulation model in the ED software. After validating the simulation model using design of experiment, various scenarios designed and run in the simulation model. Possible results for human resource and equipment variables, obtained by genetic algorithm are shown in a Pareto chart and have compared with the production line current situation
Laya Olfat; Maghsod Amiri; Ahmad Jafarian
Abstract
Cross-docking is one of the lean logistics tools that is used for uniting the shipments during the loops replacement. Cross-docking is the process of product movement form distribution centers without storage function. Vehicle routing problem in Cross-Dock external environment has much influence on cross-dock ...
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Cross-docking is one of the lean logistics tools that is used for uniting the shipments during the loops replacement. Cross-docking is the process of product movement form distribution centers without storage function. Vehicle routing problem in Cross-Dock external environment has much influence on cross-dock costs. This paper provides a model for minimizing total distance traveled by vehicles in the external environment of a cross-dock. In this paper, Vehicles routes was modeled with capacitated vehicle routing problem (CVRP) and genetic algorithm (GA) was used to solve the model. To validate responses obtained by GA, simulated annealing (SA) was used. Also, to evaluate the efficacy of two algorithms (SA & GA) in different CVRP problems in cross-dock, 10 problems with different dimensions are evaluated. The results show that in problems with smaller size GA is more efficient, whereas in large size problems SA is more efficient
Akdar Alemtabriz; Ashkan Ayough; Mahdie Baniasadi
Abstract
During the recent years, extensive research has been done on the field ofproject scheduling. There is always uncertainty in the area of projectscheduling that causes a deviation in the real plan from the scheduled plan.One of the solutions to deal with this uncertainty is using the critical chainmethod ...
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During the recent years, extensive research has been done on the field ofproject scheduling. There is always uncertainty in the area of projectscheduling that causes a deviation in the real plan from the scheduled plan.One of the solutions to deal with this uncertainty is using the critical chainmethod (CCM) in project scheduling. This method which is derived from thetheory of constraints (TOC) is a new method in project control which was firstproposed by Goldartt in 1997.In this research we attempt to use the principalsof critical chain in resource-constrained project scheduling problem. The maininnovation in this research is presentation of critical chain project schedulingproblem model with consideration of feeding buffer and using float as asupplement for feeding buffer. For this matter, the project scheduling underresources constraints with critical chain approach was first written and itsreliability was evaluated using the Lingo software. In the next step thesolution algorithm of this model was developed using the genetic algorithmand finally different sample issues were investigated. The results of thisresearch show the efficiency of the presented genetic algorithm
Hamidreza Shahabifard; Behrouz Afshar-nadjafi
Abstract
In this paper, a mathematical model is proposed for project portfolioselection and resource availability cost problem to scheduling activities inorder to maximize net present value of the selected projects preservingprecedence and resource constraints. Since the developed model belongs toNP-hard problems ...
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In this paper, a mathematical model is proposed for project portfolioselection and resource availability cost problem to scheduling activities inorder to maximize net present value of the selected projects preservingprecedence and resource constraints. Since the developed model belongs toNP-hard problems list, so a genetic based meta-heuristic algorithm isproposed to tackle the developed model. In the proposed algorithm besidecommon operators of genetic algorithms such as crossover & mutation, someintelligent operators are utilized for local search in computed resources andshifting the activities with negative cash flows. The key parameters of thealgorithm are calibrated using Taguchi method to accelerate convergence ofthe proposed algorithm. Then, the algorithm is used to solve 90 testproblems consisting 30 small-scale, 30 middle-scale and 30 large scaleproblems to examine the algorithm’s performance. It is observed that, insmall problems, the obtained solutions from the proposed genetic algorithmhave been comparably better than the local optimum solutions stemmedfrom Lingo software. On the other hand, for the middle and large sizeproblems which there is no local optimum available within the limited CPUtime, robustness of the proposed algorithm is appropriate
Roozbeh . Azizmohammadi; Maghsoud .Amiri; Reza Tavakkoli- Moghadam; Hamid Reza. Mashatzadegan
Volume 14, Issue 42 , October 2016, , Pages 103-121
Abstract
A redundancy allocation problem is a well-known NP-hard problem thatinvolves the selection of elements and redundancy levels to maximize thesystem reliability under various system-level constraints. In many practicaldesign situations, reliability apportionment is complicated because of thepresence of ...
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A redundancy allocation problem is a well-known NP-hard problem thatinvolves the selection of elements and redundancy levels to maximize thesystem reliability under various system-level constraints. In many practicaldesign situations, reliability apportionment is complicated because of thepresence of several conflicting objectives that cannot be combined into asingle-objective function. A stele communications, manufacturing and powersystems are becoming more and more complex, while requiring shortdevelopments schedules and very high reliability, it is becoming increasinglyimportant to develop efficient solutions to the RAP. In this paper, a newhybrid multi-objective competition algorithm (HMOCA)based oncompetitive algorithm (CA) and genetic algorithm (GA) is proposed for thefirst time in multi-objective redundancy allocation problems. In the multiobjectiveformulation, the system reliability is maximized while the cost andvolume of the system are minimized simultaneously. Additionally, ay RSMis employed to tune the CA parameters. The proposed HMOCA is validatedby some examples with analytical solutions. It shows its superiorperformance compared to a NSGA-II and PAES algorithms. Finally, theconclusion is given
Masoud Rabbani,; Neda Manavizadeh; Amir Farshbaf-Geranmayeh
Volume 13, Issue 37 , July 2015, , Pages 5-35
Abstract
In this paper, supply chain network design problem is modeled as a fuzzy multi objective mixed integer programming which seeks to locate the plants, DCs, and warehouses by considering disruption, supply and demand risk. Maximizing net present value of supply chain cash flow, minimizing delivery tardiness ...
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In this paper, supply chain network design problem is modeled as a fuzzy multi objective mixed integer programming which seeks to locate the plants, DCs, and warehouses by considering disruption, supply and demand risk. Maximizing net present value of supply chain cash flow, minimizing delivery tardiness and maximizing reliability of suppliers are considered as objective functions in the proposed mathematic model. In order to have a more reliable model in case of disruption, the robustness measure is used in the model. Moreover, because of the lack of information, the economic factors such as tax rate, interest rate, and inflation are considered as uncertain factors in the model. An interactive possibilistic programming approach is applied for solving the multi-objective model. To solve larger size instances, genetic algorithm is proposed. Finally numerical examples are presented to show how the model works in practice
Ali Mohtashami; Ali Fallahian-Najafabadi
Volume 11, Issue 31 , January 2014, , Pages 55-84
Abstract
In today’s competitive world, the organizations decide to establish competitive benefits by making benefit from management sciences. One of the most important management sciences arisen lots of so useful matters is the supply chain. The supply chain management is the evolved result of warehousing ...
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In today’s competitive world, the organizations decide to establish competitive benefits by making benefit from management sciences. One of the most important management sciences arisen lots of so useful matters is the supply chain. The supply chain management is the evolved result of warehousing management and is regarded as one of the infrastructure and important concepts for implementing the career so that in many of them it is essentially tried to shorten the time between the customer’s order and the real time of delivering the goods. Cross docking is one of the most important alternatives for lowering the time in supply chain. The central aim of this paper is to focus on optimizing the planning of the trucks input and output aiming to minimize total time of operation inside the supply chain in designed model. Timing the transportation in this paper makes the time between sources and destinations, time of unloading and transferring the products minimized. To find the optimum answers to the question, genetic algorithms and the particle swarm optimization have been used. Then, these algorithms have been compared with the standards such as the implementation time and quality of answers with each other and then better algorithms in each standard identified.
Majid Esmaelian; Kamran Feizi; Amir Afsar
Volume 10, Issue 26 , January 2012, , Pages 55-73
Abstract
In this paper, a nonlinear multi objective mathematical model forcrashing the PERT network is presented. The main purpose of thismodel is minimizing the pessimistic time of critical activities byallocating more budget. In fact, this model indicates how the budget isallocated among critical activities. ...
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In this paper, a nonlinear multi objective mathematical model forcrashing the PERT network is presented. The main purpose of thismodel is minimizing the pessimistic time of critical activities byallocating more budget. In fact, this model indicates how the budget isallocated among critical activities. The results show that decreasingthe pessimistic of critical activities lead to decreasing the time andvariance of project completion. This model increases the probabilityof project completion on time scheduling. A genetic algorithm is useto solve the nonlinear model
Payam Chiniforooshan; Behrooz Pourghannad; Narges Shahraki
Volume 9, Issue 23 , December 2011, , Pages 209-231
Abstract
In this paper, a mathematical model is proposed to solve cell formation problem considering alternative process routings in which more than one process route for each part can be selected. The model attempts to minimize intercellular movements and incorporates several real-life production factors and ...
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In this paper, a mathematical model is proposed to solve cell formation problem considering alternative process routings in which more than one process route for each part can be selected. The model attempts to minimize intercellular movements and incorporates several real-life production factors and practical constraints. In order to increase the flexibility provided by the multiplicity of routings, the model distributes production volume of each part among alternative routes. Also, a constraint enforcing work load balancing among machines is included in the model. Due to the complexity and combinatorial nature of this model, an enhanced algorithm comprised of a genetic algorithm and a linear programming is proposed for solving the model. The proposed algorithm is tested by a range of test problems and compared with two algorithms from the literature .The computational results show that the proposed algorithm is effective and the proposed approach offers better solution.
Hasan Shavandi; Mehdi Mardane Khameneh
Volume 8, Issue 20 , March 2011, , Pages 27-48
Abstract
On the networks existing servers and customers, each node indicates a customer demand and demand rate is estimated for them. The edges of the network indicate connective ways among the nods which is usually shown with the distance of two nods or the time of travelling. In the covering location problems, ...
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On the networks existing servers and customers, each node indicates a customer demand and demand rate is estimated for them. The edges of the network indicate connective ways among the nods which is usually shown with the distance of two nods or the time of travelling. In the covering location problems, the objective is locating some of the servers on the network in a way that the customers' demand supported by the maximum covering of the servers and optimized objective criterion. In this research the location model with Probability Structure, which the probability of choosing servers by customer is estimated based on their distance, is developed. In the presented model, supposing there is a competitive market, lost demand is considered, too. And according to the mentioned matter the objective of the model is to minimize the cost of losing demands or to maximize the earned profits of responding to the demands. Then, we propose a genetic algorithm (GA) to solve this model. In addition, we employ design of experiments and response surface methodology to both tune the GA parameters and to evaluate the performance of the proposed method in 45 test problems. The results of the performance analysis show that the efficiency of the proposed GA method is very well.
Jamshid Salehi Sadaghiani; Seyed Amir Reza Abtahi
Volume 4, Issue 13 , June 2006, , Pages 89-122
Abstract
The purpose of this article is about soft computing and its different methods for modeling phenomena. Soft Computing refers to the evolving collection of methodologies used to build intelligent systems exhibiting human-like reasoning and capable of tackling uncertainty.
In this paper, we describe the ...
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The purpose of this article is about soft computing and its different methods for modeling phenomena. Soft Computing refers to the evolving collection of methodologies used to build intelligent systems exhibiting human-like reasoning and capable of tackling uncertainty.
In this paper, we describe the neural networks approach in soft computing at first. Then, other approaches such as genetic algorithm and machine learning will be described. Since the main goal of building the model is knowledge extraction, finally, we will describe the various methods of knowledge and rule extraction from neural networks.
