Industrial management
Mohsen Kochaki; Behnam Vahdani
Abstract
The correct storage and arrangement of products in the warehouse increase efficiency in responding to requests, accelerate the identification of products, increase accessibility of items in the warehouse, make more use of available space in the warehouse, reduce the possibility of product damage, and ...
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The correct storage and arrangement of products in the warehouse increase efficiency in responding to requests, accelerate the identification of products, increase accessibility of items in the warehouse, make more use of available space in the warehouse, reduce the possibility of product damage, and increase flexibility. The review of studies in the field of warehousing and arrangement of products in warehouses revealed that the use of machine learning algorithms in this field is one of the important research gaps. Therefore, in this article, using machine learning algorithms, we aim to present an innovative algorithm for allocating goods to different parts of a warehouse, for which a real case study is used. The goal of categorizing products based on their characteristics is to minimize the total cost of the system. Hence, spatial clustering algorithms based on the density of applications with noise (DBSCAN), self-organizing mapping neural network (SOM), and AGNES are used. The obtained results show that SOM has better performance than DBSCAN. Also, the DBSCAN algorithm performs better than AGNES.IntroductionWarehouses play a crucial role in every supply chain that involves activities such as receiving, storing, picking, and transporting goods. The way goods are stored directly affects the costs associated with warehousing, so it is important to have efficient management systems in place in order to stay competitive in the global market. Having an organized warehouse layout, utilizing technology for inventory management, and implementing streamlined processes can all contribute to reducing costs and increasing efficiency in warehousing operations. By continuously optimizing operations and staying up-to-date with industry trends, businesses can ensure they are meeting customer demands and staying ahead of the competition (Jinxiang Gu et al., 2007). Storage is the primary and essential function in all warehouses. The methods used for storing items can vary depending on the type of warehouse and its specific goals and objectives (Berman, 1996). The main goal of storage and warehouses is to meet the needs of consumers or enhance service in a manner that takes into account limitations in resources. Efficient management of storage also helps to enhance the speed and reliability of deliveries, which has been identified as a crucial factor for performance in the last twenty years (Ann E. Gary et al., 1992). When looking at logistics costs from an economic perspective, the costs associated with storage and warehousing services make up around 15% of the total logistics costs in developed countries like Germany (Handfield et al., 2013). In this context, properly allocating storage can reduce costs. After deciding how to store the goods, we determine their arrangement. The purpose of this article is to determine the optimal arrangement of goods in the dedicated storage system. Arranging the goods logically in the warehouse increases efficiency in responding to requests, accelerates goods identification, increases accessibility, makes better use of space, determines the location of goods and protects them. It also provides more flexibility and more suitable conditions for storage. It should be noted that due to the functional nature of warehouses, which requires rapid response to determine optimal goods placement, innovative solutions are imperative. All algorithms proposed to solve organizing goods in warehouses must completely consider inclusiveness according to attributes like grouping, similarity, flammability, degradability, inbound/outbound amounts, and stockroom area. Therefore, according to the huge volume and diversity of data in these systems, utilizing data extraction strategies can maximize efficiency of mathematical planning models whose inputs include inbound/outbound amounts for each good and stockroom area assigned. This confirms arrangements account for qualities like item classes, quantities, traits, and warehouse restrictions. Usually, algorithms presented by these methods typically have some limitations. For example, you could reference the inventory of products stocked in your warehouse. A useful way to enhance or address existing issues is through the use of data-driven and machine learning techniques. In this work, we aim to improve an innovative algorithm described in prior studies using data-focused and collaborative learning approaches. Next, we will provide a brief overview of the framework. Then, the problem definition and mathematical model are described. Following, the methods and analyses employed and findings obtained are examined. After, the effect of the algorithm on performance metrics is assessed. Later, applications of machine learning methods for inventory are explained. Finally, results and recommendations are presented.MethodAccording to the items found in the storage facility, nine characteristics for goods were identified, such as group one, group two, similarity, combustion, combustible, corruption, violation, the quantity of goods entering and leaving the warehouse, and storage space extracted. Subsequently, 17 warehouse performance indicators were used to calculate the cost function through a mathematical programming model, analyzing 55 different scenarios. The commodities were then classified using machine learning algorithms SOM, DBSCAN, and AGNES, based on the identified characteristics and inventory performance indicators, with the cost function calculated for each algorithm. Finally, a comparison was conducted between inventory performance indicators and the cost function using the mathematical planning model and the suggested algorithm, with performance evaluated through statistical tests like the Levene test, Kruskal Wallis test, and the Brown for Syte test.ResultsBased on the inventory of 2800 different types of products in the warehouse of Farasan Industrial and Manufacturing Plant, characteristics were extracted for each product. Additionally, warehouse performance indicators and cost functions were analyzed using mathematical programming models and machine learning algorithms. The performance of three algorithms was compared with a mathematical algorithm through statistical tests such as Levene's test, Kruskal-Wallis test, and Brown-Forsythe test. The results showed that the SOM neural network was more efficient than the other two algorithms. Thus, by combining mathematical programming models and machine learning algorithms, one can improve warehouse performance and reduce costs, providing optimal solutions for factory inventory management.ConclusionIn previous research, it was found that products were stored in warehouses without any prior processing. This created a gap in the field, highlighting the importance of categorizing similar goods before storing them in warehouses to reduce storage costs for factories and manufacturing companies. To address this issue, a sophisticated algorithm was developed to enhance product quality in warehouses across all industries. Reducing storage costs is a common objective for companies and factories, influenced by various factors in their environments. This research focused on developing a model for keeping products in warehouses by considering factors such as product diversity. This study used DBSCAN, AGNES, and SOM algorithms to classify products based on 9 features extracted from the products, which resulted in 55 different classification modes with each of the machine learning algorithms. The development of this algorithm aimed to provide factory and warehouse managers with a solution for making more effective decisions in arranging warehouse products.
project management
Yahya Dorfeshan; Seyed Meysam Mousavi; Behnam Vahdani
Abstract
Critical path method is one of the most widely used approaches in planning and project control. Time is considered a determinative criterion for the critical path. But it seems necessary to regard other criteria in addition to time. Besides time criterion, effective criteria such as quality, cost, risk ...
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Critical path method is one of the most widely used approaches in planning and project control. Time is considered a determinative criterion for the critical path. But it seems necessary to regard other criteria in addition to time. Besides time criterion, effective criteria such as quality, cost, risk and safety are considered in this paper. Then, the developed problem is solved as a multi-attribute decision making problem by a new extension of MULTIMOORA method. Moreover, type-2 fuzzy sets are utilized for considering uncertainties. Type-2 fuzzy sets are more flexible and capable than type-1 fuzzy sets in reflecting uncertainties. Eventually, SWARA method is developed for determining the weights of efficient criteria such as time, cost, quality, risk and safety under type-2 fuzzy environment. Finally, an applied example has been solved to illustrate the calculations and the ability of the proposed approach. Based on the example, it is clear that the longest path in terms of time criterion is not a critical path, and other influential criteria are involved in determining the critical path. IntroductionToday, in the competitive business environment, project management, planning, scheduling, and project control hold significant importance. One of the widely used and common methods in the field of project planning and control is undoubtedly the Critical Path Method (CPM). In the Critical Path Method, activity durations are predetermined. However, in the real world, many projects and activities are executed for the first time and have considerable uncertainties. Therefore, obtaining an accurate estimate of the time and resources required for activities is challenging. However, considering a single criterion, such as time, will not yield fruitful results, and other influential parameters such as risk should also be taken into account. For example, a path that carries a high level of risk may not be the critical path at present, but it may become critical in the future due to the high risk involved. For this reason, this research explores other influential criteria besides time and considers them in determining the critical path.Materials and MethodsIn this study, the problem under investigation is the determination of the critical path while considering other influential criteria in addition to the time criterion. To achieve this, multiple criteria decision-making methods are used to consider criteria such as time, cost, quality, risk, and safety in determining the critical path. Furthermore, to account for the uncertainties of the real world and incorporate expert opinions, type-2 fuzzy sets are utilized. It should be noted that the MULTIMOORA method is employed for ranking the critical paths, while the SWARA method is used to determine the weights of the influential criteria in determining the critical path. Both methods have been extended and developed in a type-2 fuzzy environment.Discussion and Results Initially, the proposed method is solved considering only the time criterion. As observed, the critical path has changed, indicating the importance of other criteria in determining the critical path. Then, the proposed method is solved considering pairwise combinations of the criteria, where the time criterion is treated as a fixed criterion due to its high importance. In fact, the problem is solved considering time and cost, time and risk, time and quality, and time and risk. By increasing or decreasing each criterion, the critical path changes, demonstrating the significance of all criteria in determining the project's critical path. To determine the critical path, it is necessary to consider all criteria together. These variations in the criteria and the resulting change in the critical path clearly indicate the importance and influence of other criteria in determining the critical path.ConclusionIn this article, an extension of the MULTIMOORA multi-criteria decision-making method is presented in the reference section. Additionally, Type-2 fuzzy numbers, which offer more flexibility and better representation of uncertainties compared to Type-1 fuzzy numbers, are utilized. The MULTIMOORA multi-criteria decision-making method is developed to incorporate these Type-2 fuzzy numbers. The opinions of three experts are used numerically for the time and cost criteria and linguistically as linguistic variables for the quality, risk, and safety criteria. Ultimately, the weights of the influential criteria of time, cost, risk, quality, and safety are determined using the developed SWARA method under Type-2 fuzzy environment. Finally, the most critical path is determined by considering not only the time criterion but also the influential criteria of cost, quality, risk, and safety. Based on the conducted research, a set of criteria including time, cost, quality, risk, and safety are used in this article, and additional criteria can also be added to this set.
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.
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.
Behnam Vahdani
Abstract
Today, intense competition in global markets has forced companies to design and manage of supply chains in a better way. Since the role of three factors: location, routing and inventory is important to continue the life of a supply chain so, integration of these three elements will create an efficient ...
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Today, intense competition in global markets has forced companies to design and manage of supply chains in a better way. Since the role of three factors: location, routing and inventory is important to continue the life of a supply chain so, integration of these three elements will create an efficient and effective supply chain. In this study, we investigate the problem of supply chain network design that including routing and inventory problem consist of flow allocation, vehicle routing between facilities, locating distribution centers and also consider the maximum coverage for respond to customer demand. Proposed mathematical model is a nonlinear mixed integer programming model for location-routing-inventory problem in the four-echelon supply chain by considering the multiple conflicting goals of total cost, travel time and maximum coverage. In order to solve the proposed model, three meta-heuristic algorithms (MOPSO, MSGA_II and NRGA) has been used. The accuracy of mathematical model and proposed algorithms are evaluated through numerical examples
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.
Behnam Vahdani
Abstract
In this research, a multi-objective mixed integer programming model is presented to design a healthcare network with risk pooling effect. Since the model parameters have also uncertainty, for closing the model to reality, using robust optimization approach, the model is also extended in a state of uncertainty. ...
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In this research, a multi-objective mixed integer programming model is presented to design a healthcare network with risk pooling effect. Since the model parameters have also uncertainty, for closing the model to reality, using robust optimization approach, the model is also extended in a state of uncertainty. Objective functions that have been used, include minimization of transportation costs, costs related to sterilization, as well as the movement of resources. We are also looking for maximizing the minimum level of service provision of healthcare centers to customers. Also, for solving the proposed model, we utilized a multi-objective fuzzy method which is developed in recent years. Moreover, several numerical examples are brought up to show the accuracy and validity of the model. The results obtained from this analysis, showed the accuracy of behavior of the model and the proposed approach in different modes. Computational results show that the robust model provides more high-quality solutions, in a way that it has far less standard deviation compared to deterministic model
