Ehsan Yadegari; Akbar Alem Tabriz; Mostafa Zandieh
Abstract
Over the past decade, due to environmental laws and the competitive environment, development of an effective tactical plan for efficient and integrated supply chain and considering the responsibility of organizations to collect defective goods seems impossible. In this paper a mixed-integer linear programming ...
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Over the past decade, due to environmental laws and the competitive environment, development of an effective tactical plan for efficient and integrated supply chain and considering the responsibility of organizations to collect defective goods seems impossible. In this paper a mixed-integer linear programming is considered to mathematically model the essentially five stages along our supply chain network: suppliers, manufacturers, DCs, customers, and Dismantlers.Delivers raw materials from suppliers to factories and then through distribution centers, delivering the final product to customers. On the other hand, it simultaneously collects recycled goods from customers and enters the cycle of safe reconstruction or destruction.The aim of this model is minimizing the costs of establishing facilities at potential points as well as the optimal flow of materials in the network layers. Since the problem is NP-hard, to solve it, the cloud theory based simulated annealing algorithm has been used. We also used the tree-covering method to show the answer, which uses fewer arrays than other methods in the literature. To analyze the accuracy and speed of the proposed algorithm, we compared its performance with the genetic and simulated annealing algorithm. The results show that the cost function in the cloud-based refrigeration simulation algorithm provides more accurate answers than both algorithms studied in the literature. The results show that the cost function in the cloud-based simulated annealing algorithm provides more accurate answers than both algorithms studied in the literature. Also, in terms of convergence rate criterion, the proposed method has better position than the genetic algorithm, but it is not significantly different from simulated annealing algorithm.
Shamsoldin Hosseini; Parham Azimi; Mani Sharifi; Mostafa Zandieh
Abstract
The aim of dynamic facility layout problem is to find the best layout for facilities at a multi period planning horizon so that the total cost of material handling and relocating the facilities is minimized. This paper developed a bi-objective mathematical model which is able to simultaneously minimize ...
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The aim of dynamic facility layout problem is to find the best layout for facilities at a multi period planning horizon so that the total cost of material handling and relocating the facilities is minimized. This paper developed a bi-objective mathematical model which is able to simultaneously minimize the material handling costs between facilities and the cost of rearrangement facilities and material handling time. Due to probabilistic characteristic of the transporters, such as the time of handling operation and existence of the failure, calculating the time required to carry the material using analytical relationships is impossible.Therefore, this paper uses the simulation approach and artificial neural networks. In this approach, a lot of scenarios are generated by combining of various levels of variables. Each scenario shows the location of the facilities and how transportation operations in each period is performed. Then each of these scenarios is implemented through computer simulation and simulation results are considered as the response variable. Finally, using input and response variable, an artificial neural network is trained to accurately estimate the time of carrying out the transportation operations. Given that the above problem is a NP-hard; this paper proposes a new meta-heuristic algorithm to optimize the problem and compares the performance of the proposed algorithm with existing algorithms in literature.
Abdolreza Sedighpour; Mostafa Zandieh; Akbar Alem Tabriz; behroz dori
Abstract
Within two recent decades, the complexity of business environment, dynamics, uncertainty and higher environmental fluctuations, concepts such as globalization and increasing competition made many changes in the equations ruling on the industries supply chain. In such conditions, the businesses must make ...
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Within two recent decades, the complexity of business environment, dynamics, uncertainty and higher environmental fluctuations, concepts such as globalization and increasing competition made many changes in the equations ruling on the industries supply chain. In such conditions, the businesses must make themselves ready for encountering the continuous flow of challenges such as economic crises, sanctions, exchange rate and prices fluctuations, limitations of manufacturing system or natural disasters. “Resiliency” is one of strategies for dealing with such challenges. The present study aims to review the systematic studies based on the strategic position of pharmaceutical industry as a part of society’s health system and present here the resilient supply chain model. For this purpose, in addition to contemplating in the literature review, interview to the experts and using Delphi method, the resilient elements and indices of supply chain were identified and extracted, and a questionnaire was designed and provided to the population of pharmaceutical industry. The results were analyzed using structural Equations modeling technique and Lisrel software, and the proposed model of research was accepted upon explaining the associations between factors. This model studied the relationship between elements such as drivers, vulnerabilities, capabilities and empowerments of supply chain and their effect on each other. Summary of study indicates that the managers of pharmaceutical industries through making or using the capabilities and strengthening the empowerments can reduce the factors that make the companies susceptible for the disruption, and achieve the required resiliency to deal with them.
Mina Riahee; Mostafa Zandieh
Abstract
Recovering, recycling, and remanufacturing end-of-life products (disassembly line) are appropriate methods of reducing the environmental impact associated with wastes. A disassembly line is a viable option for doing so. The objective of the disassembly line balancing problem (DLBP) is to coordinate disassembly ...
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Recovering, recycling, and remanufacturing end-of-life products (disassembly line) are appropriate methods of reducing the environmental impact associated with wastes. A disassembly line is a viable option for doing so. The objective of the disassembly line balancing problem (DLBP) is to coordinate disassembly line activities so that total operating times of workstations are nearly equal. The disassembly process mainly aims to reuse components in end-of-life products and thus reduce adverse environmental effects. This paper employs an approach based on the Kano model, Fuzzy AHP, M-TOPSIS, and PROMETHEE. Furthermore, using AND/OR precedence relationships, the optimal sequence of disassembly is obtained. Tasks are assigned to workstations according to priority and precedence relationships. An illustrative example of the proposed method is solved using both M-TOPSIS and PROMETHEE. Both methods lead to a decrease of two seconds in total cycle time. Despite yielding equal results, PROMETHEE is superior to M-TOPSIS in terms of complexity and ease of use. However, it takes longer to complete.
Mehdi Yazdani; Mostafa Zandieh; Reza Tavakkoli-Moghaddam
Volume 12, Issue 33 , July 2015, , Pages 43-74
Abstract
In this paper, the dual-resource constrained flexible job-shop scheduling problem (DRCFJSP) with objective of minimizing the makespan is investigated. Under studied problem is NP-hard and mainly includes three sub-problems. The first one is to assign each operation to a machine out ...
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In this paper, the dual-resource constrained flexible job-shop scheduling problem (DRCFJSP) with objective of minimizing the makespan is investigated. Under studied problem is NP-hard and mainly includes three sub-problems. The first one is to assign each operation to a machine out of a set of capable machines, the second one is to determine a worker among a set of skilled workers for processing each operation on the selected machine and the third one deals with sequencing the assigned operations on the machines considering workers in order to optimize the performance measure. In this paper, we provide a mathematical model for this problem and then propose a hybrid meta-heuristic algorithm for solving the problem. The proposed hybrid algorithm uses variable neighborhood search and simulated annealing algorithms to search in the solution space. Computational study with randomly generated test problems is performed to evaluate the performance of the proposed algorithm. The results show the proposed algorithms are effective approaches for solving the DRCFJSP.
