|تعداد مشاهده مقاله||15,580,127|
|تعداد دریافت فایل اصل مقاله||10,888,187|
|Control and Optimization in Applied Mathematics|
|مقاله 4، دوره 7، شماره 1، فروردین 2022، صفحه 53-78 اصل مقاله (433.68 K)|
|نوع مقاله: Research Article|
|شناسه دیجیتال (DOI): 10.30473/coam.2022.60472.1173|
|Hamidreza Ayoughi1؛ Hossein Dehghani Poudeh* 2؛ Abbas Raad3؛ Davood Talebi3|
|1Department of Industrial Management, South Tehran Branch, Islamic Azad University, Tehran, Iran|
|2Department of Management, Malek Ashtar University of Technology, Faculty of Management, Tehran, Iran|
|3Department of Management, Shahid Beheshti University, Faculty of Management and Accounting, Tehran, Iran|
|In this paper, a stable multi-objective model of location, inventory, and supply chain routing is presented under conditions of uncertainty and using a passive defense approach. Parameters such as demand, cost of setting up the facility and cost of maintaining inventory are considered uncertain and in the form of triangular fuzzy numbers. Also, in order to increase supply chain resilience, the characteristics and capabilities of passive defense in the supply chain, such as ``ready flow rate'', ``security of backup routes'', ``possibility of deployment of resources and equipment'', and ``the principle of dispersion for location'' are considered. Multipurpose, multipartite algorithms, based on the Pareto archive and genetic algorithm, are used to solve the model. The results of validation show that the proposed model is valid and feasible, and the proposed algorithm is also valid and converges to the optimal solution. Sample problems, in three groups of small, medium and large, are solved by two algorithms, and the results are compared based on quality, dispersion, uniformity and execution time. The results of this section show that in all cases, the multi-objective particle mass algorithm has a higher ability than the GA to produce solutions of higher quality and to explore and extract the scalable area of the solution. Also, the comparison of the execution times of the algorithms indicates that the multi-objective particle mass algorithm has a higher solution time.|
|Supply chain؛ Sustainability؛ Passive defense؛ Multi-objective fuzzy optimization؛ Meta-heuristic algorithm|
 Aghaei M., Ebadati M. (2013). “Design supply chain management networks by new risk passive defense model and solved it by heuristic algorithm. Case study: Warehouse and retail ETKA organization”, Research Journal of Recent Sciences, 2(9), 18-24.
 Brandenburg M., Govindan K., Sarkis J., Seuring S. (2014). “Quantitative models for sustainable supply chain management: Developments and directions”, European Journal of Operational Research, 233, 299-312.
 Carter C. R., Rogers D. S. (2008). “A framework of sustainable supply chain management: moving toward new theory”, International Journal of Physical Distribution & Logistics Management, 38(5), 360-387.
 Deb K. (2001). “Multi-objective optimization using evolutionary algorithms”, Kluwer Academic.
 Gao Q., Xu H., Li A. (2022). “The analysis of commodity demand predication in supply chain network based on particle swarm optimization algorithm”, Journal of Computational and Applied Mathematics, 400 (15), Article ID 113760.
 Golinin R., Longoni A., Cagliano R. (2014). “Developing sustainability in global manufacturing networks: The role of site competence on sustainability performance”, International Journal of Production Economics, 147, 448-459.
 Golpira H., Khan S. A. R., Jian C., Zhang Y., Kumar A., Sharif A. (2019). “Environmental, social and economic growth indicators spur logistics performance: From the perspective of south Asian association for regional cooperation countries”, Journal of Cleaner Production, 214, 1011-1023.
 Golpira H., Najafi E., Zandieh M., Sadi-Nezhad S. (2017). “Robust bi-level optimization for green opportunistic supply chain network design problem against uncertainty and environmental risk”, Computers & Industrial Engineering, 107, 301-312.
 Haque M., Ahsan Akhtar Hasin M. (2021). “Fuzzy genetic algorithm-based model for bullwhip effect reduction in a multi-stage supply chain”, International Journal of Supply Chain and Inventory Management. 4(1), 1-24.
 Hsueh (2015). “A bi-level programming model for corporate social responsibility collaboration in sustainable supply chain management”, Transportation Research Part E, 73, 84-95.
 Hussain A. A., Manoj Kumar T. (2015). “An ISM-ANP integrated framework for evaluating alternatives for sustainable supply chain management”, Applied Mathematical Modelling, 40 (5-6).
 Peng Peng, Lawrence Snyder, Zumbul Atan, Burcu Sinsoysal (2016). “OR/MS Models for supply chain disruptions: A review”, IIE Transactions 48(2), 89-109.
 Salehi, M., Jabarpour, E. (2020). “Modeling and solving a multi-objective location-routing problem considering the evacuation of casualties and homeless people and fuzzy paths in relief logistics”. Control and Optimization in Applied Mathematics, 5(1), 41-65.
 Shishebor I. D. (201). “Reliable multi-product multi-vehicle multi-type link logistics network design: A hybrid heuristic algorithm”, Journal of Industrial and Systems Engineering, 9(1), 92-108.
 Tavakoli-Moghaddam, Alikhani-Kooshkak, Jamili A., Ebrahimnejad S. (2019). “Multi-objective mathematical modeling of an integrated train makeup and routing problem in an Iranian railway company”, Scientia Iranica E, 26(6), 3765-3779.
 Yixin Zh., Zhen G., (2021). “Research on intelligent solution of service industry supply chain network optimization based on genetic algorithm”, Hindawi Journal of Healthcare Engineering, Special Issue, Volume 2021, 6 pages.
 Yumei C., Xinqun F., (2021). “An optimization model of raw material supply chain using improved genetic algorithm for primary and secondary school uniform under IoT environment”, Hindawi Mobile Information Systems, Special Issue, Volume 2021, 11 pages.
تعداد مشاهده مقاله: 271
تعداد دریافت فایل اصل مقاله: 139