Engineering Optimization
Elnaz Farhang zad; Reza Ehtesham Rasi; Davood Gharakhani
Abstract
This paper examines the use of hybrid metaheuristic algorithms to optimize order quantity in a single manufacturer-multi-supplier two-level JIT supply chain in production system. Over the years, production systems have largely been controlled by either MRP (Material Requirements Planning), JIT (Just ...
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This paper examines the use of hybrid metaheuristic algorithms to optimize order quantity in a single manufacturer-multi-supplier two-level JIT supply chain in production system. Over the years, production systems have largely been controlled by either MRP (Material Requirements Planning), JIT (Just in Time) or OPT (Optimized Production Technology) paradigm. In the supply chain environment, traditional material demand planning does not consider the supplier's supply capacity and economic benefits, which is not conducive to the long-term cooperation of upstream and downstream enterprises in the supply chain. The main goal of this paper is to optimize ordering batches based on MRP and JIT in supply chain. There is limited research designing and optimizing the supply chain / procurement network. This study is among the first to integrate supplier selection to optimize performance indicators in supply chain network design considering minimization of total cost of JIT supply chain order batch coordination adjustment model. The BOM constraints and MRP formulation principles of product production are followed to minimize the supply chain the total cost of downstream companies’ inventory, transportation, out of stock, and crashing is the target. The MRP-led supply chain ordering batch collaborative optimization model is constructed; the manufacturer’s main production plan is adjusted to change the procurement plan to obtain supplier supplies according to the scheme, an improved discrete particle swarm optimization algorithm and genetic algorithm is designed to solve the model; the feasibility of the model is verified by an example. The effectiveness of the algorithm is proved through the analysis and comparison of the algorithm results.
Supply chain management
Meysam Donyavi Rad; Ehsan Sadeh; Zeinolabedin Amini Sabegh; Reza Ehtesham Rasi
Abstract
The natural disasters of the last few decades clearly reveal that natural disasters impose high financial and human costs on governments and communities. Concerns in this regard are growing day by day. Making the right decisions and taking appropriate and timely measures in each phase of the crisis management ...
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The natural disasters of the last few decades clearly reveal that natural disasters impose high financial and human costs on governments and communities. Concerns in this regard are growing day by day. Making the right decisions and taking appropriate and timely measures in each phase of the crisis management cycle will reduce potential damage at the time of the disaster and reduce the vulnerability of society. Therefore, in this research, a mathematical model of crisis logistics planning considering the problem of primary and secondary crisis in disaster relief is introduced, which is the innovation of this research. In the primary crisis, the goal is to provide services and relief goods to crisis areas, and in the second stage, the secondary crisis that occurs after the primary crisis seeks to provide relief to crisis centers and transfer the injured to relief centers. Therefore, this research proposes a mathematical fuzzy ideal programming model in two primary and secondary crises. In the primary crisis, the goal is to provide services and relief goods to crisis-stricken areas. The secondary crisis, which occurs after the primary crisis, aims to support crisis-stricken centers and move injured people to relief bases in the second step. According to the proposed model, Bertsimas-Sim’s fuzzy programming that formulation proposed by Bertsimas and Sim [1] and robust approach we initially used. The Epsilon constraint method was used to solve the low-dimensional model. Multi-objective meta-heuristic algorithms have been designed to handle the computational complexity of large-scale real-time problems. Multiple comparisons and analyses have been proposed to assess the performance of the model and problem-solving capabilities. The results indicate that the proposed approach can be applied and implemented to develop a real-world humanitarian logistics network.