论文标题
可持续闭环供应链网络的多目标优化需求不确定性:一种遗传算法
Multi-Objective Optimization for Sustainable Closed-Loop Supply Chain Network Under Demand Uncertainty: A Genetic Algorithm
论文作者
论文摘要
数十年来,供应链管理一直集中在通过精致的供应商,制造商和消费者网络管理流量的生产方法上。最近,能源和材料速率已被大大消耗以改善该行业,使可持续发展成为高级和发展中国家的核心问题。提出了一种新的供应链管理方法,以维持经济以及供应链设计的环境问题,以及计划范围内最高的可靠性,以尽可能满足客户的需求。本文旨在优化一个新的可持续闭环供应链网络,以维持财务状况以及环境因素,以最大程度地减少对环境的负面影响,并最大程度地提高向客户提高可靠性的产品的平均产品总数。仓库的可靠性已被认为是需求不确定性的。已建议这种方法最小化总成本和总CO2排放的多目标数学模型,并最大程度地利用了建立闭环供应链的可靠性。两种优化方法用于多目标遗传算法优化方法和加权总和方法。两个结果表明了这种方法的最佳性。本文还显示了使用Pareto Front的最佳点,以清晰地识别Optima。结果被批准以验证模型的效率以及维持财务,环境和可靠性问题的方法。
Supply chain management has been concentrated on productive ways to manage flows through a sophisticated vendor, manufacturer, and consumer networks for decades. Recently, energy and material rates have been greatly consumed to improve the sector, making sustainable development the core problem for advanced and developing countries. A new approach of supply chain management is proposed to maintain the economy along with the environment issue for the design of supply chain as well as the highest reliability in the planning horizon to fulfill customers demand as much as possible. This paper aims to optimize a new sustainable closed-loop supply chain network to maintain the financial along with the environmental factor to minimize the negative effect on the environment and maximize the average total number of products dispatched to customers to enhance reliability. The situation has been considered under demand uncertainty with warehouse reliability. This approach has been suggested the multi-objective mathematical model minimizing the total costs and total CO2 emissions and maximize the reliability in handling for establishing the closed-loop supply chain. Two optimization methods are used namely Multi-Objective Genetic Algorithm Optimization Method and Weighted Sum Method. Two results have shown the optimality of this approach. This paper also showed the optimal point using Pareto front for clear identification of optima. The results are approved to verify the efficiency of the model and the methods to maintain the financial, environmental, and reliability issues.