论文标题
一种用于分层枢纽和灵活流动商店问题的新型混合多目标调度模型
A New Hybrid Multi-Objective Scheduling Model for Hierarchical Hub and Flexible Flow Shop Problems
论文作者
论文摘要
近年来,技术和生活方式已越来越针对消费主义。因此,对商业企业的最终客户最重要的是价格和交付时间。因此,如今,拥有最佳交付时间的重要性变得越来越明显。调度可用于以这种方式优化供应链和生产系统,这是一种降低成本和提高生产率的实用方法。本文提出了具有三个服务级别的分层集线器结构(HHS)的多目标调度模型。工厂和客户集线器(第二层)和中央位于工厂拥有灵活的流店(FFS)环境的第一层。非中央集线器(第三级)负责将工厂在工厂生产的产品交付给客户。客户节点和工厂分别连接到第二级,非中心枢纽连接到第三级。该模型的目标是最大程度地减少运输,生产成本以及产品到达时间。为了验证和评估该模型,已经通过加权总和和电子构造方法对小实例进行了详细介绍和分析。因此,根据理想的平均距离(中)度量,比较了设计实例的两种方法。由于NP硬度导致先前提出的解决大规模问题的方法是耗时的,因此开发了一种荟萃分析方法来解决大规模的问题。
Technologies and lifestyles have been increasingly geared toward consumerism in recent years. Accordingly, it is both the price and the delivery time that matter most to the ultimate customers of commercial enterprises. Consequently, the importance of having an optimal delivery time is becoming increasingly evident these days. Scheduling can be used to optimize supply chains and production systems in this manner, which is one practical method for lowering costs and boosting productivity. This paper suggests a multi-objective scheduling model for hierarchical hub structures (HHS) with three levels of service. The factory and customers hub (second level) and central are on the first level in which the factory has a Flexible Flow Shop (FFS) environment. The noncentral hub (third level) is responsible for the delivery of products made in the factory to customers. Customer nodes and factories are connected separately to the second level, and the non-central hubs are connected to the third level. The model's objective is to minimize transportation and production costs and product arrival times. To validate and evaluate the model, small instances have been solved and analyzed in detail with the weighted sum and e-constraint methods. Consequently, based on the ideal mean distance (MID) metric, the two methods were compared for the designed instances. As NP-hardness causes the previously proposed methods to solve large-scale problems to be time-consuming, a meta-heuristic method was developed to solve the large-scale problem.