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Heuristic Scheduling in Machining-Assembly Flowshop under Supply Chain Environment

供应链环境下加工装配流水作业启发式调度

基本信息

批准号：
17510130
负责人：
MORIZAWA Kazuko
金额：
$ 0.9万
依托单位：
Osaka Prefecture University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2005
资助国家：
日本
起止时间：
2005 至 2006
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-17510130/
关键词：
Systems Engineering Production Management Scheduling Machining-Assembly Cell Supply Chain Management

项目摘要

This research dealt with scheduling problems to minimize makespan in a flexible manufacturing cell(FMC). FMC has attracted manufactures because it is effective for machining various component parts and assembling them into many kinds of products in a small lot to meet a rapid change in production-mix and its demand. We formulated the FMCs as machining-assembly flowshop(MAFS) models to minimize makespan and proposed two types of heuristic algorithms for finding a near optimum schedule to this problem efficiently.One of the proposed algorithms is a heuristic algorithm, in which some promising schedules for the original MAFS model are found by applying NEH algorithm to converted virtual flowshop models in various ways, and then better schedules are searched by applying some job-moving strategies, such as Johnson-rule-based strategy, critical-job/line-based strategies, to the schedules.Another one is a Branch-and-Bound(B＆B) based local search algorithms, which searches neighborhood of initial schedules in an enumerative manner by using a branching procedure in a branch-and-bound algorithm. In this algorithm, some initial schedules are found first by using promising heuristic methods, and then a B＆B-based parallel local search is implemented for obtaining an optimal(or a near-optimal) schedule.Numerical experiments were implemented to demonstrate that booth of the proposed algorithms can efficiently provide a nearoptimum schedule with high accuracy such as mean relative error being less than 1% and the maximum relative error being at most 3%.Extension of the proposed algorithms to the case of dynamic and multiobjective scheduling will be one of our future works.

本文研究了柔性制造单元（FMC）中最小化最大完工时间的调度问题。FMC能有效地加工各种零件，并在小批量内组装成多种产品，以满足生产结构的快速变化及其需求，因此吸引了制造商。我们将fmc定义为最小化最大完工时间的加工-装配流程车间（mffs）模型，并提出了两种启发式算法来有效地找到该问题的近最优调度。其中一种算法是启发式算法，该算法将NEH算法应用于转换后的虚拟流水车间模型，通过各种方法找到适合原mffs模型的有希望的调度，然后通过基于johnson -rule策略、基于关键作业/线策略等作业移动策略来搜索较好的调度。另一种是基于分支定界（B&B）的局部搜索算法，该算法利用分支定界算法中的分支过程，以枚举的方式搜索初始调度的邻域。在该算法中，首先使用有前途的启发式方法找到一些初始调度，然后实现基于b&b的并行局部搜索以获得最优（或接近最优）调度。数值实验结果表明，所提算法能有效地提供近最优调度，平均相对误差小于1%，最大相对误差不超过3%。将所提出的算法扩展到动态和多目标调度将是我们未来的工作之一。