A hybrid approach based on the variable neighborhood search and particle swarm optimization for parallel machine scheduling problems-A case study for solar cell industry
A hybrid approach based on the variable neighborhood search and particle swarm optimization for parallel machine scheduling problems-A case study for solar cell industry
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DOI：
10.1016/j.ijpe.2012.06.013
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发表时间：
2013-01-01
影响因子：
12
通讯作者：
Chen, Tzu-Li
中科院分区：
文献类型：
--
作者：
Chen, Yin-Yann;Cheng, Chen-Yang;Chen, Tzu-Li
研究了类似于传统的混合流水作业调度问题的太阳能电池产业调度问题。在典型的具有并行机的HFS问题中，需要事先为每个订单分配机器资源，然后才能确定每个阶段的最优多处理机任务调度。然而，太阳能电池制造中的挑战是机器的数量可以动态调整，以在尽可能短的时间内完成工作。因此，本文研究了一类具有并行处理、专用机器、安装时间无关和安装时间依赖的多阶段HFS调度问题。目标是调度作业的生产顺序，子批量的数量，并动态地将子批量分配给并行机，使完工时间最小化。将问题描述为混合整数线性规划(MILP)模型。提出了一种基于变邻域搜索和粒子群算法(VNPSO)的混合算法来获得近似最优解。初步计算结果表明，改进的VNPSO不仅在合理的时间内提供了高质量的解，而且优于经典的分枝定界法和目前CASE公司所采用的行业启发式方法。(C)2012爱思唯尔B.V.保留所有权利。
This paper studies a solar cell industry scheduling problem which is similar to the traditional hybrid flow shop scheduling (HFS). In a typical HFS with parallel machines problem, the allocation of machine resources for each order should be scheduled in advance and then the optimal multiprocessor task scheduling in each stage could be determined. However, the challenge in solar cell manufacturing is the number of machines can be dynamically adjusted to complete the job within the shortest possible time. Therefore, the paper addresses a multi-stage HFS scheduling problem with characteristics of parallel processing, dedicated machines, sequence-independent setup time, and sequence-dependent setup time. The objective is to schedule the job production sequence, number of sublots, and dynamically allocate sublots to parallel machines such that the makespan time is minimized. The problem is formulated as a mixed integer linear programming (MILP) model. A hybrid approach based on the variable neighborhood search and particle swarm optimization (VNPSO) is developed to obtain the near-optimal solution. Preliminary computational study indicates that the developed VNPSO not only provides good quality solutions within a reasonable amount of time but also outperforms the classic branch and bound method and the current industry heuristic practiced by the case company. (C) 2012 Elsevier B.V. All rights reserved.