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首页> 外文期刊>International journal of management science and engineering management >A bi-objective multi-item capacitated lot-sizing model: Two Pareto-based meta-heuristic algorithms
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A bi-objective multi-item capacitated lot-sizing model: Two Pareto-based meta-heuristic algorithms

机译：双目标多项目容量批量模型：两种基于帕累托的元启发式算法

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摘要

Lot-sizing problems (LSP) form a class of production planning problems in which available quantities are always considered as decision variables in the production plan. The goal of this paper is to present a multi-item capacitated lot-sizing problem (MICLSP) with setup times, safety stock deficit costs, demand shortage costs - both backorder and lost sale states - and different manners of production. Although a considerable amount of research has concentrated on model development and solution procedures in the terms of single-objective problems in the past decade, to make the model more realistic this paper develops a bi-objective mathematical programming model with two conflicting objectives including: (1) minimizing the total cost considered by the production plans including production costs with different manners of production, inventory costs, safety stock deficit costs, shortage costs and setup costs; (2) minimizing the required storage space. Considering that the proposed model is NP-hard, we propose two novel Pareto-based multi-objective meta-heuristic algorithms called multi-objective vibration damping optimization (MOVDO) and the non-dominated ranking genetic algorithm (NRGA) for the literature on LSP. In order to validate the performance of the proposed MOVDO and NRGA, a non-dominated sorting genetic algorithm (NSGA-Ⅱ), one of the most common multi-objective meta-heuristic algorithms, is applied. The optimal solutions are also reported to justify the results. Finally, we calibrate both algorithms by robust response surface methodology (RSM); then, the results are analysed on some test problems, both graphically and statistically.
机译：批量确定问题（LSP）构成了一类生产计划问题，在该问题中，可用数量始终被视为生产计划中的决策变量。本文的目的是提出一个多项目容量批量问题（MICLSP），其中包括建立时间，安全库存短缺成本，需求短缺成本-缺货和丢失销售状态-以及不同的生产方式。尽管在过去的十年中，大量研究集中在模型开发和求解程序方面，涉及单目标问题，但为了使模型更切合实际，本文开发了一种具有两个相互矛盾的目标的双目标数学编程模型，其中包括： 1）最小化生产计划考虑的总成本，包括具有不同生产方式的生产成本，库存成本，安全库存赤字成本，短缺成本和设置成本； （2）最小化所需的存储空间。考虑到所提出的模型是NP难的，我们针对LSP的文献提出了两种新颖的基于Pareto的多目标元启发式算法，称为多目标振动阻尼优化（MOVDO）和非支配排序遗传算法（NRGA）。 。为了验证所提出的MOVDO和NRGA的性能，应用了一种非支配的排序遗传算法（NSGA-Ⅱ），它是最常见的多目标元启发式算法之一。还报告了最佳解决方案以证明结果的合理性。最后，我们通过鲁棒的响应面方法（RSM）校准这两种算法；然后，以图形和统计方式分析一些测试问题的结果。

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