当前位置: X-MOL 学术Int. J. Inf. Technol. Decis. Mak. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
A Bi-Objective Simulation-Based Optimization Approach for Optimizing Price, Warranty, and Spare Part Production Decisions Under Imperfect Repair
International Journal of Information Technology & Decision Making ( IF 4.9 ) Pub Date : 2021-04-10 , DOI: 10.1142/s021962202150022x
Mohsen Afsahi 1 , Ali Husseinzadeh Kashan 1 , Bakhtiar Ostadi 1
Affiliation  

This paper proposes an efficient methodology based on the Monte-Carlo simulation-based bi-objective optimization, to determine base-warranty (BW) and extended warranty (EW) parameters based on the product lifecycle. The first objective, which is from the manufacturer’s perspective, maximizes the profit while the second objective minimizes the expected number of failures that occurred during the out-of-warranty period. The manufacturer can rectify failed products via minimal repair, imperfect repair and perfect repair. The optimization model has decision variables including the product price, BW length, EW length, EW price, product failure rate, imperfect repair level, and spare part production rate in each time interval. The structure of the model admits the design of a hybrid method based on the multi-objective optimization search algorithm, Monte-Carlo simulation and an exact Out-Of-Kilter (OOK) algorithm. The nondominated sorting genetic algorithm (NSGA-II) and the multi-objective particle swarm optimization (MOPSO) algorithm are used as search algorithms. The proposed approach consists of three stages, where in the first stage, product price, BW length, EW length, EW price, product failure rate, imperfect repair level are set by NSGA-II/MOPSO. In the second stage, the number of failed products is calculated by the Monte-Carlo simulation and in the third stage, we show that the spare part inventory control sub problem can be transformed to a minimum cost network flow problem which is optimized by the OOK algorithm to attain a unified solution. A Taguchi approach is used to find the optimum level of parameters. The performance of algorithms is compared based on three different metrics. Results on a real-world problem demonstrate that the NSGA-II-OOK algorithm is more effective than the MOPSO-OOK algorithm. Through a sensitivity analysis, we analyze how various levels of planning horizon can affect Pareto-set which indicates valuable managerial insight.

中文翻译:

一种基于双目标仿真的优化方法,用于在不完美修复下优化价格、保修和备件生产决策

本文提出了一种基于蒙特卡罗模拟的双目标优化的有效方法,用于根据产品生命周期确定基本保修 (BW) 和延长保修 (EW) 参数。从制造商的角度来看,第一个目标最大化利润,而第二个目标最小化在保修期内发生的预期故障数量。制造商可以通过最小修复、不完美修复和完美修复来纠正失败的产品。优化模型的决策变量包括每个时间间隔的产品价格、BW长度、EW长度、EW价格、产品故障率、不完美修复水平和备件生产率。该模型的结构允许设计一种基于多目标优化搜索算法的混合方法,蒙特卡罗模拟和精确的失衡 (OOK) 算法。非支配排序遗传算法(NSGA-II)和多目标粒子群优化(MOPSO)算法被用作搜索算法。所提出的方法由三个阶段组成,在第一阶段,产品价格、BW 长度、EW 长度、EW 价格、产品故障率、不完美修复水平由 NSGA-II/MOPSO 设置。在第二阶段,通过蒙特卡罗模拟计算不合格产品的数量,在第三阶段,我们证明了备件库存控制子问题可以转化为通过 OOK 优化的最小成本网络流问题算法得到一个统一的解决方案。田口方法用于找到参数的最佳水平。基于三个不同的指标比较算法的性能。实际问题的结果表明,NSGA-II-OOK 算法比 MOPSO-OOK 算法更有效。通过敏感性分析,我们分析了不同级别的规划视野如何影响帕累托集,这表明了有价值的管理洞察力。
更新日期:2021-04-10
down
wechat
bug