A hybrid differential evolution algorithm for a stochastic location-inventory-delivery problem with joint replenishment

Data Science and Management Pub Date : 2022-09-01 DOI:10.1016/j.dsm.2022.07.003

Sirui Wang, Lin Wang, Yingying Pi

{"title":"A hybrid differential evolution algorithm for a stochastic location-inventory-delivery problem with joint replenishment","authors":"Sirui Wang, Lin Wang, Yingying Pi","doi":"10.1016/j.dsm.2022.07.003","DOIUrl":null,"url":null,"abstract":"<div><p>A practical stochastic location-inventory-delivery problem with multi-item joint replenishment is studied. Unlike the conventional location-inventory model with a continuous-review (<em>r</em>, <em>Q</em>) inventory policy, the periodic-review inventory policy is adopted with multi-item joint replenishment under stochastic demand, and the coordinated delivery cost is considered. The proposed model considers the integrated optimization of strategic, tactical, and operational decisions by simultaneously determining (a) the number and location of distribution centers (DCs) to be opened, (b) the assignment of retailers to DCs, (c) the frequency and cycle interval of replenishment and delivery, and (d) the safety stock level for each item. An intelligent algorithm based on particle swarm optimization (PSO) and adaptive differential evolution (ADE) is proposed to address this complex problem. Numerical experiments verified the effectiveness of the proposed two-stage PSO-ADE algorithm. A sensitivity analysis is presented to reveal interesting insights that can guide managers in making reasonable decisions.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764922000297/pdfft?md5=84902aa89790bfa1a62890f17b06f72d&pid=1-s2.0-S2666764922000297-main.pdf","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data Science and Management","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666764922000297","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 10

Abstract

A practical stochastic location-inventory-delivery problem with multi-item joint replenishment is studied. Unlike the conventional location-inventory model with a continuous-review (r, Q) inventory policy, the periodic-review inventory policy is adopted with multi-item joint replenishment under stochastic demand, and the coordinated delivery cost is considered. The proposed model considers the integrated optimization of strategic, tactical, and operational decisions by simultaneously determining (a) the number and location of distribution centers (DCs) to be opened, (b) the assignment of retailers to DCs, (c) the frequency and cycle interval of replenishment and delivery, and (d) the safety stock level for each item. An intelligent algorithm based on particle swarm optimization (PSO) and adaptive differential evolution (ADE) is proposed to address this complex problem. Numerical experiments verified the effectiveness of the proposed two-stage PSO-ADE algorithm. A sensitivity analysis is presented to reveal interesting insights that can guide managers in making reasonable decisions.

查看原文本刊更多论文

联合补货随机定位-库存-交货问题的混合差分进化算法

研究了一个实际的多物品联合补货随机定位-库存-交货问题。与传统的连续评审(r, Q)库存策略不同，采用随机需求下多项目联合补货的周期评审库存策略，并考虑协调配送成本。提出的模型通过同时确定(a)要开设的配送中心(dc)的数量和位置，(b)零售商到dc的分配，(c)补货和交付的频率和周期间隔，以及(d)每件商品的安全库存水平，考虑了战略、战术和运营决策的综合优化。为了解决这一复杂问题，提出了一种基于粒子群优化(PSO)和自适应差分进化(ADE)的智能算法。数值实验验证了所提两阶段PSO-ADE算法的有效性。提出了一个敏感性分析，揭示有趣的见解，可以指导管理者做出合理的决策。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Data Science and Management

CiteScore

7.50

自引率

0.00%

发文量