A stochastic multi-item replenishment and delivery problem with lead-time reduction initiatives and the solving methodologies

Ligang Cui, Jie Deng, Rui Liu, Dongyang Xu, Yajun Zhang and Maozeng Xu

Applied Mathematics and Computation, 2020, vol. 374, issue C

Abstract: With the intensification of time-based competition, the importance of reducing lead-time by rapidly delivering multi-orders has been underscored in the process of replenishment-storage-transportation. This perception has prompted enterprises to increase expenditure on purchasing modern time-tracing technologies (e.g. RFID), and equipping facilities for item fast movement (e.g. high-rack automatic shelves) to retain customers. In this study, we explore a novel extension of the multi-item joint replenishment problem (JRP) with lead-time compressing initiatives. By assuming controllable lead-time, we construct a stochastic periodic-review joint replenishment and delivery (JRD) model to investigate impacts of capital investment in lead-time reduction to the decisions of multi-item joint replenishment and delivery. To solve the proposed JRD, two heuristics and a differential evolutionary algorithm are presented based on the model property analyses. The experimental results reveal the performance differences (e.g., searching speed, robustness and searching effectiveness) of three algorithms. Furthermore, our findings have managerial implications that proper investment in lead-time reduction not only helps shorten replenishment time and cut major ordering cost, but can also reduce the system cost.

Keywords: Joint replenishment; Lead-time reduction; Stochastic demand; Heuristic; Differential evolution (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:374:y:2020:i:c:s0096300320300242

DOI: 10.1016/j.amc.2020.125055

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