A novel locust swarm algorithm for the joint replenishment problem considering multiple discounts simultaneously

被引:28
作者
Cui, Ligang [1 ]
Deng, Jie [2 ]
Wang, Lin [3 ]
Xu, Maozeng [1 ]
Zhang, Yajun [3 ]
机构
[1] Chongqing Jiaotong Univ, Sch Econ & Management, 66 Xuefu Ave, Chongqing 400074, Peoples R China
[2] Chongqing Univ Technol, Intellectual Property Inst Chongqing, 69 Hongguang Ave,7F Room 15, Chongqing 400054, Peoples R China
[3] Huazhong Univ Sci & Technol, Sch Management, 1037 Luoyu Rd, Wuhan 430074, Peoples R China
基金
中国国家自然科学基金;
关键词
joint replenishment; quantity discount; PSO; locust swarms; DIFFERENTIAL EVOLUTION ALGORITHM; OPTIMIZATION ALGORITHM; SUPPLIER SELECTION; WAREHOUSE; CLASSIFICATION; MODEL;
D O I
10.1016/j.knosys.2016.08.007
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In B2C E-Commerce operations, multiple quantity discount offers are commonly practiced in the multi item replenishment environment. In this paper, a novel joint replenishment model (JRP) is presented considering two quantity discounts, all-unit quantity discount, incremental quantity discount, simultaneously. A novel swarms search technique, locust swarms algorithm (IS) is introduced and redesigned to solve the novel formulated JRP model. Numerical experiments and parameter sensitivity analyses reveal that IS is an effective and efficient algorithm for solving the proposed model in terms of solution quality and searching stableness comparing to some other meta-heuristic algorithms, such as GA, DE and PSO. Moreover, management insights such as the mutual effects of multiple quantity discounts to the total cost, and the role of multiple quantity discounts to different stakeholders in replenishment are outlined. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:51 / 62
页数:12
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