Multi-objective evolutionary approach for supply chain network design problem within online customer consideration

doi:10.1051/ro/2016010

機構典藏 > College of Business and Management > Department of Management Sciences > Journal Article > Item 987654321/119629

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/119629

Title:	Multi-objective evolutionary approach for supply chain network design problem within online customer consideration
Authors:	Liao, Shu-Hsien;Hsieh, Chia-Lin;Ho, Wei-Chung
Keywords:	Supply chain network design;location inventory problem;dual channel;multi-objective programming;evolutionary computation
Date:	2017-01-05
Issue Date:	2020-11-24 12:10:42 (UTC+8)
Abstract:	Supply chain network design is one of the most important strategic decisions that need to be optimized for long-term efficiency. Critical decisions include facility location, inventory, and transportation issues. This study proposes that with a dual-channel supply chain network design model, the traditional location-inventory problem should be extended to consider the vast amount of online customers at the strategic level, since the problem usually involves multiple and conflicting objectives. Therefore, a multi-objective dual-channel supply chain network model involving three conflicting objectives is initially proposed to allow a comprehensive trade-off evaluation. In addition to the typical costs associated with facility operation and transportation, we explicitly consider the pivotal online customer service rate between the distribution centers (DCs) and their assigned customers. This study proposes a heuristic solution scheme to resolve this multi-objective programming problem, by integrating genetic algorithms, a clustering analysis, a Non-dominated Sorting Genetic Algorithm II (NSGA-II), and a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Several experiments are simulated to demonstrate the possibility and efficacy of the proposed approach. A scenario analysis is conducted to understand the model’s performance.
Relation:	RAIRO Operations Research 51(1), p.135-155
DOI:	10.1051/ro/2016010
Appears in Collections:	[Department of Management Sciences] Journal Article

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	50	View/Open

Loading...