Bi-objective optimization of distribution scheduling using MOPSO optimizer

Purpose - The paper aims at the bi-objective optimization of a two-echelon distribution network model for facility location and capacity allocation where in a set of customer locations with demands and a set of candidate facility locations will be known in advance. The problem is to find the locatio...

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Bibliographic Details
Published in:Journal of modelling in management Vol. 7; no. 3; pp. 304 - 327
Main Authors: Latha Shankar, B, Basavarajappa, S, Kadadevaramath, Rajeshwar S
Format: Journal Article
Language:English
Published: Bradford Emerald Group Publishing Limited 01.01.2012
Emerald
Subjects:
Commodities
Customer services
Decision making
Distribution costs
Integrated approach
Inventory
Investments
Kapazitätsplanung
Lieferkette
Location analysis
Management decisions
Management-Informationssystem
Optimization
Scheduling-Verfahren
Studies
Supply chains
Warehouses
Supply chain management
Capacity allocation
Decision making
Capacitated plant location
Non-dominated
Hybrid MOPSO
Plant location and layout
ISSN:1746-5664, 1746-5672
Online Access:Get full text
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Summary:Purpose - The paper aims at the bi-objective optimization of a two-echelon distribution network model for facility location and capacity allocation where in a set of customer locations with demands and a set of candidate facility locations will be known in advance. The problem is to find the locations of the facilities and the shipment pattern between the facilities and the distribution centers (DCs) to minimize the combined facility location and shipment costs subject to a requirement that maximum customer demands be met.Design methodology approach - To optimize the two objectives simultaneously, the location and distribution two-echelon network model is mathematically represented in this paper considering the associated constraints, capacity, production and shipment costs and solved using hybrid multi-objective particle swarm optimization (MOPSO) algorithm.Findings - This paper shows that the heuristic based hybrid MOPSO algorithm can be used as an optimizer for characterizing the Pareto optimal front by computing well-distributed non-dominated solutions. These aolutions represent trade-off solutions out of which an appropriate solution can be chosen according to industrial requirement.Originality value - Very few applications of hybrid MOPSO are mentioned in literature in the area of supply chain management. This paper addresses one of such applications.
Bibliography:SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:1746-5664
1746-5672
DOI:10.1108/17465661211283296