An improved multi-objective genetic algorithm for heterogeneous coverage RFID network planning

Recent research has demonstrated the potential benefits of radio frequency identification (RFID) technology in the supply chain and production management via its item-level visibility. However, the RFID coverage performance is largely impacted by the surrounding environment and potential collisions...

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Bibliographic Details
Published in:International journal of production research Vol. 54; no. 8; pp. 2227 - 2240
Main Authors: Tang, Lin, Zheng, Li, Cao, Hui, Huang, Ningjian
Format: Journal Article
Language:English
Published: London Taylor & Francis 17.04.2016
Taylor & Francis LLC
Subjects:
Algorithms
Computation
Electronic devices
Evolutionary
Genetic algorithms
Greedy algorithms
heterogeneous coverage
Heuristic methods
Mathematical models
mixed-model assembly line
multi-objective genetic algorithm
Multiple objective analysis
Optimization
Pareto optimum
Production management
Production scheduling
Radio frequency identification
RFID network planning
Robustness (mathematics)
Sequential scheduling
Supply chains
Visibility
ISSN:0020-7543, 1366-588X
Online Access:Get full text
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Summary:Recent research has demonstrated the potential benefits of radio frequency identification (RFID) technology in the supply chain and production management via its item-level visibility. However, the RFID coverage performance is largely impacted by the surrounding environment and potential collisions between the RFID devices. Thus, through RFID network planning (RNP) to achieve the desired coverage within the budget becomes a key factor for success. In this study, we establish a novel and generic multi-objective RNP model by simultaneously optimising two conflicted objectives with satisfying the heterogeneous coverage requirements. Then, we design an improved multi-objective genetic algorithm (IMOGA) integrating a divide-and-conquer greedy heuristic algorithm to solve the model. We further construct a number of computational cases abstracted from an automobile mixed-model assembly line to illustrate how the proposed model and algorithm are applied in a real RNP application. The results show that the proposed IMOGA achieves highly competitive solutions compared with Pareto optimal solutions and the solutions given by four recently developed well-known multi-objective evolutionary and swarm-based optimisers (SPEA2, NSGA-II, MOPSO and MOPS 2 O) in terms of solution quality and computational robustness.
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ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2015.1057299