Agile two-stage lot-sizing and scheduling problem with reliability, customer satisfaction and behaviour under uncertainty: a hybrid metaheuristic algorithm

This article proposes a new multi-objective model for a lot-sizing and scheduling problem (LSSP) under uncertainty. The model considers economic aspects, reliability and quality inspection, and customer satisfaction and behaviour in designing the LSSP. A utility function is applied to increase custo...

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Bibliographic Details
Published in:Engineering optimization Vol. 52; no. 8; pp. 1323 - 1343
Main Authors: Aghamohammadi-Bosjin, Soroush, Rabbani, Masoud, Tavakkoli-Moghaddam, Reza
Format: Journal Article
Language:English
Published: Abingdon Taylor & Francis 02.08.2020
Taylor & Francis Ltd
Subjects:
Adaptive systems
agility
Algorithms
Artificial neural networks
Clustering
clustering method
Customer satisfaction
Economic models
Fuzzy logic
Fuzzy systems
Genetic algorithms
Heuristic methods
Inspection
Lot sizing
Lot-sizing and scheduling
multi-objective metaheuristics
Multiple objective analysis
Particle swarm optimization
reliability
Reliability aspects
Scheduling
Sorting algorithms
Uncertainty
ISSN:0305-215X, 1029-0273
Online Access:Get full text
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Summary:This article proposes a new multi-objective model for a lot-sizing and scheduling problem (LSSP) under uncertainty. The model considers economic aspects, reliability and quality inspection, and customer satisfaction and behaviour in designing the LSSP. A utility function is applied to increase customer satisfaction and maximize responsiveness. In addition, the adaptive neuro-fuzzy inference system is employed to address uncertain demands. The presented model uses a fuzzy c-means clustering method to assess customers' behaviour. A hybrid multi-objective metaheuristic algorithm, comprised of the multi-objective red deer algorithm and parallel non-dominated sorting genetic algorithm-II, is applied to solve the model efficiently. The results obtained from experiments on several problem instances show the superiority of the proposed metaheuristic algorithm over other algorithms, such as multi-objective particle swarm optimization, used in this article. Finally, a real case study is presented to show the applicability of the model, and several analyses are implemented to extend managerial insights.
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ISSN:0305-215X
1029-0273
DOI:10.1080/0305215X.2019.1650923