Multicriterion decision making

Horn, Jeffrey

doi:10.1887/0750308958/b386c85

Cited by 106 publications

(57 citation statements)

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“…In such cases a multi-objective optimisation approach is often favoured as it searches for optimality in problems with multiple conflicting objectives, which often results in the generation of a set of solutions that are insensitive to each solution, implying that no enhancement can be achieved by altering any of the constraints without negatively influencing the performance of one or more of the objectives (Hwang & Masud 1979;Horn 1997). This set of non-dominated solutions is referred to as the Pareto-optimal solution frontier.…”

Section: Optimisation and Pareto Frontier Curvature Analysismentioning

confidence: 99%

Robustness analysis of pull strategies in multi-product systems

Onyeocha¹,

Khoury²,

Geraghty³

2015

JIEM

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Abstract:Purpose: This paper examines the behaviour of shared and dedicated Kanban allocation policies of Hybrid Kanban-CONWIP and Basestock-Kanban-CONWIP control strategies in multi-product systems; with considerations to robustness of optimal solutions to environmental and system variabilities.Design/methodology/approach: Discrete event simulation and evolutionary multi-objective optimisation approach were utilised to develop Pareto-frontier or sets of non-dominated optimal solutions and for selection of an appropriate decision set for the control parameters in the shared Kanban allocation policy (S-KAP) and dedicated Kanban allocation policy (D-KAP).Simulation experiments were carried out via ExtendSim simulation application software. The outcomes of PCS+KAP performances were compared via all pairwise comparison and Nelson's screening and selection procedure for superior PCS+KAP under negligible environmental and system stability. To determine superior PCS+KAP under systems' and environmental variability, the optimal solutions were tested for robustness using Latin hypercube sampling technique and stochastic dominance test. Findings:The outcome of this study shows that under uncontrollable environmental variability, dedicated Kanban allocation policy outperformed shared Kanban allocation policy in serial manufacturing system with negligible and in complex assembly line with setup times.Moreover, the BK-CONWIP is shown as superior strategy to HK-CONWIP.-1125-Journal of Industrial Engineering and Management -http://dx.doi.org/10.3926/jiem.1407Research limitations/implications: Future research should be conducted to verify the level of flexibility of BK-CONWIP with respect to product mix and product demand volume variations in a complex multi-product system Practical implications: The outcomes of this work are applicable to multi-product manufacturing industries with significant setup times and systems with negligible setup times.The multi-objective optimisation provides decision support for selection of control-parameters such that operations personnel could easily change parameter settings to achieve a new service level without additional optimisations of the system parameters.Originality/value: The examination of the behaviour of the two Kanban allocation policies in HK-CONWIP and BK-CONWIP in a complex multi-product assembly line with setup-times and environmental variabilities, under erratic demand profiles.

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Section: Optimisation and Pareto Frontier Curvature Analysismentioning

confidence: 99%

Robustness analysis of pull strategies in multi-product systems

Onyeocha¹,

Khoury²,

Geraghty³

2015

JIEM

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“…Surveys on such multiobjective evolutionary algorithms (MOEAs) are given by Fonseca and Fleming (1995), Horn (1997), and Tamaki, Kita, and Kobayashi (1996). More recent research results are included in the proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization edited by Zitzler et al (2001)).…”

Section: Introductionmentioning

confidence: 99%

A multiobjective evolutionary algorithm for scheduling and inspection planning in software development projects

Hanne

Nickel

2005

European Journal of Operational Research

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In this article, we consider the problem of planning inspections and other tasks within a software development (SD) project with respect to the objectives quality (no. of defects), project duration, and costs. Based on a discrete-event simulation model of SD processes comprising the phases coding, inspection, test, and rework, we present a simplified formulation of the problem as a multiobjective optimization problem. For solving the problem (i.e. finding an approximation of the efficient set) we develop a multiobjective evolutionary algorithm. Details of the algorithm are discussed as well as results of its application to sample problems.

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“…A couple of conventional approaches to multi-objective optimization have been adopted to EAs [4][5][6]. In most cases, ambiguities are eliminated before the Paretoset is known, e.g., by weighting the criteria or Euclidean distances to the single-criteria optimizers.…”

Section: Introductionmentioning

confidence: 99%

A spatial predator-prey approach to multi-objective optimization: A preliminary study

Laumanns

Rudolph

Schwefel

1998

Lecture Notes in Computer Science

133

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Abstract. This paper presents a novel evolutionary approach of approximating the shape of the Pareto-optimal set of multi-objective optimization problems. The evolutionary algorithm (EA) uses the predator-prey model from ecology. The prey are the usual individuals of an EA that represent possible solutions to the optimization task. They are placed at vertices of a graph, remain stationary, reproduce, and are chased by predators that traverse the graph. The predators chase the prey only within its current neighborhood and according to one of the optimization criteria. Because there are several predators with different selection criteria, those prey individuals, which perform best with respect to all objectives, are able to produce more descendants than inferior ones. As soon as a vertex for the prey becomes free, it is refilled by descendants from alive parents in the usual way of EA, i.e., by inheriting slightly altered attributes. After a while, the prey concentrate at Pareto-optimal positions. The main objective of this preliminary study is the answer to the question whether the predator-prey approach to multi-objective optimization works at all. The performance of this evolutionary algorithm is examined under several step-size adaptation rules.

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Multicriterion decision making

Cited by 106 publications

References 0 publications

Robustness analysis of pull strategies in multi-product systems

Robustness analysis of pull strategies in multi-product systems

A multiobjective evolutionary algorithm for scheduling and inspection planning in software development projects

A spatial predator-prey approach to multi-objective optimization: A preliminary study

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