Multi‐objective combinatorial optimization problems: A survey

Ulungu, E. L.; Teghem, Jacques

doi:10.1002/mcda.4020030204

Cited by 271 publications

(92 citation statements)

References 48 publications

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“…The most popular ones are as follows: metric LP methods, bounded objective method, lexicographic method, goal programming (GP), goal attainment method, method of Zionts-Wallenius, the methods as step method (STEM) and related method, sequential multi-objective problem solving and sequential information generator for multi-objective problems method, GP STEM, and C-constraint method and adaptive search method (Hwang & Masud, 1979;Szidarovszky, Gershon, & Duchstein, 1986;Ulungu & Teghem, 1994;Zionts, 1979). Also, papers such as the work by Do et al could be considered relevant as they combine simulation under uncertainty with heuristics to find suitable solutions.…”

Section: Madm and Modm Decision-making Techniquesmentioning

confidence: 99%

Developing location indicators for Agricultural Service Center: a Delphi–TOPSIS–FAHP approach

Zangeneh

Akram

Nielsen

et al. 2015

Production & Manufacturing Research

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The main goal of this paper is to construct objectives and attributes of the service centers location problem. This problem is often found within production management as a location problem, when designing for example supply chains or manufacturing layouts. The main contribution of this paper is constructing related location indicators. Since there was no similar literature in this discipline, so a Delphi survey applied to quantify expert's attitudes about location problem of Agricultural Service Center (ASC) and construct location selection attributes and also ASC objectives. A TOPSIS survey is done to rank extracted attributes to import in fuzzy analytical hierarchy process (AHP) study. Then a fuzzy AHP technique is applied to compute the weight of these most important attributes using four objectives, which obtained by Delphi technique too. In the simplest form with this assumption that all objectives have the same priority, the results illustrated that the service, cost, speed, and ASC profit are first to last important objectives, respectively. At the end of this paper, the multi-choice goal programming method recommended to use when the priorities of objectives are not the same. Finally, the weight of ASC location attributes computed to consider in ASC location problem.

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Section: Madm and Modm Decision-making Techniquesmentioning

confidence: 99%

Developing location indicators for Agricultural Service Center: a Delphi–TOPSIS–FAHP approach

Zangeneh

Akram

Nielsen

et al. 2015

Production & Manufacturing Research

View full text Add to dashboard Cite

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“…Basically, the first extensions were proposed by Serafini [24,25] and by Ululgu and Teghem [26], where various ways of defining the probability in the multi-objective framework and how they affect the performance of SA based multi-objective algorithms. Czyzak et al [27] combined mono-criterion SA and genetic algorithm to provide efficient solutions for multi-criteria shortest path problem.…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

Design and Identification Problems of Rotor Bearing Systems Using the Simulated Annealing Algorithm

Lobato¹,

Assis²,

Steffen³

et al. 2012

Simulated Annealing - Single and Multiple Objective Problems

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“…Multiobjective optimization, also known as multiobjective programming, vector optimization, multi-criteria optimization, multiattribute optimization or Pareto optimization, has a rich history, dating back to the emergence of rigorous mathematical programming [21]. Several books have been written on the topic [11,28,14], and many in-depth surveys [34,37,12,13,15].…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Optimization by Decision Diagrams

Bergman

Ciré

2016

Lecture Notes in Computer Science

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Always cite the published version, so the author(s) will receive recognition through services that track citation counts, e.g. Scopus. If you need to cite the page number of the author manuscript from TSpace because you cannot access the published version, then cite the TSpace version in addition to the published version using the permanent URI (handle) found on the record page.This article was made openly accessible by U of T Faculty. Please tell us how this access benefits you. Your story matters. Abstract. In this paper we present a technique for solving multiobjective discrete optimization problems using decision diagrams. The proposed methodology is related to an algorithm designed for multiobjective optimization for dynamic programming, except utilizing decision diagram theory to reduce the state space, which can lead to orders of magnitude performance gains over existing algorithms. The decision diagrambased technique is applied to knapsack, set covering, and set partitioning problems, exhibiting improvements over state-of-the-art general-purpose multiobjective optimization algorithms. Multiobjective Optimization by Decision Diagrams

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Multi‐objective combinatorial optimization problems: A survey

Cited by 271 publications

References 48 publications

Developing location indicators for Agricultural Service Center: a Delphi–TOPSIS–FAHP approach

Developing location indicators for Agricultural Service Center: a Delphi–TOPSIS–FAHP approach

Design and Identification Problems of Rotor Bearing Systems Using the Simulated Annealing Algorithm

Multiobjective Optimization by Decision Diagrams

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