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dc.contributor.authorGören, Selçuk
dc.contributor.authorPierreval, Henri
dc.description4th International Conference on Industrial Engineering and Systems Management (IESM) - Innovative Approaches and Technologies for Networked Manufacturing Enterprises Management -- MAY 25-27, 2011 -- Metz, FRANCEen_US
dc.descriptionGoren, Selcuk/0000-0002-5320-4213en_US
dc.description.abstractSchedules have implications that are experienced collectively by a number of different persons with different responsibilities. It is, therefore, reasonable to make scheduling decisions in such a way that satisfies the considerations of all the involved partners. Unfortunately, even though there is a vast body of literature on production scheduling, the existing research generally concentrates on generating schedules that optimize one or more performance measures and does not address the problem of how to find a schedule that can be found acceptable by several users. Moreover, the considerations of the users may not be fully known in advance, can be implicit or qualitative, and therefore may not be included in the initial problem definition. In this study, we tackle with this problem and propose an approach that aims at determining a schedule that is the result of an agreement between different partners rather than at imposing an optimal solution to everyone. To alleviate difficulties, we suggest that it is first necessary to find a set of different schedules that can be considered efficient by everyone. The solutions can afterwards be passed on to the users to decide on the most appropriate schedule according to their priorities. The proposed two-step approach is illustrated on a hybrid flow shop environment. We propose a multimodal genetic algorithm to solve the first sub-problem. Our computational experiments on a set of benchmark problems from the literature indicate not only that the proposed algorithm is very competitive when compared to the existing exact or heuristic state-of-the-art methods, but that it is also quite promising in obtaining a diverse set of efficient (mostly optimal) alternative schedules. We address the second sub-problem using a multiplicative variant of the popular analytic hierarchy processing(AHP) technique, which does not suffer from dependence on irrelevant alternatives as the original version.en_US
dc.description.sponsorshipInt Inst Innovat, Ind Engn & Entrepreneurship, ENIM, INRIA, Igipm, Conseil Gen Moselle, Metz Metropole, Univ Valenciennes Hainaut Cambresis, Lab Automatique Mecanique & Informatique Ind & Humaines, Thermique Ecoulement Mecanique Materiaux Mise Forme Prod, Gdr Macs, IBM, Innovat Prod Machines & Systen_US
dc.publisherInt Inst Innovation, Industrial Engineering & Entrepreneurshipen_US
dc.subjectMultimodal Optimizationen_US
dc.subjectHybrid Flow Shopen_US
dc.subjectGenetic Algorithmen_US
dc.subjectProduction Schedulingen_US
dc.subjectGroup Decision Makingen_US
dc.subjectPreference Aggregationen_US
dc.titleHybrid flow shop scheduling with several usersen_US
dc.departmentMühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.department-temp[Goren, Selcuk; Pierreval, Henri] Clermont Univ, IFMA, LIMOS, UMR CNRS 6158, Campus Clermont Ferrand, F-63175 Aubiere, France; [Goren, Selcuk] Istanbul Kemerburgaz Univ, Dept Ind Engn, TR-34217 Bagcilar Istanbul, Turkeyen_US
dc.contributor.institutionauthorGören, Selçuk
dc.relation.journalProceedings of International Conference on Industrial Engineering and Systems Management (Iesm'2011): Innovative Approaches and Technologies For Networked Manufacturing Enterprises Managementen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US

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