Details

Title

Multi-Objective Approach for Production Line Equipment Selection

Journal title

Management and Production Engineering Review

Yearbook

2012

Issue

No 1

Authors

Keywords

Wydział IV Nauk Technicznych

Divisions of PAS

Wydział IV Nauk Technicznych

Publisher

Production Engineering Committee of the Polish Academy of Sciences, Polish Association for Production Management

Date

2012

Identifier

DOI: 10.2478/v10270-012-0001-5

Source

Management and Production Engineering Review; 2012; No 1

References

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(1989), A comprehensive literature review and analysis of the design, balancing and scheduling of assembly systems, International Journal of Production Research, 27, 637, doi.org/10.1080/00207548908942574 ; Erel E. (1998), A survey of the assembly line balancing procedures, Production Planning and Control, 9, 5, 414, doi.org/10.1080/095372898233902 ; Rekiek B. (2002), State of art of assembly lines design optimization, Annual Reviews in Control, 26, 2, 163, doi.org/10.1016/S1367-5788(02)00027-5 ; Scholl A. (2006), State-of-the-art exact and heuristic solution procedures for simple assembly line balancing, European Journal of Operational Research, 168, 666, doi.org/10.1016/j.ejor.2004.07.022 ; Guschinskaya O. (2010), Equilibrage de lignes de production: état de l'art, Journal Européen des Systèmes Automatisés, 44, 1081, doi.org/10.3166/jesa.44.1079-1117 ; Dolgui A. (2005), An heuristic approach for transfer lines balancing, Journal of Intelligent Manufacturing, 16, 159, doi.org/10.1007/s10845-004-5886-6 ; Dolgui A. (2006), MIP approach to balancing transfer lines with blocks of parallel operations, IIE Transactions, 38, 10, 869, doi.org/10.1080/07408170500531334 ; Belmokhtar S. (2006), An integer programming model for logical layout design of modular machining lines, Computers and Industrial Engineering, 51, 3, 502, doi.org/10.1016/j.cie.2006.08.010 ; Dolgui A. (2009), Branch and bound algorithm for a transfer line design problem: Stations with sequentially activated multi-spindle heads, European Journal of Operational Research, 197, 1119, doi.org/10.1016/j.ejor.2008.03.028 ; Graves S. (1988), Equipment selection and task assignment for multiproduct assembly system design, International Journal of Flexible Manufacturing Systems, 1, 31, doi.org/10.1007/BF00713158 ; Szadkowski J. (1997), Critical path concept for multitool cutting processes optimization, null, 393. ; Bukchin J. (2000), Design of flexible assembly line to minimize equipment cost, IIE Transactions, 32, 585, doi.org/10.1080/07408170008967418 ; Ishibushi H. (2003), Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling, IEEE Transactions on Evolutionary Computation, 7, 2, 204, doi.org/10.1109/TEVC.2003.810752 ; Sysoev V. (1999), A Pareto optimization approach for manufacturing system design, null, 75. ; Collette Y. (2000), Optimisation Multiobjectif. ; Srinivas N. (1994), Multiobjective function optimization using non-dominated sorting genetic algorithms, Evolutionary Computation Journal, 2, 3, 221, doi.org/10.1162/evco.1994.2.3.221 ; Fonseca C. (1995), An overview of evolutionary algorithms in multi-objective optimisation, Evolutionary Computation, 3, 1, 1, doi.org/10.1162/evco.1995.3.1.1 ; Sarker R. (2002), A new multi-objective evolutionary algorithm, European Journal of Operational Research, 140, 12, doi.org/10.1016/S0377-2217(01)00190-4 ; Coello C. (1999), A comparative survey of evolutionary-based multiobjective optimization techniques, Knowledge and Information Systems, 1, 269, doi.org/10.1007/BF03325101 ; Horn J., Nafpliotis N., <i>Multiobjective optimization using the Niched Pareto Genetic Algorithm</i>, IlliGAL Report No.93005, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA, 1993. ; Zitzler E. (1999), Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach, IEEE Transactions on Evolutionary Computation, 3, 257, doi.org/10.1109/4235.797969 ; Ponnambalam S. (2000), A multi-objective genetic algorithm for solving assembly line balancing problem, International Journal of Advanced Manufacturing Technology, 16, 341, doi.org/10.1007/s001700050166 ; Younes A. (2002), An adaptive genetic algorithm for multi-objective flexible manufacturing systems, null, 1241. ; Rekiek B. (1999), A resource planner for hybrid assembly lines, null, 1. ; Makdessian L. (2008), Optimisation de lignes de production, Partie I: une approche monocritère, Journal of Decision Systems, 17, 313. ; Makdessian L. (2008), Optimisation de lignes de production, Partie II: une approche multicritère, Journal of Decision Systems, 17, 337, doi.org/10.3166/jds.17.337-368 ; Riise A. (2002), Comparing genetic algorithms and tabu search for multiobjective optimization, null. ; Lacomme P. (2006), A genetic algorithm for a bi-objective capacitated arc routing problem, Computers & Operations Research, 33, 3473, doi.org/10.1016/j.cor.2005.02.017 ; Deb K. (2000), A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II, null, 849. ; Deb K. (1999), Multi-objective genetic algorithms: Problem difficulties and construction of test problems, Evolutionary Computation Journal, 7, 3, 205, doi.org/10.1162/evco.1999.7.3.205 ; Deb K. (2002), A fast and elitist multi-objective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, 6, 2, 182, doi.org/10.1109/4235.996017 ; Chehade H. (2009), A new hybrid multiobjective algorithm for assembly lines design, null. ; Dugardin F. (2009), Méthodes multi-objectif pour l'ordonnancement de lignes réentrantes, Journal of Decision Systems, 18, 2, 233, doi.org/10.3166/jds.18.231-255

Aims and scope

MISSION STATEMENT Management and Production Engineering Review (MPER) is a peer-refereed, international, multidisciplinary journal covering a broad spectrum of topics in production engineering and management. Production engineering is a currently developing stream of science encompassing planning, design, implementation and management of production and logistic systems. Orientation towards human resources factor differentiates production engineering from other technical disciplines. The journal aims to advance the theoretical and applied knowledge of this rapidly evolving field, with a special focus on production management, organisation of production processes, manage- ment of production knowledge, computer integrated management of production flow, enterprise effectiveness, maintainability and sustainable manufacturing, productivity and organisation, forecasting, modelling and simu- lation, decision making systems, project management, innovation management and technology transfer, quality engineering and safety at work, supply chain optimization and logistics. Management and Production Engineering Review is published under the auspices of the Polish Academy of Sciences Committee on Production Engineering and Polish Association for Production Management. The main purpose of Management and Production Engineering Review is to publish the results of cutting- edge research advancing the concepts, theories and implementation of novel solutions in modern manufacturing. Papers presenting original research results related to production engineering and management education are also welcomed. We welcome original papers written in English. The Journal also publishes technical briefs, discussions of previously published papers, book reviews, and editorials. Letters to the Editor-in-Chief are highly encouraged.
SUBMISSION Papers for submission should be prepared according to the Authors Instructions available at: www.journals.pan.pl/mper
SUBSCRIPTION Only subscription guarantees receiving this journal. Subscription orders stating the period of time, along with the subscriber’s name and address should be sent directly to biuro@ptzp.org.pl. Back issues of all previously published volumes are available on request. Subscription price for 2023, Volume 14, including postage and handling, is 240 PLN.

Abstracting & Indexing

Index Copernicus
Web of Science - Clarivate (ESCI)
Scopus - Elsevier
SCIMAGO:
(CiteScore 2020 - 2.5
SJR 2020 - 0.332
SNIP 2020 - 1.061)


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