Publicerad: 2012-10-11
ISBN: 978-91-7393-381-0
ISSN: 1650-3686 (tryckt), 1650-3740 (online)
Industrial Product-Service-Systems (IPS²) are specified by delivering value in use to the customer while both product and service shares occur integrative over the whole life cycle. Thus they comprise several degrees of freedom; such as the partial substitution of product and service shares or the integration of customers’ resources; which the operational resource planning of IPS² deals with. Furthermore it has to optimize the schedule regarding several aims like costs or constant work load. This article describes the Heuristic Resource Planning Approach for IPS² which combines e.g. randomized search heuristics and evolutionary algorithms.
Industrial Product-Service Systems (IPS²); advanced planning and scheduling; process planning
[1] Levitt; T.; 1969; The Marketing Mode; Pathways to Corporate Growth
[2] Cunha; P. F.; Caldeira Duarte; J. A.; 2004; Development of a Productive Service Module Based on a Life Cycle Perspective of Maintenance Issues; Annals of the CIRP; Vol 53/1; 13-16; ISSN: 0007- 8506
[3] Tan; A. R.; McAloone; T. C.; 2006; Characteristics of strategies in Product/Service-System Development; International Design Conference – Design 2006; Dubrovnik – Croatia; May 15-18
[4] Matzen; D.; Tan; A. R.; Myrup Andreasen; M.; 2005; Product/Service-Systems: Proposal for Models and Terminology; 16. Symposium “Design For X”; Neukirchen; October 13-15;
[5] Arai; T.; Shimomura; Y. 2005; Service CAD System – Evaluation and Quantification; Annals of the CIRP; Vol 54/1; 463-466
[6] Meier; H.; Kortmann; D.; 2007; Leadership – From Technology to Use; 14th CIRP Conference on Life Cycle Engineering; 159-163
[7] Meier; H.; Krug; C. M.; 2008; System for Planning of Resources in IPS²-Delivery; 41st CIRP Conference on Manufacturing Systems; Tokyo; Japan
[8] Meier; H.; Krug; C. M.; 2006; Strategische Kapazitätsplanung hybrider Leistungsbündel; PPS Management 11; 48-51
[9] Klamroth; K.; 2009; Discrete Multiobjective Optimization; Evolutionary Multi-Criterion Optimization 5th International Conference; EMO 2009; Nantes; France; April 7-10; Proceedings ISBN 978-3-642-01019-4; 4
[10] Sathe; M.; Schenk; O.; Burkhart; H.; 2009; Solving Bi-objective Many-Constraint Bin Packing Problems in Automobile Sheet Metal Forming Processes; Evolutionary Multi-Criterion Optimization 5th International Conference; EMO 2009; Nantes; France; April 7-10; Proceedings ISBN 978-3-642- 01019-4; 246-260
[11] Datta; D.; Deb; K.; Fonseca; C. M.; 2007; Multiobjective Evolutionary Algorithms for Resource Allocation Problems; Evolutionary Multi-Criterion Optimization 4th International Conference; EMO 2007; Matsushima; Japan; March 5-8; Proceedings ISBN 978-3-540-70927-5; 401-416
[12] Hutzschenreuter; A.; Bosman; P.; La Poutré; H.; 2009; Evolutionary Multiobjective Optimization for Dynamic Hospital Resource Management; Evolutionary Multi-Criterion Optimization 5th International Conference; EMO 2009; Nantes; France; April 7-10; Proceedings ISBN 978-3-642- 01019-4; 320-334
[13] Garcia-Najera; A.; Bullinaria; J.; 2009; Bi-objective Optimization for the Vehicle Routing Problem with Time Windows: Using Route Similarity to Enhance Performance; Evolutionary Multi-Criterion Optimization 5th International Conference; EMO 2009; Nantes; France; April 7-10; Proceedings ISBN 978-3-642-01019-4; 275-289
[14] Geiger; M. J.; 2009; Multi-criteria curriculum based course timetabling - A comparison of a weighted sum and a reference point based approach; Evolutionary Multi-Criterion Optimization 5th International Conference; EMO 2009; Nantes; France; April 7-10; Proceedings ISBN 978-3-642-01019-4; 290-304
[15] Raidl; G. R.; 2006; A Unified View on Hybrid Metaheuristics; Hybrid Metaheuristics; Third International Workshop; HM 2006 Gran Canaria; Spain; October 13-15; Proceedings ISBN 978-3-540- 46384-9; 1-12
[16] Glover; F.; Kochenberger; G. A. (eds.); 2003; Handbook of Metaheuristics; International Series in Operations Research & Management Science; Vol. 57; Springer; ISBN 978-1-4020-7263-5
[17] Blum; C.; Roli; A.; 2003; Metaheuristics in combinatorial optimization: Overview and conceptual comparison; ACM Computing Surveys 35(3); 268- 308.
[18] Crescenzi; P.; Kann; V.; 1997; Approximation on the web: A compendium of NP optimization problems; Randomization and Approximation Techniques in Computer Science; International Workshop RANDOM’97; Bologna; Italy; July 11-12; Proceedings ISBN 978-3-540-63248-1; 111-118
[19] Talbi; E. G.; 2009; Metaheuristics: from design to implementation; Wiley; ISBN 978-0-470-27858-1
[20] Vöcking; B.; Alt; H.; Dietzfelbinger; M.; Reischuk; R.; Scheideler; C.; Vollmer; H.; Wagner; D. (Ed.); 2008; Taschenbuch der Algorithmen; Springer; ISBN: 978- 3-540-76393-2
[21] Zitzler; E.; Laumanns; M.; Bleuler; S.; 2004; A tutorial on evolutionary multiobjective optimization; Metaheuristics for Multiobjective Optimisation; Springer; ISBN: 978-3-540-20637-8; 3-38
[22] Eiben; A. E.; Smith; J. E.; 2003; Introduction to Evolutionary Computing; Springer; ISBN: 978-3-540- 40184-1
[23] Konaka; A.; Coitb; D. W.; Smithc; A. E.; 2006; Multiobjective optimization using genetic algorithms: A tutorial; Reliability Engineering & System Safety; Vol. 91; Issue 9; 992-1007
[24] van Laarhoven; P. J.; Aarts; E. H.; 1987; Simulated Annealing: Theory and Applications; Springer; ISBN: 978-90-277-2513-4
[25] Martin; O. C.; Otto; S. W.; 1996; Combining simulated annealing with local search heuristics; Annals of Operations Research 63 (1); Springer Netherlands; ISSN 0254-5330
[26] Gendreau; M.; 2003; An introduction to tabu search; Handbook of Metaheuristics; International Series in Operations Research & Management Science; Vol. 57; Springer; ISBN 978-1-4020-7263-5; 37-54
[27] Cormen; T.; H.; 2001; Introduction to algorithms; MIT Press; ISBN 0262032937
[28] Dorigo; M.; Stützle; T.; 2004; Ant colony optimization; MIT Press; ISBN 0-262-04219-3
