Improvements of constraint programming and hybrid methods for scheduling of tests on vehicle prototypes
In the automotive industry, a manufacturer must perform several hundreds of tests on prototypes of a vehicle before starting its mass production. Tests must be allocated to suitable prototypes and ordered to satisfy temporal constraints and various kinds of test dependencies. The manufacturer aims t...
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| Published in | Constraints : an international journal Vol. 17; no. 2; pp. 172 - 203 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Boston
Springer US
01.04.2012
Springer |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1383-7133 1572-9354 |
| DOI | 10.1007/s10601-012-9118-y |
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| Abstract | In the automotive industry, a manufacturer must perform several hundreds of tests on prototypes of a vehicle before starting its mass production. Tests must be allocated to suitable prototypes and ordered to satisfy temporal constraints and various kinds of test dependencies. The manufacturer aims to minimize the number of prototypes required. We present improvements of constraint programming (CP) and hybrid approaches to effectively solve random instances from an existing benchmark. CP mostly achieves better solutions than the previous heuristic technique and genetic algorithm. We also provide customized search schemes to enhance the performance of general search algorithms. The hybrid approach applies mixed integer linear programming (MILP) to solve the planning part and CP to find the complete schedule. We consider several logical principles such that the MILP model can accurately estimate the prototype demand, while its size particularly for large instances does not exceed memory capacity. Moreover, the robustness is alleviated when we allow CP to partially change the allocation obtained from the MILP model. The hybrid method can contribute to optimal solutions in some instances. |
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| AbstractList | In the automotive industry, a manufacturer must perform several hundreds of tests on prototypes of a vehicle before starting its mass production. Tests must be allocated to suitable prototypes and ordered to satisfy temporal constraints and various kinds of test dependencies. The manufacturer aims to minimize the number of prototypes required. We present improvements of constraint programming (CP) and hybrid approaches to effectively solve random instances from an existing benchmark. CP mostly achieves better solutions than the previous heuristic technique and genetic algorithm. We also provide customized search schemes to enhance the performance of general search algorithms. The hybrid approach applies mixed integer linear programming (MILP) to solve the planning part and CP to find the complete schedule. We consider several logical principles such that the MILP model can accurately estimate the prototype demand, while its size particularly for large instances does not exceed memory capacity. Moreover, the robustness is alleviated when we allow CP to partially change the allocation obtained from the MILP model. The hybrid method can contribute to optimal solutions in some instances. |
| Author | Limtanyakul, Kamol Schwiegelshohn, Uwe |
| Author_xml | – sequence: 1 givenname: Kamol surname: Limtanyakul fullname: Limtanyakul, Kamol email: kamoll@kmutnb.ac.th organization: Department of Software Systems Engineering, The Sirindhorn International Thai-German Graduate School of Engineering, King Mongkut’s University of Technology North Bangkok – sequence: 2 givenname: Uwe surname: Schwiegelshohn fullname: Schwiegelshohn, Uwe organization: Robotics Research Institute, Technische Universität Dortmund |
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| CitedBy_id | crossref_primary_10_1016_j_omega_2016_05_003 crossref_primary_10_1007_s10462_018_9667_6 crossref_primary_10_1287_inte_2016_0855 |
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| Keywords | Mixed integer linear programming Benders decomposition Automotive industry Constraint programming Mass production Partition method Linear programming Mixed integer programming Scheduling Temporal constraint Modeling Search algorithm Constrained optimization Genetic algorithm Automobile industry Heuristic method Memory capacity Robustness Planning |
| Language | English |
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| References_xml | – reference: Nübel, H. (1998). A branch-and-bound procedure for the resource investment problem with generalized precedence constraints. Tech. rep., Institut für Wirtschaftstheorie und Operations Research, University of Karlsruhe. – reference: ILOG (2006). ILOG scheduler 6.2 user’s manual. ILOG S.A. – reference: NeumannKSchwindtCZimmermannJProject scheduling with time windows and scarce resources: Temporal and resource-constrained project scheduling with regular and nonregular objective functions2003New YorkSpringer1059.90001 – reference: DemeulemeesterEMinimizing resource availability costs in time-limited project networksManagement Science19954110159015990861.9007110.1287/mnsc.41.10.1590 – reference: ILOG (2006). ILOG CPLEX 10.0 user’s manual. ILOG S.A. – reference: Limtanyakul, K., & Schwiegelshohn, U. (2007). Scheduling tests on vehicle prototypes using constraint programming. In Proceedings of the 3rd multidisciplinary international scheduling conference: Theory and applications (pp. 336–343). – reference: Schwindt, C. (1998). Generation of resource-constrained project scheduling problems subject to temporal constraints. Tech. rep. WIOR-543, Institut für Wirtschaftstheorie und Operations Research, University of Karlsruhe. – reference: MöhringRHMinimizing costs of resource requirements in project networks subject to a fixed completion timeOperations Research1984321891200531.9004910.1287/opre.32.1.89 – reference: Scheffermann, R., Clausen, U., & Preusser, A. (2005). Test-scheduling on vehicle prototypes in the automotive industry. In Proceedings of sixth Asia-Pacific industrial engineering and management systems (APIEMS) (Vol. 11, pp. 1817–1830). – reference: BartelsJ-HZimmermannJScheduling tests in automotive R&D projectsEuropean Journal of Operational Research200919338058191179.9012310.1016/j.ejor.2007.11.010 – reference: Junker, U. (2001). Quickxplain: Conflict detection for arbitrary constraint propagation algorithms. In Proceedings of IJCAI’01 Workshop on Modelling and Solving Problems with Constraints. – reference: HookerJAn integrated method for planning and scheduling to minimize tardinessConstraints200611213915722248481103.6881110.1007/s10601-006-8060-2 – reference: LockledgeJMihailidisDSidelkoJChelstKPrototype fleet optimization modelJournal of the Operational Research Society20025388338411130.9036710.1057/palgrave.jors.2601321 – reference: HsuCCKimDSA new heuristic for the multi-mode resource investment problemJournal of the Operational Research Society20055644064131104.9101310.1057/palgrave.jors.2601827 – reference: van BeckPRossiFvan BeekPWalshTBacktracking search algorithmsHandbook of constraint programming2006New YorkElsevier85134 – reference: Le Pape, C., Couronné, P., Vergamini, D., & Gosselin, V. (1994). Time-versus-capacity compromises in project scheduling. In Proceedings of the thirteenth workshop of the UK planning special interest group. – reference: BeniniLBertozziDGuerriAMilanoMAllocation and scheduling for MPSoCs via decomposition and no-good generationPrinciples and Practice of constraint programming—CP 20052005New YorkSpringer10712110.1007/11564751_11 – reference: NeumannKZimmermannJResource levelling for projects with schedule-dependent time windowsEuropean Journal of Operational Research199911735916050937.9003410.1016/S0377-2217(98)00272-0 – reference: LiHWomerKScheduling projects with multi-skilled personnel by a hybrid MILP/CP benders decomposition algorithmJournal of Scheduling200912328129825114831185.9011210.1007/s10951-008-0079-3 – reference: Limtanyakul, K. (2009). Scheduling of tests on vehicle prototypes. PhD thesis, Dortmund University of Technology. – reference: PinedoMScheduling-theory: Algorithm and systems20022Englewood Cliffs, NJPrentice Hall – reference: ZakarianAA methodology for the performance analysis of product validation and test plansInternational Journal of Product Development201010436939210.1504/IJPD.2010.031979 – reference: BaptistePLe PapeCNuijtenWConstraint-based scheduling: Applying constraint programming to scheduling problems2001KluwerNorwell, MA1094.90002 – reference: PardalosPMXueJThe maximum clique problemJournal of Global Optimization19944330132812663240797.9010810.1007/BF01098364 – reference: JainVGrossmannIEAlgorithms for hybrid MILP/CP models for a class of optimization problemsINFORMS Journal on Computing2001134258276186988010.1287/ijoc.13.4.258.9733 – reference: Bartels, J.-H. (2008). Anwendung von Methoden der Ressourcenbeschränkten Projektplanung mit multiplen Ausführungsmodi in der betriebswirtschaftlichen Praxis. PhD thesis, Clausthal University of Technology. – reference: YamashitaDSArmentanoVALagunaMScatter search for project scheduling with resource availability costEuropean Journal of Operational Research2006169262363721723631079.9006610.1016/j.ejor.2004.08.019 – ident: 9118_CR9 – start-page: 107 volume-title: Principles and Practice of constraint programming—CP 2005 year: 2005 ident: 9118_CR4 doi: 10.1007/11564751_11 – volume-title: Scheduling-theory: Algorithm and systems year: 2002 ident: 9118_CR22 – ident: 9118_CR14 – volume-title: Constraint-based scheduling: Applying constraint programming to scheduling problems year: 2001 ident: 9118_CR1 doi: 10.1007/978-1-4615-1479-4 – volume: 32 start-page: 89 issue: 1 year: 1984 ident: 9118_CR17 publication-title: Operations Research doi: 10.1287/opre.32.1.89 – ident: 9118_CR12 – volume: 11 start-page: 139 issue: 2 year: 2006 ident: 9118_CR6 publication-title: Constraints doi: 10.1007/s10601-006-8060-2 – volume: 56 start-page: 406 issue: 4 year: 2005 ident: 9118_CR7 publication-title: Journal of the Operational Research Society doi: 10.1057/palgrave.jors.2601827 – ident: 9118_CR24 – ident: 9118_CR2 – volume: 10 start-page: 369 issue: 4 year: 2010 ident: 9118_CR27 publication-title: International Journal of Product Development doi: 10.1504/IJPD.2010.031979 – volume: 169 start-page: 623 issue: 2 year: 2006 ident: 9118_CR26 publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2004.08.019 – ident: 9118_CR20 – ident: 9118_CR11 – start-page: 85 volume-title: Handbook of constraint programming year: 2006 ident: 9118_CR25 doi: 10.1016/S1574-6526(06)80008-8 – volume-title: Project scheduling with time windows and scarce resources: Temporal and resource-constrained project scheduling with regular and nonregular objective functions year: 2003 ident: 9118_CR19 doi: 10.1007/978-3-540-24800-2 – volume: 41 start-page: 1590 issue: 10 year: 1995 ident: 9118_CR5 publication-title: Management Science doi: 10.1287/mnsc.41.10.1590 – ident: 9118_CR8 – volume: 117 start-page: 591 issue: 3 year: 1999 ident: 9118_CR18 publication-title: European Journal of Operational Research doi: 10.1016/S0377-2217(98)00272-0 – ident: 9118_CR15 – volume: 4 start-page: 301 issue: 3 year: 1994 ident: 9118_CR21 publication-title: Journal of Global Optimization doi: 10.1007/BF01098364 – volume: 193 start-page: 805 issue: 3 year: 2009 ident: 9118_CR3 publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2007.11.010 – ident: 9118_CR23 – volume: 13 start-page: 258 issue: 4 year: 2001 ident: 9118_CR10 publication-title: INFORMS Journal on Computing doi: 10.1287/ijoc.13.4.258.9733 – volume: 12 start-page: 281 issue: 3 year: 2009 ident: 9118_CR13 publication-title: Journal of Scheduling doi: 10.1007/s10951-008-0079-3 – volume: 53 start-page: 833 issue: 8 year: 2002 ident: 9118_CR16 publication-title: Journal of the Operational Research Society doi: 10.1057/palgrave.jors.2601321 |
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| Title | Improvements of constraint programming and hybrid methods for scheduling of tests on vehicle prototypes |
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