Meta-heuristics for System Design Engineering

Chelouah, Rachid; Baron, Claude; Zholghadri, Marc; Gutierrez, Citlalih

doi:10.1007/978-3-642-01085-9_13

Cited by 3 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hence, the next step is to find solutions that belong to the Pareto front (time/cost) among the solution space restricted during the first step. Multicriteria optimization techniques and more accurately Evolutionary Algorithm (EA) (see [6] and [7]) have the advantage to avoid the aggregation of criteria and can provide solutions on a Pareto front in a rather simple way. Thanks to an evolutionary approach, the second step will perform a second reduction of the solution space for both product and plan and provide solutions on the Pareto front.…”

Section: Introductionmentioning

confidence: 99%

Using constraints filtering and evolutionary algorithms for interactive configuration and planning

Pitiot

Aldanondo

Djefel

et al. 2010

2010 IEEE International Conference on Industrial Engineering and Engineering Management

View full text Add to dashboard Cite

This communication aims to associate the product configuration task with the planning of its production process in order to make consistent decisions while trying to minimize cost and cycle time. A two step approach is described with relevant aiding tools. During the first one, configuration and planning are considered as two constraint satisfaction problems and are interactively assisted by constraint propagation. The second one, thanks to a multicriteria optimisation relying on a constrained evolutionary algorithm, proposes a set of solutions belonging to a Pareto front minimizing cost and cycle time to the user. After a problem introduction and a global description of the aiding system, the paper focuses on the optimisation process with interesting quantified results.Keywordsproduct configuration, process planning, constraint satisfaction problem, evolutionary algorithm 978-1-4244-8503-1/10/$26.00 ©2010 IEEE Proceedings of the 2010 IEEE IEEM

show abstract

Section: Introductionmentioning

confidence: 99%

Using constraints filtering and evolutionary algorithms for interactive configuration and planning

Pitiot

Aldanondo

Djefel

et al. 2010

2010 IEEE International Conference on Industrial Engineering and Engineering Management

View full text Add to dashboard Cite

show abstract

“…Hence, the next step is to find solutions that belong to the Pareto front (time/cost) among the solution space restricted during the first step. Multi-criteria optimization techniques and more accurately Evolutionary Algorithm (EA) (see [6] and [7]) have the advantage to avoid the aggregation of criteria and can provide solutions on a Pareto front in a rather simple way. Thanks to an evolutionary approach, the second step will perform a second reduction of the solution space for both product and plan and provide solutions on the Pareto front.…”

Section: Introductionmentioning

confidence: 99%

Aiding Interactive Configuration and Planning: A Constraint and Evolutionary Approach

Pitiot

Aldanondo

Vareilles

et al. 2010

IFIP Advances in Information and Communication Technology

Self Cite

View full text Add to dashboard Cite

This communication aims to propose a two step interactive aiding system dealing with product configuration and production planning. The first step assists interactively and simultaneously the configuration of a product and the planning of its production process. Then a second step complete the two previous tasks thanks to a constrained multi-criteria optimisation that proposes to the user a set of solutions belonging to a Pareto front minimizing cost and cycle time. The first section of the paper introduces the problem. The second one proposes a solution for the first step relying on constraint filtering for both configuration and planning. The following ones propose an evolutionary optimisation process and first computation results.

show abstract

Multi-agent system-based fuzzy constraints offer negotiation of workflow scheduling in Fog-Cloud environment

et al. 2023

View full text Add to dashboard Cite

Meta-heuristics for System Design Engineering

Cited by 3 publications

References 10 publications

Using constraints filtering and evolutionary algorithms for interactive configuration and planning

Using constraints filtering and evolutionary algorithms for interactive configuration and planning

Aiding Interactive Configuration and Planning: A Constraint and Evolutionary Approach

Multi-agent system-based fuzzy constraints offer negotiation of workflow scheduling in Fog-Cloud environment

Contact Info

Product

Resources

About