Artificial Intelligence - Special issue on knowledge representation
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Iterative Flattening: A Scalable Method for Solving Multi-Capacity Scheduling Problems
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Robotics and Computer-Integrated Manufacturing
Computers and Operations Research
From precedence constraint posting to partial order schedules: A CSP approach to Robust Scheduling
AI Communications - Constraint Programming for Planning and Scheduling
Discrepancy search for the flexible job shop scheduling problem
Computers and Operations Research
Stochastic procedures for generating feasible schedules
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Parallel meta2heuristics for the flexible job shop problem
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
Flexible job shop scheduling using hybrid differential evolution algorithms
Computers and Industrial Engineering
An integrated search heuristic for large-scale flexible job shop scheduling problems
Computers and Operations Research
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This paper presents a meta-heuristic algorithm for solving the Flexible Job Shop Scheduling Problem (FJSSP). This strategy, known as Iterative Flattening Search (IFS), iteratively applies a relaxation-step, in which a subset of scheduling decisions are randomly retracted from the current solution; and a solving-step, in which a new solution is incrementally recomputed from this partial schedule. This work contributes two separate results: (1) it proposes a constraint-based procedure extending an existing approach previously used for classical Job Shop Scheduling Problem; (2) it proposes an original relaxation strategy on feasible FJSSP solutions based on the idea of randomly breaking the execution orders of the activities on the machines and opening the resource options for some activities selected at random. The efficacy of the overall heuristic optimization algorithm is demonstrated on a set of well-known benchmarks.