Flexible job-shop scheduling with routing flexibility and separable setup times using ant colony optimisation method

Rossi, Andrea; Dini, Gino

doi:10.1016/j.rcim.2006.06.004

Cited by 140 publications

(44 citation statements)

References 28 publications

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“…The taboo search algorithm is embedded in the ACO algorithm to improve the solution quality. Rossi and Dini (2007) proposed an ACO based software system for solving flexible job shop problem (FJSP) with routing flexibility, sequence-dependent set up and transportation time. They showed the effectiveness of their system by comparing with other alternative approaches (including GA) for various benchmark instances.…”

Section: Review Of Solution Methodologiesmentioning

confidence: 99%

An Ant Colony Optimization Algorithm for Flexible Job Shop Scheduling Problem

Ponnambalam¹,

Jawahar²,

Girish³

2010

New Advanced Technologies

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Section: Review Of Solution Methodologiesmentioning

confidence: 99%

An Ant Colony Optimization Algorithm for Flexible Job Shop Scheduling Problem

Ponnambalam¹,

Jawahar²,

Girish³

2010

New Advanced Technologies

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“…The RCPSP/max ( [17]) is an extended formulation of the Resource Constrained Project Scheduling Problem (RCPSP) frequently used to model manufacturing, logistics and project management scheduling problems, and is considered particularly difficult due to the presence of temporal separation constraints (in particular maximum time lags) between project activities (even the search of a feasible solution is NP-hard ). The FJSSP ( [22]) is an extension of the Job Shop Scheduling Problem (JSSP), of particular interest in the field of Flexible Manufacturing Systems development, whose resolution integrates a routing policy search to the ordinary sequencing of activities that concurrently use the same resource; being an extension of the JSSP, also the FJSSP is NP-hard.…”

Section: Putting the Ifs To The Testmentioning

confidence: 99%

Surveying the Versatility of Constraint-Based Large Neighborhood Search for Scheduling Problems

Rasconi

Oddi

Cesta

2015

Beyond Databases, Architectures and Structures

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Abstract. Constraint-based search techniques have gained increasing attention in recent years as a basis for scheduling procedures that are capable of accommodating a wide range of constraints. Among these, the Large Neighborhood Search (lns) has largely proven to be a very effective heuristic-based methodology. Its basic optimization cycle consists of a continuous iteration of two steps where the solution is first relaxed and then re-constructed. In Constraint Programming terms, relaxing entails the retraction of some previously imposed constraints, while re-constructing entails imposing new constraints, searching for a better solution. Each iteration of constraint removal and re-insertion can be considered as the examination of a large neighborhood move, hence the procedure's name. Over the years, LNS has been successfully employed over a wide range of different problems; this paper intends to provide an overview of some utilization examples that demonstrate both the versatility and the effectiveness of the procedure against significantly difficult scheduling benchmarks known in literature.

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“…Since using optimal methods to solve realistic problems is computationally intensive, nearly optimal methods try to reduce the algorithm complexity by relaxing certain problem requirements and constraints, with for (Baker, 1998). For instance, the FMS problem can be modeled as the travelling salesman problem or the flexible job shop problem (Toth, 2000;Rossi and Dini, 2007). Some examples implementing integer linear programs in FMS control are reported by Pach (2013) and Gou et al, (1994).…”

Section: Optimal and Nearly Optimal Approachesmentioning

confidence: 99%