Tabu search for resource-constrained scheduling

Verhoeven, M.G.A.

doi:10.1016/s0377-2217(98)80001-5

Cited by 21 publications

(7 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…EA (Hartmann 1998;Valls et al 2001Valls et al , 2008, TS (Thomas et al 1998;Tsai et al 1998;Verhoeven 1998), SA (Bouleimen et al 2003;Das et al 2011) are well explored and widely applied to solve MS-RCPSP. It is worth a mention that ACO is not the only swarm intelligence metaheuristic used in solving (MS-) RCPSP.…”

Section: Related Workmentioning

confidence: 99%

Hybrid ant colony optimization in solving multi-skill resource-constrained project scheduling problem

et al. 2014

View full text Add to dashboard Cite

In this paper, hybrid ant colony optimization (HAntCO) approach in solving multi-skill resource-constrained project scheduling problem (MS-RCPSP) has been presented. We have proposed hybrid approach that links classical heuristic priority rules for project scheduling with ant colony optimization (ACO). Furthermore, a novel approach for updating pheromone value has been proposed based on both the best and worst solutions stored by ants. The objective of this paper is to research the usability and robustness of ACO and its hybrids with priority rules in solving MS-RCPSP. Experiments have been performed using artificially created dataset instances based on real-world ones. We published those instances that can be used as a benchmark. Presented results show that ACO-based hybrid method is an efficient approach. More directed search process by hybrids makes this approach more stable and provides mostly better results than classical ACO.

show abstract

Section: Related Workmentioning

confidence: 99%

Hybrid ant colony optimization in solving multi-skill resource-constrained project scheduling problem

et al. 2014

View full text Add to dashboard Cite

show abstract

“…In terms of changes made to the resource requirements of operations, the multiplecapacitated JSSP (Nuijtne andAarts 1996 andVerhoeven 1998) and JSSP with temporal relaxation of capacity constraints due to subcontracting (Chung et al 2005), allow the processing of more than one operation at a time on a single resource. The multiple resource constrained JSSP or multiple processor task JSSP, in turn, requires that an operation be processed simultaneously on two or more resources (Brucker andKramer 1996 andFredendall et al 1996).…”

Section: The Problem Contextmentioning

confidence: 99%

Metaheuristics for the multi-objective FJSP with sequence-dependent set-up times, auxiliary resources and machine down time

Grobler

Engelbrecht²,

Kok³

et al. 2009

Ann Oper Res

View full text Add to dashboard Cite

This paper investigates the application of particle swarm optimization (PSO) to the multi-objective flexible job shop scheduling problem with sequence-dependent set-up times, auxiliary resources and machine down time. To achieve this goal, alternative particle representations and problem mapping mechanisms were implemented within the PSO paradigm. This resulted in the development of four PSO-based heuristics. Benchmarking on real customer data indicated that using the priority-based representation resulted in a significant performance improvement over the existing rule-based algorithms commonly used to solve this problem. Additional investigation into algorithm scalability led to the development of a priority-based differential evolution algorithm. Apart from the academic significance of the paper, the benefit of an improved production schedule can be generalized to include cost reduction, customer satisfaction, improved profitability, and overall competitive advantage.

show abstract

“…There have been proposed many heuristic algorithms for RCPSP [1,2,5,7,8,10,12,13,14,15,18,19,21,22]. Among them, some effective RCPSP algorithms (e.g., genetic algorithms by Hartmann [7,8], an ant colony optimization algorithm by Merkle et al [13] and a tabu search algorithm by Baar et al [1]) represent a solution by a permutation of all the activities (which is often called a list), from which a schedule is generated by the serial or parallel schedule generation scheme [10].…”

Section: Tabu Search For Rcpspmentioning

confidence: 99%

“…Recently, to deal with problems of large scale, meta-heuristic algorithms have been studied and brought successful computational results. In these developments [1,2,5,7,8,12,13,14,15,19,21,22], most literature employs a rather simple model of RCPSP, whose objective is to minimize the makespan, and whose constraints are only those of renewable resources and precedence constraints. In real situations, however, more complicated constraints often arise, which cannot be formulated in such a simple form of RCPSP.…”

Section: Introductionmentioning

confidence: 99%

Formulation and Tabu Search Algorithm for the Resource Constrained Project Scheduling Problem

Nonobe

Ibaraki

2002

Operations Research/Computer Science Interfaces Series

129

View full text Add to dashboard Cite

Abstract:The resource constrained project scheduling problem (RCPSP) can formulate many scheduling problems including jobshop and flowshop scheduling problems. In this paper, we extend the definition of RCPSP further so that various complicated constraints and objective functions arising in practice can be handled; for example, each activity can be processed in one of the selectable modes, the available amounts of renewable resources may vary depending on the periods, setup activities can be dealt with, and complex objective functions can be handled. Then, we develop a tabu search based heuristic algorithm, which contains elaborations in representing solutions and in constructing neighborhood. Our code was tested for many benchmarks of RCPSP, and also for some problems from real applications. For a number of RCPSP instances, we found better solutions than the best ones found so far. These computational results indicate the effectiveness and usefulness of our approach.

show abstract

Tabu search for resource-constrained scheduling

Cited by 21 publications

References 12 publications

Hybrid ant colony optimization in solving multi-skill resource-constrained project scheduling problem

Hybrid ant colony optimization in solving multi-skill resource-constrained project scheduling problem

Metaheuristics for the multi-objective FJSP with sequence-dependent set-up times, auxiliary resources and machine down time

Formulation and Tabu Search Algorithm for the Resource Constrained Project Scheduling Problem

Contact Info

Product

Resources

About