LSSPER: Solving the Resource-Constrained Project Scheduling Problem with Large Neighbourhood Search

Palpant, Mireille; Artigues, Christian; Michelon, Philippe

doi:10.1023/b:anor.0000039521.26237.62

Cited by 108 publications

(76 citation statements)

References 49 publications

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“…This neighborhood search repeatedly removes a small subset of the jobs from the schedule and reinsert them by means of an ILP solution. This last technique is based on the work of Palpant et al [2004].…”

Section: Deriving and Solving The Resulting Tcpspmentioning

confidence: 99%

Time-constrained project scheduling with adjacent resources

Hurink

Kok

Paulus³

et al. 2011

Computers & Operations Research

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We develop a decomposition method for the Time-Constrained Project Scheduling Problem (TCPSP) with Adjacent Resources. For adjacent resources the resource units are ordered and the units assigned to a job have to be adjacent. On top of that, adjacent resources are not required by single jobs, but by job groups. As soon as a job of such a group starts, the adjacent resource units are occupied, and they are not released before all jobs of that group are completed. The developed decomposition method separates the adjacent resource assignment from the rest of the scheduling problem. Test results demonstrate the applicability of the decomposition method. The presented decomposition forms a first promising approach for the TCPSP with adjacent resources and may form a good basis to develop more elaborated methods.

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Section: Deriving and Solving The Resulting Tcpspmentioning

confidence: 99%

Time-constrained project scheduling with adjacent resources

Hurink

Kok

Paulus³

et al. 2011

Computers & Operations Research

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“…The final results of our computational experiments have been compared with the results provided for LSSPER in [32]. LSSPER is a state-of-the-art procedure that for the first time has improved 14, 9, and 4 of the best solutions for the set J 60, J 90, and J 120, respectively.…”

Section: Computational Experimentsmentioning

confidence: 99%

A polarized adaptive schedule generation scheme for the resource-constrained project scheduling problem

Zamani

2012

RAIRO-Oper. Res.

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This paper presents a hybrid schedule generation scheme for solving the resource-constrained project scheduling problem. The scheme, which is called the Polarized Adaptive Scheduling Scheme (PASS), can operate in a spectrum between two poles, namely the parallel and serial schedule generation schemes. A polarizer parameter in the range between zero and one indicates how similarly the PASS behaves like each of its two poles. The presented hybrid is incorporated into a novel genetic algorithm that never degenerates, resulting in an effective self-adaptive procedure. The key point of this genetic algorithm is the embedding of the polarizer parameter as a gene in the genomes used. Through this embedding, the procedure learns via monitoring its own performance and incorporates this knowledge in conducting the search process. The computational experiments indicate that the procedure can produce optimal solutions for a large percentage of benchmark instances. Abstract. This paper presents a hybrid schedule generation scheme for solving the resource-constrained project scheduling problem. The scheme, which is called the Polarized Adaptive Scheduling Scheme (PASS), can operate in a spectrum between two poles, namely the parallel and serial schedule generation schemes. A polarizer parameter in the range between zero and one indicates how similarly the PASS behaves like each of its two poles. The presented hybrid is incorporated into a novel genetic algorithm that never degenerates, resulting in an effective self-adaptive procedure. The key point of this genetic algorithm is the embedding of the polarizer parameter as a gene in the genomes used. Through this embedding, the procedure learns via monitoring its own performance and incorporates this knowledge in conducting the search process. The computational experiments indicate that the procedure can produce optimal solutions for a large percentage of benchmark instances.

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“…We use a neighborhood search based on a method proposed by Palpant et al (2004). This method selects a number of jobs, 'freezes' the remainder of the schedule, and calculates for the resulting ILP an optimal schedule.…”

Section: Stagementioning

confidence: 99%

Time-constrained project scheduling

Guldemond

Hurink

Paulus

et al. 2008

J Sched

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We propose a new approach for scheduling with strict deadlines and apply this approach to the TimeConstrained Project Scheduling Problem (TCPSP). To be able to meet these deadlines, it is possible to work in overtime or hire additional capacity in regular time or overtime. For this problem, we develop a two stage heuristic. The key of the approach lies in the first stage in which we construct partial schedules. In these partial schedules, jobs may be scheduled for a shorter duration than required. The second stage uses an ILP formulation of the problem to turn a partial schedule into a feasible schedule, and to perform a neighborhood search. The developed heuristic is quite flexible and, therefore, suitable for practice. We present experimental results on modified RCPSP benchmark instances. The two stage heuristic solves many instances to optimality, and if we substantially decrease the deadline, the rise in cost is only small.

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LSSPER: Solving the Resource-Constrained Project Scheduling Problem with Large Neighbourhood Search

Cited by 108 publications

References 49 publications

Time-constrained project scheduling with adjacent resources

Time-constrained project scheduling with adjacent resources

A polarized adaptive schedule generation scheme for the resource-constrained project scheduling problem

Time-constrained project scheduling

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