Justification and RCPSP: A technique that pays

Valls, Vicente; Ballestín, Francisco; Quintanilla, Sacramento

doi:10.1016/j.ejor.2004.04.008

Cited by 231 publications

(124 citation statements)

References 26 publications

Supporting

Mentioning

112

Contrasting

Unclassified

Order By: Relevance

“…The method applies serial forward backward scheduling iteratively until no further improvement in the project duration is achieved. In [35,38], it has been shown that even if this technique, in the backward/forward direction, is applied only once to a schedule, it may still produce significant improvements. The performances of several major heuristics for the RCPSP have been investigated in [22].…”

Section: Related Workmentioning

confidence: 99%

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

Zamani

2012

RAIRO-Oper. Res.

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

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

Zamani

2012

RAIRO-Oper. Res.

View full text Add to dashboard Cite

show abstract

“…• Dual population: The population based heuristic splits the total number of generated schedules into two separate populations containing left-and right-justified schedules, inspired by the promising results found by Valls et al (2005).…”

Section: Rcpsp Scheduling Stepmentioning

confidence: 99%

The Multi-Mode Resource-Constrained Project Scheduling Problem

Coelho

Vanhoucke

2014

Handbook on Project Management and Scheduling Vol.1

View full text Add to dashboard Cite

This chapter reports on a new solution approach for the multi-mode resource-constrained project scheduling problem (MRCPSP, MPS|prec|C max ). This problem type aims at the selection of a single activity mode from a set of available modes in order to construct a precedence and a (renewable and non-renewable) resource-feasible project schedule with a minimal makespan. The problem type is known to be N P-hard and has been solved using various exact as well as (meta-)heuristic procedures. The new algorithm splits the problem type into a mode assignment and a single mode project scheduling step. The mode assignment step is solved by a satisfiability (SAT) problem solver and returns a feasible mode selection to the project scheduling step. The project scheduling step is solved using an efficient meta-heuristic procedure from literature to solve the resourceconstrained project scheduling problem (RCPSP). However, unlike many traditional meta-heuristic methods in literature to solve the MRCPSP, the new approach executes these two steps in one run, relying on a single priority list. Straightforward adaptations to the pure SAT solver by using pseudo boolean non-renewable resource constraints has led to a high quality solution approach in a reasonable computational time. Computational results show that the procedure can report similar or sometimes even better solutions than found by other procedures in literature, although it often requires a higher CPU time.

show abstract

“…Finalmente, Valls et al [99] diseña un simulated annealing y utiliza los métodos de mejora forward backward, siendo también eficiente en la librería de pruebas.…”

Section: Simulated Annealingunclassified

“…En cuanto al RCPSP los AG son el método más utilizado para su solución. Destacan los trabajos desarrollados por Alcaraz y Maroto [1], [2]; Debels [24]; Hartman [38]; Valls et al [99], [98].…”

Section: Algoritmos Genéticosunclassified

See 1 more Smart Citation

Nuevos métodos meta heurísticos para la asignación eficiente, optimizada y robusta de recursos limitados

Posada¹,

Margarita²

View full text Add to dashboard Cite

Justification and RCPSP: A technique that pays

Cited by 231 publications

References 26 publications

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

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

The Multi-Mode Resource-Constrained Project Scheduling Problem

Nuevos métodos meta heurísticos para la asignación eficiente, optimizada y robusta de recursos limitados

Contact Info

Product

Resources

About