A biased random-key genetic algorithm for the project scheduling problem with flexible resources

Almeida, Bernardo F.; Correia, Isabel; Saldanha-da-Gama, Francisco

doi:10.1007/s11750-018-0472-9

Cited by 27 publications

(12 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A hybrid Benders decomposition approach is developed in Li and Womer (2009) and Bellenguez-Morineau and Néron (2007) present a branch-and bound procedure. Almeida, Correia, and Saldanha-da Gama (2016) and Almeida, Correia, and Saldanha-da Gama (2018a) address the multi-skill project scheduling problem using respectively a heuristic framework and a biased random-key genetic algorithm. In the literature, multi-skilled resources in projects have been utilised in software development ( Alba & Chicano, 2007 ), the service sector ( Valls et al, 2009 ), the building sector ( Liu & Wang, 2012 ) and consulting ( Correia et al, 2012 ).…”

Section: Literature Review On Multi-skilled Resourcesmentioning

confidence: 99%

See 1 more Smart Citation

A new algorithm for resource-constrained project scheduling with breadth and depth of skills

Snauwaert

Vanhoucke

2021

European Journal of Operational Research

View full text Add to dashboard Cite

Section: Literature Review On Multi-skilled Resourcesmentioning

confidence: 99%

“…Hartmann (1998) presented a similar list to apply priority rules to schedule activities. An alternative to the PL is the resource list (RL), which was proposed by Almeida et al (2018a) in a multi-skilled environment. The resource list is a single list of the resources in the project and has length | R | .…”

Section: Representationmentioning

confidence: 99%

A new algorithm for resource-constrained project scheduling with breadth and depth of skills

Snauwaert

Vanhoucke

2021

European Journal of Operational Research

View full text Add to dashboard Cite

“…Almeida et al (2016) propose a priority rule-based parallel scheduling heuristic for the MSRCPSP by applying two new concepts, namely, resource weight and activity grouping. In another work, Almeida et al (2018) use a biased random key-based genetic algorithm (GA) (Mendes et al , 2009) for the PSPFR and improve the previous results obtained by the priority rule-based heuristics.…”

Section: Introductionmentioning

confidence: 98%

An effective teaching-learning-based optimization algorithm for the multi-skill resource-constrained project scheduling problem

Joshi

Mittal

Sharma

et al. 2019

JM2

View full text Add to dashboard Cite

Purpose The purpose of this paper is to consider one of the recent and practical extensions of the resource-constrained project scheduling problem (RCPSP) termed as the multi-skill resource-constrained project scheduling problem (MSRCPSP) for investigation. The objective is the minimization of the makespan or total project duration. Design/methodology/approach To solve this complex problem, the authors propose a teaching–learning-based optimization (TLBO) algorithm in which self-study and examination have been used as additional features to enhance its exploration and exploitation capabilities. An activity list-based encoding scheme has been modified to include the resource assignment information because of the multi-skill nature of the algorithm. In addition, a genetic algorithm (GA) is also developed in this work for the purpose of comparisons. The computational experiments are performed on 216 test instances with varying complexity and characteristics generated for the purpose. Findings The results obtained after computations show that the TLBO has performed significantly better than GA in terms of average percentage deviation from the critical path-based lower bound for different combinations of three parameters, namely, skill factor, network complexity and modified resource strength. Research limitations/implications The modified TLBO proposed in this paper can be conveniently applied to any product or service organization wherein human resources are involved in executing project activities. Practical implications The developed model can suitably handle resource allocation problems faced in real-life large-sized projects usually administered in software development companies, consultancy firms, R&D-based organizations, maintenance firms, big construction houses, etc. wherein human resources are involved. Originality/value The current work aims to propose an effective metaheuristic for a more realistic version of MSRCPSP, in which resource requirements of activities may be more than one. Moreover, to enhance the exploration and exploitation capabilities of the original TLBO, the authors use two additional concepts, namely, self-study and examination in the search process.

show abstract

“…However, this time, the authors propose a multi-pass version of the algorithm, where different priority lists for activities and different criticality functions for resource allocation are tested and the best solution found is kept. Almeida et al (2018) propose a biased random-key genetic algorithm that significantly improved the heuristic of Almeida et al (2016). In independently carried-out research efforts, Myszkowski et al (2013) test different priority rules, the traditional ones and some more complex ones, with an SGS algorithm.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Heuristic and metaheuristic methods for the multi‐skill project scheduling problem with partial preemption

Polo-Mejía

Artigues

López

et al. 2021

Int Trans Operational Res

View full text Add to dashboard Cite

The multi-skill project scheduling problem (MSPSP) has been first addressed in the scheduling community for more than 15 years. This paper deals with a new variant of this problem, the multi-skill project scheduling problem with partial preemption (MSPSP-PP), where only a subset of resources can be released during the preemption periods. Like the standard problem, this variant is NP-hard, because of that we propose in this article a series of heuristic algorithms to solve instances arising from an industrial application. First, we present a serial greedy algorithm, based on priority rules and a flow problem for resource allocation. To improve the solutions of the greedy algorithm, we then introduce a binary-tree-based search algorithm and a greedy randomised adaptive search procedure (GRASP). Finally, we propose a large neighbourhood search (LNS) algorithm integrating exact and heuristic methods. The best results in terms of solution quality and execution time are obtained by combining the GRASP algorithm and the LNS approach. Furthermore, the proposed GRASP algorithm is able to find new best results on 56 instances out of 216 on a standard MSPSP instance set which shows the quality of the approach even on special cases of the considered problem.

show abstract

A biased random-key genetic algorithm for the project scheduling problem with flexible resources

Cited by 27 publications

References 31 publications

A new algorithm for resource-constrained project scheduling with breadth and depth of skills

A new algorithm for resource-constrained project scheduling with breadth and depth of skills

An effective teaching-learning-based optimization algorithm for the multi-skill resource-constrained project scheduling problem

Heuristic and metaheuristic methods for the multi‐skill project scheduling problem with partial preemption

Contact Info

Product

Resources

About