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

Nonobe, Koji; Ibaraki, Toshihide

doi:10.1007/978-1-4615-1507-4_25

Cited by 129 publications

(88 citation statements)

References 20 publications

(43 reference statements)

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“…Among the approaches for solving MRCPSPs with the use of metaheuristics, several algorithms were proposed: genetic algorithms (GA) [1,7,23,24,28,40]; simulated annealing (SA) [12,33]; tabu search (TS) [19,27,30,31]; particle swarm optimisation (PSO) [10,13,42]; ant colony optimisation (ACO) [39], or hybrid algorithms [29].…”

Section: Approaches To Solving the Problemmentioning

confidence: 99%

The multi-mode, resource-constrained project scheduling problem in construction: state of art review and research challenges

Rosłon¹

2017

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In this article, the author analyses the state of the research of the multi-mode, resource-constrained, projectscheduling problem (MRCPSP). Both general and construction-industry-specific applications were studied and compared-in particular, algorithms and methodologies used for solving the different variations of this scheduling problem. Based on the analysis, the author suggests future research ideas.

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Section: Approaches To Solving the Problemmentioning

confidence: 99%

The multi-mode, resource-constrained project scheduling problem in construction: state of art review and research challenges

Rosłon¹

2017

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“…The first algorithm, Algorithm A, is the tabu-search algorithm of Nonobe and Ibaraki (2002). This algorithm was not specially designed for the MRCPSP and can handle more general resource-constrained project scheduling problems with other extensions as well.…”

Section: Empirical Hardness Modelsmentioning

confidence: 99%

An automatic algorithm selection approach for the multi-mode resource-constrained project scheduling problem

Messelis

Causmaecker

2014

European Journal of Operational Research

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This paper investigates the construction of an automatic algorithm selection tool for the multi-mode resourceconstrained project scheduling problem (MRCPSP). The research described relies on the notion of empirical hardness models. These models map problem instance features onto the performance of an algorithm. Using such models, the performance of a set of algorithms can be predicted. Based on these predictions, one can automatically select the algorithm that is expected to perform best given the available computing resources. The idea is to combine different algorithms in a super-algorithm that performs better than any of the components individually. We apply this strategy to the classic problem of project scheduling with multiple execution modes. We show that we can indeed significantly improve on the performance of state-of-the-art algorithms when evaluated on a set of unseen instances. This becomes important when lots of instances have to be solved consecutively. Many state-of-the-art algorithms perform very well on a majority of benchmark instances, while performing worse on a smaller set of instances. The performance of one algorithm can be very different on a set of instances while another algorithm sees no difference in performance at all. Knowing in advance, without using scarce computational resources, which algorithm to run on a certain problem instance, can significantly improve the total overall performance.

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“…Varma et al [25] considered a multi-mode problem without nonrenewable resources. Multi-mode problems with generalized precedence constraints have been considered by De Reyck and Herroelen [26], Drexl et al [21], Heilmann [27,28], Nonobe and Ibaraki [29], Brucker and Knust [30], Calhoun et al [31], Sabzehparvar and Seyed-Hosseini [32] and Barrios et al [33].…”

Section: Multi-mode Resource Constrained Project Scheduling Problemmentioning

confidence: 99%

An Entropy-Based Upper Bound Methodology for Robust Predictive Multi-Mode RCPSP Schedules

Chen¹,

Liang

Padilla

2014

Entropy

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Abstract:Projects are an important part of our activities and regardless of their magnitude, scheduling is at the very core of every project. In an ideal world makespan minimization, which is the most commonly sought objective, would give us an advantage. However, every time we execute a project we have to deal with uncertainty; part of it coming from known sources and part remaining unknown until it affects us. For this reason, it is much more practical to focus on making our schedules robust, capable of handling uncertainty, and even to determine a range in which the project could be completed. In this paper we focus on an approach to determine such a range for the Multi-mode Resource Constrained Project Scheduling Problem (MRCPSP), a widely researched, NP-complete problem, but without adding any subjective considerations to its estimation. We do this by using a concept well known in the domain of thermodynamics, entropy and a three-stage approach. First we use Artificial Bee Colony (ABC)-an effective and powerful meta-heuristic-to determine a schedule with minimized makespan which serves as a lower bound. The second stage defines buffer times and creates an upper bound makespan using an entropy function, with the advantage over other methods that it only considers elements which are inherent to the schedule itself and does not introduce any subjectivity to the buffer time generation. In the last stage, we use the ABC algorithm with an objective function that seeks to maximize robustness while staying within the makespan boundaries defined OPEN ACCESSEntropy 2014, 16 5033 previously and in some cases even below the lower boundary. We evaluate our approach with two different benchmarks sets: when using the PSPLIB for the MRCPSP benchmark set, the computational results indicate that it is possible to generate robust schedules which generally result in an increase of less than 10% of the best known solutions while increasing the robustness in at least 20% for practically every benchmark set. And, in an attempt to solve larger instances with 50 or 100 activities, we also used the MRCPSP/max benchmark sets, where the increase of the makespan is approximately 35% with respect to the best known solutions at the same time as with a 20% increase in robustness.

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

Cited by 129 publications

References 20 publications

The multi-mode, resource-constrained project scheduling problem in construction: state of art review and research challenges

The multi-mode, resource-constrained project scheduling problem in construction: state of art review and research challenges

An automatic algorithm selection approach for the multi-mode resource-constrained project scheduling problem

An Entropy-Based Upper Bound Methodology for Robust Predictive Multi-Mode RCPSP Schedules

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