Experimental evaluation of state-of-the-art heuristics for the resource-constrained project scheduling problem

Hartmann, Sönke; Kolisch, Rainer

doi:10.1016/s0377-2217(99)00485-3

Cited by 405 publications

(225 citation statements)

References 35 publications

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“…The design of the first part of the chromosomes was based on the activity sequence proposed by Hartmann (2000). For MRCPSP problems, the chromosomes should consist of three parts (Arjmand & Najafi, 2015).…”

Section: Chromosome Representation and Objective Functionmentioning

confidence: 99%

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A new approach for solving resource constrained project scheduling problems using differential evolution algorithm

Eshraghi¹

2016

10.5267/j.ijiec

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One subcategory of project scheduling is the resource constrained project scheduling problem (RCPSP). The present study proposes a differential evolution algorithm for solving the RCPSP making a small change in the method to comply with the model. The RCPSP is intended to program a group of activities of minimal duration while considering precedence and resource constraints. The present study introduces a differential evolution algorithm and local search was added to improve the performance of the algorithm. The problems were then solved to evaluate the performance of the algorithm and the results are compared with genetic algorithm. Computational results confirm that the differential evolution algorithm performs better than genetic algorithm.

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Section: Chromosome Representation and Objective Functionmentioning

confidence: 99%

“…They compared the algorithms using the MADM approach called TOPSIS. Hartmann and Kolisch (1999, 2000 and Kolisch and Padman (2001) developed meta-heuristic methods to solve large-sized RCPSP. Near-optimal solutions were generated using this method in large-sized RCPSP (Rahmani et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

A new approach for solving resource constrained project scheduling problems using differential evolution algorithm

Eshraghi¹

2016

10.5267/j.ijiec

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“…For the RCPSP problem, the most effective (Hartmann & Kolisch, 2000;Kolisch & Padman, 2001) is the solution coding, used also here, in which the problem is coded as a list of activities, that is a permutation setoff tasks 1, 2, . .…”

Section: Two-phase Algorithm Of Project's Npv Maximisationmentioning

confidence: 99%

A Two-Phase Algorithm for a Resource Constrained Project Scheduling Problem with Discounted Cash Flows

Klimek

Łebkowski

2013

DMMS

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Abstract. This paper presents a Resource-Constrained Project Scheduling Problem (RCPSP) settled by contractual milestones. The criterion analysed here is the maximisation of aggregate discounted cash flows from the contractor's perspective, known as an RCPSP problem with Discounted Cash Flows (RCPSPDCF). The cash flows analysed here cover the contractor's cash outflows (negative cash flows), related to the commencement of individual activities, and cash inflows (positive cash flows) after the fulfilment of individual milestones. The authors propose a two-phase algorithm for solving the problem defined. In the first phase, the simulated annealing metaheuristics is used, designed to identify a forward schedule with as high total DCF as possible. In the second phase, the best first-phase schedule is improved by right shifts of activities. To this end, the procedure which iteratively shifts tasks by one unit is applied, with a view to maximising the objective function. Activity shifts take into consideration precedence and resource constraints, and they are performed for a specified resource allocation to activities. This paper also includes an analysis of the problem for a sample project. The results of computational experiments are then analysed. The experiments were run with the use of standard test problems from the Project Scheduling Problem LIBrary (PSPLIB), with additionally defined cash flows and contractual milestones.

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“…Differentiating factors between problem instances should be noted prior to any experimentation, and heuristic performance on each type of problem instance should be discussed. A good example of such analysis is found in [17], an experimental evaluation of heuristics for the resource-constrained project scheduling problem. That paper split problem instances by three key problem instance parameters, the network complexity, resource factor, and resource strength, analyzing the effects of each on the performance of the heuristics.…”

Section: Problem Instance Classificationmentioning

confidence: 99%

Comparison of Metaheuristics

Silberholz

Golden

2010

International Series in Operations Research &Amp; Management Science

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Experimental evaluation of state-of-the-art heuristics for the resource-constrained project scheduling problem

Cited by 405 publications

References 35 publications

A new approach for solving resource constrained project scheduling problems using differential evolution algorithm

A new approach for solving resource constrained project scheduling problems using differential evolution algorithm

A Two-Phase Algorithm for a Resource Constrained Project Scheduling Problem with Discounted Cash Flows

Comparison of Metaheuristics

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