An effective branch-and-price algorithm for the Preemptive Resource Constrained Project Scheduling Problem based on minimal Interval Order Enumeration

Moukrim, Aziz; Quilliot, Alain; Toussaint, Hélène

doi:10.1016/j.ejor.2014.12.037

Cited by 32 publications

(15 citation statements)

References 23 publications

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“…Similar to PRCPSP problems, Van Peteghem and Vanhoucke (2010) assumed that preemption of activities was allowed at any time of performing the activities. In addition, it was observed in Moukrim et al (2015) that no fee was regarded as penalty for preemption of activities. Shadrokh (2010), Afshar-Nadjafi and Majlesi (2014), Afshar-Nadjafi (2014b), Ballestín et al (2009), and Demeulemeester and Herroelen (1996) allowed the interruption of activities in discrete points of time.…”

Section: Preemptive Schedulingmentioning

confidence: 99%

Resource-constrained project scheduling problem: review of past and recent developments

Habibi¹,

Barzinpour²,

Sadjadi³

2018

10.5267/j.jpm

106

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The project scheduling problem is both practically and theoretically of paramount importance. From the practical perspective, improvement of project scheduling as a critical part of project management process can lead to successful project completion and significantly decrease of the relevant costs. From the theoretical perspective, project scheduling is regarded as one of the interesting optimization issues, which has attracted the attention of many researchers in the area of operations research. Therefore, the project scheduling issue has been significantly evaluated over time and has been developed from various aspects. In this research, the topics related to Resource-Constrained Project Scheduling Problem (RCPSP) are reviewed, recent developments in this field are evaluated, and the results are presented for future studies. In this regard, first, the standard problem of RCPSP is expressed and related developments are presented from four aspects of resources, characteristics of activities, type of objective functions, and availability level of information. Following that, details about 216 articles conducted on RCPSP during 1980-2017 are expressed. At the end, in line with the statistics obtained from the evaluation of previous articles, suggestions are made for the future studies in order to help the development of new issues in this area.

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Section: Preemptive Schedulingmentioning

confidence: 99%

Resource-constrained project scheduling problem: review of past and recent developments

Habibi¹,

Barzinpour²,

Sadjadi³

2018

10.5267/j.jpm

106

View full text Add to dashboard Cite

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“…Peteghem and Vanhoucke (2010) solved the Preemption multi-mode resource-constrained project scheduling problem by applying a bi-population genetic algorithm that uses two populations and extends the serial schedule generation scheme by introducing a mode improvement procedure. Preemption without a penalty was discussed by Moukrim, Quilliot and Toussaint (2015). An effective branch-and-price algorithm was used to minimize the project duration based on minimal interval order enumeration involving column generation and constraint propagation.…”

Section: Resource Allocation Scheduling Problemmentioning

confidence: 99%

Optimized resource-constrained method for project schedule compression

Elkabalawy

Moselhi

2021

ECAM

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PurposeThis paper aims to present an integrated method for optimized project duration and costs, considering the size and cost of crews assigned to project activities' execution modes.Design/methodology/approachThe proposed method utilizes fuzzy set theory (FSs) for modeling uncertainties associated with activities' duration and cost and genetic algorithm (GA) for optimizing project schedule. The method has four main modules that support two optimization methods: modeling uncertainty and defuzzification module; scheduling module; cost calculations module; and decision-support module. The first optimization method uses the elitist non-dominated sorting genetic algorithm (NSGA-II), while the second uses a dynamic weighted optimization genetic algorithm. The developed scheduling and optimization methods are coded in python as a stand-alone automated computerized tool to facilitate the developed method's application.FindingsThe developed method is applied to a numerical example to demonstrate its use and illustrate its capabilities. The method was validated using a multi-layered comparative analysis that involves performance evaluation, statistical comparisons and stability evaluation. Results indicated that NSGA-II outperformed the weighted optimization method, resulting in a better global optimum solution, which avoided local minima entrapment. Moreover, the developed method was constructed under a deterministic scenario to evaluate its performance in finding optimal solutions against the previously developed literature methods. Results showed the developed method's superiority in finding a better optimal set of solutions in a reasonable processing time.Originality/valueThe novelty of the proposed method lies in its capacity to consider resource planning and project scheduling under uncertainty simultaneously while accounting for activity splitting.

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“…use a linear programming model that is based on the PRCPSP to compute a lower bound for the RCPSP [26], and Moukrim et al propose an effective branch-and-price algorithm based on minimal interval order enumeration that involves column generation as well as constraint propagation [27]. For more research efforts on project scheduling problems with activity splitting, we refer to [28] - [30].…”

Section: Introductionmentioning

confidence: 99%

A Genetic Algorithm for the Proactive Resource-Constrained Project Scheduling Problem With Activity Splitting

Wang

et al. 2019

IEEE Trans. Eng. Manage.

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Proactive scheduling aims at the generation of robust baseline schedules, which has been studied for many years with the assumption that activity splitting is not allowed. In this paper, we focus on the proactive resource-constrained project scheduling problem in which each activity can be split at discrete time instants under the constraints of a maximum number of splitting and a minimum period of continuous execution. Besides, in this problem setup times are considered. Two properties of the established model and one lemma are proposed and applied in the developed genetic algorithm. After linearizing the proposed model, we use a commercial mathematical programming solver as a benchmark to solve the problem. From the computational results, we find that the developed genetic algorithm is effective and efficient in solving the defined problem, and activity splitting improves robustness. With the growth of the maximum number of splitting, the decline in the minimum execution time, the decrease in the setup times, and the extension of the project due date, robustness increases.

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An effective branch-and-price algorithm for the Preemptive Resource Constrained Project Scheduling Problem based on minimal Interval Order Enumeration

Cited by 32 publications

References 23 publications

Resource-constrained project scheduling problem: review of past and recent developments

Resource-constrained project scheduling problem: review of past and recent developments

Optimized resource-constrained method for project schedule compression

A Genetic Algorithm for the Proactive Resource-Constrained Project Scheduling Problem With Activity Splitting

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