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

Habibi, Farhad; Barzinpour, Farnaz; Sadjadi, Seyed Jafar

doi:10.5267/j.jpm.2018.1.005

Cited by 105 publications

(61 citation statements)

References 103 publications

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“…There are three basic solution methods, namely precise methods, heuristic methods, and meta-evolutionary approaches to its solution. A good overview of these methods can be found in [20,21]. Different variants of genetic algorithm [22][23][24], colony intellect method [25,26], ant colony [27], bee colony methods [28][29][30], and DE algorithm [31,32] have been proposed for solving the RCPSP problem.…”

Section: Review Of Related Workmentioning

confidence: 99%

A Model for Optimal Planning of Information Security Incident Response Operations

İmamverdiyev¹

2018

JPIT

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A quick and adequate response to handling of information security incidents is critical for ensuring business continuity. To handle such incidents, special CERT commands are required, but the cost of maintaining them is a burden for most organizations, and they prefer to use the services of special CERT service providers. This study proposes a model for the optimal distribution of information security incident response operations between CERT groups; the model is formulated as an optimization problem, and differential evolution algorithm is developed to solve it.

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Section: Review Of Related Workmentioning

confidence: 99%

A Model for Optimal Planning of Information Security Incident Response Operations

İmamverdiyev¹

2018

JPIT

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“…question, we are dealing with a scheduling problem consisting in defining the time in which to start different activities. The classical objective is to find a feasible schedule so that the project duration has to be minimized [29]. Also in this case many models have been proposed, considering different aspects of the problem such as capacity constraints [35] or robustness of the problem [1].…”

Section: Introductionmentioning

confidence: 99%

A general space-time model for combinatorial optimization problems (and not only)

Barbati

Corrente

Greco

2020

Omega

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We consider the problem of defining a strategy consisting of a set of facilities taking into account also the location where they have to be assigned and the time in which they have to be activated. The facilities are evaluated with respect to a set of criteria. The plan has to be devised respecting some constraints related to different aspects of the problem such as precedence restrictions due to the nature of the facilities. Among the constraints, there are some related to the available budget. We consider also the uncertainty related to the performances of the facilities with respect to considered criteria, and plurality of stakeholders participating to the decision. The considered problem can be seen as the combination of some prototypical operations research problems: knapsack problem, location problem and project scheduling. Indeed, the basic brick of our model is a variable x ilt which takes value 1 if facility i is activated in location l at time t, and 0 otherwise. Due to the conjoint consideration of a location and a time in the decision variables, what we propose can be seen as a general space-time model for operations research problems. We discuss how such a model permits to handle complex problems using several methodologies including multiple attribute value theory and multiobjective optimization. With respect to the latter point, without any loss of the generality, we consider the compromise programming and an interactive methodology based on the Dominance-based Rough Set Approach. We illustrate the application of our model with a simple didactic example.

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“…Therefore, RCPSP determines a proper sequence of activities so that we involve in two main constraints: (1) Resource constraints and 2) Precedence relationships are satisfied at fashion time, and measurement criteria, such as time, cost, and quality are optimized [1]. The classification of RCPSP is summarized as follows [13] Since the RCPSP is NP-hard, and for more adaptation of the RCPSP with reality, some researchers have considered various extensions and different methods for solving the problem and working on exact methods using mixed-integer programming, constraint programming, and satisfiability modulo theories [14][15][16][17][18][19][20][21][22]. Bibiks et al [23] presented a discrete cuckoo search (DCS) algorithm for RCPSP and improved the DCS algorithm in their study [24].…”

Section: Introductionmentioning

confidence: 99%

Designing a resource-constrained project scheduling model considering multiple routes for flexible project activities: meta-heuristic algorithms

2019

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Resource constrained project scheduling problem with multiple routes for flexible project activities (RCPSP-MR) is a generalization of the RCPSP, in which for the implementation of each flexible activity in main structure of the project, several exclusive sub-networks are considered. Each sub-network is regarded as a route for the flexible activity. The routes are considered for each flexible activity that are varied in terms of: 1) Number of activities required to execute; 2) Precedence relationship between activates; 3) Allocation of different renewable and nonrenewable resources to each activity; and 4) Effectiveness on the duration and cost of project completion. In this paper, a new mathematical formulation of RCPSP-MR is firstly presented. Then, two solving approaches based on particle swarm optimization (PSO) and genetic algorithm (GA) are proposed to minimize costs of project completion. To evaluate the effectiveness of these proposed approaches, 50 problems (in very small, small, medium, and large-sized test problems) are designed and then are solved. Finally, comparisons are provided. Computational results show that the proposed GA generates high-quality solutions in a timely fashion. Keywords: Resource constrained project scheduling problem (RCPSP), flexible activities, multiple routes, particle swarm optimization (PSO), genetic algorithm (GA) 0,1, 2,..., 1 JN  1 J Set of total project fixed activities, 1 JJ  2 J Set of total flexible project activities, 21 J J J    Set of routes of each flexible activity 2 J   h  Set of total sub network for flexible activity

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

Cited by 105 publications

References 103 publications

A Model for Optimal Planning of Information Security Incident Response Operations

A Model for Optimal Planning of Information Security Incident Response Operations

A general space-time model for combinatorial optimization problems (and not only)

Designing a resource-constrained project scheduling model considering multiple routes for flexible project activities: meta-heuristic algorithms

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