In this paper, a new representation for resource-constrained project scheduling problems (RCPSPs), namely moving block sequence (MBS), is proposed. In RCPSPs, every activity has fixed duration and resource demands, th...
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In this paper, a new representation for resource-constrained project scheduling problems (RCPSPs), namely moving block sequence (MBS), is proposed. In RCPSPs, every activity has fixed duration and resource demands, therefore, it can be modelled as a rectangle block whose height represents the resource demand and width the duration. Naturally, a project that consists of N activities can be represented as the permutation of N blocks that satisfy the precedence constraints among activities. To decode an MBS to a valid schedule, four move modes are designed according to the situations that how every block can be moved from its initial position to an appropriate location that can minimise the makespan of the project. Based on MBS, the multiagent evolutionary algorithm (MAEA) is used to solve RCPSPs. The proposed algorithm is labelled as MBSMAEA-RCPSP, and by comparing with several state-of-the-art algorithms on benchmark J30, J60, J90 and J120, the effectiveness of MBSMAEA-RCPSP is clearly illustrated.
The resourceconstrainedprojectscheduling problem (RCPSP) has been considered as a scheduling problem which has a wide range of applications in construction industries, manufacturing, production planning and project...
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ISBN:
(纸本)9781728138046
The resourceconstrainedprojectscheduling problem (RCPSP) has been considered as a scheduling problem which has a wide range of applications in construction industries, manufacturing, production planning and project management domains. To solve such RCPSPs, in this paper we propose a hybrid algorithm that utilizes the strengths of both differential evolution (DE) and cuckoo search (CS) algorithm in one frame-work called hybrid differential evolution with cuckoo search (DECS) algorithm. In it, a selection mechanism based on the solutions' quality and populations' diversity is used to select the most appropriate algorithm during the evolutionary process. A linear population reduction mechanism is utilized to update the DE population size. A number of data sets of single-mode RCPSPs from the projectscheduling library (PSPLIB) have been considered and solved by the proposed hybrid DECS algorithm. Computational results and comparisons with some recent state-of-the-art algorithms show that DECS is able to produce very high quality results.
resource-constrained project scheduling problems (RCPSPs) represent an important class of practical problems. Over the years, many optimization algorithms for solving them have been proposed, with their performances e...
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resource-constrained project scheduling problems (RCPSPs) represent an important class of practical problems. Over the years, many optimization algorithms for solving them have been proposed, with their performances evaluated using well-established test instances with various levels of complexity. While it is desirable to obtain a high-quality solution and fast rate of convergence from an optimization algorithm, no single one performs well across the entire space of instances. Furthermore, even fora given algorithm, the optimal choice of its operators and control parameters may vary from one problem to another. To deal with this issue, we present a generic framework for solving RCPSPs in which various meta-heuristics, each with multiple search operators, are self-adaptively used during the search process and more emphasis is placed on the better-performing algorithms, and their underlying search operators. To further improve the rate of convergence and introduce good-quality solutions into the population earlier, a local search approach is introduced. The experimental results clearly indicate the capability of the proposed algorithm to attain high-quality results using a small population. Compared with several state-of-the-art algorithms, the proposed one delivers the best solutions for problems with 30 and 60 activities, and is very competitive for those involving 120 activities. (C) 2017 Elsevier Inc. All rights reserved.
In recent years, the resource-constrainedprojectscheduling problem (RCPSP) with multiple execution modes is becoming more and more popular. In this paper, a new cooperative coevolutionary algorithm based on the conc...
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ISBN:
(纸本)9783319135632;9783319135625
In recent years, the resource-constrainedprojectscheduling problem (RCPSP) with multiple execution modes is becoming more and more popular. In this paper, a new cooperative coevolutionary algorithm based on the concept of organizations, namely Organizational Cooperative Coevolutionary Algorithm for MRCPSPs (OCCA-MRCPSPs), is proposed for solving this problem. The objective is to find a schedule of activities together with their execution modes so that the makespan is minimized. In the OCCA-MRCPSPs, the population is divided into two subpopulations, for activities execution modes, respectively. The two subpopulations evolve independently, and each subpopulation is composed of organizations. During the evolutionary process, the global searching and the local searching are combined efficiently by conducting different operators. At first, each subpopulation searches the whole space of its domain through the splitting operator, the annexing operator, and the cooperation operator. Afterwards, the two subpopulations are combined to form complete solutions, and a local search operator is performed. In the experiments, the performance of OCCA-MRCPSPs is validated on benchmark problem sets J10, J12, J14, and J16 from the PLPSIB, and the experimental results show that the OCCA-MRCPSPs obtains a good performance not only in terms of the optimal solutions found but also in terms of the average deviations from optimal solutions.
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