Abstract
In order to find a feasible solution for the multimode resource-constrained project scheduling problem (MRCPSP), a hybrid of particle swarm optimization (PSO) and differential evolution (DE) algorithm is proposed in this paper. The proposed algorithm uses a two-level coding structure. The upper-level structure is coded for scheduling sequence, which is optimized by PSO algorithm. The lower-level structure is coded for project execution mode, and DE algorithm is used to solve the optimal scheduling model. The effectiveness and advantages of the proposed algorithm are illustrated by using the test function of project scheduling problem library (PSPLIB) and comparing with other scheduling methods. The results show that the proposed algorithm can well solve MRCPSP.
Highlights
Resource-constrained project scheduling problem (RCPSP) is a project scheduling problem of how to arrange the task start-up time to minimize the total project time reasonably in the conditions of resource constraints and project timing constraints [1]
Multimode resource-constrained project scheduling problem (MRCPSP) is an extension of RCPSP, which is a kind of Nondeterministic Polynomial (NP) problem
A new algorithm for MRCPSP combined particle swarm optimization (PSO) algorithm and differential evolution (DE) algorithm is proposed in this paper
Summary
Resource-constrained project scheduling problem (RCPSP) is a project scheduling problem of how to arrange the task start-up time to minimize the total project time reasonably in the conditions of resource constraints and project timing constraints [1]. On the basis of traditional RCPSP, MRCPSP considers a variety of optional modes of the task and the dependent relationships of resources. Precise algorithm adopts the branch-and-bound method as the main solving method, and it can get the optimal solution It is not suitable for solving large-scale scheduling problem. Heuristic algorithm has a strong ability to solve large-scale scheduling problem and the characteristic of the computing speed It cannot guarantee obtaining the optimal solution. Intelligent optimization algorithm is an effective solving method for MRCPSP. Liu et al proposed a novel hybrid algorithm named PSO-DE, which integrates PSO with DE algorithm to solve constrained numerical and engineering optimization problems [11]. Based on the verification of many MRCPSP’s instances in PSPLIB, the simulation results show that the proposed algorithm can effectively solve the MRCPSP
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