In this paper, we aim to solve the problem of resource-constrained project scheduling with multiple modes (rc-PSP/mM), in which multiple execution modes are available for each of the project's activity and with minimization of makespan as objective. We present a new genetic algorithm approach in order to solve this problem. In this procedure, we propose a new mutation operator that exploits a critical path and two new local search procedures, i.e. critical path improvement local search (cpiLS ) and iterative forward/backward local search (ifbLS ), using activity list characteristics. The cpiLS reduces the critical path and the ifbLS improves resource allocation of the schedule of rc-PSP/mM. Also, to evaluate the proposed approach, we compare the results of the computational experiment on certain standard project instances with the several competing heuristic procedures presented in the literature, and it has been revealed that our procedure is one of the most competitive among the algorithms.
In this paper, we aim to solve the resource-constrained multiproject scheduling problem (rc-mPSP), in which more than one project are scheduled simultaneously, projects share global resources, and the average project delay and total project time are minimized as objectives. In order to solve this problem by a centralized scheduling method, we present a new genetic algorithm (GA) approach. In this procedure, we follow the GA described in Okada et al . (2014) and improve its genetic operators, such as crossover and mutation, and local search so as to work better on rc-mPSP. Furthermore, in order to evaluate the proposed approach, we compare the results of the computational experiment on certain standard project instances with the several competing decentralized methods and centralized methods presented in the literature. We show that our procedure is one of the most competitive among such algorithms.
SUMMARYIn this paper, we propose a hybrid genetic algorithm with fuzzy logic controller (flc-rkGA) to solve the resourceconstrained multiple project scheduling problem (rcmPSP) which is well known as an NP-hard problem and the objective in this paper is to minimize total complete time in the project. It is difficult to treat the rc-mPSP problems with traditional optimization techniques. The new approach proposed is based on the hybrid genetic algorithm (flc-rkGA) with fuzzy logic controller (FLC) and random-key encoding. For these rc-mPSP problems, we demonstrate that the proposed flc-rkGA to solve the rc-mPSP problem yields better results than several heuristic genetic algorithms presented in the computation result.
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