Abstract
This paper addresses an energy-based extension of the Multimode Resource-Constrained Project Scheduling Problem (MRCPSP) called MRCPSP-ENERGY. This extension considers the energy consumption as an additional resource that leads to different execution modes (and durations) of the activities. Consequently, different schedules can be obtained. The objective is to maximize the efficiency of the project, which takes into account the minimization of both makespan and energy consumption. This is a well-known NP-hard problem, such that the application of metaheuristic techniques is necessary to address real-size problems in a reasonable time. This paper shows that the Activity List representation, commonly used in metaheuristics, can lead to obtaining many redundant solutions, that is, solutions that have different representations but are in fact the same. This is a serious disadvantage for a search procedure. We propose a genetic algorithm (GA) for solving the MRCPSP-ENERGY, trying to avoid redundant solutions by focusing the search on the execution modes, by using the Mode List representation. The proposed GA is evaluated on different instances of the PSPLIB-ENERGY library and compared to the results obtained by both exact methods and approximate methods reported in the literature. This library is an extension of the well-known PSPLIB library, which contains MRCPSP-ENERGY test cases.
Highlights
The energy consumption in the industry sector is growing by leaps and bounds
This paper addresses an energy-based extension of the Multimode Resource-Constrained Project Scheduling Problem (MRCPSP) called MRCPSP-ENERGY
The performed empirical assessment of the proposed genetic algorithm (GA) and the obtained computational results are divided into two groups: the first group is focused on the impact of the redundant solutions of the Activity List-based representation and the second group is focused on the performance assessment of the proposed GA by using the PSPLIB-ENERGY library
Summary
The energy consumption in the industry sector is growing by leaps and bounds. Based on the U.S Energy Information Administration report, in 2016, the industry sector, including manufacturing, consumed approximately a third of the total delivered energy in the world [1]. The environmental implications of the industrial process are gaining more and more importance. Energy consumption reduction in resource-allocation projects is a critical aspect in the industry sector [2]. For this reason, the interest of researchers is increasingly focused on the development of methodologies for obtaining energy-sustainable solutions. The energyefficiency oriented scheduling is an actual challenge and a feasible way to save energy in process planning [3]
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