h-NSDE—A Solution Algorithm for the Multi-objective Resource Leveling Problem

Marinos Aristotelous and Andreas C. Nearchou ()
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Marinos Aristotelous: University of Patras
Andreas C. Nearchou: University of Patras

SN Operations Research Forum, 2025, vol. 6, issue 1, 1-22

Abstract: Abstract Consideration is given to the resource leveling problem (RLP) in resource-constrained project scheduling (RCPS). Although RLP has gained an increasing research interest, multiple optimization criteria are rarely considered simultaneously in the literature. In this paper, a multi-objective version of RLP is investigated aiming to simultaneously minimize the resource imbalance, the peak of the resource usage as well as the makespan. A metaheuristic algorithm is presented devoted to the search for Pareto-optimal RLP solutions. This algorithm constitutes an adaptation of h-NSDE (the hybrid non-dominated sorting differential evolution) which has recently shown excellent performance over a particular class of machine scheduling problems. Using existing benchmark data sets, we test the performance of h-NSDE in comparison to three of the most famous in the literature multi-objective population-based metaheuristics namely NSGA-II, SPEA2, and PAES. The results obtained are quite promising demonstrating a clear superiority of h-NSDE in terms of both the solution quality and diversity in regard to Pareto-front.

Keywords: Combinatorial optimization; Metaheuristics; Evolutionary algorithms; Project; Scheduling; Smoothing; Leveling; Variable neighborhood search; Differential evolution (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s43069-025-00415-2

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