Multiple-objective heuristics for scheduling unrelated parallel machines

This research proposes two heuristics and a Genetic Algorithm (GA) to find non-dominated solutions to multiple-objective unrelated parallel machine scheduling problems. Three criteria are of interest, namely: makespan, total weighted completion time, and total weighted tardiness. Each heuristic seeks to simultaneously minimize a pair of these criteria; the GA seeks to simultaneously minimize all three. The computational results show that the proposed heuristics are computationally efficient and provide solutions of reasonable quality. The proposed GA outperforms other algorithms in terms of the number of non-dominated solutions and the quality of its solutions.

► We propose three heuristics to find solutions to parallel machine scheduling problems. ► We consider makespan, total weighted completion time, and total weighted tardiness. ► We seek to find the set of non-dominated solutions for pairs of objectives and all 3. ► Results show heuristics are efficient and provide solutions of reasonable quality. ► Proposed GA outperforms other heuristics.