Abstract
Scheduling–Location (ScheLoc) problems integrate the separate fields of scheduling and location problems. In ScheLoc problems the objective is to find locations for the machines and a schedule for each machine subject to some production and location constraints such that some scheduling objective is minimized. In this paper we consider the discrete parallel machine makespan ScheLoc problem where the set of possible machine locations is discrete and a set of n jobs has to be taken to the machines and processed such that the makespan is minimized. Since the separate location and scheduling problem are both \(\mathcal {NP}\)-hard, so is the corresponding ScheLoc problem. Therefore, we propose an integer programming formulation and different versions of clustering heuristics, where jobs are split into clusters and each cluster is assigned to one of the possible machine locations. Since the IP formulation can only be solved for small scale instances we propose several lower bounds to measure the quality of the clustering heuristics. Extensive computational tests show the efficiency of the heuristics.
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Acknowledgements
Partially supported by the Federal Ministry of Education and Research Germany, Grant DSS_Evac_Logistic, FKZ 13N12229 and Grant RobEZiS, FKZ 13N13198 and by the French National Research Agency as ANR-11-SECU-002-01 (CSOSG 2011) and the European Union with the European Regional Development Fund and the Regional Council of Normandy.
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Heßler, C., Deghdak, K. Discrete parallel machine makespan ScheLoc problem. J Comb Optim 34, 1159–1186 (2017). https://doi.org/10.1007/s10878-017-0138-9
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DOI: https://doi.org/10.1007/s10878-017-0138-9