Abstract
Motivated by a real-world problem situation faced by a large manufacturing company in the last step of their production process, we model and solve an offline, deterministic, non-preemptive, identical, parallel machine scheduling problem with processing set restrictions, availability constraints, release dates, bounded tardiness, blocking constraints and due windows. The objective is to minimize the total tardiness while penalizing the assignment of jobs to larger than necessary machines. We develop a mixed integer linear program and we propose a heuristic solution approach relying on the well known large neighborhood search paradigm which relies on tailored destroy and repair operators, embedded into a simulated annealing framework. The proposed algorithm is tested on artificial instances as well as on real-world data provided by the company and compared to optimal solutions obtained by means of CPLEX. The developed tool is now used for decision support by the company partner.
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