VARIABLE NEIGHBORHOOD DESCENT FOR THE UNRELATED PARALLEL MACHINE SCHEDULING PROBLEM

The NP-hard problem of scheduling jobs on unrelated parallel machines with the objective of minimizing the makespan is addressed. A variable neighborhood descent heuristic hybridized with mathematical programming is proposed. A new constructive heuristic is also introduced to seed the search with a solution residing in a promising region. The strength of the approach lies in the adoption of appropriately chosen optimization criteria that avoid early local optimum entrapment and in the hybridization of the heuristic with mathematical programming for the exploration of the large neighborhood structures. Experimental results on a large set of benchmark problem instances attest to the efficacy of the proposed approach.