Abstract

A modified genetic algorithm (GA) is presented for the solution of the economic manpower shift planning (EMSP) problem. This is an NP-hard capacity planning problem arising in various industrial settings including the packing stage of production in process industries and maintenance operations. Given a set of independent jobs, their production targets, and a planning horizon, EMSP seeks the manpower to be planned for each workday shift in order to complete all the jobs within the specified time horizon at minimum cost. These are the key innovative aspects of the developed GA: (1) it uses a problem-specific encoding of the solution structure at the genotypic level; (2) it adopts a special greedy algorithm for mapping genotypes to phenotypes (i.e., to actual EMSP solutions); (3) it employs an adaptive parameter control scheme to adjust the mutation rate during its run. Extensive experiments over various industrial simulated environments and comparisons with the best existing EMSP heuristic (namely the block planning greedy algorithm) show the superiority of the proposed solution approach in terms of solution quality. Furthermore, comparisons to the results obtained by the standard CPLEX optimizer showed the proposed solution's performance to be very satisfactory.

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