An efficient evolutionary algorithm for joint optimization of maintenance grouping and routing

Geographically Dispersed Production System (GDPS) is a system network in which production sites are located far away from each other and subjected to management of an administration center. In maintenance planning for such system, planners intend to group maintenance activities to save preparation cost and transportation cost. Specially, this work is extremely meaningful in framework of sustainability (reduction of environmental pollution). In fact, when grouping maintenance activities of components located at different sites, maintenance resources need to be travelled from the maintenance center to the production sites where these components are located on, and then come back to the maintenance center when the maintenance is completed. By that way, travel distance as well as emissions from means of transports can reduce compared to individual maintenance. Although grouping maintenance has the positive impacts, it causes difficulties due to the shift of maintenance dates of components. Moreover, maintenance planning optimization for a GDPS is multi-groups and multiroutes problem. Hence, optimizing maintenance grouping and routing for GDPS should be considered simultaneously. However, the joint optimization leads to be a NP-hard problem to be solved. To face this issue, in this paper an evolutionary algorithm based on Genetic Algorithm (GA) and Branch and Bound algorithm (BAB), named GA-BAB, is developed. The numerical study shows that the proposed algorithm can efficiently solve maintenance grouping and routing optimization problem in a GDPS context.