Scheduling of periodic services to customers in dispersed locations from heterogeneous multi-agent companies considering uncertainty: A real case study

This study investigates the problem of scheduling periodic services from service providers to customers located in dierent places that need dierent services. The service centers are also located in dierent positions, each of which has a limited number of teams with the capability of performing one or some services. The goal is to simultaneously minimize 'total service costs' and 'total earliness/tardiness' in providing services for customers. Providing an optimal maintenance schedule is a signicant challenge for those companies with dispersed supply centers. In this paper, a novel bi-objective mixed integer linear programming model along with augmented epsilon constraint method is presented to exactly solve this problem. Then, a bi-objective meta-heuristic technique based on genetic algorithm is proposed and its performance in solving large-scale problems is assessed. Companies may face uncertain parameters when using the robust possibilistic programming approach to diminish the risk of decision-making. Finally, the performance of the proposed model and solution approaches is evaluated in the context of a real case study in maintenance scheduling of Compressed Natural Gas (CNG) stations equipment in Iran.