Agent scheduling in unrelated parallel machines with sequence and agent-machine dependent setup times

Abstract

The assignation-sequencing models have played since the mid 50’s a critical role in economic competitivity within companies and organizations. Models have evolved from simple assignations to complex constrained formulations, and still is an area of deep interest to researchers. This thesis presents a better representation of the reality regarding assignation-sequencing models, a model to schedule agents in unrelated parallel machines with sequence and agent-machine dependent setup times (ASUPM), with minimization of maximum makespan criteria and a relationship one agent to one machine. Six mixed-integer linear formulations are proposed, and since the problem complexity belongs to the NP-hard category, two heuristic algorithms are presented. The linear models and heuristics algorithms are tested using randomly generated instances. In addition, it is presented the inclusion and solution of other assignation-sequencing models in the/with ASUPM problem. Finally, two applications of the model are presented: scheduling in automotive seat safety tests, and hemodialysis scheduling in hospitals.