Multi-Task Simultaneous Supervision Dual-Resource Constrained Scheduling Problem With Automated And Manual Work Units

This paper investigates an extended problem of the Multi- Task Simultaneous Supervision with Dual-Resource Con- strained (MTSSDRC), observed in a service provider spe- cializing in industrial computed tomography-based quality control. Unlike previous studies that consider a job with an automated work unit, this paper schedules each job com- posed of an automated work unit and a manual work unit. Additionally, working shifts of technicians are incorpo- rated. To address this scheduling problem, an optimization model is developed to minimize total job tardiness. The Genetic Algorithm (GA) and the Particle Swarm Opti- mization (PSO) are employed to solve the optimization model, and their performance is evaluated through a series of numerical experiments.