Heuristic Genetic Algorithm for Workforce Scheduling with Minimum Total Worker-Location Changeover - Asawarungsaengkul, Krisada; Nanthavanij, Suebsak (International Journal of Industrial Engineering: Theory, Applications and Practice, 2022)
This paper presents a heuristic genetic algorithm (GA) to find daily work assignments without hazard exposure. Its objective is twofold: (1) to determine a minimum number of workers for a given set of worker locations, and (2) to determine safety work assignments with a minimum total worker-location changeover. Firstly, a hybrid procedure to determine a lower bound and the minimum number of workers is applied to generate an initial population. Then, the GA with heuristic crossover and mutation is utilized to search for a safety work assignment solution. The swap and multi-start algorithms are also employed to improve the GA solution. The heuristic GA is able to solve both balanced and unbalanced work assignment problems. Comparing with an optimization approach, the GA can generate the safety work assignments with the minimum total worker-location changeover in much shorter computation time