STUDY ON RESOURCE ALLOCATION SCHEDULING PROBLEM WITH LEARNING FACTORS AND GROUP TECHNOLOGY

This paper investigates the single-machine resource allocation scheduling problem with learning effects and group technology. The objective is to determine the optimal job and group schedules, and resource allocations such that total completion time is minimized subject to limited resource availability. For some special cases, we show that the problem remains polynomially solvable. For general case of the problem, we propose the heuristic algorithm, tabu search algorithm and branch-and-bound algorithm. Numerical experiments are tested to evaluate the performance of the heuristic and branch-and-bound algorithms.

Automated summary

This paper investigates the problem of single-machine resource allocation scheduling with learning effects and group technology. The objective is to minimize total completion time by determining optimal job and group schedules, and resource allocations under limited availability. The problem is shown to be polynomially solvable for some special cases, and for the general case, heuristic, tabu search, and branch-and-bound algorithms are proposed. Numerical experiments are conducted to evaluate the performance of the heuristic and branch-and-bound algorithms.