Solving Non-sharable Machine and Resource Scheduling Problem using Simulated Annealing Algorithm

Abstract

Scheduling problems exist in many applications, and most of them have demonstrated their complexities as NP complete. A lot of schemes have been introduced to solve scheduling problems, such as linear programming, artificial neural network and fuzzy logic. Among them, simulated annealing is a highly effective means of obtaining an optimal solution which is capable of preventing the local minimum. In this paper we try to use simulated annealing algorithm to solve a non-sharable machine and resource-based scheduling problem which closely reflects a scheduling problem in the real world. We assume that both machine and resource are not sharable, and each job to process requires machine and resource. In contrast to the most scheduling problems which only resolve the machine problem, job process time and deadline, we believe that resource constrains are critical issues, although it will make scheduling problem more complicated. In this work, we try to use a genetic algorithm(GA)-based simulated annealing algorithm to solve the scheduling problem in reducing the penalty of resource factor involved. Simulation results demonstrate that our method not only can solve machine and resource constraint problem, but also can work effectively.