This paper is proposing a novel machine scheduling model for the unrelated parallel machine scheduling problem without setup times to minimize the total completion time, also known as “makespan”. This problem is a NP-complete problem, and to date, most approaches for real-life situations are based on the operator’s experience or simple heuristics. The new model based on the Memetic Algorithm, which was proposed by P. Moscato in 1989, is a hybrid algorithm that includes genetic algorithm and local search optimization. The new model is tested on randomly generated datasets, and is compared to optimal solution, and four scheduling models; three rule-based heuristic algorithms, and a genetic algorithm based scheduling model from literature; the test results show that the new model performed better than scheduling models from literature.

We focus on the fire scheduling problem (FSP), the problem of determining the sequence of targets to be fired at, for the objective of minimizing makespan to achieve tactical goals. In this paper, we assume that there are m available weapons to fire at n targets (> m) and the weapons are already allocated to targets. One weapon or multiple weapons can fire at one target and these fire operations should start simultaneously while the finish time of them may be different. We develop several dominance properties and a lower bound for the problem, and suggest a branch and bound algorithm implementing them. Also, In addition, heuristic algorithms that can be used for obtaining an initial upper bound in the B&B algorithm and for obtaining good solutions in a short time were developed. Computational experiments are performed on randomly generated test problems and results show that the suggested algorithm solves problems of a medium size in a reasonable amount of computation time. The proposed lower bound, the dominance properties, and the heuristics for upper bound are tested in B&B respectively, and the result showed that lower bound is effective to fathoming nodes and the dominance properties and heuristics also worked well. Also, it is showed that the CPU time required by this algorithm increases rapidly as the problem size increases. Therefore, the suggested B&B algorithm would be limited to solve large size problems. However, the employed heuristic algorithms can be effectively used in the B&B algorithm and can give good solutions for large problems within a few seconds.

  This paper deals with the problem of scheduling jobs and rate-modifying activities on parallel machines. A rate-modifying activity is an activity that changes the production rate of equipment such as maintenance and readjustment. If a job is scheduled a

  In this paper, we study flowshop scheduling problems with availability constraints. In such problems, n jobs have to be scheduled on m machines sequentially under assumption that the machines are unavailable during some periods of planning horizon. The

This paper deals with the problem of scheduling jobs and rate-modifying activities on parallel machines, motivated by a problem commonly found in the surface-mount technology of electronic assembly lines. A rate-modifying activity involving repair, maintenance and readjust is an activity that changes the production rate of equipment under consideration. In this paper, the processing time of a job scheduled before the activity is equal in each parallel machines. On the other hand, if the job is scheduled after the rate-modifying activity than the process time depends on the modifying rate of the activity. Our purpose is to schedule the rate-modifying activities and jobs to minimize the makespan on parallel machines which is NP-hard. We propose a branch and bound algorithm to solve medium size problems optimally. Also we develop three heuristics, Modified Longest Processing Time (MLPT), MULTIFIT and COMBINE algorithms to solve large size problems.

This paper concerns on a multiprocessor task scheduling problem with precedence relation, in which each task requires several processors simultaneously Meta-heuristic generally finds a good solution If It starts from a good solution In this paper, a tabu

This paper concerns on a multiprocessor task scheduling problem with precedence relation, in which each task requires several processors at a time. The problem is to find a schedule of minimal time to complete all tasks. In this paper, a tabu search is presented. Numerical results show that tabu search yields a better performance than the previous studies.