This paper proposes an algorithm for the Unrelated Parallel Machine Scheduling Problem(UPMSP) without setup times, aiming to minimize total tardiness. As an NP-hard problem, the UPMSP is hard to get an optimal solution. Consequently, practical scenarios are solved by relying on operator's experiences or simple heuristic approaches. The proposed algorithm has adapted two methods: a policy network method, based on Transformer to compute the correlation between individual jobs and machines, and another method to train the network with a reinforcement learning algorithm based on the REINFORCE with Baseline algorithm. The proposed algorithm was evaluated on randomly generated problems and the results were compared with those obtained using CPLEX, as well as three scheduling algorithms. This paper confirms that the proposed algorithm outperforms the comparison algorithms, as evidenced by the test results.

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.

This paper deals with rescheduling on unrelated parallel-machines with compressible processing times, assuming that the arrival of a set of new jobs triggers rescheduling. It formulates this rescheduling problem as an assignment problem with a side constraint and proposes a heuristic to solve it. Computational tests evaluate the efficacy of the heuristic.

This paper considers a parallel-machine scheduling problem with dedicated and common processing machines using GA (Genetic Algorithm). Non-identical setup times, processing times and order lot size are assumed for each machine. The GA is proposed to minimize the total-tardiness objective measure. In this paper, heuristic algorithms including EDD (Earliest Due-Date), SPT (Shortest Processing Time) and LPT (Longest Processing Time) are compared with GA. The effectiveness and suitability of the GA are derived and tested through computational experiments.

  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

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 proposes a heuristic scheduling algorithm to satisfy the customer''s due date in the production process under make to order cnvironment. The goal is to achicve the machine scheduling in the transformer winding process, in which consists of para