Abstract
The paper presents a novel hybrid genetic algorithm (HGA) for a deterministic scheduling problem where multiple jobs with arbitrary precedence constraints are processed on multiple unrelated parallel machines. The objective is to minimize total tardiness, since delays of the jobs may lead to punishment cost or cancellation of orders by the clients in many situations. A priority rule-based heuristic algorithm, which schedules a prior job on a prior machine according to the priority rule at each iteration, is suggested and embedded to the HGA for initial feasible schedules that can be improved in further stages. Computational experiments are conducted to show that the proposed HGA performs well with respect to accuracy and efficiency of solution for small-sized problems and gets better results than the conventional genetic algorithm within the same runtime for large-sized problems.
Highlights
We consider an unrelated parallel machine scheduling problem with arbitrary precedence constraints to minimize total tardiness
We develop a priority rule-based heuristic algorithm (PRHA) consisting of several iterations
We propose a hybrid genetic algorithm (HGA) which combines the PRHA approach with conventional genetic algorithm
Summary
We consider an unrelated parallel machine scheduling problem with arbitrary precedence constraints to minimize total tardiness. Such a problem typically occurs in an office or project management environment, where unrelated machines represent workers who have different skills in office scheduling problem, or represent various types of resources which are allocated to activities in multimode project scheduling problem. Mangers should adopt some methods to select the suitable workers (or resources) to undertake each subtask (or activity) separately, in order to maximize utilization of these workers (or resources), improve productivity, and reduce overall cost. In. Section 5, a hybrid genetic algorithm (HGA), taking the solutions of the PRHA as a part of initial population, is proposed for the final solution.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have