A tutorial survey of job-shop scheduling problems using genetic algorithms—I: representation
Computers and Industrial Engineering
Genetic algorithms for flowshop scheduling problems
Computers and Industrial Engineering
A hybrid genetic algorithm for the re-entrant flow-shop scheduling problem
Expert Systems with Applications: An International Journal
Deadlock control methods in automated manufacturing systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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In this paper, we develop a simulation-based two-phase genetic algorithm for the capacitated re-entrant line scheduling problem. The structure of a chromosome consists of two sub-chromosomes for buffer allocation and server allocation, respectively, while considering all possible states of the system in terms of buffer levels of workstations and assigning a preferred job stage to each component of the chromosome. As an implementation of the suggested algorithm, a job priority-based randomized policy is defined, which reflects the job priority and the properness of local non-idling in allocating buffering and processing capacity to available job instances. The algorithm is combined with a polynomial time sub-optimal deadlock avoidance policy, namely, Bankers algorithm, and the fitness of a chromosome was evaluated based on simulation. The performance of the proposed algorithm is evaluated through a numerical experiment, showing that the suggested approach holds considerable promise for providing effective and computationally efficient approximations to the optimal scheduling policy that consistently outperforms the typically employed heuristics.