A self-adaptive differential evolution heuristic for two-stage assembly scheduling problem to minimize maximum lateness with setup times

A variable neighborhood search for minimizing single machine weighted earliness and tardiness with common due date

In this paper we address the two-stage assembly flowshop scheduling problem with respect to the makespan criterion where setup times are considered as separate from processing times. We formulate the problem and obtain a dominance relation. Moreover, we propose two evolutionary heuristics: a Particle Swarm Optimization (PSO) and a Tabu search. We also propose a simple and yet efficient algorithm with negligible computational time. We have conducted extensive computational experiments to compare the two heuristics and the algorithm along with a random solution. The computational analysis indicates that both heuristics and the algorithm perform significantly well. The computational analysis also indicates that PSO is the best and that the difference between the average errors of PSO and the algorithm becomes small as the number of jobs increases, while the computational time of PSO becomes much larger. Moreover, the difference between the two errors becomes even smaller as the number of machines (at the first stage) and the ratio of setup times to processing times becomes smaller. Therefore, PSO is recommended for a number of jobs up to 50, whereas the algorithm is suggested for larger numbers of jobs and larger numbers of machines at the first stage.

Evolutionary heuristics and an algorithm for the two-stage assembly scheduling problem to minimize makespan with setup times

This paper investigates an integrated bi-objective optimisation problem with non-resumable jobs for production scheduling and preventive maintenance in a two-stage hybrid flow shop with one machine on the first stage and m identical parallel machines on the second stage. Sequence-dependent set-up times and preventive maintenance (PM) on the first stage machine are considered. The scheduling objectives are to minimise the unavailability of the first stage machine and to minimise the makespan simultaneously. To solve this integrated problem, three decisions have to be made: determine the processing sequence of jobs on the first stage machine, determine whether or not to perform PM activity just after each job, and specify the processing machine of each job on the second stage. Due to the complexity of the problem, a multi-objective tabu search (MOTS) method is adapted with the implementation details. The method generates non-dominated solutions with several parallel tabu lists and Pareto dominance concept. ...

Two-stage hybrid flow shop scheduling with preventive maintenance using multi-objective tabu search method

Intelligent scheduling covers various tools and techniques for successfully and efficiently solving the scheduling problems. In this paper, we provide a survey of intelligent scheduling systems by categorizing them into five major techniques containing fuzzy logic, expert systems, machine learning, stochastic local search optimization algorithms and constraint programming. We also review the application case studies of these techniques.

C. Fedjki

Papers

Tabu search for a class of single-machine scheduling problems