Swarm lexicographic goal programming for fuzzy open shop scheduling

In this work we consider a multiobjective open shop scheduling problem with uncertain processing times and flexible due dates, both modelled using fuzzy sets. We adopt a goal programming model based on lexicographic multiobjective optimisation of both makespan and due-date satisfaction and propose a particle swarm algorithm to solve the resulting problem. We present experimental results which show that this multiobjective approach achieves as good results as single-objective algorithms for the objective with the highest priority, while greatly improving on the second objectiv

A simheuristic for bi-objective stochastic permutation flow shop scheduling problem

This paper addresses the stochastic permutation flow shop problem (SPFSP) in which the stochastic parameters are the processing times. This allows the modeling of setups and machine breakdowns. Likewise, it is proposed a multi-objective greedy randomized adaptive search procedure (GRASP) coupled with Monte-Carlo Simulation to obtain expected makespan and expected tardiness. To manage the bi-objective function, a sequential combined method is considered in the construction phase of the meta-heuristic. Moreover, the local Search combines 2-optimal interchanges with a Pareto Archived Evolution Strategy (PAES) to obtain the Pareto front. Also, some Taillard benchmark instances of deterministic permutation flow shop problem were adapted in order to include the variation in processing times. Accordingly, two coefficients of variation (CVs) were tested: one depending on expected processing times values defined as twice the expected processing time of a job, and a fixed value of 0.25. Thus, the computational results on benchmark instances show that the variable CV provided lower values of the expected makespan and tardiness, while the con-stant CV presented higher expected measures. The computational results present insights for further analysis on the behavior of stochastic scheduling problems for a better approach in real-life scenarios at industrial and service systems