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

Abstract

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 constant 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.

Solving NP-Hard Challenges in Logistics and Transportation under General Uncertainty Scenarios Using Fuzzy Simheuristics

“…This property arises from the usage of a general-purpose simulation schema that can be adjusted to suit any problem domain [37][38][39]. Additionally, the heuristic algorithms used in simheuristics can be customized to fulfill the unique requirements of each problem by choosing from a collection of suitable techniques [40,41]. Despite the many advantages offered by simheuristics, the field is still considered relatively new and requires further research to fully unlock its potential.…”

A Fuzzy Simheuristic for the Permutation Flow Shop Problem under Stochastic and Fuzzy Uncertainty

“…For example, González-Neira et al [36] efficiently solved a multiobjective stochastic PFSP with a simheuristic approach based on a tabu search metaheuristic, an evolutionary strategy, and Monte Carlo simulation. For a bi-objective PFSP, González-Neira and Montoya-Torres [37] proposed a simheuristic approach based on a multi-objective greedy randomized adaptive search procedure (GRASP) coupled with Monte Carlo simulation to obtain the expected duration and the expected delay. Similarly, Villarinho et al [38] analyzed the PFSP with cumulative delivery dates and stochastic processing times.…”

A simheuristic approach for the flexible job shop scheduling problem with stochastic processing times

“…Their approach consists of a two-stage simulation approach, consisting of a fast estimation and subsequent simulation of promising solutions to evaluate the offspring, thereby reducing the computational effort. The PFSSP has been investigated simheuristics considering several variants such as distributed assembly flow shop problem, 64,65 parallel flow shop problem, 13 integrated resource allocation permutation flow shop scheduling problem, 66 multi-objective PFSSP, 12 and blocking lot-streaming flow shop scheduling problem. 67 De Armas et al 18 addressed the uncapacitated facility location problem and employed an iterated local search (ILS) incorporating a saving-based heuristic to solve the problem.…”

“…9–11 In real-world instances, the probability distribution to be employed needs to be based on historical data. To overcome these shortcomings we employ a simheuristic approach, which is gaining recognition as a frequent choice to solve stochastic combinatorial optimization problems (SCOPs) in areas of production scheduling, 12–16 facility location, 17,18 vehicle routing, 19–23 healthcare, 24 internet services, 25 human resource management, 26 and financial considerations, 27 due to their inherent flexibility and efficiency. These approaches integrate a simulation technique into a metaheuristic framework, thereby acquiring the characteristics of both the methodologies.…”