Risk measure of job shop scheduling with random machine breakdowns

Field-synchronized Digital Twin framework for production scheduling with uncertainty

“…swapping randomly two rows of the individual to create a new sequence. This is in line with literature, where values used for mutation rate span from 0.01 to 0.3, but the most suggested rates lie between 0.01 and 0.03 (Nguyen and Bagajewicz 2008;Snaselova and Zboril 2015;Uslu et al 2019;Wu et al 2018).…”

“…Robustness has been defined as the capability to handle small delays, resist to imprecision, tolerate a certain degree of uncertainty, and deal with unexpected disruptions without having to thoroughly modify the production schedule. Thus, a schedule can be defined as robust when it minimizes the impact of disruptions: not necessarily it is the best under a specific performance, but it is the one that performs better in terms of realistic implementation of operations (Vieira et al 2017;Wu et al 2018;Zandieh et al 2010). To cope with uncertainties, two approaches are widely explored (Dias and Ierapetritou 2016;Herroelen and Leus 2005).…”

“…Numerous works in literature implement it. (Wu et al 2018) propose a GA coupled with Monte Carlo simulation in order to optimize the schedule for a Job Shop Scheduling Problem (JSSP) with random machine breakdowns, evaluating the risk based on makespan delay. (Aramon Bajestani and Beck 2015) design an iterative twostage algorithm to solve a Flow Shop Scheduling Problem (FSSP), considering machine maintenance and minimizing costs.…”

A Meta-Model-Based Multi-Objective Evolutionary Approach to Robust Job Shop Scheduling

“…In this study, the operation-based machine breakdown model presented in [21] will be applied. More specifically, the machine breakdown is modeled by two parameters: the downtime required to repair the machine after its breakdown, and the breakdown probability during a time interval.…”

“…Recently, robust optimization has gained intensive interest [19], where most of existing studies focus on addressing the scheduling problems under known uncertainties. The robustness of a schedule indicates the ability of the schedule to preserve a specified level of solution quality in the presence of uncertainties [20], which is generally measured by the expected deviation of the performance from its initial performance under uncertainties [21]. Liu et al [22] defined the robust schedule as a schedule that is insensitive to uncertainties, such as that a schedule may degrade its performance to a very small degree under disruptions.…”