Modelling and Solving Rescheduling Problems in Dynamic Permutation Flow Shop Environments

Abstract

The aim of this paper is to analyse, model, and solve the rescheduling problem in dynamic permutation flow shop environments while considering several criteria to optimize. Searching optimal solutions in multiobjective optimization problems may be difficult as these objectives are expressing different concepts and are not directly comparable. Thus, it is not possible to reduce the problem to a single-objective optimization, and a set of efficient (nondominated) solutions, a so-called Pareto front, must be found. Moreover, in manufacturing environments, disruptive changes usually emerge in scheduling problems, such as machine breakdowns or the arrival of new jobs, causing a need for fast schedule adaptation. In this paper, a mathematical model for this type of problem is proposed and a restarted iterated Pareto greedy (RIPG) metaheuristic is used to find the optimal Pareto front. To demonstrate the appropriateness of this approach, the algorithm is applied to a benchmark specifically designed in this study, considering three objective functions (makespan, total weighted tardiness, and steadiness) and three classes of disruptions (appearance of new jobs, machine faults, and changes in operational times). Experimental studies indicate the proposed approach can effectively solve rescheduling tasks in a multiobjective environment.

Solving Rescheduling Problems in Dynamic Permutation Flow Shop Environments with Multiple Objectives Using the Hybrid Dynamic Non-Dominated Sorting Genetic II Algorithm

“…Ref. [51] proposes the application of a state-of-the-art greedy algorithm for scheduling problems, called RIPG (restarted iterated Pareto greedy) to solve a three-objective permutation flow shop rescheduling problem.…”

“…The selection of these objective functions seeks to improve the productivity in the production environment (by minimizing the makespan), the customer service (by minimizing the total weighted tardiness), and the schedule stability in the rescheduling process when sudden disruptions arise. The mathematical formulation of the problem studied in this paper is described below and can be found in detail in [51].…”

Sequencing mixed-model assembly lines with risk-averse stochastic mixed-integer programming

“…2020), minimization of total workload of machines (Zhang et al 2017), and maximization of resource utilization (Henning and Cerda 2000). Other objectives include schedule stability (Iima 2005, Zhang et al 2018, Valledor et al 2020, economic performance (Shafaei andBrun 1999, Tiacci 2017) and energy consumption (Zhang et al 2017 andSalido et al 2017).…”

Intelligent Decision-Making of Scheduling for Dynamic Permutation Flowshop via Deep Reinforcement Learning

“…Thus, dynamic scheduling has more practical implications for a real workshop. Several dynamic characteristics have been considered, such as dynamic Sensors 2021, 21, 1019 2 of 20 job/order arrival [28][29][30][31], stochastic processing time [32][33][34], machine breakdown [35], process interruptions [36,37], etc. Among these dynamic characteristics, new job arrival has been recently receiving arising attention.…”