Taxonomy of Scheduling Problems with Learning and Deterioration Effects

Abstract

In traditional scheduling problems, job processing times are considered constant and known in advance. This assumption is, however, a simplification when it comes to hand-intensive real-life production contexts because workers usually induce variability in the job processing times due to several factors such as learning, monotony, fatigue, psychological factors, etc. These effects can decrease or increase the actual processing time when workers execute a job. The academic literature has reported several modeling and resolution approaches to deal with the phenomenon in a variety of configurations. However, there is no comprehensive review of these research outputs to the best of our knowledge. In this paper, we follow a systematic approach to review relevant contributions addressing the scheduling problem with learning and deterioration effects. Modeling approaches for learning and deterioration effects, objective functions, and solution methods employed in the literature are the main topics for the taxonomy proposed in this review. A total of 455 papers from 1999 to 2021 are included and analyzed. Different areas of interest are presented, and some opportunities for future research are identified.

A Reinforcing-Learning-Driven Artificial Bee Colony Algorithm for Scheduling Jobs and Flexible Maintenance under Learning and Deteriorating Effects

“…As a result of their efficiency and ability to be used in real-world production environments, metaheuristics as approximation methods have made breakthroughs in solving various complex optimization problems in the industry and become the main methods used to solve large-scale scheduling problems [47]. Nonetheless, it is worth noting that few studies in the literature have proposed metaheuristics to solve scheduling problems with learning and/or deteriorating effects [1,2].…”

“…We chose a full factorial design in which all possible combinations of the following ABC factors were tested: All the cited factors resulted in a total of 3 × 3 × 3 × 3 = 81 different combinations run on a set of n × m Taillard instances with n ∈ {20; 70; 120; 170; 220} and m ∈ {5; 10; 15; 20}. The job processing times of the instances were generated uniformly in the interval [1,99], and maintenance durations were generated according to the first maintenance mode (M1) described in Section 5.1. Each instance was executed five times, which meant a total of 16,200 executions.…”

“…On the other hand, machines wear out after long periods of processing. Therefore, recent trends in scheduling theory have focused on modeling real-world production scheduling problems where human learning, as well as machine deteriorating effects, are taken into account when studying scheduling problems [1,2]. The learning effect [3] is a natural human-oriented phenomenon appearing among operators after repeating similar tasks frequently.…”

“…Furthermore, incorporating effects further increases the problem's complexity. Additionally, while most research on deteriorating jobs and learning effects has focused on single-machine problems [1], it is the multimachine problems that are more interesting and closer to realworld problems. Therefore, the intent of this paper is to explore the permutation flowshop scheduling problem (PFSP) with flexible maintenance under learning and deteriorating effects, through the perspective of reinforcement learning algorithms.…”