A concise guide to scheduling with learning and deteriorating effects

Pei, Jun; Zhou, Yuenan; Yan, Ping; Pardalos, Pãnos M.

doi:10.1080/00207543.2022.2049911

Cited by 18 publications

(6 citation statements)

References 136 publications

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“…For the single machine problem, NP-hardness in the strong sense has been proven. Finally, we mention that a very recent survey about deterioration (and learning) effects in scheduling has been given by Pei et al [15].…”

Section: Literature Reviewmentioning

confidence: 94%

Scheduling a single machine with compressible jobs to minimize maximum lateness

Vakhania¹,

Werner²,

Reynoso³

2022

Preprint

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The problem of scheduling non-simultaneously released jobs with due dates on a single machine with the objective to minimize the maximum job lateness is known to be strongly NP-hard. Here we consider an extended model in which the compression of the job processing times is allowed. The compression is accomplished at the cost of involving additional emerging resources, whose use, however, yields some cost. With a given upper limit U on the total allowable cost, one wishes to minimize the maximum job lateness. This setting is realistic in practice because by completing some emerging jobs earlier using some available resources, the objective function value might be decreased. In particular, for minimizing the maximum job lateness, shortening the processing time of specific jobs turns out to be very helpful. Although the generalized problem remains strongly NP-hard and is considerably more complicated than the generic non-compressible version, given a "sufficient amount" of additional resources, it might be possible to solve the problem optimally. In particular, we establish the compression rate for some specific jobs and develop an algorithm that obtains an optimal solution for the processing times reduced in this way. Such an approach has an obvious benefit in practice since the manufacturer can be provided with an information about the required amount of additional resources in order to solve the problem optimally.

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Section: Literature Reviewmentioning

confidence: 94%

Scheduling a single machine with compressible jobs to minimize maximum lateness

Vakhania¹,

Werner²,

Reynoso³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…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].…”

Section: Metaheuristic Solution Approachmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Touafek,

Benbouzid-Si Tayeb,

Ladj

2023

Algorithms

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In the last decades, the availability constraint as well as learning and deteriorating effects were introduced into the production scheduling theory to simulate real-world case studies and to overcome the limitation of the classical models. To the best of our knowledge, this paper is the first in the literature to address the permutation flowshop scheduling problem (PFSP) with flexible maintenance under learning and deterioration effects to minimize the makespan. Firstly, we address the PFSP with flexible maintenance and learning effects. Then, the deteriorating effect is also considered. Adaptive artificial bee colony algorithms (ABC) enhanced with Q-learning are proposed, in which the Nawaz–Enscore–Ham (NEH) heuristic and modified NEH heuristics are hybridized with a maintenance insertion heuristic to construct potential integrated initial solutions. Furthermore, a Q-learning (QL)-based neighborhood selection is applied in the employed bees phase to improve the quality of the search space solutions. Computational experiments performed on Taillard’s well-known benchmarks, augmented with both prognostic and health management (PHM) and maintenance data, demonstrate the effectiveness of the proposed QL-driven ABC algorithms.

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“…For the proposed system, their experimental results indicated a better and fairer distribution of energy expenditure among workers. Some researchers have investigated some human factors like fatigue in project scheduling, 40 fatigue and learning in the field of human-robot collaboration, 41 work motivation in job rotation, 42 learning and forgetting effects on machine scheduling problem [43][44][45][46] and human factors in the problem of fuzzy integrated cell formation and production scheduling with guided vehicles. 47 The survey of the previous studies indicates that no paper examined the effects of human factors of learning, forgetting, fatigue, and rest at the same time on the human reliability in the field of shift scheduling.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing human reliability in work shifts scheduling: A real case of clothing job shop

Arekamari

Namaghi

2023

Quality & Reliability Eng

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In this research, we provide a mathematical model with an objective function of minimizing human errors for the problem of shift scheduling optimization. The unique aspects which are considered in our model are learning, forgetting, fatigue, and rest parameters (which affect the human reliability). Various problems with different human parameters, based on real cases of clothing job shops, are solved to study the efficiency and effectiveness of the optimization model. Two improved bacterial foraging algorithms are presented to handle the complexity of our problem. To examine the capability of the presented swarm intelligence algorithms in obtaining global optimum problem, we compare the results of the algorithms against the solution of basic bacterial foraging algorithm in terms of solutions quality. Results indicate that the solution approaches are more efficient in searching solution space and convergence. Analysis of different shift scheduling shows that in moderate fatigue condition, the scenario of scheduling with continuous work hours is better in terms of productivity. Also, experiments demonstrate that optimum reliable shift schedules are related to human factors. The findings of our research indicate that the presented scheduling model and improved algorithms can provide optimum shift schedules regarding human factors.

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A concise guide to scheduling with learning and deteriorating effects

Cited by 18 publications

References 136 publications

Scheduling a single machine with compressible jobs to minimize maximum lateness

Scheduling a single machine with compressible jobs to minimize maximum lateness

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

Optimizing human reliability in work shifts scheduling: A real case of clothing job shop

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