Single machine scheduling with time-dependent linear deterioration and rate-modifying maintenance

Rustogi, Kabir; Strusevich, Vitaly A.

doi:10.1057/jors.2014.18

Cited by 26 publications

(5 citation statements)

References 18 publications

(34 reference statements)

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“…In addition, we identified alternative approaches to model the learning effect, using a perceptron neural network learning algorithm [72]. We also found some articles corresponding to the inclusion of rate-modifying activities (RMA) [73][74][75][76][77][78][79][80], which changes the processing conditions in a system and adjusts the production rate of a resource. Two examples are maintenance activities or rest periods, which usually increase production rates.…”

Section: Dejong's Learning Effect (Dj-le)mentioning

confidence: 99%

Taxonomy of Scheduling Problems with Learning and Deterioration Effects

2022

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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.

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Section: Dejong's Learning Effect (Dj-le)mentioning

confidence: 99%

Taxonomy of Scheduling Problems with Learning and Deterioration Effects

2022

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“…Of course, many extended variants of SMTWT were developed such as Single machine scheduling with time-dependent linear deterioration and rate-modifying maintenance (Rustogi, & Strusevich, 2015), and Two-agent single machine scheduling with forbidden intervals (Qi Feng, & Li, & Shang, 2015), and many others. However, this paper focuses on the basic SMTWT as a validation problem for the proposed discrete Fireworks Algorithm.…”

Section: Total Weighted Tardiness Problem (Smtwt)mentioning

confidence: 99%

Discrete Fireworks Algorithm for Single Machine Scheduling Problems

Majdouli

Imrani

2016

International Journal of Applied Metaheuristic Computing

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Over the recent years, Fireworks Algorithm has recorded an increasing success on solving continuous optimization problems, due to its efficiency, simplicity and more importantly its rapid convergence to good optimums. Thus, the Fireworks Algorithm performance is now widely comparable with the most popular methods in the optimization field such as evolutionary computation and swarm intelligence techniques. This paper introduces a discrete Fireworks Algorithm for combinatorial single machine scheduling problems. Taking advantage of the robust design of the original Fireworks Algorithm, a new adaptation of sparks generation is proposed with a novel use of the control parameters. To verify the explorative performance of the algorithm, a hybridization with Variable Neighborhood Search heuristic is implemented. To validate it, the proposed method is tested with several benchmarks instances of the single machine total weighted tardiness. A comparison with other optimization algorithms is also included. The obtained results exhibit the high performance of the proposed method.

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“…That is, time-based and condition-based maintenance needed to be treated as a whole. For more recent information about maintenance scheduling, please refer to [13,16,31,37,41,43,51,53,60].…”

Section: Introductionmentioning

confidence: 99%

A three-agent scheduling problem for minimizing the flow time on two machines

Lee

Wang

2020

RAIRO-Oper. Res.

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This study introduces a two-machine three-agent scheduling problem. We aim to minimize the total tardiness of jobs from agent 1 subject to that the maximum completion time of jobs from agent 2 cannot exceed a given limit and that two maintenance activities from agent 3 must be conducted within two maintenance windows. Due to the NP-hardness of this problem, a genetic algorithm (named GA+) is proposed to obtain approximate solutions. On the other hand, a branch-and-bound algorithm (named B&B) is developed to generate the optimal solutions. When the problem size is small, we use B&B to verify the solution quality of GA+. When the number of jobs is large, a relative deviation is proposed to show the gap between GA+ and another ordinary genetic algorithm. Experimental results show that the proposed genetic algorithm can generate approximate solutions by consuming reasonable execution time.

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Single machine scheduling with time-dependent linear deterioration and rate-modifying maintenance

Cited by 26 publications

References 18 publications

Taxonomy of Scheduling Problems with Learning and Deterioration Effects

Taxonomy of Scheduling Problems with Learning and Deterioration Effects

Discrete Fireworks Algorithm for Single Machine Scheduling Problems

A three-agent scheduling problem for minimizing the flow time on two machines

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