The third comprehensive survey on scheduling problems with setup times/costs

Allahverdi, Ali

doi:10.1016/j.ejor.2015.04.004

Cited by 413 publications

(239 citation statements)

References 388 publications

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“…(3) Performance criteria and scheduling objectives. In the traditional workshop, the scenario is usually that factories schedule their own machines, and thus the scheduling objective is usually how to minimise the makespan and flow times without concerning cost and quality (Allahverdi 2015). However, in cloud manufacturing, due to resources are owned by different enterprises, many other metrics such as cost, quality, service evaluation, and enterprise reputation need also to be considered.…”

Section: Workhop Schedulingmentioning

confidence: 99%

“…A great variety of issues have been addressed, including single machine scheduling, parallel machine scheduling, flow shop and flexible flow shop scheduling, job shop scheduling, open shop scheduling and many different models, deterministic or stochastic, have been built (Allahverdi 2015). Many approaches have been proposed, from the early mathematical programming (Blazewicz, Dror, and Weglarz 1991) and branch-and-bound technique (Brucker, Jurisch, and Sievers 1994) for smallsize problems to various approximation methods such as local search (Vaessens, Aarts, and Lenstra 1996) and AI (Wang 2005;Çaliş and Bulkan 2015), etc.…”

Section: Workhop Schedulingmentioning

confidence: 99%

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Scheduling in cloud manufacturing: state-of-the-art and research challenges

Liu

Wang

et al. 2018

International Journal of Production Research

210

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For the past eight years, cloud manufacturing as a new manufacturing paradigm has attracted a large amount of research interest worldwide. The aim of cloud manufacturing is to deliver on-demand manufacturing services to consumers over the Internet. Scheduling is one of the critical means for achieving the aim of cloud manufacturing. Thus far, about 158 articles have been published on scheduling in cloud manufacturing. However, research on scheduling in cloud manufacturing faces numerous challenges. Thus, there is an urgent need to ascertain the current status and identify issues and challenges to be addressed in the future. Covering articles published on the subject over the past eight years, this article aims to provide a state-of-the-art literature survey on scheduling issues in cloud manufacturing. A detailed statistical analysis of the literature is provided based on the data gathered from the Elsevier's Scopus abstract and citation database. Typical characteristics of scheduling issues in cloud manufacturing are systematically summarised. A comparative analysis of scheduling issues in cloud manufacturing and other scheduling issues such as cloud computing scheduling, workshop scheduling and supply chain scheduling is also carried out. Finally, future research issues and challenges are identified.

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Section: Workhop Schedulingmentioning

confidence: 99%

Section: Workhop Schedulingmentioning

confidence: 99%

Scheduling in cloud manufacturing: state-of-the-art and research challenges

Liu

Wang

et al. 2018

International Journal of Production Research

210

View full text Add to dashboard Cite

show abstract

“…This gap has also existed in optimization models as well as another field of engineering (Ehrgott et al, 2014;Gabrel et al, 2014;Goerigk & Schöbel, 2015;Wang & Shan, 2007). (Ghodratnama et al, 2015) √ √ √ √ √ √ 16 (Pishvaee & Fazli Khalaf, 2016 (Gul & Zoubir, 2017) √ √ √ 33 (Goerigk & Schöbel, 2015) √ √ √ √ √ 34 (Gorissen, 2015) √ √ √ √ √ √ 35 (Liu et al, 2015) √ √ √ √ 36 (Sun et al, 2015) √ √ √ 37 √ √ √ √ 38 (Wu , 2015) √ √ √ √ √ 39 (Khan et al, 2015) √ √ √ 40 (Park, 2016) √ √ √ 41 (Goberna et al, 2015) √ √ √ √ 42 √ √ √ 43 (Wang & Pedrycz, 2015) √ √ √ √ √ 44 (Yu & Zeng, 2015) √ √ √ 45 (Asafuddoula et al, 2015) √ √ √ √ √ √ 46 (Dellino et al, 2015) √ √ √ √ √ √ √ √ √ 47 (Auzins et al, 2015) √ √ √ √ √ √ 48 (Cao et al, 2015) √ √ √ √ √ √ 49 (Ng et al, 2015) √ √ √ √ √ √ √ 50 (Allahverdi, 2015) √ √ …”

Section: Discussionmentioning

confidence: 99%

“…Herroelen and Leus (2004) and (2005) in two different comprehensive review papers have tried to investigate the methods of reactive and proactive scheduling project under uncertain conditions. In addition, recent survey on scheduling problems based on time and cost can be found in (Allahverdi, 2015). …”

Section: Production and Project Management Under Uncertaintymentioning

confidence: 99%

An overview on robust design hybrid metamodeling: Advanced methodology in process optimization under uncertainty

Parnianifard¹,

Azfanizam²,

Ariffin³

et al. 2018

10.5267/j.ijiec

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Nowadays, process optimization has been an interest in engineering design for improving the performance and reducing cost. In practice, in addition to uncertainty or noise parameters, a comprehensive optimization model must be able to attend other circumstances which might be imposed in problems under real operational conditions such as dynamic objectives, multiresponses, various probabilistic distribution, discrete and continuous data, physical constraints to design parameters, performance cost, computational complexity and multi-process environment. The main goal of this paper is to give a general overview on topics with brief systematic review and concise discussions about the recent development on comprehensive robust design optimization methods under hybrid aforesaid circumstances. Both optimization methods of mathematical programming based on Taguchi approach and robust optimization based on scenario sets are briefly described. Metamodels hybrid robust design is discussed as an appropriate methodology to decrease computational complexity in problems under uncertainty. In this context, the authors' policy is to choose important topics for giving a systematic picture to those who wish to be more familiar with recent studies about robust design optimization hybrid metamodels, also by attending real circumstances in practice. In particular, production and project management are considered as two important methodologies that could be improved by applications of advanced robust design combining with metamodel methods.

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“…Recently, Rossi (2014) investigate the SDST-FJSP with transportation times using ant-colony algorithm with reinforced pheromone. The most recent comprehensive survey of scheduling problem with setup times is given by Allahverdi (2015).…”

Section: Introductionmentioning

confidence: 99%

A hybrid algorithm for flexible job-shop scheduling problem with setup times

Azzouz

Ennigrou

Saïd

2017

Int. J. Prod. Manag. Eng.

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Job-shop scheduling problem is one of the most important fields in manufacturing optimization where a set of n jobs must be processed on a set of m specified machines. Each job consists of a specific set of operations, which have to be processed according to a given order. The Flexible Job Shop problem (FJSP) is a generalization of the above-mentioned problem, where each operation can be processed by a set of resources and has a processing time depending on the resource used. The FJSP problems cover two difficulties, namely, machine assignment problem and operation sequencing problem. This paper addresses the flexible job-shop scheduling problem with sequence-dependent setup times to minimize two kinds of objectives function: makespan and bi-criteria objective function. For that, we propose a hybrid algorithm based on genetic algorithm (GA) and variable neighbourhood search (VNS) to solve this problem. To evaluate the performance of our algorithm, we compare our results with other methods existing in literature. All the results show the superiority of our algorithm against the available ones in terms of solution quality.

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The third comprehensive survey on scheduling problems with setup times/costs

Cited by 413 publications

References 388 publications

Scheduling in cloud manufacturing: state-of-the-art and research challenges

Scheduling in cloud manufacturing: state-of-the-art and research challenges

An overview on robust design hybrid metamodeling: Advanced methodology in process optimization under uncertainty

A hybrid algorithm for flexible job-shop scheduling problem with setup times

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