A review on job shop scheduling with setup times

Sharma, Pankaj; Jain, Anuj

doi:10.1177/0954405414560617

Cited by 34 publications

(14 citation statements)

References 78 publications

(149 reference statements)

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“…Scheduling is a critical means for achieving the aim of cloud manufacturing. Scheduling has been a research topic in various domains for decades such as process and threat scheduling in operating systems (Tanenbaum and Woodhull 1987), job shop scheduling (Chaudhry and Khan 2016;Sharma and Jain 2016) and flow shop scheduling (Yenisey and Yagmahan 2014) in production environments, and task scheduling in computing and manufacturing systems such as in computing grid (Rahman et al 2013), cloud computing (Singh and Chana 2016) and manufacturing grid (Tao et al 2009). …”

Section: Introductionmentioning

confidence: 99%

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

Liu

Wang

et al. 2018

International Journal of Production Research

221

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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: Introductionmentioning

confidence: 99%

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

Liu

Wang

et al. 2018

International Journal of Production Research

221

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“…At present, various swarm intelligence algorithms, such as ant colony optimization (ACO), genetic algorithm (GA), PSO, artificial bee colony (ABC) algorithm and artificial fish swarm algorithm (AFSA), have been used to solve the combination optimization problem. 14 For example, Lin and Chong 15 presented a GA-based resource-constraint project scheduling, incorporating a number of new ideas (enhancements and local search) for solving computing resources allocation problems. Seghir and Khababa 16 proposed a hybrid GA to solve the QoS-aware cloud service composition problem.…”

Section: Brief Reviewmentioning

confidence: 99%

An improved firefly algorithm for collaborative manufacturing chain optimization problem

Zhang

Jing

Zhu

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part B:

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In order to conduct resource sharing and deployment in cloud manufacturing environment, a concept of collaborative manufacturing chain was proposed. Based on machining tasks with the sequential characteristics, the proposed model considering the criteria of service cost, service time, service quality and service utilization was constructed. Fuzzy analytical hierarchy process was adopted to add the above multi-criteria model to a single objective problem. Then, an improved firefly algorithm was used to solve a reasonable collaborative manufacturing chain scheme. Based on the discrete characteristics of the collaborative manufacturing chain, iterative position function was improved to make the solution space to be a discrete domain. Furthermore, particle swarm optimization was used to optimize the step length factor α, attraction degree β0 and light absorption coefficient γ so as to prevent the firefly algorithm from local optimum. Compared with the genetic algorithm, numerical result suggests that the improved firefly algorithm has more advantages in convergence speed and solving efficiency. It is expected that this study can provide a useful reference for the service composition of collaborative manufacturing chain.

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“…These areas have been explored extensively for subtractive manufacturing processes but not for the emerging additive manufacturing field. [5][6][7][8][9] For example, Figure 2 shows results from a review by Mukherjee and Ray 10 who classified the optimisation techniques that have been employed to the cutting process of metals. They highlight that each technique is able to enhance certain characteristics of the cutting process however, such is the multi-objective nature and number of variables that can be altered, there remains opportunities in being able to combine the affordances of the various optimisation techniques.…”

Section: Related Workmentioning

confidence: 99%

“…It is contended that the assumptions represent a reasonable starting point for defining the problem within this new 3D MPS context. 6,9 The assumptions and justification are as follows:…”

Section: Modelling the Scheduling Model Of 3d Mpsmentioning

confidence: 99%

Investigating the effect of scale and scheduling strategies on the productivity of 3D managed print services

Gopsill

Hicks

2017

Proceedings of the Institution of Mechanical Engineers, Part B:

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Sales of extrusion 3D printers have seen a rapid growth and the market value is expected to triple over the next decade. This rapid growth can be attributed to a step change in capability and an increase in demand for 3D printed parts within mechanical, industrial and civil engineering processes. Correspondingly, a new technical prototyping platformcommonly referred to as FabLabs -has emerged to provide a stimulus for local education, entrepreneurship, innovation and invention through the provision of on-demand 3D printing and prototyping services. Central to the effectiveness of the on-demand 3D printing and prototyping services -hereby referred to as 3D Managed Print Services (3D MPS) -is their ability to handle multiple users with varying knowledge and understanding of the manufacturing processes, and scaling numbers of 3D printers in order to maximise productivity of the service.It is this challenge of productivity and more specifically, the scalability and scheduling of prints that is considered in this paper. The effect of scale and scheduling strategies on productivity is investigated through the modelling of four scheduling strategies for 3D MPS of varying scale by altering the number of available printers and level of user demand. The two most common approaches (First-come first serve & On-line continuous queue) and two alternatives based on bed space optimisation (First-fit decreasing height & First-fit decreasing height with a genetic algorithm) have been considered. Through Monte-Carlo simulation and comparison of the strategies, it is shown that increasing the scale of a 3D MPS improves the peak productivity and range of user demands at which the 3D MPS remains productive. In addition, the alternative strategies are able to double the peak productivity of a 3D MPS as well as increase the user demand range where the 3D MPS remains productive.

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A review on job shop scheduling with setup times

Cited by 34 publications

References 78 publications

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

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

An improved firefly algorithm for collaborative manufacturing chain optimization problem

Investigating the effect of scale and scheduling strategies on the productivity of 3D managed print services

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