A survey of case studies in production scheduling: Analysis and perspectives

Fuchigami, Helio Yochihiro; Rangel, Socorro

doi:10.1016/j.jocs.2017.06.004

Cited by 82 publications

(47 citation statements)

References 86 publications

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“…For instance, Fuchigami and Rangel (2017) found, in a review regarding case studies in production scheduling, that almost half of the case studies were concerned with solving hybrid flow-shops problems, a machine environment that can describe the flow pattern of stress shops. Furthermore, they found that setup constraints were present in 42% of the cases where specific constraints existed, which is a constraint that is commonly found in hybrid flow-shop environments (Romero-Silva et al, 2016a) and which could create the need for building and implementing a complete schedule.…”

Section: Previous Research Supporting the Proposed Fitmentioning

confidence: 99%

Shop-floor scheduling as a competitive advantage: A study on the relevance of cyber-physical systems in different manufacturing contexts

Romero-Silva

Hernández-López

2020

International Journal of Production Economics

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Section: Previous Research Supporting the Proposed Fitmentioning

confidence: 99%

Shop-floor scheduling as a competitive advantage: A study on the relevance of cyber-physical systems in different manufacturing contexts

Romero-Silva

Hernández-López

2020

International Journal of Production Economics

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“…The literature distinguishes between different problem variants, and for many problem variants, one or even several surveys have been published, most of them studying the problems through a theoretical lens. Indeed, the literature review carried out by Fuchigami and Rangel (2018), the publication real-life cases on scheduling problems have been scarce, with their frequency only increasing very recently. In addition, according to Allahverdi (2015), more than 90% of the literature on scheduling problems ignores setup times/costs.…”

Section: Scheduling Problemmentioning

confidence: 99%

Improving effectiveness of parallel machine scheduling with earliness and tardiness costs: A case study

Muñoz-Villamizar¹,

Santos²,

Montoya‐Torres³

et al. 2019

10.5267/j.ijiec

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This paper assesses the effectiveness in scheduling independent jobs with earliness/tardiness costs and variable setup times applying the Overall Equipment Effectiveness (OEE). The OEE is a common metric for measuring the manufacturing productivity. We defined a mixed-integer linear programming formulation of the parallel machine scheduling problem with four different objective functions in order to compare different scheduling configurations. Real data, from a plastic container manufacturing company located in the Basque Country (Spain), were used to validate this approach. A sensitivity analysis was performed with different production capacities and earliness/tardiness costs in order to evaluate the trade-offs between economic performance (i.e., costs) and the partial rates of OEE (i.e., quality, performance and availability). The objective of this study is to propose a guideline to help management make decisions regarding the measurement and improvement of scheduling effectiveness through contemplating earliness, tardiness and variable setup times.

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“…The scheduling of tasks in a production line is not a straightforward problem and, therefore, proposed solutions can be found in the literature [9][10][11][12][13]. Some solutions use artificial intelligence considering multiple restrictions [14,15]. In Ðurasević et al (2016) a genetic algorithm is proposed to schedule tasks in several unrelated machines [16].…”

Section: Introductionmentioning

confidence: 99%

Production Line Optimization to Minimize Energy Cost and Participate in Demand Response Events

et al. 2021

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The scheduling of tasks in a production line is a complex problem that needs to take into account several constraints, such as product deadlines and machine limitations. With innovative focus, the main constraint that will be addressed in this paper, and that usually is not considered, is the energy consumption cost in the production line. For that, an approach based on genetic algorithms is proposed and implemented. The use of local energy generation, especially from renewable sources, and the possibility of having multiple energy providers allow the user to manage its consumption according to energy prices and energy availability. The proposed solution takes into account the energy availability of renewable sources and energy prices to optimize the scheduling of a production line using a genetic algorithm with multiple constraints. The proposed algorithm also enables a production line to participate in demand response events by shifting its production, by using the flexibility of production lines. A case study using real production data that represents a textile industry is presented, where the tasks for six days are scheduled. During the week, a demand response event is launched, and the proposed algorithm shifts the consumption by changing task orders and machine usage.

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A survey of case studies in production scheduling: Analysis and perspectives

Cited by 82 publications

References 86 publications

Shop-floor scheduling as a competitive advantage: A study on the relevance of cyber-physical systems in different manufacturing contexts

Shop-floor scheduling as a competitive advantage: A study on the relevance of cyber-physical systems in different manufacturing contexts

Improving effectiveness of parallel machine scheduling with earliness and tardiness costs: A case study

Production Line Optimization to Minimize Energy Cost and Participate in Demand Response Events

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