Optimizing the production scheduling of a single machine to minimize total energy consumption costs

Shrouf, Fadi; Ordieres-Meré, Joaquín; García-Sánchez, Álvaro; Ortega-Mier, Míguel

doi:10.1016/j.jclepro.2013.12.024

Cited by 409 publications

(189 citation statements)

References 32 publications

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“…Consequently, the involved energy cost is implicitly reduced due to the reduction of peak power consumption. A single-machine scheduling model is proposed in [13] to minimize the electricity cost for job processing under volatile electricity price. However, this model cannot assign job sequence, either.…”

Section: B Energy-cost-aware Production Schedulingmentioning

confidence: 99%

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A power data driven energy-cost-aware production scheduling method for sustainable manufacturing at the unit process level

Gong

Pessemier

Joseph

et al. 2016

2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA)

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Abstract-Nowadays, the energy price is rising. The consciousness of environmental sustainability of governments and customers has been ever increasing. Consequently, manufacturing enterprises are increasingly motivated to reduce the energy cost involved in their production activities. This paper proposes a novel production scheduling method to minimize the energy cost involved in the production at the unit process level. Compared to the emerging energy-conscious production scheduling methods, this method builds the finite state machine based energy model from power data that are measured from the shop floor. By following the formulated mixed integer linear programming model, the power states and changeovers of a unit process can be additionally scheduled, and the potential multiple process idle modes can be optimally selected between two jobs. In addition, the process power consumption behavior can be predicted along with the optimal schedule. This method was demonstrated in an extrusion blow molding process in a Belgian plastic bottle manufacturer. Compared to two conventional schedules, i.e., "as-early-as-possible" and "as-late-as-possible", the schedule given by the proposed method is able to reduce 21% and 11% of electricity cost for completing the same production task before a due date.

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Section: B Energy-cost-aware Production Schedulingmentioning

confidence: 99%

“…Based on the above philosophy, some energy-cost-aware production scheduling approaches can be found in literature [12][13][14][15][16][17][18]. Almost all these approaches emerged in the very recent years.…”

Section: Introductionmentioning

confidence: 99%

A power data driven energy-cost-aware production scheduling method for sustainable manufacturing at the unit process level

Gong

Pessemier

Joseph

et al. 2016

2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA)

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“…The fourth area is oriented towards savings and better economical acceptance. In this area, there can be found several studies about reducing the costs and reducing power consumption [9,15,28,29,39,43].…”

Section: The Basics Of Virtual Factorymentioning

confidence: 99%

An innovative training of production planners through virtual production performing

et al. 2016

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Original scientific paper Nowadays the production process with as few as possible deadlocks is the most important goal of management in every company. An optimized production process can be reached with well-trained personnel and especially with well-trained schedule planners. In this contribution, a new strategy on how to train production planners for different types of production by using virtual factory is presented. Also, two specially developed parametric simulation models for typical make-to-order and large-scale types of production are presented. Every model describes the real production process in detail so that it enables the observation of responses to the different input data. In the first step, the strategy covers the training of personnel so that they learn how varied input data reflect in the output results. The second strategy step is supporting schedule planning by using virtual factory where variants of schedule plans are tested much earlier than the real production process is performed. Keywords: modelling; optimization; simulation; schedule plan; training; virtual factory Inovativna obuka za planere proizvodnje kroz odvijanje virtualne proizvodnjeIzvorni znanstveni članak Danas je proces proizvodnje s najmanje zastoja najvažniji cilj upravljanja u svakom poduzeću. Optimizirani proces proizvodnje može se postići s dobro obučenim osobljem i osobito s dobro obučenim planerima proizvodnje. U ovom članku predstavljena je nova strategija treniranja planera proizvodnje za više tipova proizvodnje uz korištenje virtualne tvornice. Također su prikazana dva posebno razvijena parametarska simulacijska modela za tipične vrste pojedinačne i velikoserijske proizvodnje. Svaki model opisuje stvarni proizvodni proces u detalje i tako omogućuje promatranje odgovora na različite ulazne podatke. U prvom koraku ova strategija obuhvaća obuku osoblja, da nauče kako se različiti ulazni podaci odražavaju u izlaznim rezultatima. Drugi korak ove strategije je podupiranje planiranja proizvodnje uz pomoć virtualne tvornice, u kojoj se varijante proizvodnog plana testiraju mnogo ranije nego što se realni proizvodni proces odvije.

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“…To solve the issue, they proposed a turn-on/turn-off scheduling framework (which determines whether and when to turn off a machine or to keep it idle) to reduce the overall energy consumption of the machines. This framework has been utilized and extended in later research such as [5][6][7][8] for single machine scheduling or flow shop scheduling settings. Another research framework is based on machine speed scaling [9].…”

Section: Introductionmentioning

confidence: 99%

Sustainable Scheduling of Cloth Production Processes by Multi-Objective Genetic Algorithm with Tabu-Enhanced Local Search

Zhang

2017

Sustainability

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Abstract:The dyeing of textile materials is the most critical process in cloth production because of the strict technological requirements. In addition to the technical aspect, there have been increasing concerns over how to minimize the negative environmental impact of the dyeing industry. The emissions of pollutants are mainly caused by frequent cleaning operations which are necessary for initializing the dyeing equipment, as well as idled production capacity which leads to discharge of unconsumed chemicals. Motivated by these facts, we propose a methodology to reduce the pollutant emissions by means of systematic production scheduling. Firstly, we build a three-objective scheduling model that incorporates both the traditional tardiness objective and the environmentally-related objectives. A mixed-integer programming formulation is also provided to accurately define the problem. Then, we present a novel solution method for the sustainable scheduling problem, namely, a multi-objective genetic algorithm with tabu-enhanced iterated greedy local search strategy (MOGA-TIG). Finally, we conduct extensive computational experiments to investigate the actual performance of the MOGA-TIG. Based on a fair comparison with two state-of-the-art multi-objective optimizers, it is concluded that the MOGA-TIG is able to achieve satisfactory solution quality within tight computational time budget for the studied scheduling problem.

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Optimizing the production scheduling of a single machine to minimize total energy consumption costs

Cited by 409 publications

References 32 publications

A power data driven energy-cost-aware production scheduling method for sustainable manufacturing at the unit process level

A power data driven energy-cost-aware production scheduling method for sustainable manufacturing at the unit process level

An innovative training of production planners through virtual production performing

Sustainable Scheduling of Cloth Production Processes by Multi-Objective Genetic Algorithm with Tabu-Enhanced Local Search

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