Production scheduling optimisation with machine state and time-dependent energy costs

Aghelinejad, Mohammad Mohsen; Ouazène, Yassine; Yalaoui, Alice

doi:10.1080/00207543.2017.1414969

Cited by 53 publications

(22 citation statements)

References 31 publications

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“…The auxiliary variables γ i,t connect the constraints Equations (8) and (9). The constraints Equation (10) provide that the value of ε t is not less than the consumption of the EGS energy in the slot t. The variables ε t are used in the Equation ( 5) representing total cost of the energy which value is minimised.…”

Section: Discrete-time Linear Programming Modelmentioning

confidence: 99%

“…In the first group, there are works concerning the optimisation based on schedule-dependent energy cost of the production [3][4][5][6][7]. In particular, the manufacturing with machines of variable speed [8,9] or having multiple working states [10][11][12][13] is considered in this group, where the speed or state determines instantaneous machine power consumption and the related cost. The second group includes problems related to manufacturing cost optimisation under time-of-use (TOU) price tariffs of EGS energy.…”

Section: Introductionmentioning

confidence: 99%

“…The second group includes problems related to manufacturing cost optimisation under time-of-use (TOU) price tariffs of EGS energy. This subject is quite well known and studied for a few decades, but it is continuously extended by new research in which some specific manufacturing environments and conditions are considered [10][11][12][14][15][16][17][18] or new approaches are proposed, e.g., optimal production planning for continuous power-intensive processes [19] or scheduling of machine unavailability related to preventive maintenance [20]. One can notice that a few of the mentioned works belong to the two groups simultaneously, namely [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Energy Cost-Efficient Task Positioning in Manufacturing Systems

Bożek

2020

Energies

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A problem to determine a production schedule which minimises the cost of energy used for manufacturing is studied. The scenario assumes that each production task has assigned constant power consumption, price of power from conventional electrical grid system is defined by time-of-use tariffs, and a component of free of charge renewable energy is available for the manufacturing system. The objective is to find the most cost-efficient production plan, subject to constraints involving predefined precedence relationships between the tasks and a bounded makespan. Two independent optimisation approaches have been developed, based on significantly different paradigms, namely mixed-integer linear programming and tabu search metaheuristic. Both of them have been verified and compared in extensive computational experiments. The tabu search-based approach has turned out to be generally more efficient in the sense of the obtained objective function values, but advantages of the use of linear programming have also been identified. The results confirm that it is possible to develop efficient computational methods to optimise energy cost under circumstances typical of manufacturing companies. The set of numerous benchmark instances and their solutions have been archived and it can be reused in further research.

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Section: Discrete-time Linear Programming Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Energy Cost-Efficient Task Positioning in Manufacturing Systems

Bożek

2020

Energies

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“…So for single machine scheduling problems, Aghelinejad et al [25] proposed a dynamic programming approach to solve these problems by using a finite graph. A non-preemptive single-machine manufacturing environment had been investigated by Aghelinejad et al [26] to reduce the total costs. They Improved the mathematical formulation of scheduling problem in a predetermined order at machine level to process the jobs.…”

Section: Literature Reviewmentioning

confidence: 99%

Multisources of Energy Contracting Strategy with an Ecofriendly Factor and Demand Uncertainties

et al. 2019

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This study presents a mathematical formulation to optimize contracting capacity strategies of multisources of energy for institutional or industrial consumers considering demand uncertainties. The objective consists of minimizing the total costs composed of the different types of energy contract capacity costs, penalty price, and an ecofriendly factor. The penalty price is charged on the demand of energy exceeding the total contract capacities. The ecofriendly factor encourages the use of renewable energy and reduces the traditional energy used in the optimal mix of energy sources. The proposed model is tested based on demand of energy inspired from real data. These numerical experiments are analyzed to illustrate the impact of encouraging the use of renewable energy sources by introducing the ecofriendly factor and the influence of penalty price and uncertainty in the demand of energy. The results show that in the presence of low penalty price or low uncertainty a large amount of ecofriendly support is needed for using more renewable energy sources in the optimal contract capacity combination.

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“…Aghelinejad vd. [20] ve Zhang vd. [21], sezgisel algoritma önermişlerdir.…”

Section: Gi̇ri̇ş (Introduction)unclassified

Enerji etkin ve sıra bağımlı hazırlık süreli tek makine çizelgeleme problemi için tavlama benzetimi algoritması tabanlı hibrit sezgisel çözüm önerisi

Bektur

2020

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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 Energy efficient single machine scheduling problem  Multi-objective heuristic algorithm  Sequence dependent setup times In this study, energy efficient single machine scheduling problem with sequence dependent setup times is considered. Speed scaling mechanism is taken in to account. Objective functions of the problem are minimization of the total number of tardy jobs and total energy consumption. A multi-objective heuristic algorithm is proposed for the problem. The proposed heuristic algorithm is compared with NSGA II and εconstraint method. The Pareto front obtained by NSGA II and proposed heuristic algorithm is given in Figure A. The proposed heuristic algorithm outperforms NSGA II significantly.

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Production scheduling optimisation with machine state and time-dependent energy costs

Cited by 53 publications

References 31 publications

Energy Cost-Efficient Task Positioning in Manufacturing Systems

Energy Cost-Efficient Task Positioning in Manufacturing Systems

Multisources of Energy Contracting Strategy with an Ecofriendly Factor and Demand Uncertainties

Enerji etkin ve sıra bağımlı hazırlık süreli tek makine çizelgeleme problemi için tavlama benzetimi algoritması tabanlı hibrit sezgisel çözüm önerisi

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