An Ant Optimization Model for Unrelated Parallel Machine Scheduling with Energy Consumption and Total Tardiness

Peng, Liang; Yang, Haidong; Liu, Guosheng; Guo, Jianhua

doi:10.1155/2015/907034

Cited by 30 publications

(19 citation statements)

References 18 publications

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“…A hybridization of two methods, namely truncated branch-and-bound and TS, is proposed and compared to some of the metaheuristic algorithms from the exited approaches. The authors of [64] addressed UPMSPs by employing the power-down scheme in order to minimize total tardiness and total energy consumption. A mathematical model is formulated, then, Ant Optimization algorithm is proposed with Apparent Tardiness Cost (ATC) heuristic rule.…”

Section: Literature Reviewmentioning

confidence: 99%

Modified Harris Hawks Optimizer for Solving Machine Scheduling Problems

et al. 2020

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Scheduling can be described as a decision-making process. It is applied in various applications, such as manufacturing, airports, and information processing systems. More so, the presence of symmetry is common in certain types of scheduling problems. There are three types of parallel machine scheduling problems (PMSP): uniform, identical, and unrelated parallel machine scheduling problems (UPMSPs). Recently, UPMSPs with setup time had attracted more attention due to its applications in different industries and services. In this study, we present an efficient method to address the UPMSPs while using a modified harris hawks optimizer (HHO). The new method, called MHHO, uses the salp swarm algorithm (SSA) as a local search for HHO in order to enhance its performance and to decrease its computation time. To test the performance of MHHO, several experiments are implemented using small and large problem instances. Moreover, the proposed method is compared to several state-of-art approaches used for UPMSPs. The MHHO shows better performance in both small and large problem cases.

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Section: Literature Reviewmentioning

confidence: 99%

Modified Harris Hawks Optimizer for Solving Machine Scheduling Problems

et al. 2020

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“…The turning On and Off strategy was first proposed by Mouzon et al (2007) for single-machine scheduling problem and had been proven highly effective in saving idle energy when a machine tool was idling for a long time. Subsequently, the turning On and Off strategy has been gradually applied to single-machine scheduling (Yildirim and Mouzon 2012;Shrouf et al 2014;Che et al 2017), parallel machine scheduling (Liang et al 2015), HFSP-UPM (Dai et al 2013) and flexible job-shop scheduling ). Yildirim and Mouzon (2012) developed a nonlinear mathematical model to minimise the energy consumption and the total completion time of a single machine simultaneously, and a multi-objective genetic algorithm was employed to solve this problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Mathematical modelling and optimisation of energy-conscious hybrid flow shop scheduling problem with unrelated parallel machines

Meng

Zhang

Shao

et al. 2018

International Journal of Production Research

112

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This paper investigates an energy-conscious hybrid flow shop scheduling problem with unrelated parallel machines (HFSP-UPM) with the energy-saving strategy of turning off and on. We first analyse the energy consumption of HFSP-UPM and formulate five mixed integer linear programming (MILP) models based on two different modelling ideas namely idle time and idle energy. All the models are compared both in size and computational complexities. The results show that MILP models based on different modelling ideas vary dramatically in both size and computational complexities. HFSP-UPM is NP-Hard, thus, an improved genetic algorithm (IGA) is proposed. Specifically, a new energy-conscious decoding method is designed in IGA. To evaluate the proposed IGA, comparative experiments of different-sized instances are conducted. The results demonstrate that the IGA is more effective than the genetic algorithm (GA), simulating annealing algorithm (SA) and migrating birds optimisation algorithm (MBO). Compared with the best MILP model, the IGA can get the solution that is close to an optimal solution with the gap of no more than 2.17% for small-scale instances. For large-scale instances, the IGA can get a better solution than the best MILP model within no more than 10% of the running time of the best MILP model.

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“…An unrelated parallel machine scheduling problem is investigated in [16]. Idling and setup are explicitly defined as machine states, which require to be assigned.…”

Section: B Energy-cost-aware Production Schedulingmentioning

confidence: 99%

“…Only one job is executed at one time without preemption, as constrained in (15) and (16). All jobs should be scheduled to a unique position, as described in (17).…”

Section: Ts T T T T T T Dt T Dt Dmentioning

confidence: 99%

See 1 more Smart Citation

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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An Ant Optimization Model for Unrelated Parallel Machine Scheduling with Energy Consumption and Total Tardiness

Cited by 30 publications

References 18 publications

Modified Harris Hawks Optimizer for Solving Machine Scheduling Problems

Modified Harris Hawks Optimizer for Solving Machine Scheduling Problems

Mathematical modelling and optimisation of energy-conscious hybrid flow shop scheduling problem with unrelated parallel machines

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

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