Mathematical modeling and evolutionary generation of rule sets for energy-efficient flexible job shops

Zhang, Liping; Tang, Qiuhua; Wu, Zhengjia; Wang, Fang

doi:10.1016/j.energy.2017.07.005

Cited by 79 publications

(45 citation statements)

References 39 publications

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“…A common objective in these investigations is makespan, as it not only directly measures the time to complete a set of jobs, but also is linked to other important production metrics, e.g., earliness/tardiness of finished jobs and workload. The energy-related objectives include minimization of total energy consumption to process a set of jobs [3,16,19,22,28,33], non-processing energy [16,25], peak power consumption [17], and energy cost [20,25].…”

Section: Literature Review On Energy-aware (Flexible) Job Shop Schedumentioning

confidence: 99%

“…This power input model encompasses idle, switch, and cutting states, where the switch state is a transition to runtime mode. Besides processing and standby states, startup and shutdown states are considered in [33]. The duration of the latter two states enables the switching off-on decision: a machine is switched off if the duration between two adjacent operations is longer than the breakeven duration (i.e., energy consumption of shutdown and startup divided by standby power).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Energy- and labor-aware flexible job shop scheduling under dynamic electricity pricing: A many-objective optimization investigation

Gong

Pessemier

Martens

et al. 2019

Journal of Cleaner Production

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Energy-aware production scheduling is a promising way to adapt the factories' energy consumption behavior to the volatile electricity prices in the demand response initiative of smart grids. However, it may not be economical by simply scheduling production loads to the periods with lower electricity prices, as these periods often have higher labor wage, e.g., nights and weekends. Based on this gap, this paper proposes a many-objective integrated energy-and labor-aware flexible job shop scheduling model. Many objectives refer to the number of optimization objectives surpasses three (i.e., five objectives: makespan, total energy cost, total labor cost, maximal workload, and total workload), whereas the existing energy-aware production scheduling research is limited within three objectives. To enable energy awareness in the conventional production scheduling algorithms, a state-based shop floor wide energy model is proposed. To enable labor awareness, the number and type of human workers are matched to the scheduled production loads, with varying labor wage over shifts. As one of the most complex shop floor configurations, the partial flexible job shop further considers job recirculation and operation sequence-dependent machine setup times. The recently-proposed nondominated sorting genetic algorithm-III (NSGA-III) is tailored for this many-objective optimization problem (MaOP), including scheduling solution encoding and decoding, crossover, mutation, and solution evaluation using the energy-and labor-aware discrete-event simulation framework. Through numerical experiments under real-time pricing (RTP) and time-of-use pricing (ToUP), insights are statistically obtained on the relation among these five production objectives; the effectiveness and efficiency of NSGA-III in solving a MaOP are also demonstrated. This proposed scheduling method can be used to automated and enhance the decision making of factory managers in jointly allocating machine, human worker, and energy resources on the shop floor, such that the production cost is minimized even under time-varying electricity and labor prices.

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Section: Literature Review On Energy-aware (Flexible) Job Shop Schedumentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy- and labor-aware flexible job shop scheduling under dynamic electricity pricing: A many-objective optimization investigation

Gong

Pessemier

Martens

et al. 2019

Journal of Cleaner Production

View full text Add to dashboard Cite

show abstract

“…However, three measure criteria are separately optimized in this paper. Zhang et al [47] developed an efficient Gene Expression Programming (eGEP) algorithm for generating rules automatically to achieve effective machine selection, job sequencing, and machine off-on decision making. The single objective function is to minimize the total energy consumption.…”

Section: Literature Reviewmentioning

confidence: 99%

Hyper-Heuristic Coevolution of Machine Assignment and Job Sequencing Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling

2019

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Nowadays, real-time scheduling is one of the key issues in cyber-physical system. In real production, dispatching rules are frequently used to react to disruptions. However, the man-made rules have strong problem relevance, and the quality of results depends on the problem itself. The motivation of this paper is to generate effective scheduling policies (SPs) through off-line learning and to implement the evolved SPs online for fast application. Thus, the dynamic scheduling effectiveness can be achieved, and it will save the cost of expertise and facilitate large-scale applications. Three types of hyper-heuristic methods were proposed in this paper for coevolution of the machine assignment rules and job sequencing rules to solve the multi-objective dynamic flexible job shop scheduling problem, including the multi-objective cooperative coevolution genetic programming with two sub-populations, the multi-objective genetic programming with two sub-trees, and the multi-objective genetic expression programming with two chromosomes. Both the training and testing results demonstrate that the CCGP-NSGAII method is more competitive than other evolutionary approaches. To investigate the generalization performance of the evolved SPs, the nondominated SPs were applied to both the training and testing scenarios to compare with the 320 types of man-made SPs. The results reveal that the evolved SPs can discover more useful heuristics and behave more competitive than the man-made SPs in more complex scheduling scenarios. It also demonstrates that the evolved SPs have a strong generalization performance to be reused in new unobserved scheduling scenarios. INDEX TERMS Scheduling, flexible job shop, hyper-heuristic, multi-objective, genetic programming. NOMENCLATURE MO-DFJSP multi-objective dynamic flexible job shop scheduling problem MAR machine assignment rule JSR job sequencing rule SP scheduling policy GEP genetic expression programming CCGP cooperative coevolution genetic programming with two sub-populations TTGP genetic programming with single population that a GP individual contains two sub-trees NSGAII nondominated sorting genetic algorithm II SPEA2 strength Pareto evolutionary algorithm 2

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“…Che et al (2017) were the first to develop a mixed integer linear programming (MILP) model for single-machine scheduling problem with turning On and Off strategy, the effectiveness of which is verified by CPLEX SOLVER. A MILP model with the objective of minimising the total energy consumption for flexible job-shop scheduling problem (FJSP) with considering turning On and Off strategy was firstly proposed by Zhang et al (2017).…”

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

117

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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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Mathematical modeling and evolutionary generation of rule sets for energy-efficient flexible job shops

Cited by 79 publications

References 39 publications

Energy- and labor-aware flexible job shop scheduling under dynamic electricity pricing: A many-objective optimization investigation

Energy- and labor-aware flexible job shop scheduling under dynamic electricity pricing: A many-objective optimization investigation

Hyper-Heuristic Coevolution of Machine Assignment and Job Sequencing Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling

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

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