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

Meng, Leilei; Zhang, Chaoyong; Shao, Xinyu; Ren, Yaping

doi:10.1080/00207543.2018.1501166

Cited by 117 publications

(53 citation statements)

References 48 publications

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“…In the HFSP, the m jobs are processed through n stages ( 1 ≥ n ) and there is at least one stage that has more than one parallel machine [49]. The minimum makespan is chosen as the optimization objective of the HFSP.…”

Section: Hybrid Flow Shop Scheduling Problemsmentioning

confidence: 99%

Hybrid Flow Shop Scheduling Problems Using Improved Fireworks Algorithm for Permutation

et al. 2020

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Prior studies are lacking which address permutation flow shop scheduling problems and hybrid flow shop scheduling problems together to help firms find the optimized scheduling strategy. The permutation flow shop scheduling problem and hybrid flow shop scheduling problems are important production scheduling types, which widely exist in industrial production fields. This study aimed to acquire the best scheduling strategy for making production plans. An improved fireworks algorithm is proposed to minimize the makespan in the proposed strategies. The proposed improved fireworks algorithm is compared with the fireworks algorithm, and the improvement strategies include the following: (1) A nonlinear radius is introduced and the minimum explosion amplitude is checked to avoid the waste of optimal fireworks; (2) The original Gaussian mutation operator is replaced by a hybrid operator that combines Cauchy and Gaussian mutation to improve the search ability; and (3) An elite group selection strategy is adopted to reduce the computing costs. Two instances from the permutation flow shop scheduling problem and hybrid flow shop scheduling problems were used to evaluate the improved fireworks algorithm's performance, and the computational results demonstrate the improved fireworks algorithm's superiority.

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Section: Hybrid Flow Shop Scheduling Problemsmentioning

confidence: 99%

Hybrid Flow Shop Scheduling Problems Using Improved Fireworks Algorithm for Permutation

et al. 2020

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“…e biobjective function minimizes the makespan (C max ) and the total energy consumption (TEC) which mainly includes the energy consumption of machine tools and the common energy consumption. Energy consumption of machine tools can further be divided into processing energy consumption (PEC) and idle energy consumption (IEC) [20][21][22][23][24] which is computed as follows:…”

Section: Problem Description and Modellingmentioning

confidence: 99%

Green Scheduling of Jobs and Flexible Periods of Maintenance in a Two-Machine Flowshop to Minimize Makespan, a Measure of Service Level and Total Energy Consumption

Assia

Abbassi

Barkany

et al. 2020

Advances in Operations Research

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e success of an industry today depends on its ability to innovate. In terms of energy performance, this innovation is reflected in the ability of manufacturers to implement new solutions or technologies that enable better energy management. In this regard, this paper aims to address this gap by incorporating energy consumption as an explicit criterion in flowshop scheduling of jobs and flexible preventive maintenance. Leveraging the variable speed of machining operations leading to different energy consumption levels, we explore the potential for energy saving in manufacturing. We develop a mixed integer linear multiobjective optimization model for minimizing the makespan and the total energy consumption. In the literature, no papers considering both production scheduling and flexible periods of maintenance with minimizing both objective the total of energy consumption in flowshop and makespan. e performance of the proposed mixed binary integer programming model is evaluated based on the exact method of branch and bound algorithm. A study of the results proved the performance of the model developed.

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“…Dai et al [38] adopted the same technique for both makespan and total energy consumption reduction using a genetic-SA algorithm in flexible flow shop scheduling optimization. Recently, Meng et al [39] developed some novel MILP models for the energy-conscious hybrid flow shop scheduling problem with unrelated parallel machines; again, the strategy of machines turning off and on has been adopted in their model. Due to the considerable amount of additional energy in restarting machines as well as the damage to the machine tools caused by frequent machine switch "ons" and "offs", Zhang et al [40] adopted the machine speed scaling-based paradigm [41] to reduce energy consumption in a job shop.…”

Section: Literature Reviewmentioning

confidence: 99%

Realizing Energy Savings in Integrated Process Planning and Scheduling

2019

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The integration of scheduling and process planning can eliminate resource conflicts and hence improve the performance of a manufacturing system. However, the focus of most existing works is mainly on the optimization techniques to improve the makespan criterion instead of more efficient uses of energy. In fact, with a deteriorating global climate caused by massive coal-fired power consumption, carbon emission reduction in the manufacturing sector is becoming increasingly imperative. To ease the environmental burden caused by energy consumption, e.g., coal-fired power consumption in use of machine tools, this research considers both makespan as well as environmental performance criteria, e.g., total power consumption, in integrated process planning and scheduling using a novel multi-objective memetic algorithm to facilitate a potential amount of energy savings; this can be realized through a better use of resources with more efficient scheduling schemes. A mixed-integer linear programming (MILP) model based on the network graph is formulated with both makespan as well as total power consumption criteria. Due to the complexity of the problem, a multi-objective memetic algorithm with variable neighborhood search (VNS) technique is then developed for this problem. The Kim’s benchmark instances are employed to test the proposed algorithm. Moreover, the TOPSIS decision method is used to determine the most satisfactory non-dominated solution. Several scenarios are considered to simulate different machine automation levels and different machine workload levels. Computational results show that the proposed algorithm can strike a balance between the makespan criterion and the total power consumption criterion, and the total power consumption can be affected by machine tools with different automation levels and different workloads. More importantly, results also show that energy saving can be realized by completing machining as early as possible on a machine tool and taking advantage of machine flexibility.

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

Cited by 117 publications

References 48 publications

Hybrid Flow Shop Scheduling Problems Using Improved Fireworks Algorithm for Permutation

Hybrid Flow Shop Scheduling Problems Using Improved Fireworks Algorithm for Permutation

Green Scheduling of Jobs and Flexible Periods of Maintenance in a Two-Machine Flowshop to Minimize Makespan, a Measure of Service Level and Total Energy Consumption

Realizing Energy Savings in Integrated Process Planning and Scheduling

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