Two-level imperialist competitive algorithm for energy-efficient hybrid flow shop scheduling problem with relative importance of objectives

Li, Ming; Lei, Deming; Cai, Jingcao

doi:10.1016/j.swevo.2019.05.006

Cited by 69 publications

(30 citation statements)

References 39 publications

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“…The population size and the maximum iterations are set as the same value to compare the performance of IMOEA/DTL, MOEA/D-MR, SPEA2, and NSGA-II. The parameters of NSGA-II, SPEA2, and MOEA/D-MR are set based on some literature and the trial and error method [31][32][33][34][35]. Moreover, considering the randomness of the evolutionary algorithm, three algorithms are run ten times for each scheduling problem, and the result is the average of ten times.…”

Section: Parameters Settingmentioning

confidence: 99%

Energy Saving for Tissue Paper Mills by Energy-Efficiency Scheduling under Time-of-Use Electricity Tariffs

2021

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Environmental concerns and soaring energy prices have brought huge pressure of energy saving and emission reduction to tissue paper mills. Electricity is one of the main energy sources of tissue paper mills. The production characteristics of tissue paper mills make it easy to decrease energy cost by using time-of-use (TOU) electricity tariffs. This study investigates the bi-objective energy-efficiency scheduling of tissue paper mills under time-of-use electricity tariffs, the objectives of which are makespan and energy cost. First, considering the processing energy cost, setup energy cost, and transportation energy cost, an energy cost model of a tissue paper mill under TOU electricity tariffs is established. Second, the energy-efficiency scheduling model under TOU electricity tariffs is built based on the energy cost model. Finally, on the basis of decomposition and teaching–learning optimization, this study proposes a novel multi-objective evolutionary algorithm and further combined with the variable neighborhood search to solve the problem. The case study results demonstrate that our study of tissue paper mill energy saving is feasible, and the proposed method has better performance than the existing methods.

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Section: Parameters Settingmentioning

confidence: 99%

Energy Saving for Tissue Paper Mills by Energy-Efficiency Scheduling under Time-of-Use Electricity Tariffs

2021

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“…The authors of [44] adopted a chance-constrain approach to describe decision-makers' awareness for the total tardiness when minimizing the bi-objective of makespan and energy consumption. The authors of [45] considered the makespan, tardiness, and energy consumption and assumed that the third objective is less important than other ones. The authors of [46] proposed an adaptive multi-objective variable neighborhood search algorithm to solve the no-wait flow shop problem, and the authors of [47] designed a multi-objective grey wolf optimization algorithm to solve the flexible job shop problem.…”

Section: Literature Reviewmentioning

confidence: 99%

A Bi-Objective Approach to Minimize Makespan and Energy Consumption in Flow Shops with Peak Demand Constraint

Cui

2020

Sustainability

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With the growing concern of energy shortage and environment pollution, the energy aware operation management problem has emerged as a hot topic in industrial engineering recently. An integrated model consisting of production scheduling, preventive maintenance (PM) planning, and energy controlling is established for the flow shops with the PM constraint and peak demand constraint. The machine’s on/off and the speed level selection are considered to save the energy consumption in this problem. To minimize the makespan and the total energy consumption simultaneously, a multi-objective algorithm founded on NSGA-II is designed to solve the model effectively. The key decision variables are coded into the chromosome, while the others are obtained heuristically using the proposed decoding method when evaluating the chromosome. Numerical experiments were conducted to validate the effectiveness and efficiency by comparing the proposed algorithm and the traditional rules in manufacturing plant. The impacts of constraints on the Pareto frontier are also shown when analyzing the tradeoff between two objectives, which can be used to explicitly assess the energy consumption.

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“…Optimizing the manufacturing industry's scheduling is a critical approach to improve the indicators for energy and environmental sustainability [6]. One of the common problems in manufacturing industry scheduling is the hybrid flow shop scheduling (HFSS) [7]. HFSS is one of the problems in NP-hard problems, which is also the generalization of the flow shop scheduling problem [8] [9] [10] [11].…”

Section: Introductionmentioning

confidence: 99%

“…Xiang, et al [20] designed a model to solve three-stage HFSS, which involved three machines in stage 1, 2 machines in stage 2, and 3 machines in stage 3 to solve the HFSS problems proposed GA algorithm to find the solutions. Several other algorithms to solve HFSP by considering energy efficiency which has been proposed so far include A-novel Teaching-learning-based Optimization Algorithm [21], two-level imperialist competitive algorithm [7], Particle swarm optimization [22], Genetic Algorithm [23], Lagrangian Relaxation Algorithm [24], and Improved Genetic Algorithm [25].…”

Section: Introductionmentioning

confidence: 99%

Energy-Aware Scheduling in Hybrid Flow Shop using Firefly Algorithm

Jabari

Hasan

2021

JTI

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Nowadays, the industrial sector takes up a significant portion of the world's total energy consumption. This sector is responsible for half of the total energy consumed in the world. Therefore, efficiency in the industrial sector becomes an essential issue. One of the main factors triggering the high energy consumption in this sector is that many machines are left idle. Idle machines during the manufacturing process require electricity and other energies. This research aimed to develop a firefly algorithm that can minimize the energy consumption in the hybrid flow shop scheduling problem. This algorithm is used to determine the optimum order of the jobs. The ultimate goal is to minimize energy consumption. The experiment on the algorithm was conducted by employing iteration and population variations. The research results show that population and iteration affect the quality of the hybrid flow shop scheduling solution.

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Two-level imperialist competitive algorithm for energy-efficient hybrid flow shop scheduling problem with relative importance of objectives

Cited by 69 publications

References 39 publications

Energy Saving for Tissue Paper Mills by Energy-Efficiency Scheduling under Time-of-Use Electricity Tariffs

Energy Saving for Tissue Paper Mills by Energy-Efficiency Scheduling under Time-of-Use Electricity Tariffs

A Bi-Objective Approach to Minimize Makespan and Energy Consumption in Flow Shops with Peak Demand Constraint

Energy-Aware Scheduling in Hybrid Flow Shop using Firefly Algorithm

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