Hybrid Flow Shop Scheduling with Energy Consumption in Machine Shop Using Moth Flame Optimization

Author(s):  
Mohd Fadzil Faisae Ab. Rashid ◽  
Ahmad Nasser Mohd Rose ◽  
Nik Mohd Zuki Nik Mohamed
Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Kaifeng Geng ◽  
Chunming Ye ◽  
Zhen hua Dai ◽  
Li Liu

Re-entrant hybrid flow shop scheduling problem (RHFSP) is widely used in industries. However, little attention is paid to energy consumption cost with the raise of green manufacturing concept. This paper proposes an improved multiobjective ant lion optimization (IMOALO) algorithm to solve the RHFSP with the objectives of minimizing the makespan and energy consumption cost under Time-of-Use (TOU) electricity tariffs. A right-shift operation is then used to adjust the starting time of operations by avoiding the period of high electricity price to reduce the energy consumption cost as far as possible. The experimental results show that IMOALO algorithm is superior to multiobjective ant lion optimization (MOALO) algorithm, NSGA-II, and MOPSO in terms of the convergence, dominance, and diversity of nondominated solutions. The proposed model can make enterprises avoid high price period reasonably, transfer power load, and reduce the energy consumption cost effectively. Meanwhile, parameter analysis indicates that the period of TOU electricity tariffs and energy efficiency of machines have great impact on the scheduling results.


2014 ◽  
Vol 573 ◽  
pp. 362-367
Author(s):  
Senthil Vairam ◽  
V. Selladurai

Parallel machine shop scheduling problem can be stated as finding a schedule for a general task graph to execute on a customed flow so that the schedule length can be minimized. Parallel Flow Shop Scheduling with a case study has been . In this study we present an effective memetic algorithm to solve the problem. Also evaluating the performance of two algorithms (genetic algorithm and memetic algorithm) in terms of both the quality of the solutions produced and the efficiency. These results demonstrate that the memetic algorithm produces better and quality solutions and hence it is very efficient . KEY WORDS: Hybrid Flow Shop Scheduling, Multiprocessor, Memetic algorithm.


2021 ◽  
Vol 22 (1) ◽  
pp. 18-30
Author(s):  
Ahmed Nedal Abid Al Kareem Jabari ◽  
Afif Hasan

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.


2020 ◽  
Vol 90 (9) ◽  
pp. 1315-1343 ◽  
Author(s):  
Sven Schulz ◽  
Udo Buscher ◽  
Liji Shen

Abstract Energy costs play an important role in industrial production and are closely related to environmental concerns. As sustainability aspects are coming into focus in recent years, energy-oriented objectives are increasingly being taken into account in scheduling. At the same time, requirements for punctual delivery become more and more important in times of just-in-time delivery and highly networked supply chains. In this paper, a hybrid flow shop scheduling problem with variable discrete production speed levels is considered with the aim of minimizing both energy costs and total tardiness. Although lower speeds can reduce energy consumption, they also increase processing times, which counteract the objective of punctual delivery. Two new model formulations additionally taking time-of-use energy prices into account are presented and compared. The influence of variable discrete production speed levels on energy costs, energy consumption and punctual delivery as well as the interdependencies between these objectives are analysed in a numerical case study.


Author(s):  
Jingcao Cai ◽  
Deming Lei

AbstractDistributed hybrid flow shop scheduling problem (DHFSP) has attracted some attention; however, DHFSP with uncertainty and energy-related element is seldom studied. In this paper, distributed energy-efficient hybrid flow shop scheduling problem (DEHFSP) with fuzzy processing time is considered and a cooperated shuffled frog-leaping algorithm (CSFLA) is presented to optimize fuzzy makespan, total agreement index and fuzzy total energy consumption simultaneously. Iterated greedy, variable neighborhood search and global search are designed using problem-related features; memeplex evaluation based on three quality indices is presented, an effective cooperation process between the best memeplex and the worst memeplex is developed according to evaluation results and performed by exchanging search times and search ability, and an adaptive population shuffling is adopted to improve search efficiency. Extensive experiments are conducted and the computational results validate that CSFLA has promising advantages on solving the considered DEHFSP.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 223782-223796
Author(s):  
Xixing Li ◽  
Hongtao Tang ◽  
Zhipeng Yang ◽  
Rui Wu ◽  
Yabo Luo

2012 ◽  
Vol 252 ◽  
pp. 354-359
Author(s):  
Xin Min Zhang ◽  
Meng Yue Zhang

A main-branch hybrid Flow shop scheduling problem in production manufacturing system is studied. Under the premise of JIT, targeting of smallest cost, a Flow-Shop production line scheduling model is built in cycle time of buffer. Two stages Quantum Genetic Algorithm (QGA) is proposed. By the results of numerical example, the effective and advantageous of QGA was shown.


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