Multi-objective genetic algorithm for energy-efficient hybrid flow shop scheduling with lot streaming

2018 ◽  
Vol 290 (1-2) ◽  
pp. 813-836 ◽  
Author(s):  
Tzu-Li Chen ◽  
Chen-Yang Cheng ◽  
Yi-Han Chou
Keyword(s):  
Genetic Algorithm ◽  
Energy Efficient ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Lot Streaming ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Hybrid Flow Shop Scheduling

A fuzzy logic based methodology for multi-objective hybrid flow shop scheduling with multi-processor tasks problems and solving with an efficient genetic algorithm

2021 ◽  
pp. 1-13
Author(s):  
Orhan Engin ◽  
Mustafa Kerim Yılmaz
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Processing Time ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Due Dates ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Hybrid Flow Shop Scheduling

In the conventional scheduling problem, the parameters such as the processing time for each job and due dates are usually assumed to be known exactly, but in many real-world applications, these parameters may very dynamically due to human factors or operating faults. During the last decade, several works on scheduling problems have used a fuzzy approach including either uncertain or imprecise data. A fuzzy logic based tool for multi-objective Hybrid Flow-shop Scheduling with Multi-processor Tasks (HFSMT) problem is presented in this paper. In this study, HFSMT problems with a fuzzy processing time and a fuzzy due date are formulated, taking Oğuz and Ercan’s benchmark problems in the literature into account. Fuzzy HFSMT problems are formulated by three-objectives: the first is to maximize the minimum agreement index and the second is to maximize the average agreement index, and the third is to minimize the maximum fuzzy completion time. An efficient genetic algorithm(GA) is proposed to solve the formulated fuzzy HFSMT problems. The feasibility and effectiveness of the proposed method are demonstrated by comparing it with the simulated annealing (SA) algorithm in the literature.

scholarly journals A cooperated shuffled frog-leaping algorithm for distributed energy-efficient hybrid flow shop scheduling with fuzzy processing time

2021 ◽  
Author(s):  
Jingcao Cai ◽  
Deming Lei
Keyword(s):  
Energy Efficient ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Distributed Energy ◽  
Shuffled Frog Leaping Algorithm ◽  
Shop Scheduling ◽  
Fuzzy Processing Time ◽  
Hybrid Flow Shop Scheduling ◽  
Shuffled Frog Leaping

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.

An energy-efficient hybrid flow shop scheduling problem in steelmaking plants

2021 ◽  
Vol 162 ◽  
pp. 107683
Author(s):  
Xiaoyuan Lian ◽  
Zhong Zheng ◽  
Cheng Wang ◽  
Xiaoqiang Gao
Keyword(s):  
Energy Efficient ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Scheduling Problem ◽  
Shop Scheduling ◽  
Hybrid Flow Shop Scheduling

Optimization of Two-Stage Hybrid Flow Shop Scheduling Problems Using Genetic Algorithm

2015 ◽  
Vol 766-767 ◽  
pp. 962-967
Author(s):  
M. Saravanan ◽  
S. Sridhar ◽  
N. Harikannan
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing Algorithm ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Computational Time ◽  
Hybrid Flow Shop ◽  
Scheduling Problems ◽  
Two Stage ◽  
Shop Scheduling ◽  
Hybrid Flow Shop Scheduling

The two-stage Hybrid flow shop (HFS) scheduling is characterized n jobs m machines with two-stages in series. The essential complexities of the problem need to solve the hybrid flow shop scheduling using meta-heuristics. The paper addresses two-stage hybrid flow shop scheduling problems to minimize the makespan time with the batch size of 100 using Genetic Algorithm (GA) and Simulated Annealing algorithm (SA). The computational results observed that the GA algorithm is finding out good quality solutions than SA with lesser computational time.

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