A hybrid genetic algorithm for the batch sequencing problem on identical parallel machines

2002 ◽  
Vol 13 (3) ◽  
pp. 243-252 ◽  
Author(s):  
Do Thanh Luu ◽  
Erik L. J. Bohez ◽  
Anulark Techanitisawad
Keyword(s):  
Genetic Algorithm ◽  
Parallel Machines ◽  
Hybrid Genetic Algorithm ◽  
Identical Parallel Machines ◽  
Sequencing Problem

A Hybrid Genetic Algorithm for Hybrid Flow Shop Scheduling with Load Balancing

2008 ◽  
Vol 392-394 ◽  
pp. 250-255
Author(s):  
Yong Zhan ◽  
Chang Hua Qiu ◽  
Kai Xue
Keyword(s):  
Genetic Algorithm ◽  
Load Balancing ◽  
Parallel Machines ◽  
Flow Shop ◽  
Hybrid Genetic Algorithm ◽  
Flow Shop Scheduling ◽  
Neighborhood Search ◽  
Hybrid Flow Shop ◽  
Initial Population ◽  
Hybrid Flow Shop Scheduling

This paper considers the practical manufacturing environment of the hybrid flow shop (HFS) with non-identical machines in parallel. In order to significantly enhance the performance level of manufacturing, maintaining load balancing among parallel machines is very important. The aim of this paper is to minimize makespan with load balancing in a non-identical parallel machine environment by using hybrid genetic algorithm (HGA). In the HGA, the neighborhood search-based method is used together with genetic algorithm as local optimization method to balance the exploration and exploitation abilities. The representation of chromosome used in this paper is composed of two layers: allocation layer and sequencing layer, which can be encode and decoded easily. In generating initial population, a special constraint of load balancing between parallel machines is used to reduce the number of individuals. And particular crossover operation is used, which generates multiple offspring at a time, so that the efficiency of the algorithm can be well improved. At last, the proposed algorithm is tested on a benchmark, and numerical example shows good result.

scholarly journals A Hybrid Genetic Algorithm to Minimize Total Tardiness for Unrelated Parallel Machine Scheduling with Precedence Constraints

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Chunfeng Liu
Keyword(s):  
Genetic Algorithm ◽  
Parallel Machines ◽  
Hybrid Genetic Algorithm ◽  
Parallel Machine Scheduling ◽  
Precedence Constraints ◽  
Total Tardiness ◽  
Priority Rule ◽  
Deterministic Scheduling ◽  
Unrelated Parallel Machine Scheduling ◽  
Conventional Genetic Algorithm

The paper presents a novel hybrid genetic algorithm (HGA) for a deterministic scheduling problem where multiple jobs with arbitrary precedence constraints are processed on multiple unrelated parallel machines. The objective is to minimize total tardiness, since delays of the jobs may lead to punishment cost or cancellation of orders by the clients in many situations. A priority rule-based heuristic algorithm, which schedules a prior job on a prior machine according to the priority rule at each iteration, is suggested and embedded to the HGA for initial feasible schedules that can be improved in further stages. Computational experiments are conducted to show that the proposed HGA performs well with respect to accuracy and efficiency of solution for small-sized problems and gets better results than the conventional genetic algorithm within the same runtime for large-sized problems.

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