scholarly journals Improved Hybrid Heuristic Algorithm Inspired by Tissue-Like Membrane System to Solve Job Shop Scheduling Problem

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 219
Author(s):  
Xiang Tian ◽  
Xiyu Liu
Keyword(s):  
Local Search ◽  
Heuristic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Membrane System ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Hybrid Heuristic ◽  
Shop Scheduling ◽  
Hybrid Heuristic Algorithm

In real industrial engineering, job shop scheduling problem (JSSP) is considered to be one of the most difficult and tricky non-deterministic polynomial-time (NP)-hard problems. This study proposes a new hybrid heuristic algorithm for solving JSSP inspired by the tissue-like membrane system. The framework of the proposed algorithm incorporates improved genetic algorithms (GA), modified rumor particle swarm optimization (PSO), and fine-grained local search methods (LSM). To effectively alleviate the premature convergence of GA, the improved GA uses adaptive crossover and mutation probabilities. Taking into account the improvement of the diversity of the population, the rumor PSO is discretized to interactively optimize the population. In addition, a local search operator incorporating critical path recognition is designed to enhance the local search ability of the population. Experiment with 24 benchmark instances show that the proposed algorithm outperforms other latest comparative algorithms, and hybrid optimization strategies that complement each other in performance can better break through the original limitations of the single meta-heuristic method.

scholarly journals A Modified Genetic Algorithm with Local Search Strategies and Multi-Crossover Operator for Job Shop Scheduling Problem

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5440 ◽  
Author(s):  
Monique Simplicio Viana ◽  
Orides Morandin Junior ◽  
Rodrigo Colnago Contreras
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Crossover Operator ◽  
Local Optima ◽  
Shop Scheduling ◽  
Solution Methods

It is not uncommon for today’s problems to fall within the scope of the well-known class of NP-Hard problems. These problems generally do not have an analytical solution, and it is necessary to use meta-heuristics to solve them. The Job Shop Scheduling Problem (JSSP) is one of these problems, and for its solution, techniques based on Genetic Algorithm (GA) form the most common approach used in the literature. However, GAs are easily compromised by premature convergence and can be trapped in a local optima. To address these issues, researchers have been developing new methodologies based on local search schemes and improvements to standard mutation and crossover operators. In this work, we propose a new GA within this line of research. In detail, we generalize the concept of a massive local search operator; we improved the use of a local search strategy in the traditional mutation operator; and we developed a new multi-crossover operator. In this way, all operators of the proposed algorithm have local search functionality beyond their original inspirations and characteristics. Our method is evaluated in three different case studies, comprising 58 instances of literature, which prove the effectiveness of our approach compared to traditional JSSP solution methods.

An Efficient Memetic Algorithm for Dynamic Flexible Job Shop Scheduling with Random Job Arrivals

2013 ◽  
Vol 5 (1) ◽  
pp. 63-77 ◽  
Author(s):  
Liping Zhang ◽  
Xinyu Li ◽  
Long Wen ◽  
Guohui Zhang
Keyword(s):  
Local Search ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Memetic Algorithm ◽  
Dynamic Environment ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

Much of the research on flexible job shop scheduling problem has ignored dynamic events in dynamic environment where there are complex constraints and a variety of unexpected disruptions. This paper proposes an efficient memetic algorithm to solve the flexible job shop scheduling problem with random job arrivals. Firstly, a periodic policy is presented to update the problem condition and generate the rescheduling point. Secondly, the efficient memetic algorithm with a new local search procedure is proposed to optimize the problem at each rescheduling point. Five kinds of neighborhood structures are presented in the local search. Moreover, the performance measures investigated respectively are: minimization of the makespan and minimization of the mean tardiness. Finally, several experiments have been designed to test and evaluated the performance of the memetic algorithm. The experimental results show that the proposed algorithm is efficient to solve the flexible job shop scheduling problem in dynamic environment.

Solving the Flexible Job Shop Scheduling Problem Based on Memetic Algorithm

2012 ◽  
Vol 544 ◽  
pp. 1-5 ◽  
Author(s):  
Guo Hui Zhang
Keyword(s):  
Local Search ◽  
Search Strategy ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Memetic Algorithm ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

Flexible job shop scheduling problem (FJSP) is a well known NP-hard combinatorial optimization problem due to its very large search space and many constraint between jobs and machines. Evolutionary algorithms are the most widely used techniques in solving FJSP. Memetic algorithm is a hybrid evolutionary algorithm that combines the local search strategy and global search strategy. In this paper, an effective memetic algorithm is proposed to solve the FJSP. In the proposed algorithm, variable neighborhood search is adopted as local search algorithm. The neighborhood functions is generated by exchanging and inserting the key operations which belong to the critical path. The optimization objective is to minimize makespan. The experimental results obtained from proposed algorithm show that the proposed algorithm is very efficient and effective for all tested problems.

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