Using A Bee Colony Algorithm For Neighborhood Search In Job Shop Scheduling Problems

2007 ◽  
Author(s):  
C. S. Chong ◽  
M. Y. H. Low ◽  
A. I. Sivakumar ◽  
K. L. Gay
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Neighborhood Search ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Bee Colony

scholarly journals A Hybrid Artificial Bee Colony Algorithm for Flexible Job Shop Scheduling Problems

2011 ◽  
Vol 6 (2) ◽  
pp. 286 ◽  
Author(s):  
Jun-qing Li ◽  
Sheng-xian Xie ◽  
Quan-ke Pan ◽  
Song Wang
Keyword(s):  
Hybrid Algorithm ◽  
Artificial Bee Colony ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Food Sources ◽  
Flexible Job Shop ◽  
Scheduling Problems ◽  
Flexible Job Shop Scheduling ◽  
Shop Scheduling ◽  
Bee Colony

<p>In this paper, we propose a hybrid Pareto-based artificial bee colony (HABC) algorithm for solving the multi-objective flexible job shop scheduling problem. In the hybrid algorithm, each food sources is represented by two vectors, i.e., the machine assignment vector and the operation scheduling vector. The artificial bee is divided into three groups, namely, employed bees, onlookers, and scouts bees. Furthermore, an external Pareto archive set is introduced to record non-dominated solutions found so far. To balance the exploration and exploitation capability of the algorithm, the scout bees in the hybrid algorithm are divided into two parts. The scout bees in one part perform randomly search in the predefined region while each scout bee in another part randomly select one non-dominated solution from the Pareto archive set. Experimental results on the well-known benchmark instances and comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm.</p>

scholarly journals A Hybrid Genetic Algorithm with a Knowledge-Based Operator for Solving the Job Shop Scheduling Problems

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Hamed Piroozfard ◽  
Kuan Yew Wong ◽  
Adnan Hassan
Keyword(s):  
Genetic Algorithm ◽  
Production Management ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Neighborhood Search ◽  
Function Evaluation ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Knowledge Based

Scheduling is considered as an important topic in production management and combinatorial optimization in which it ubiquitously exists in most of the real-world applications. The attempts of finding optimal or near optimal solutions for the job shop scheduling problems are deemed important, because they are characterized as highly complex andNP-hard problems. This paper describes the development of a hybrid genetic algorithm for solving the nonpreemptive job shop scheduling problems with the objective of minimizing makespan. In order to solve the presented problem more effectively, an operation-based representation was used to enable the construction of feasible schedules. In addition, a new knowledge-based operator was designed based on the problem’s characteristics in order to use machines’ idle times to improve the solution quality, and it was developed in the context of function evaluation. A machine based precedence preserving order-based crossover was proposed to generate the offspring. Furthermore, a simulated annealing based neighborhood search technique was used to improve the local exploitation ability of the algorithm and to increase its population diversity. In order to prove the efficiency and effectiveness of the proposed algorithm, numerous benchmarked instances were collected from the Operations Research Library. Computational results of the proposed hybrid genetic algorithm demonstrate its effectiveness.

Approach of hybrid GA for multi-objective job-shop scheduling

2016 ◽  
Vol 07 (04) ◽  
pp. 1643006 ◽  
Author(s):  
Qiaofeng Meng ◽  
Linxuan Zhang ◽  
Yushun Fan
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Neighborhood Search ◽  
Scheduling Problems ◽  
Actual System ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Weighted Sum Method ◽  
Local Space ◽  
Local Search Strategy

In recent years, scholars have made many research results on job-shop scheduling (JSP) problem, especially in single objective such as the maximum completion time. But most of the actual system scheduling problems are more than one object. Therefore, the research of multi-objective scheduling problem is very important and meaningful. In this paper, we proposed a multi-objective scheduling model which adopts weighted sum method to optimize two important indexes (makespan and total flow time). Genetic algorithm (GA) has diversified global search ability, while simulated annealing (SA) combined with tabu search (TS) have intensified capabilities in local neighborhood search. To overcome the drawback of the GA, we proposed a new hybrid GA (NewHGA) which produces initial solutions by GA firstly, and then take SA operator incorporate TS operator to search in the local space. By adding the novel local search strategy, the diversity of solutions will be improved greatly so that it can ensure the algorithm jump out of the local optimal value. We test this algorithm using the benchmark instances of different sizes taken from the OR-Library, and the results show that the algorithm is efficient than another hybrid algorithm.

Solving the job shop scheduling problem with an enhanced artificial bee colony algorithm through local search heuristic

2020 ◽  
Vol 13 ◽  
Author(s):  
Leila Asadzadeh
Keyword(s):  
Local Search ◽  
Artificial Bee Colony ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Neighborhood Search ◽  
Discrete Optimization Problem ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Bee Colony

: Job shop scheduling problem (JSSP) is an important problem in manufacturing systems This problem is one of the NP-hard combinatorial problems so the best scheduling solution is not polynomially bounded. Meta-heuristic approaches are widely applied to solve the job shop scheduling problem and find the near optimal solutions in polynomial time. In this paper, we propose an enhanced artificial bee colony (ABC) algorithm for the job shop scheduling problem. We designed more suitable local search procedures that are based on neighborhood search mechanisms to better adapt ABC for this discrete optimization problem. The experimental results showed that the proposed algorithm is effective.

Bee colony optimisation algorithm with big valley landscape exploitation for job shop scheduling problems

2010 ◽  
Vol 2 (2) ◽  
pp. 85 ◽  
Author(s):  
Li Pei Wong ◽  
Chi Yung Puan ◽  
Malcolm Yoke Hean Low ◽  
Yi Wen Wong ◽  
Chin Soon Chong
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Optimisation Algorithm ◽  
Bee Colony
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