Optimization of Multi Objective Job Shop Scheduling Problems Using Firefly Algorithm

2014 ◽  
Vol 591 ◽  
pp. 157-162 ◽  
Author(s):  
K.C. Udaiyakumar ◽  
M. Chandrasekaran
Keyword(s):  
Firefly Algorithm ◽  
Job Shop ◽  
Heuristic Algorithms ◽  
Job Shop Scheduling ◽  
Optimization Techniques ◽  
Computational Time ◽  
Scheduling Problems ◽  
Mean Flow ◽  
Practical Applications ◽  
Mean Flow Time

Scheduling is the allocation of resources over time to carry out a collection of tasks assigned in any field of engineering and non engineering. Majority of JSSP are categorized into non deterministic (NP) hard problem because of its complexity. Scheduling are generally solved by using heuristics to obtain optimal or near optimal solutions because problems found in practical applications cannot be solved to optimality using available resources in many cases. Many researchers attempted to solve the problem by applying various optimization techniques. While using traditional methods they observed huge difficulty in solving high complex problems and meta-heuristic algorithms were proved most efficient algorithms to solve various JSSP so far. The objective of this paper i) to make use of a newly developed meta heuristic called Firefly algorithm (FA) because of inspiration on Firefly and its characteristic. ii) To find the combined objective function by determining optimal make span, mean flow time and tardiness of different size problems (using Lawrence 1-40 problems) as a bench marking dataset and to find the actual computational time. Iii) to prove that the proposed FFA algorithm is a good problem solving technique for JSSP with multi criteria.

A New GT Heuristic for Solving Multi Objective Job Shop Scheduling Problems

2014 ◽  
Vol 591 ◽  
pp. 184-188
Author(s):  
D. Lakshmipathy ◽  
M. Chandrasekaran ◽  
T. Balamurugan ◽  
P. Sriramya
Keyword(s):  
Production Scheduling ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Heuristic Method ◽  
Flow Time ◽  
Scheduling Problems ◽  
Mean Flow ◽  
Practical Applications ◽  
Shop Scheduling ◽  
Mean Flow Time

The n-job, m-machine Job shop scheduling (JSP) problem is one of the general production scheduling problems in manufacturing system. Scheduling problems vary widely according to specific production tasks but most are NP-hard problems. Scheduling problems are usually solved using heuristics to get optimal or near optimal solutions because problems found in practical applications cannot be solved to optimality using reasonable resources in many cases. In this paper, optimization of three practical performance measures mean job flow time, mean job tardiness and makespan are considered. New Game theory based heuristic method (GT) is used for finding optimal makespan, mean flow time, mean tardiness values of different size problems. The results show that the GT Heuristic is an efficient and effective method that gives better results than Genetic Algorithm (GA). The proposed GT Heuristic is a good problem-solving technique for job shop scheduling problem with multi criteria.

scholarly journals A Hybrid Firefly Algorithm Approach for Job Shop Scheduling Problem

2021 ◽  
Vol 9 (12) ◽  
pp. 1436-1444
Author(s):  
Gayathri Devi K
Keyword(s):  
Simulated Annealing ◽  
Firefly Algorithm ◽  
Job Shop ◽  
Heuristic Algorithms ◽  
Job Shop Scheduling ◽  
Computational Time ◽  
Greedy Heuristics ◽  
Hybrid Technique ◽  
Scheduling Problems ◽  
Shop Scheduling

Abstract: Job shop scheduling has always been one of the most sought out research problems in Combinatorial optimization. Job Shop Scheduling problems (JSSP) are categorized under NP hard problems. In recent years the meta heuristic algorithms have been proved effective to solve hardcore NP problem. Firefly Algorithm is one of such meta heuristic techniques which is nature inspired from firefly characteristic. Its potential can be enhanced further by hybridizing it with other known evolutionary algorithms and thereby getting improved results in less computational time. In this paper we have proposed a new hybrid technique christened as HyFA, by hybridizing Firefly algorithm(FA) with simulated annealing (SA) and Greedy heuristics approach (GHA). The hybrid technique has the advantages of all three algorithms and are combined in such a way that a quicker and better optimal solution is obtained. Our proposed HyFA is coded in Matlab with an objective to minimize the makespan (Cm). The novel hybrid technique is then used to evaluate 1-25 Lawrence problems taken from literature. The results show the proposed technique is more effective not only in getting optimal results but has significantly reduced computational time. Keywords: Scheduling, Optimisation, Job shop scheduling, Meta-heuristics, Firefly, Simulated Annealing, Greedy heuristics Approach.

A Genetic Algorithm for Hybrid Job-Shop Scheduling Problems with Minimizing the Makespan or Mean Flow Time

2018 ◽  
Vol 17 (04) ◽  
pp. 461-486
Author(s):  
Omid Gholami ◽  
Yuri N. Sotskov ◽  
Frank Werner
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Heuristic Algorithms ◽  
Flow Time ◽  
Scheduling Problems ◽  
Mean Flow ◽  
Average Percentage ◽  
Type Formula ◽  
Mean Flow Time ◽  
Specific Subset

We address a generalization of the classical job-shop problem which is called a hybrid job-shop problem. The criteria under consideration are the minimization of the makespan and mean flow time. In the hybrid job-shop, machines of type [Formula: see text] are available for processing the specific subset [Formula: see text] of the given operations. Each set [Formula: see text] may be partitioned into subsets for their processing on the machines of type [Formula: see text]. Solving the hybrid job-shop problem implies the solution of two subproblems: an assignment of all operations from the set [Formula: see text] to the machines of type [Formula: see text] and finding optimal sequences of the operations for their processing on each machine. In this paper, a genetic algorithm is developed to solve these two subproblems simultaneously. For solving the subproblems, a special chromosome is used in the genetic algorithm based on a mixed graph model. We compare our genetic algorithms with a branch-and-bound algorithm and three other recent heuristic algorithms from the literature. Computational results for benchmark instances with 10 jobs and up to 50 machines show that the proposed genetic algorithm is rather efficient for both criteria. Compared with the other heuristics, the new algorithm gives most often an optimal solution and the average percentage deviation from the optimal function value is about 4%.

Using the Memetic Algorithm for Multi Objective Job Shop Scheduling Problems

2012 ◽  
Vol 544 ◽  
pp. 245-250 ◽  
Author(s):  
Guo Hui Zhang
Keyword(s):  
Search Strategy ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Memetic Algorithm ◽  
Optimization Problems ◽  
Search Space ◽  
Scheduling Problems ◽  
Mean Flow ◽  
Shop Scheduling ◽  
Multi Objective

The multi objective job shop scheduling problem is well known as one of the most complex optimization problems due to its very large search space and many constraint between machines and jobs. In this paper, an evolutionary approach of the memetic algorithm is used to solve the multi objective job shop scheduling problems. Memetic algorithm is a hybrid evolutionary algorithm that combines the global search strategy and local search strategy. The objectives of minimizing makespan and mean flow time are considered while satisfying a number of hard constraints. The computational results demonstrate the proposed MA is significantly superior to the other reported approaches in the literature.

Improved Adaptive Genetic Algorithms for Job Shop Scheduling Problems

2010 ◽  
Vol 97-101 ◽  
pp. 2473-2476 ◽  
Author(s):  
Mei Hong Liu ◽  
Xiong Feng Peng
Keyword(s):  
Genetic Algorithms ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Mathematical Function ◽  
Scheduling Problems ◽  
Practical Applications ◽  
Shop Scheduling ◽  
Adaptive Genetic Algorithms ◽  
Mutation Probability ◽  
Crossover Probability

In this paper, the adaptability of the genetic algorithm (GA) is considered. Two improved adaptive genetic algorithms (AGA) which are called Ch-AGA and Th-AGA for short are proposed based on the previous AGA. The crossover probability and the mutation probability of the Ch-AGA and the Th-AGA are non-linear changed between some a certain region, and adopted the mathematical function of chx and thx respectively. The two improved adaptive genetic algorithms are used to solve the classical job shop scheduling problems and the results indicate that the algorithms are more effective and more efficient than previous AGA, and should be used in practical applications.

scholarly journals Firefly algorithm applied to the job-shop scheduling problem

2021 ◽  
Author(s):  
Piotr Świtalski ◽  
Arkadiusz Bolesta
Keyword(s):  
Firefly Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimal Parameter ◽  
Optimal Solution ◽  
Experimental Program ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

The job shop scheduling problem (JSSP) is one of the most researched scheduling problems. This problem belongs to the NP-hard class. An optimal solution for this category of problems is rarely possible. We try to find suboptimal solutions using heuristics or metaheuristics. The firefly algorithm is a great example of a metaheuristic. In this paper, this algorithm is used to solve JSSP. We used some benchmarking JSSP datasets for experiments. The experimental program was implemented in the aitoa library. We investigated the optimal parameter settings of this algorithm in terms of JSSP. Analysis of the experimental results shows that the algorithm is useful to solve scheduling problems.

scholarly journals AIS-SFHM APPROACH FOR OPTIMIZATION OF MULTI OBJECTIVE JOB SHOP PROBLEMS

2017 ◽  
Vol 13 (7) ◽  
pp. 6363-6368
Author(s):  
Chandrasekaran Manoharan
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Hybrid Approach ◽  
Flow Time ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Job Shop Scheduling Problem ◽  
Mean Flow ◽  
Shop Scheduling ◽  
Multi Objective

The n-job, m-machine Job shop scheduling (JSP) problem is one of the general production scheduling problems. The JSP problem is a scheduling problem, where a set of ‘n’ jobs must be processed or assembled on a set of ‘m’ dedicated machines. Each job consists of a specific set of operations, which have to be processed according to a given technical precedence order. Job shop scheduling problem is a NP-hard combinatorial optimization problem.  In this paper, optimization of three practical performance measures mean job flow time, mean job tardiness and makespan are considered. The hybrid approach of Sheep Flocks Heredity Model Algorithm (SFHM) is used for finding optimal makespan, mean flow time, mean tardiness. The hybrid SFHM approach is tested with multi objective job shop scheduling problems. Initial sequences are generated with Artificial Immune System (AIS) algorithm and results are refined using SFHM algorithm. The results show that the hybrid SFHM algorithm is an efficient and effective algorithm that gives better results than SFHM Algorithm, Genetic Algorithm (GA). The proposed hybrid SFHM algorithm is a good problem-solving technique for job shop scheduling problem with multi criteria.

A Hybrid Genetic Algorithm for Solving Job Shop Scheduling Problems

2013 ◽  
Vol 845 ◽  
pp. 559-563
Author(s):  
Hamed Piroozfard ◽  
Adnan Hassan ◽  
Ali Mokhtari Moghadam ◽  
Ali Derakhshan Asl
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Computational Time ◽  
Insertion Mutation ◽  
Scheduling Problems ◽  
Exact Methods ◽  
Shop Scheduling ◽  
Mutation Operators

Job shop scheduling problems are immensely complicated problems in machine scheduling area, and they are classified as NP-hard problems. Finding optimal solutions for job shop scheduling problems with exact methods incur high cost, therefore, looking for approximate solutions with meta-heuristics are favored instead. In this paper, a hybrid framework which is based on a combination of genetic algorithm and simulated annealing is proposed in order to minimize maximum completion time i.e. makespan. In the proposed algorithm, precedence preserving order-based crossover is applied which is able to generate feasible offspring. Two types of mutation operators namely swapping and insertion mutation are used in order to maintain diversity of population and to perform intensive search. Furthermore, a new approach is applied for arranging operations on machines, which improved solution quality and decreased computational time. The proposed hybrid genetic algorithm is tested with a set of benchmarking problems, and simulation results revealed efficiency of the proposed hybrid genetic algorithm compared to conventional genetic based algorithm.

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