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.

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.

An Improved Simulated Annealing Algorithm for Real-Time Dynamic Job-Shop Scheduling

2011 ◽  
Vol 186 ◽  
pp. 636-639 ◽  
Author(s):  
Yan Cao ◽  
Jiang Du
Keyword(s):  
Simulated Annealing ◽  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Combinatorial Problem ◽  
Manufacturing Execution System ◽  
Scheduling Problems ◽  
Time Dynamic ◽  
Shop Scheduling

Job-shop scheduling is one of the core research aspects of Manufacturing Execution System (MES). It is significant for improving the utilization of enterprise resources, enhancing product quality, shortening delivery periods, reducing product cost, and raising enterprise competitive power in market economy. In order to solve this problem, Simulated Annealing (SA) algorithm is improved to solve large-scale combinatorial problem of job-shop scheduling. To make the SA algorithm more effective to solve job-shop scheduling problems, a solution encoding mode, scheduling scheme generation, initial temperature selection, temperature updating function, Markov chain length, end rule, and so on of the improved SA algorithm are discussed that affect the computation speed and convergence of the SA algorithm. Finally, the improved SA algorithm is validated by a job–shop scheduling problem of 10 workpieces and 10 machines.

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.

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