scholarly journals Simulated annealing approach to solve dual resource constrained job shop scheduling problems: layout impact analysis on solution quality

2015 ◽  
Vol 7 (6) ◽  
pp. 609 ◽  
Author(s):  
Maurizio Faccio ◽  
Jana Ries ◽  
Nicola Saggiorno
Keyword(s):  
Simulated Annealing ◽  
Impact Analysis ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problems ◽  
Resource Constrained ◽  
Solution Quality ◽  
Shop Scheduling

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 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.

scholarly journals Cooperative Threads with Effective-Address in Simulated Annealing Algorithm to Job Shop Scheduling Problems

2019 ◽  
Vol 9 (16) ◽  
pp. 3360 ◽  
Author(s):  
Marco Antonio Cruz-Chávez ◽  
Jesús del C. Peralta-Abarca ◽  
Martín H. Cruz-Rosales
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Distributed Processing ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Parallel And Distributed Processing ◽  
Annealing Algorithm ◽  
Cooperative Algorithm

This paper presents a parallel algorithm applied to the job shop scheduling problem (JSSP). The algorithm generates a set of threads, which work in parallel. Each generated thread, executes a procedure of simulated annealing which obtains one solution for the problem. Each solution is directed towards the best solution found by the system at the present, through a procedure called effective-address. The cooperative algorithm evaluates the makespan for various benchmarks of different sizes, small, medium, and large. A statistical analysis of the results of the algorithm is presented and a comparison of performance with other (sequential, parallel, and distributed processing) algorithms that are found in the literature is presented. The obtained results show that the cooperation of threads carried out by means of effective-address procedure permits to simulated annealing to work with increased efficacy and efficiency for problems of JSSP.

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