scholarly journals On Neighborhood Structures and Repair Techniques for Blocking Job Shop Scheduling Problems

Algorithms ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 242 ◽  
Author(s):  
Julia Lange ◽  
Frank Werner
Keyword(s):  
Simulated Annealing Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Heuristic Method ◽  
General Purpose ◽  
Future Research ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Feasible Solutions ◽  
The One

The job shop scheduling problem with blocking constraints and total tardiness minimization represents a challenging combinatorial optimization problem of high relevance in production planning and logistics. Since general-purpose solution approaches struggle with finding even feasible solutions, a permutation-based heuristic method is proposed here, and the applicability of basic scheduling-tailored mechanisms is discussed. The problem is tackled by a local search framework, which relies on interchange- and shift-based operators. Redundancy and feasibility issues require advanced transformation and repairing schemes. An analysis of the embedded neighborhoods shows beneficial modes of implementation on the one hand and structural difficulties caused by the blocking constraints on the other hand. The applied simulated annealing algorithm generates good solutions for a wide set of benchmark instances. The computational results especially highlight the capability of the permutation-based method in constructing feasible schedules of valuable quality for instances of critical size and support future research on hybrid solution techniques.

scholarly journals Recent Research Trends in Genetic Algorithm Based Flexible Job Shop Scheduling Problems

2018 ◽  
Vol 2018 ◽  
pp. 1-32 ◽  
Author(s):  
Muhammad Kamal Amjad ◽  
Shahid Ikramullah Butt ◽  
Rubeena Kousar ◽  
Riaz Ahmad ◽  
Mujtaba Hassan Agha ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Future Research ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

Flexible Job Shop Scheduling Problem (FJSSP) is an extension of the classical Job Shop Scheduling Problem (JSSP). The FJSSP is known to be NP-hard problem with regard to optimization and it is very difficult to find reasonably accurate solutions of the problem instances in a rational time. Extensive research has been carried out in this area especially over the span of the last 20 years in which the hybrid approaches involving Genetic Algorithm (GA) have gained the most popularity. Keeping in view this aspect, this article presents a comprehensive literature review of the FJSSPs solved using the GA. The survey is further extended by the inclusion of the hybrid GA (hGA) techniques used in the solution of the problem. This review will give readers an insight into use of certain parameters in their future research along with future research directions.

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.

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 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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