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%.

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.

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.

scholarly journals Pengembangan Algoritma Non Delay Pada Kasus Penjadwalan Non-Permutation Hybrid Flowshop Untuk Minimasi Mean Flowtime

2018 ◽  
Vol 19 (2) ◽  
pp. 148
Author(s):  
Siti Muhimatul Khoiroh
Keyword(s):  
Production Scheduling ◽  
Manufacturing Industry ◽  
Success Factors ◽  
Flow Shop ◽  
Flow Time ◽  
Hybrid Flow Shop ◽  
Scheduling Problems ◽  
Mean Flow ◽  
Mean Flow Time ◽  
Permutation Flow Shop

Production scheduling is one of the key success factors in the production process. Scheduling approach with Non-Permutation flow shop is a generalization of the traditional scheduling problems Permutation flow shop for the manufacturing industry to allow changing the job on different machines with the flexibility of combinations. This research tries to develop a heuristic approach that is non-delay algorithm by comparing Shortest Processing Time (SPT) and Largest Remaining Time (LRT) in the case of non-permutation flow shop to produce minimum mean flow time ratio. The result of simulation shows that the SPT algorithm gives less mean flow time value compared to LRT algorithm which means that SPT algorithm is better than LRT in case of non-permutation hybrid flow shop.

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.

The complexity of mean flow time scheduling problems with release times

2007 ◽  
Vol 10 (2) ◽  
pp. 139-146 ◽  
Author(s):  
Philippe Baptiste ◽  
Peter Brucker ◽  
Marek Chrobak ◽  
Christoph Dürr ◽  
Svetlana A. Kravchenko ◽  
...  
Keyword(s):  
Flow Time ◽  
Scheduling Problems ◽  
Mean Flow ◽  
Release Times ◽  
Mean Flow Time ◽  
Time Scheduling
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