A BAT ALGORITHM FOR REALISTIC HYBRID FLOWSHOP SCHEDULING PROBLEMS TO MINIMIZE MAKESPAN AND MEAN FLOW TIME

The scheduling problems have been discussed in the literature extensively under the assumption that the machines are permanently available without any breakdown. However, in the real manufacturing environments, the machines could be unavailable inevitably for many reasons. In this paper, the authors introduce the hybrid flowshop scheduling problem with random breakdown (RBHFS) together with a discrete group search optimizer algorithm (DGSO). In particular, two different working cases, preempt-resume case, and preempt-repeat case are considered under random breakdown. The proposed DGSO algorithm adopts the vector representation and several discrete operators, such as insert, swap, differential evolution, destruction, and construction in the producers, scroungers, and rangers phases. In addition, an orthogonal test is applied to configure the adjustable parameters in the DGSO algorithm. The computational results in both cases indicate that the proposed algorithm significantly improves the performances compared with other high performing algorithms in the literature.

Download Full-text

Minimisation of total tardiness in hybrid flowshop scheduling problems with sequence dependent setup times using a discrete firefly algorithm

International Journal of Operational Research ◽

10.1504/ijor.2018.091204 ◽

2018 ◽

Vol 32 (1) ◽

pp. 114 ◽

Cited By ~ 1

Author(s):

M.K. Marichelvam ◽

A. Azhagurajan ◽

M. Geetha

Keyword(s):

Firefly Algorithm ◽

Setup Times ◽

Scheduling Problems ◽

Flowshop Scheduling ◽

Hybrid Flowshop ◽

Hybrid Flowshop Scheduling ◽

Discrete Firefly Algorithm ◽

Sequence Dependent Setup ◽

Dependent Setup Times ◽

Sequence Dependent Setup Times

Download Full-text

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

Jurnal Teknik Industri ◽

10.22219/jtiumm.vol19.no2.148-156 ◽

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.

Download Full-text

Solving Distributed Hybrid Flowshop Scheduling Problems by a Hybrid Brain Storm Optimization Algorithm

IEEE Access ◽

10.1109/access.2019.2917273 ◽

2019 ◽

Vol 7 ◽

pp. 66879-66894 ◽

Cited By ~ 9

Author(s):

Jian-Hua Hao ◽

Jun-Qing Li ◽

Yu Du ◽

Mei-Xian Song ◽

Peng Duan ◽

...

Keyword(s):

Optimization Algorithm ◽

Scheduling Problems ◽

Flowshop Scheduling ◽

Hybrid Flowshop ◽

Hybrid Flowshop Scheduling ◽

Brain Storm Optimization

Download Full-text

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.591.184 ◽

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.

Download Full-text

Resolution of some 2-stage hybrid flowshop scheduling problems

1996 IEEE International Conference on Systems, Man and Cybernetics. Information Intelligence and Systems (Cat. No.96CH35929) ◽

10.1109/icsmc.1996.561412 ◽

2002 ◽

Cited By ~ 5

Author(s):

A. Vignier ◽

J.-C. Billaut ◽

C. Proust ◽

V. Tkindt

Keyword(s):

Scheduling Problems ◽

Flowshop Scheduling ◽

Hybrid Flowshop ◽

Hybrid Flowshop Scheduling

Download Full-text

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

Journal of Advanced Manufacturing Systems ◽

10.1142/s0219686718500269 ◽

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%.

Download Full-text