A Heuristic Algorithm for Flowshop Scheduling Problem

2012 ◽  
Vol 591-593 ◽  
pp. 626-630
Author(s):  
Dan Tang ◽  
Hong Ping Shu
Keyword(s):  
Heuristic Algorithm ◽  
Flow Shop ◽  
New Method ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Work Shop ◽  
And Performance ◽  
Flowshop Scheduling Problem ◽  
Better Than

Flow Shop Scheduling Problem is a class of scheduling problems with a work shop in which the flow control shall enable an appropriate sequencing for each job and for processing on a set of machines in compliance with given processing orders. In this paper, we propose a new heuristic algorithm based on the analysis and research of which problem, the new method introducing a evaluate mechanism of the relative position of any two jobs to the completion time, and the efficiency and performance has been improved .The result of simulation experiments shows that, our new heuristic algorithm has good performance, and the average quality and stability of scheduling sequences generated by new method is significantly better than other heuristic algorithm which has the same complexity.

The Research of Heuristic Algorithm for Flow Shop Scheduling Problem

2012 ◽  
Vol 605-607 ◽  
pp. 528-531
Author(s):  
Dan Tang ◽  
Hong Ping Shu
Keyword(s):  
Heuristic Algorithm ◽  
Processing Time ◽  
Flow Shop ◽  
Heuristic Method ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Average Quality ◽  
Simulation Experiments ◽  
Shop Scheduling ◽  
Better Than

For the flow shop scheduling problem which aims to minimize makespan, this paper gives a new derivation about its mathematical definition, and mining characteristics of the problem itself further. By which analysis, the new heuristic method proposed in the paper shorten the waiting time of each job as much as possible on the basis of reduce the processing time of the first machine and last job. The result of simulation experiments shows that, our new heuristic algorithm has good performance, and the average quality and stability of scheduling sequences generated by new method is significantly better than other heuristic algorithm which has the same complexity.

Alternative Heuristic Algorithm for Flow Shop Scheduling Problem

2013 ◽  
pp. 277-297
Author(s):  
Vladimír Modrák ◽  
R. Sudhakra Pandian ◽  
Pavol Semanco
Keyword(s):  
Heuristic Algorithm ◽  
Flow Shop ◽  
Idle Time ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Realistic Situation ◽  
Permutation Flow Shop

In this chapter an alternative heuristic algorithm is proposed that is assumed for a deterministic flow shop scheduling problem. The algorithm is addressed to an m-machine and n-job permutation flow shop scheduling problem for the objective of minimizing the make-span when idle time is allowed on machines. This chapter is composed in a way that the different scheduling approaches to solve flow shop scheduling problems are benchmarked. In order to compare the proposed algorithm against the benchmarked, selected heuristic techniques and genetic algorithm have been used. In realistic situation, the proposed algorithm can be used as it is without any modification and come out with acceptable results.

An Effective Immune Based Approach for the No-Wait Flow Shop Scheduling Problems with Multiple Machines

2010 ◽  
Vol 97-101 ◽  
pp. 2432-2435 ◽  
Author(s):  
Yi Chih Hsieh ◽  
Y.C. Lee ◽  
Peng Sheng You ◽  
Ta Cheng Chen
Keyword(s):  
Flow Shop ◽  
Immune Algorithm ◽  
Flow Shop Scheduling ◽  
Benchmark Problems ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Practical Applications ◽  
Shop Scheduling ◽  
No Wait ◽  
Better Than

For scheduling problems, no-wait constraint is an important requirement for many industries. As known, the no-wait scheduling problem is NP-hard and has several practical applications. This paper applies an immune algorithm to solve the multiple-machine no-wait flow shop scheduling problem with minimizing the makespan. Twenty-three benchmark problems on the OR-Library are solved by the immune algorithm. Limited numerical results show that the immune algorithm performs better than the other typical approaches in the literature for most of instances.

An Efficient Hybrid Based on HS and GA Solving Blocking Flow Shop Scheduling Problems

2012 ◽  
Vol 479-481 ◽  
pp. 1893-1896
Author(s):  
Yun Bao ◽  
Liping Zheng ◽  
Hua Jiang
Keyword(s):  
Genetic Algorithm ◽  
Computational Result ◽  
Flow Shop ◽  
Harmony Search ◽  
Hybrid Algorithms ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Better Than

This paper presents an efficient hybrid algorithms (EHA) based on harmony search (HS) algorithms and genetic algorithm (GA) for solving blocking flow shop scheduling problem. An improved GA is used to get better results. The computational result shows that EHA is not only better than GA , but also better than HS algorithm.

scholarly journals A Reinforcement Learning Method for a Hybrid Flow-Shop Scheduling Problem

Algorithms ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 222 ◽  
Author(s):  
Han ◽  
Guo ◽  
Su
Keyword(s):  
Reinforcement Learning ◽  
Flow Shop ◽  
Optimal Solution ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
First Choice ◽  
Hybrid Flow Shop Scheduling

The scheduling problems in mass production, manufacturing, assembly, synthesis, and transportation, as well as internet services, can partly be attributed to a hybrid flow-shop scheduling problem (HFSP). To solve the problem, a reinforcement learning (RL) method for HFSP is studied for the first time in this paper. HFSP is described and attributed to the Markov Decision Processes (MDP), for which the special states, actions, and reward function are designed. On this basis, the MDP framework is established. The Boltzmann exploration policy is adopted to trade-off the exploration and exploitation during choosing action in RL. Compared with the first-come-first-serve strategy that is frequently adopted when coding in most of the traditional intelligent algorithms, the rule in the RL method is first-come-first-choice, which is more conducive to achieving the global optimal solution. For validation, the RL method is utilized for scheduling in a metal processing workshop of an automobile engine factory. Then, the method is applied to the sortie scheduling of carrier aircraft in continuous dispatch. The results demonstrate that the machining and support scheduling obtained by this RL method are reasonable in result quality, real-time performance and complexity, indicating that this RL method is practical for HFSP.

scholarly journals Multi-criteria blocking flow shop scheduling problems: Formulation and performance analysis

2018 ◽  
Vol 13 (2) ◽  
pp. 136-146 ◽  
Author(s):  
G. Lebbar ◽  
I. El Abbassi ◽  
A. Jabri ◽  
A. El Barkany ◽  
M. Darcherif
Keyword(s):  
Performance Analysis ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
And Performance

scholarly journals A Hybrid Discrete Bacterial Memetic Algorithm with Simulated Annealing for Optimization of the Flow Shop Scheduling Problem

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1131
Author(s):  
Anita Agárdi ◽  
Károly Nehéz ◽  
Olivér Hornyák ◽  
László T. Kóczy
Keyword(s):  
Simulated Annealing ◽  
Production Scheduling ◽  
Simulated Annealing Algorithm ◽  
Flow Shop ◽  
Memetic Algorithm ◽  
Research Work ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Shop Scheduling

This paper deals with the flow shop scheduling problem. To find the optimal solution is an NP-hard problem. The paper reviews some algorithms from the literature and applies a benchmark dataset to evaluate their efficiency. In this research work, the discrete bacterial memetic evolutionary algorithm (DBMEA) as a global searcher was investigated. The proposed algorithm improves the local search by applying the simulated annealing algorithm (SA). This paper presents the experimental results of solving the no-idle flow shop scheduling problem. To compare the proposed algorithm with other researchers’ work, a benchmark problem set was used. The calculated makespan times were compared against the best-known solutions in the literature. The proposed hybrid algorithm has provided better results than methods using genetic algorithm variants, thus it is a major improvement for the memetic algorithm family solving production scheduling problems.

scholarly journals An Efficient Flow Shop Scheduling Problem With Makespan Objective

2021 ◽  
Vol 12 (4) ◽  
pp. 461-466
Author(s):  
S. Jayasankari, Et. al.
Keyword(s):  
Flow Shop ◽  
Manufacturing Industries ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Shop Scheduling ◽  
Elapsed Time ◽  
Research Scholar ◽  
Better Than

Scheduling ‘n’ job in ‘m’ machine is a tedious task for the Research scholar to solve this type of problem.  Flow shop scheduling has its origin in the manufacturing industries in the early 1954.  In this paper, we have developed an algorithm with the objective is to minimize the total elapsed time (makespan). Examples are illustrated to demonstrate the proposed approach in detail.  Finally, the result obtained under the proposed method is compared with the existing methods available in the literature.  It is found that our algorithm performs better than the existing algorithm and the result have been incorporated in this article.

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