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 A Combination of Palmer Algorithm and Gupta Algorithm for Scheduling Problem in Apparel Industry

2021 ◽  
Vol 11 (1) ◽  
pp. 1-12
Author(s):  
Cecilia E. Nugraheni ◽  
Luciana Abednego ◽  
Maria Widyarini
Keyword(s):  
Genetic Programming ◽  
Computer Program ◽  
Production Scheduling ◽  
Textile Industry ◽  
Flow Shop ◽  
Apparel Industry ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Shop Scheduling

The apparel industry is a class of textile industry. Generally, the production scheduling problem in the apparel industry belongs to Flow Shop Scheduling Problems (FSSP). There are many algorithms/techniques/heuristics for solving FSSP. Two of them are the Palmer Algorithm and the Gupta Algorithm. Hyper-heuristic is a class of heuristics that enables to combine of some heuristics to produce a new heuristic. GPHH is a hyper-heuristic that is based on genetic programming that is proposed to solve FSSP [1]. This paper presents the development of a computer program that implements the GPHH. Some experiments have been conducted for measuring the performance of GPHH. From the experimental results, GPHH has shown a better performance than the Palmer Algorithm and Gupta Algorithm.

scholarly journals Analysis of flow shop problems and their algorithms

2009 ◽  
Vol 50 ◽  
Author(s):  
Lina Rajeckaitė ◽  
Narimantas Listopadskis
Keyword(s):  
Simulated Annealing ◽  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Flow Shop ◽  
Exact Algorithm ◽  
Combinatorial Optimization Problem ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Shop Scheduling

The combinatorial optimization problem considered in this paper is flow shop scheduling problem arising in logistics, management, business, manufacture and etc. A set of machines and a set of jobs are given. Each job consists of a set of operations. Machines are working with unavailability intervals. The task is to minimize makespan, i.e. the overall length of the schedule. There is overview of combinatorial optimization, scheduling problems and methods used to solve them. There is also presented and realized one exact algorithm – Branch and Bound, and two meta-heuristics: Simulated Annealing and Tabu Search. Analysis of these three algorithms is made.

A SIMULATED ANNEALING BASED BEAM SEARCH ALGORITHM FOR THE FLOW-SHOP SCHEDULING PROBLEM

2008 ◽  
Vol 22 (01) ◽  
pp. 65-75 ◽  
Author(s):  
FENG JIN ◽  
SHI-JI SONG ◽  
CHENG WU
Keyword(s):  
Simulated Annealing ◽  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Flow Shop ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Beam Search ◽  
Shop Scheduling

Beam search algorithm, as an adaptation of branch and bound method, is regarded as one of the effective approaches in solving combinational optimization problems. In this paper, a new beam search algorithm for the large-scale permutation flow shop scheduling problem (FSP) is proposed. A new branching scheme is addressed and compared with the traditional branching scheme. With the new branching scheme, the number of partial schedules in the search tree can be greatly reduced. Based on a simple simulated annealing algorithm, partial schedules are globally evaluated. Numerical experiments show that good solutions of large-scale FSPs could be found with the proposed algorithm in a short time.

Memetic Algorithm Approach to Two-Stage Hybrid Flow Shop Scheduling Problem with Identical Parallel Machines

2013 ◽  
Vol 651 ◽  
pp. 548-552
Author(s):  
Parinya Kaweegitbundit
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Parallel Machines ◽  
Flow Shop ◽  
Memetic Algorithm ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Scheduling Problems ◽  
Two Stage ◽  
Hybrid Flow Shop Scheduling

This paper considers two stage hybrid flow shop (HFS) with identical parallel machine. The objectives is to determine makespan have been minimized. This paper presented memetic algorithm procedure to solve two stage HFS problems. To evaluated performance of propose method, the results have been compared with two meta-heuristic, genetic algorithm, simulated annealing. The experimental results show that propose method is more effective and efficient than genetic algorithm and simulated annealing to solve two stage HFS scheduling problems.

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