On the Effectiveness of Particle Swarm Optimization and Variable Neighborhood Descent for the Continuous Flow-Shop Scheduling Problem

2008 ◽  
pp. 61-89 ◽  
Author(s):  
Jens Czogalla ◽  
Andreas Fink
Keyword(s):  
Particle Swarm Optimization ◽  
Continuous Flow ◽  
Flow Shop ◽  
Particle Swarm ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Variable Neighborhood Descent ◽  
Swarm Optimization ◽  
Shop Scheduling

scholarly journals A particle swarm optimization approach for permutation flow shop scheduling problem

2014 ◽  
Vol 5 ◽  
pp. A20 ◽  
Author(s):  
T. Radha Ramanan ◽  
Muhammed Iqbal ◽  
K. Umarali
Keyword(s):  
Particle Swarm Optimization ◽  
Flow Shop ◽  
Particle Swarm ◽  
Flow Shop Scheduling ◽  
Variable Neighbourhood Search ◽  
Benchmark Problems ◽  
Optimization Approach ◽  
Scheduling Problem ◽  
Swarm Optimization ◽  
Shop Scheduling

Flow shop scheduling problem (FSSP) is a combinatorial optimization problem. This work, with the objective of optimizing the makespan of an FSSP uses a particle swarm optimization (PSO) approach. The problems are tested on Taillard’s benchmark problems. The results of Nawaz, Encore and Ham (NEH) heuristic are initialized to the PSO to direct the search into a quality space. Variable neighbourhood search (VNS) is employed to overcome the early convergence of the PSO and helps in global search. The results are compared with standalone PSO, traditional heuristics and the Taillard’s upper bounds. Five problem set are taken from Taillard’s benchmark problems and are solved for various problem sizes. Thus a total of 35 problems are solved. The experimental results show that the solution quality of FSSP can be improved if the search is directed in a quality space based on the proposed PSO approach (PSO-NEH-VNS).

A New Hybrid Particle Swarm Optimization for Solving Flow Shop Scheduling Problem with Fuzzy due Date

2011 ◽  
Vol 189-193 ◽  
pp. 2746-2753 ◽  
Author(s):  
Hai Bo Tang ◽  
Chun Ming Ye ◽  
Liang Fu Jiang
Keyword(s):  
Particle Swarm Optimization ◽  
Flow Shop ◽  
Particle Swarm ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Due Date ◽  
Swarm Optimization ◽  
Evolutionary Mechanism ◽  
Shop Scheduling ◽  
Evolution Algorithm

Coping with the characteristic of flow shop scheduling problem with uncertain due date, fuzzy arithmetic on fuzzy numbers is applied to describe the problem, and then a new hybrid algorithm model which integrate particle swarm optimization into the evolutionary mechanism of the knowledge evolution algorithm is presented to solve the problem. By the evolutionary mechanism of knowledge evolution algorithm, we can exploit the global search ability. By the operating characteristic of PSO, we can enhance the local search ability. The algorithm is tested with MATLAB simulation. The result, compared with Genetic algorithm and modified particle swarm optimization, shows the feasibility and effectiveness of the proposed algorithm.

scholarly journals HYBRID FUZZY LOGIC-BASED PARTICLE SWARM OPTIMIZATION FOR FLOW SHOP SCHEDULING PROBLEM

2011 ◽  
Vol 10 (03) ◽  
pp. 335-356 ◽  
Author(s):  
SAI HO LING ◽  
FRANK JIANG ◽  
HUNG T. NGUYEN ◽  
KIT YAN CHAN
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Local Search ◽  
Flow Shop ◽  
Particle Swarm ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Swarm Optimization ◽  
Shop Scheduling ◽  
Inertia Weight

This paper, proposes a hybrid fuzzy logic-based particle swarm optimization (PSO) with cross-mutated operation method for the minimization of makespan in permutation flow shop scheduling problem. This problem is a typical non-deterministic polynomial-time (NP) hard combinatorial optimization problem. In the proposed hybrid PSO, fuzzy inference system is applied to determine the inertia weight of PSO and the control parameter of the proposed cross-mutated operation by using human knowledge. By introducing the fuzzy system, the inertia weight becomes adaptive. The cross-mutated operation effectively forces the solution to escape the local optimum. To make PSO suitable for solving flow shop scheduling problem, a sequence-order system based on the roulette wheel mechanism is proposed to convert the continuous position values of particles to job permutations. Meanwhile, a new local search technique namely swap-based local search for scheduling problem is designed and incorporated into the hybrid PSO. Finally, a suite of flow shop benchmark functions are employed to evaluate the performance of the proposed PSO for flow shop scheduling problems. Experimental results show empirically that the proposed method outperforms the existing hybrid PSO methods significantly.

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