K. Rameshkumar

Discrete particle swarm optimization (DPSO) algorithm for permutation flowshop scheduling to minimize makespan

In this paper a discrete particle swarm optimization (DPSO) algorithm is proposed to solve permutation flowshop scheduling problems with the objective of minimizing the makespan. A discussion on implementation details of DPSO algorithm is presented. The proposed algorithm has been applied to a set of benchmark problems and performance of the algorithm is evaluated by comparing the obtained results with the results published in the literature. Further, it is found that the proposed improvement heuristic algorithm performs better when local search is performed. The results are presented.

Application of particle swarm intelligence algorithms in supply chain network architecture optimization

In todays globalization, the success of an industry is dependent on cost effective supply chain management under various markets, logistics and production uncertainties. Uncertainties in the supply chain usually decrease profit, i.e. increase total supply chain cost. Demand uncertainty and constraints posed by the every echelon are important factors to be considered in the supply chain design operations. Optimization is no longer a luxury but has become the order of the day. This paper specifically deals with the modeling and optimization of a three echelon supply chain network using the particle swarm optimization/intelligence algorithms.

Finite element simulation of effect of residual stresses during orthogonal machining using ALE approach

In this paper, a finite element model has been developed to predict the effect of residual stress induced in the work material during multiple pass turning of AISI 4340 steel. Chip morphology and force variation during machining are also quantified using the FE model. Finite element model was developed using arbitrary Lagrangian-Eulerian formulation along with Johnson-Cook material model and Johnson-Cook damage model. The finite element model developed in this study was validated experimentally by studying the chip morphologogy and cutting force variation during the machining. Results indicate that there is good correlation existing between numerical results and experimental results.

Discrete particle swarm optimisation algorithms for minimising the completion-time variance of jobs in flowshops

In this paper, the problem of scheduling in the permutation flowshop scheduling problem is considered with the objective of minimising the completion-time variance of jobs (CTV). Two particle swarm optimisation algorithms (PSOAs) are proposed and analysed. The first PSOA is inspired from the solution construction procedures that are used in ant colony optimisation algorithms. The second algorithm is a newly developed one. The proposed algorithms are applied to a set of benchmark flowshop scheduling problems, and performances of the algorithms are evaluated by comparing the obtained results with the results published in the literature. The performance analysis demonstrates the effectiveness of the proposed algorithms in solving the permutation flowshop sequencing problem with the CTV objective.

Production and distribution scheduling of Supply Chain structure using intelligent Particle Swarm Optimisation algorithm

In todays global market, managing the entire Supply Chain (SC) becomes a key factor for the successful business and businesses have to be more adaptive to change. World class organisations now realise that non-integrated manufacturing processes, non-integrated distribution processes and poor relationship with suppliers and customers are inadequate for their success. Recently, however, there has been increasing attention placed on the performance, design and analysis of the SC as a whole. This paper specifically deals with the modelling and optimisation of a four-stage SC using the Particle Swarm Optimisation (PSO) algorithm and the problem was solved for optimal distribution of components and products made by them using PSO algorithm. And it was found that the PSO algorithm has been successfully applied to solve problems in SC network optimisation and gives quality results.

A novel particle swarm optimisation algorithm for continuous function optimisation

Particle swarm optimisation (PSO) algorithms are applied in a variety of fields. A good quality solution for a problem depends on the PSO parameters chosen for the problem under study. In this paper, a continuous particle swarm optimisation algorithm is proposed to solve the unconstrained optimisation problems. This paper proposes a novel and simple variation of the PSO algorithm by introducing dynamic updating of velocity without any parameters, such as inertia weight and constriction coefficients that are commonly used in the traditional PSO algorithms. The proposed algorithm is applied to well-known benchmark functions which are commonly used to test the performance of numeric optimisation algorithms, and the results are compared with the existing PSO algorithms. It is found that the proposed algorithm gives superior results in terms of speed of convergence and the ability of finding the solutions of excellent quality.

A novel discrete particle swarm clustering algorithm for data clustering

In this paper, a novel Discrete Particle Swarm Clustering algorithm (DPSC) for data clustering has been proposed. The particle positions and velocities are defined in a discrete form and an efficient approach is developed to move the particles for constructing new clustering solutions. DPSC algorithm has been applied to solve the data clustering problems by considering two performance metrics, such as TRace Within criteria (TRW) and Variance Ratio Criteria (VRC). The result obtained by the proposed algorithm has been compared with the published results of Combinatorial Particle Swarm Optimization (CPSO) algorithm and Genetic Algorithm (GA). The performance analysis demonstrates the effectiveness of the proposed algorithm in solving the partitional data clustering problems.

Acoustic Signature Based Weld Quality Monitoring for SMAW Process Using Data Mining Algorithms

The quality of weld depends upon welding parameters and exposed environment conditions. Improper selection of welding process parameter is one of the important reasons for the occurrence of weld defect. In this work, arc sound signals are captured during the welding of carbon steel plates. Statistical features of the sound signals are extracted during the welding process. Data mining algorithms such as Naive Bayes, Support Vector Machines and Neural Network were used to classify the weld conditions according to the features of the sound signal. Two weld conditions namely good weld and weld with defects namely lack of fusion, and burn through were considered in this study. Classification efficiencies of machine learning algorithms were compared. Neural network is found to be producing better classification efficiency comparing with other algorithms considered in this study.

A novel discrete PSO algorithm for solving job shop scheduling problem to minimize makespan

In this work, a discrete version of PSO algorithm is proposed to minimize the makespan of a job-shop. A novel schedule builder has been utilized to generate active schedules. The discrete PSO is tested using well known benchmark problems available in the literature. The solution produced by the proposed algorithms is compared with best known solution published in the literature and also compared with hybrid particle swarm algorithm and variable neighborhood search PSO algorithm. The solution construction methodology adopted in this study is found to be effective in producing good quality solutions for the various benchmark job-shop scheduling problems.

Neighborhood Search Assisted Particle Swarm Optimization (NPSO) Algorithm for Partitional Data Clustering Problems

New variant of PSO algorithm called Neighborhood search assisted Particle Swarm Optimization (NPSO) algorithm for data clustering problems has been proposed in this paper. We have proposed two neighborhood search schemes and a centroid updating scheme to improve the performance of the PSO algorithm. NPSO algorithm has been applied to solve the data clustering problems by considering three performance metrics, such as TRace Within criteria (TRW), Variance Ratio Criteria (VRC) and Marriott Criteria (MC). The results obtained by the proposed algorithm have been compared with the published results of basic PSO algorithm, Combinatorial Particle Swarm Optimization (CPSO) algorithm, Genetic Algorithm (GA) and Differential Evolution (DE) algorithm. The performance analysis demonstrates the effectiveness of the proposed algorithm in solving the partitional data clustering problems.