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The objective of this paper is to develop intelligent search heuristics to solve n-jobs, m-machines lot streaming problem in a flow shop with equal size sub-lots where the objective is to minimise makespan and total flow time independently. Improved sheep flock heredity algorithm (ISFHA) and artificial bee colony (ABC) algorithms are applied to the problem above mentioned. The performance of these algorithms is evaluated against the algorithms reported in the literature. The computational analysis shows the better performance of ISFHA and ABC algorithms. [ABSTRACT FROM AUTHOR]

In this paper, two evolutionary heuristics are proposed to determine the cutting parameters in multipass turning operations. Optimal selection of cutting speed, feed rate, and depth of cut is important in machining operations due to significant influence of these parameters on machining quality and machining economics. This paper is about optimising these three parameters using heuristic methods. The first heuristic implemented in the paper is a simple Particle Swarm Optimisation (PSO) algorithm, and the second heuristic implemented is a hybrid of Genetic Algorithm and Artificial Immune System (GA-AIS). A multipass turning operation with rough machining and then finish machining is considered in this paper. The objective function considered is minimisation of unit production cost that optimises the machining parameters. The performance of the PSO and GA-AIS are evaluated by comparing with the heuristics reported in the literature using a problem set used by other researchers for this problem. The results are presented in tables and figures and indicates the better performance of GA-AIS over PSO and other heuristics reported in the literature. [ABSTRACT FROM PUBLISHER]

Quality of an assembly is mainly based on the quality of mating parts. Due to random variation in sources such as materials, machines, operators and measurements, even those mating parts manufactured by the same process vary in their dimensions. When mating parts are assembled linearly, the resulting variation will be the sum of the mating part tolerances. Many assemblies are not able to meet the assembly specification in the available assembly methods. This will decrease the manufacturing system efficiency. Batch selective assembly is helpful to keep the assembly requirement and also to increase the manufacturing system efficiency. In traditional selective assembly, the mating part population is partitioned to form selective groups, and the parts of corresponding selective groups are assembled interchangeably. After the invention of advanced dimension measuring devices and the computer, today batch selective assembly plays a vital role in the manufacturing system. In batch selective assembly, all dimensions of a batch of mating parts are measured and stored in a computer. Instead of forming selective groups, each and every part is assigned to its best matching part. In this work, a particle swarm optimisation based algorithm is proposed by applying the batch selective assembly methodology to a multi-characteristic assembly environment, to maximise the assembly efficiency and thereby maximising the manufacturing system efficiency. The proposed algorithm is tested with a set of experimental problem data sets and is found to outperform the traditional selective assembly and sequential assembly methods, in producing solutions with higher manufacturing system efficiency. [ABSTRACT FROM AUTHOR]

In this paper, a machine loading problem in a flexible manufacturing system (FMS) is discussed, with bi-criterion objectives of minimising system imbalance and maximising system throughput in the occurrence of technological constraints such as available machining time and tool slots. A mathematical model is used to select machines, assign operations and the required tools in order to minimise the system's imbalance while maximising the throughput. An efficient evolutionary algorithm by hybridising the genetic algorithm (GA) and simulated annealing (SA) algorithm called GASA is proposed in this paper. The performance of the GASA is tested by using 10 sample dataset and the results are compared with the heuristics reported in the literature. The influence of genetic operators on the evolutionary search in GASA is studied and reported. Two machine selection heuristics are proposed and their influence on the quality of the solution is also studied. Extensive computational experiments have been carried out to evaluate the performance of the proposed evolutionary heuristics and the results are presented in tables and figures. The results clearly support the better performance of GASA over the algorithms reported in the literature. [ABSTRACT FROM AUTHOR]

Byline: R. Maheswaran, S.G. Ponnambalam, N. Jawahar This paper addresses on solving a well known Non Polynomial (NP) hard type problem, namely the single machine total weighted-tardiness problem. The performances of three hybrid heuristic algorithms to solve the single machine scheduling problems with the objective of minimising the total weighted tardiness are presented and compared. In the first hybrid algorithm, a dynamic dispatching rule, namely Modified Due Date (MDD), is hybridised with local search mechanism. In the second hybrid algorithm, a greedy heuristic, namely backward phase, is proposed and hybridised with local search mechanisms. The third hybrid algorithm hybridises the backward phase heuristics with an iterated local search (ILS) having an evolutionary perturbation tool. The algorithms are tested by solving all the 125 benchmark problem instances available in the OR-Library for different sizes and compared with the best known values. It is observed that the hybrid algorithm with evolutionary perturbation tool is performing better than the others.