M. Victor Raj

Maximising manufacturing system efficiency for multi-characteristic linear assembly by using particle swarm optimisation in batch selective assembly

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.

Bi-criteria optimization in integrated layout design of cellular manufacturing systems using a genetic algorithm

Traditionally the design of the physical layout of the manufacturing system and that of the material flow path and material handling system are carried out in isolation. In this work, an attempt was made on the integrated layout design, that is, to concurrently design the physical layout and the material handling system using a Genetic Algorithm-based methodology. The proposed algorithm was employed to simultaneously optimize two contradicting objectives viz., 1. Total material handling cost 2. Distance-weighted cost of closeness rating score. The algorithm was tested on four different benchmark layouts and with different initial problem data sets. It was found that the proposed algorithm is able to produce satisfactory solutions consistently within a reasonable computational limit.

GA approach to optimise material flow and makespan in an AGV–based flexible jobshop manufacturing system

Flexible jobshop scheduling problem (FJSP) is an extended traditional jobshop scheduling problem, which more approximates to practical scheduling problems. This paper presents a genetic algorithm–based (GA) algorithm to solve the multi objective FJSP. Flexible jobshop manufacturing system (FJMS) is a complex network of processing, inspecting, and buffering nodes connected by system of transportation mechanisms. For an FJMS, it is desirable to be capable to increase or decrease the output with the rise and fall of demand. Such specifications show the complexity of decision making in the field of FJMSs and the need for concise and accurate modelling methods. Therefore, in this paper, an AGV–based flexible jobshop automated manufacturing system is considered to optimise the material flow and makespan. The flexibility is on the multishops of the same type and also multiple products that can be produced. An automated guided vehicle is applied for material handling. The objective is to optimise the material flow regarding the demand fluctuations and machine specifications and the makespan. An illustrative example is adopted from the literature to test the validity of the proposed algorithm.

Cell formation using tabu search for design of cellular manufacturing system

A large number of non–traditional search algorithms are available for function optimisation. The cell formation problem is the important step in the design of a cellular manufacturing system. The objective is to identify part families and machine groups and consequently to form manufacturing cells with respect to minimising the number of exceptional elements. An efficient tabu search (TS) algorithm is proposed to solve cell formation problem because it perform considerable search before terminating to provide a good solution to the problem. In this work the implementation of tabu search for the design of cell formation problem and minimise the number of exceptional elements has been done by this method and it is compared with other existing methods.

Ant Colony Optimization to Improve Precision of Complex Assembly

An assembly consists of two or more mating parts. The quality of the assembly is mainly based on the quality of mating parts. The mating parts may be manufactured using different machines and processes with different standard deviations. Therefore, the dimensional distributions of the mating parts are not similar. This results in clearance between the mating parts. To obtain high precision assemblies, clearance variation has to be reduced. Selective assembly helps to reduce this clearance variation. In this paper, appropriate selective group combination for assembling the mating parts is obtained using an ant colony optimization (ACO). The combination obtained has resulted in an appreciable reduction in clearance variations.

A new approach to nullify surplus parts in selective assembly

Selective assembly is a generally accepted method for producing high precision assemblies from relatively low precision components. The mating parts are manufactured with wide tolerances. The mating part population is partitioned to form selective groups. The corresponding selective groups are then assembled interchangeably. The accuracy of selective assembly is mainly based on the number of selective groups (fixed before the assembly) and the range of selective groups. However, there are often surplus parts in some groups due to the imbalance of mating parts, especially in the case of undesired dimensional distributions. This paper presents a new approach to nullify the surplus parts in selective assembly.