Determination of an optimal assembly sequence using the psychoclonal algorithm

Abstract

Owing to conflicting objectives, assembly planning has become a difficult task for decision makers to devise an effective plan that can satisfy the majority of system goals. This is a tedious job and its quick and effective solution is the subject of much research. In recent years, artificial immune systems (AISs) have captured the attention of various researchers due to their ability to perform tasks such as learning and memory acquisition. The approach is suitable for solving multi-modal and combinatorial optimization problems. This paper extends the AIS approach by proposing a new methodology, termed the ‘psychoclonal algorithm’, to handle the assembly-planning problem. It inherits its traits from Maslow's need hierarchy theory and the theory of clonal selection. The special features of this algorithm are the various levels of needs, immune memory, and affinity maturation. Various levels of needs and immune memory help to preserve the feasibility of solution, whereas affinity maturation guides the solution to general rather than local optima. The algorithm has been initially validated on a known data set that had been previously solved using both the genetic algorithm and the immune algorithm approach. Using this data set the new psychoclonal algorithm was shown to provide a significant improvement over the other two approaches.

Solving the response time variability problem by means of a psychoclonal approach

“…The psychoclonal metaheuristic has been recently proposed by Tiwari et al (2005) which has been successfully used for solving the following scheduling and combinatorial problems: the disassembly line balancing problem , the assembly configuration problem , the flow shop problem (Kumar et al, 2006a), the make-to-stock inventory deployment problem (Kumar et al, 2006b) and the product mix decision problem (Singh et al, 2006). This metaheuristic is inspired by the need hierarchy theory of Maslow (1954) and the clonal selection principle developed by Gaspar and Collard (2000).…”

“…The aim of Tiwari et al (2005) when they developed the psychoclonal metaheuristic was to obtain a generic scheme based on the need of explotation (i.e. the local search) and the exploration (i.e.…”

“…the global search) of the search space. The need hierarchy theory of Maslow and the clonal selection principle were used by Tiwari et al (2005) to develop the psychoclonal metaheuristic, which scheme is as follows:…”

“…Psychoclonal is a very new population-based metaheuristic that was first proposed by Tiwari et al (2005). This metaheuristic inherits its attributes from the need hierarchy theory of Maslow (1954) and the artificial immune system (AIS) approach, specifically the clonal selection principle (Gaspar and Collard, 2000;de Castro and von Zuben, 2002).…”

“…The psychoclonal metaheuristic has yielded very good results when it has been used to solve several scheduling and combinatorial optimization problems Tiwari et al, 2005;Kumar et al, 2006a;Kumar et al, 2006b;Singh et al, 2006). The proposed psychoclonal algorithm for solving the RTVP is compared with the more efficient procedures for solving non-small instances published to date: three metaheuristic procedures presented by García et al (2006), which are a multi-start, a GRASP (Greedy Randomized Adaptive Search Procedure) and a PSO (Particle Swarm Optimization) algorithm.…”