Abstract
This paper conceptualises the integration of tangible and intangible factors into the design consideration of a resource assignment problem for a product-driven supply chain. The problem is formulated mathematically as a multi-objective optimisation model to maximise the broad objectives of profit, ahead of time of delivery, quality, and volume flexibility. Product characteristics are associated with the design requirements of a supply chain. Different types of resources are considered, each differing in its characteristics, thereby providing various alternatives during the design process. The aim is to design integrated supply chains that maximise the weighted sum of the objectives, the weights being decided by the desired product characteristics. The problem is solved through the proposed Taguchi-based DNA algorithm that draws its traits from random search optimisation and the statistical design of experiments. In order to minimise the effect of the causes of variations, the fundamental Taguchi method is integrated with the DNA-metaheuristic. The suggested methodology exhibits the global exploration capability to exploit the optimal or near-optimal DNA strands with a faster convergence rate. In order to authenticate the performance of the proposed solution methodology, a set of ten problem instances are considered and the results obtained are compared with that of the basic DNA, particle swarm optimisation (PSO) and its variant (PSO time varying acceleration coefficients). The results demonstrate the benefits of the proposed algorithm for solving this type of problem.
Original language | English |
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Pages (from-to) | 2345-2371 |
Number of pages | 27 |
Journal | International Journal of Production Research |
Volume | 47 |
Issue number | 9 |
DOIs | |
Publication status | Published - 1 Jan 2009 |
Externally published | Yes |
Keywords
- Fuzzy analytical hierarchical process
- Hybridisation
- Multi-objective optimisation
- Nucleotide
- Resource assignment
- Supply chain
ASJC Scopus subject areas
- Strategy and Management
- Management Science and Operations Research
- Industrial and Manufacturing Engineering