(141i) Robustness Analysis Of Supply Chain Networks

While managing their assets, companies may invest in new facilities and disinvest/relocate some of their existing facilities, which essentially mean they redesign their supply chains. In our previous work (Naraharisetti et al., 2006a), we developed a novel MILP model to address various issues in supply chain redesign and used a branch and bound algorithm for solving this model. The various features that were considered include investments, disinvestments/relocations, regulatory factors, depreciation, production changeover costs, transportation costs, taxes etc. To the best of our knowledge, we were the first to address the issues of disinvestment and relocation, where relocation is simply a disinvestment at one location and investment at another.

As problem size increases, it becomes increasingly difficult to solve an MILP formulation by the branch and bound algorithm. Even for a moderate-size problem, computation times are of the order of days just to reach a relative gap of 10%. Hence, it is important to seek alternative optimization techniques. In our later work (Naraharisetti et al., 2006b), we reported on a genetic algorithm to solve this supply chain redesign problem and compared its efficiency with the branch and bound algorithm. Our algorithm was able to reach >97% of the objective value when compared with CPlex 9.0 for small problems and was able to outperform CPlex 9.0 for large problems.

In the above examples, we obtained a plan for a given set of data. However, the demand for products and supply of raw materials are ever changing due to various reasons. New and huge markets are opening up in Asia and globalization and lifting of trade barriers are facilitating global growth. This is giving unprecedented opportunities for MNCs to venture into new and unknown markets and there is risk associated with uncertainties. To this end, a methodology/framework to obtain a SCR plan that is robust to the various uncertainties needs to be developed. In this work, we report on the use of clustering analysis to identify different supply chain networks. We analyze the performance of each of these networks against the uncertainties and obtain a robustness index for each of the networks. Such an analysis gives the decision maker the option to choose one plan from a set of plans based on his aptitude for risk.

Keywords: capacity, planning, distribution, optimization

*To whom correspondence should be addressed: Tel: +65-6874-2186, Fax: +65-6779-1936 Email: cheiak@nus.edu.sg

References:

Naraharisetti, P. K., Karimi, I. A., Srinivasan, R. Capacity Management in the Chemical Supply Chain. International Symposium on Advanced Control of Chemical Processes - ADCHEM, 2006a, 2-5 April, Gramado, Brazil.

Naraharisetti, P. K., Karimi, I. A., Srinivasan, R. Optimal Supply Chain Redesign and Asset Management using Genetic Algorithm. AIChE Annual Meeting, 2006b, 12-17 November, San Francisco, USA.