Genetic Algorithm for Inventory Levels and Routing Structure Optimization in Two Stage Supply Chain

P. Sivakumar, K. Ganesh, M. Punnniyamoorthy and S.C. Lenny Koh
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P. Sivakumar: Department of Mechanical Engineering, Vickram College of Engineering, Sivagangai, Tamil Nadu, India
K. Ganesh: Global Business Services - Global Delivery, IBM India Private Limited, Mumbai, Maharashtra, India
M. Punnniyamoorthy: Department of Management Studies, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India
S.C. Lenny Koh: Management School, University of Sheffield, Sheffield, England, UK

International Journal of Information Systems and Supply Chain Management (IJISSCM), 2013, vol. 6, issue 2, 33-49

Abstract: Several analytical models have been developed to solve the integrated production distribution problems in Supply Chain Management (SCM). In certain multi-stage service supply chain like blood banks, the term ‘production’ is referred as collection. It is often crucial to consider the inventory and distribution costs for successful decision making in multi-stage service supply chain. In this paper, the authors have explored this problem by considering a Two - Stage Collection - Distribution (TSCD) Model for blood collection and distribution that faces a deterministic stream of external demands for blood product. A finite supply and collection of blood at stage one Central Blood Bank (CBB) has been assumed. Blood is collected at stage one CBB and distributed to stage two Regional Blood Bank (RBB), where the storage capacity of the RBB is limited. Packaging is completed at stage two (that is, value is added to each item, but no new items are created), and the packed blood bags are stored which is used to meet the final demand of customer zone. During each period, the optimal collection rate at CBB, distribution rate between CBB and RBB and routing structure from the CBB to RBB and then to customer zone, must be determined. This TSCD model with capacity constraints at both stages is optimized using Genetic Algorithms (GA) and compared with the standard operations research software LINDO for small problems.

Date: 2013
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