Genetic Algorithm to Solve Multi-Period, Multi-Product, Bi-Echelon Supply Chain Network Design Problem

Genetic Algorithm to Solve Multi-Period, Multi-Product, Bi-Echelon Supply Chain Network Design Problem

R. Dhanalakshmi (IBM India Pvt. Ltd, Bangalore, India), P. Parthiban (National Institute of Technology, India), K. Ganesh (Tata Consultancy Services Limited, India) and T. Arunkumar (Vellore Institute of Technology, India)
DOI: 10.4018/978-1-60960-135-5.ch019
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Abstract

In many multi-stage manufacturing supply chains, transportation related costs are a significant portion of final product costs. It is often crucial for successful decision making approaches in multi-stage manufacturing supply chains to explicitly account for non-linear transportation costs. In this article, we have explored this problem by considering a Two-Stage Production-Transportation (TSPT). A two-stage supply chain that faces a deterministic stream of external demands for a single product is considered. A finite supply of raw materials, and finite production at stage one has been assumed. Items are manufactured at stage one and transported to stage two, where the storage capacity of the warehouses is limited. Packaging is completed at stage two (that is, value is added to each item, but no new items are created), and the finished goods inventories are stored which is used to meet the final demand of customers. During each period, the optimized production levels in stage one, as well as transportation levels between stage one and stage two and routing structure from the production plant to warehouses and then to customers, must be determined. The authors consider “different cost structures,” for both manufacturing and transportation. This TSPT model with capacity constraint at both stages is optimized using Genetic Algorithms (GA) and the results obtained are compared with the results of other optimization techniques of complete enumeration, LINDO, and CPLEX.
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Literature Survey

The literature review in aspect of distribution inventory management of supply chain, states clearly that no optimal inventory policy has been developed for a serial Supply Chain in view of the complexity of the problem. Hence the objective of the study is to optimize the inventory level for a two-echelon single product serial supply chain using Genetic Algorithm. So as to minimize the total supply chain cost comprising the distribution and production related cost.

Cakravastia (2002) developed a analytical model for the supplier selection process in designing a supply chain network. The constraints on the capacity of each potential supplier are considered in process. The assumed objective of the supply chain is to minimize the level of customer dissatisfaction, which is evaluated by two performance criteria. (1) Price and (2) delivery lead time. The overall model operates at two levels of decision making: the operational level and the chain level. An optimal solution in terms of the models for the two levels can be obtained by using a mixed integer programming technique.

Abdul-Jalbar et al. (2005) addresses a multi-echelon inventory system with one-warehouse and N-retailers. The demand at each retailer is assumed to be known and satisfied by the warehouse. Shortages are not allowed and lead times are negligible. Costs at each facility consist of a fixed charge per order and a holding cost.

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