An Integrated Model for Optimization of Production-Distribution inventory Levels and Routing Structure for a Multi-Period, Multi-Product, Bi-Echelon Supply Chain
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The International Journal of Applied Management and Technology
An integrated model for Optimization of production-distribution inventory levels and routing Structure for a multi-period, multi- product, bi-echelon supply chain
P.Parthiban 0
National Institute of Technology 0
Tamilnadu 0
K.Ganesh 0
Tamilnadu 0
0 M.Punniyamoorthy, National Institute of Technology , Tamilnadu
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 paper, we have explored this problem by considering a Two-Stage Production-Transportation (TSPT). A twostage 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. We 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 C-plex. supply chain is to optimize the inventory level by considering various costs in _______________________________________________________________________ The International Journal of Applied Management and Technology, Vol 6, Num 1
eol>TSPT; Genetic Algorithms; complete enumeration; LINDO; C-plex
Introduction
To exploit economies of scale and order in large lots, the important issue in
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maintaining a high service level towards the customer. Since, the cost of capital tied up in
inventory is more, the inventory decision in the supply chain should be coordinated
without disturbing the service level. The coordination of inventory decision within an
entity is viable, but not between the entities. So the integration of the entities to centralize
the inventory control is needed.
Several factors influence inventory policy and practice for the firm includes
distribution savings demand seasonality in production and purchasing economies with the
desired level of customer service. Transportation is the spatial linkage for the physical
flows of a supply chain. Today supply chain has to examine the effective use of supply
chain with value added processes in distribution center with minimum inventory levels
for delivering the product in time. The gateway of the supply chain is to know our
customer and serve the needs by considering the aspects of speed and reliable service
with the value propositions. The effective supply chain management needs to improve
customer service, reduction of costs across the supply chain, optimal management of
existing inventory with optimized manufacturing schedules. The impact of distribution
inventory will enhance the customer value in the form of lower costs across the supply
chain.
This project presents the Genetic Algorithm Distribution Inventory Model
(GADIM) approach for optimizing the inventory levels of supply chain entities with the
consideration of two-echelon plant to warehouse and warehouse to customers. This
model is especially suitable for inventory control and cost reduction by unlocking the
hidden profits through the genetic algorithm optimization. The generalized GADIM
evaluate the transportation link between the two entities and finding the best route for
transporting the product from plant to warehouse and then to customer. The demand of
the customer is known in advance, so that the production rate and the inventory levels can
be adjusted in the plant and warehouse. Since the logistics plays an important role in the
supply chain that the two important sectors transportation and distribution inventory has
taken into consideration for redesigning the allocation and routing through optimization.
This approach is based on genetic algorithm. It searches the population of solutions of an
optimization problem towards the improvement by simulating the natural search and
selection process associated with natural genetics.
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