Optimising integrated inventory policy for perishable items in a multi-stage supply chain

Abstract

The value of perishable products is most affected by the time delays in a supply chain. A major issue is how to integrate the existing practices in production, inventory holding and distribution, besides considering the perishable nature of the products, so as to deliver an optimized policy for the perishable commodities. Standard inventory control models are often not adequate for perishable products and there is a need for a new integrated model to focus on consolidation of production, inventory and distribution processes. We develop such a mathematical model to search for an optimal integrated inventory policy for perishable items in a multi-stage supply chain. We specifically assume the exponential deterioration rate so as to be consistent with the growth rate of the micro-organisms responsible for deterioration. We propose and analyze some general properties of the model and apply it to a three-stage supply chain. We show that this integrated model which includes inventory control and fleet selection can be optimized with an evolutionary technique like genetic algorithm. A novel genetic algorithm that avoids revisits and employs a parameter-less self-adaptive mutation operator is developed. The results are compared with those obtained with CPLEX for small sized problems. We show that our model and optimization approach gives near optimal results for varied demand scenarios.

A holistic framework for lot sizing problem in fast-moving perishable products

“…The trend observed in the proposed framework is validated from Dolgui et al 2018, as our results are in conformance with the results obtained by Dolgui et al [21].…”

“…The proposed model is based on Dolgui et al [21]. Dolgui et al [21] have illustrated the supply chain network for perishable product.…”

“…The proposed model is based on Dolgui et al [21]. Dolgui et al [21] have illustrated the supply chain network for perishable product. In this paper, we have considered the highlights of Dolgui et al [21] and further investigated the same through sensitivity analysis using commercial software and the results obtained in this manuscript will facilitate the decision makers in effective decision making for lot sizing problem for perishable product under different fleet selection environment.…”

“…There are four markets; two warehouses; one plant; and, five time periods which have been considered here for sensitivity analysis. The data [21] used for sensitivity analysis is illustrated in Table 2, Table 3 and, Table 4. In this paper, we have calculated the percentage change in the decision variables, by varying each input parameter separately, with respect to the base case.…”

“…Literature survey reveals that there is a significant research gap between the old practices and new practices for handling the lot size problem of perishable goods. Available literature survey [13][14][15][16][17][18][19][20][21][22] in the domain of this area mainly focuses on the production and inventory of perishable products. Now, due to increasing global competitive environment, it is necessary to handle the perishable product with low cost and less environmental impact.…”