SUNConferences, Computers and Industrial Engineering 42

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Mobile Enabled Operations Management Using Multi-Objective Based Logistics Planning For Perishable Products
Krishna Sundar Diatha, Ravikumar Karumanchi, Shashank Garg

Last modified: 2012-06-27

Abstract


The management of the mushroom supply chain is a complex task since mushrooms have a short shelf-life after production. Mushroom farming requires an environment in which the quality of the soil, the temperature and humidity of the chamber and flow of air between stacks of mushroom beds are to be strictly controlled under specified conditions in order to give maximum yield. Once mushrooms are plucked, sorted and packaged for delivery they must be delivered to the market within a few hours since the quality of mushrooms degrades very rapidly after harvesting. Small farmers may not have the necessary infrastructure for processing and packaging which is generally be done at nearby production centres where aggregation takes place. Hence, mushroom farming is generally done in regions which are close to the markets. In such a scenario, the role of logistics in transporting fresh produce to the retail markets or even end consumers is critical to the success of the mushroom business. Procurement of raw material (inbound logistics) in the form of harvested mushrooms as well as the distribution of mushrooms from production centres (outbound logistics) plays a vital role because of the perishable nature of the finished product. This situation warrants a dynamic procurement and distribution strategy. This paper describes a mobile technology based solution which models the typical mushroom supply chain and tracks distribution in real-time through a workflow based system. A multi-objective location-routing model with fractional and linear objectives is deployed to solve the procurement and supply problems in the mushroom supply chain. In the first stage of routing, a feasible solution is provided through TSP transformation and solving it through LIFO implicit enumeration and back-tracking, and this is further improved by a heuristic in the second stage.


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