Strategy of Solution for the Inventory Routing Problem Based on Separable Cross Decomposition

Abstract

The Inventory-Routing Problem (IRP) involves a central warehouse, a fleet of trucks wlth finlte capacity, a set of customers, and a known storage capacity. The objective is to determine when to serve each customer, as well as what route each truck should take, with the lowest expense. IRP is a NP-hard problem, this means that searching for solutions can take a very long time. A three-phase strategy is used to solve the problem. This strategy is constructedn by answering the key questions: Which customers should be attended in a planned period? What volume of n products should be delivered to each customer? And, which route should be followed by each truck? The second phase uses Cross Separable Decomposition to solve an Allocation Problem, in order to answer questions two and three, solving a location problem. The result is a very efficient ranking algorithm O(n3) for large cases of the lRP.

Referral service and customer incentive in online retail supply Chain

“…Assume that all the potential customers influenced by the price and the referral customers are also dependent on the amount of rebate. The similar linear demand functions are frequently used in supply chains research [19][20][21]. The common concave function 2 2 r  denotes the customer incentive cost ( 0   ) [22].…”

Placing Safety Stock in Logistic Networks under Guaranteed-Service Time Inventory Models: An Application to the Automotive Industry

“…In order to reach this aim, companies have realised that a global approach is required to coordinate operations across the entire Logistic Network (LN) or Supply Chain, e.g. share information to minimise the bullwhip effect [1]; pass products' demand to upstream members to reduce inventory levels [2] or solve the routing and inventory problem simultaneously [23,26]. Moreover, companies have to dynamically evaluate the LN operations [24] and reduce the complexity generated by the product diversification [25] to reach the global aim of cost reduction.…”