Inventory routing and on-line inventory routing file format

This document presents a simple extension of the TSPLIB file format to serve our needs in the Inventory Routing Problem types. Instead of creating a new file format or putting ASCII files online with a simple description, we have chosen to extend the TSPLIB file format

Inventory-routing model, for a multi-period problem with stochastic and deterministic demand

The need for integration in the supply chain management leads us to consider the coordination of two logistic planning functions: transportation and inventory. The coordination of these activities can be an extremely important source of competitive advantage in the supply chain management. The battle for cost reduction can pass through the equilibrium of transportation versus inventory managing costs. In this work, we study the specific case of an inventory-routing problem for a week planning period with different types of demand. A heuristic methodology, based on the Iterated Local Search, is proposed to solve the Multi-Period Inventory Routing Problem with stochastic and deterministic demand.Inventory-Routing, iterated local search, logistics

A Genetic Algorithm on Inventory Routing Problem

Inventory routing problem can be defined as forming the routes to serve to the retailers from the manufacturer, deciding on the quantity of the shipment to the retailers and deciding on the timing of the replenishments. The difference of inventory routing problems from vehicle routing problems is the consideration of the inventory positions of retailers and supplier, and making the decision accordingly. Inventory routing problems are complex in nature and they can be solved either theoretically or using a heuristics method. Metaheuristics is an emerging class of heuristics that can be applied to combinatorial optimization problems. In this paper, we provide the relationship between vendor-managed inventory and inventory routing problem. The proposed genetic for solving vehicle routing problem is described in detail

Inventory routing problem with non-stationary stochastic demands

In this paper we solve Stochastic Periodic Inventory Routing Problem (SPIRP) when the accuracy of expected demand is changing among the periods. The variability of demands increases from period to period. This variability follows a certain rate of uncertainty. The uncertainty rate shows the change in accuracy level of demands during the planning horizon. To deal with the growing uncertainty, we apply a safety stock based SPIRP model with different levels of safety stock. To satisfy the service level in the whole planning horizon, the level of safety stock needs to be adjusted according to the demand's variability. In addition, the behavior of the solution model in long term planning horizons for retailers with different demand accuracy is taken into account. We develop the SPIRP model for one retailer with an average level of demand, and standard deviation for each period. The objective is to find an optimum level of safety stock to be allocated to the retailer, in order to achieve the expected level of service, and minimize the costs. We propose a model to deal with the uncertainty in demands, and evaluate the performance of the model based on the defined indicators and experimentally designed cases

Practical inventory routing: A problem definition and an optimization method

The global objective of this work is to provide practical optimization methods to companies involved in inventory routing problems, taking into account this new type of data. Also, companies are sometimes not able to deal with changing plans every period and would like to adopt regular structures for serving customers

The Military Inventory Routing Problem with Direct Delivery

The inventory routing problem coordinates inventory management and transportation policies when implementing vendor managed inventory replenishment, the business practice were a vendor monitors the inventory of its customers and determines a strategy to replenish each customer. The United States Army uses vendor managed inventory replenishment during combat situations to manage resupply. The military variant of the stochastic inventory routing problem considers delivery failure due to hostile actions. We formulate a Markov decision process model for the military inventory routing problem, with the objective to determine an optimal unmanned tactical airlift policy for resupplying brigade combat team elements in a combat situation using cargo unmanned aerial systems for delivery. Computational results are presented for the military inventory routing problem with direct deliveries. Results indicate that unmanned aerial systems are capable of performing brigade combat team resupply, given the dynamics of the threat situation. An experimental design is employed to determine the set of factors important in a more general context

Modeling and solving the multi-period inventory routing problem with constant demand rates

The inventory routing problem (IRP) is one of the challenging optimization problems in supply chain logistics. It combines inventory control and vehicle routing optimization. The main purpose of the IRP is to determine optimal delivery times and quantities to be delivered to customers, as well as optimal vehicle routes to distribute these quantities. The IRP is an underlying logistical optimization problem for supply chains implementing vendor-managed inventory (VMI) policies, in which the supplier takes responsibility for the management of the customers' inventory. In this paper, we consider a multi-period inventory routing problem assuming constant demand rates (MP-CIRP). The proposed model is formulated as a linear mixed-integer program and solved with a Lagrangian relaxation method. The solution obtained by the Lagrangian relaxation method is then used to generate a close to optimal feasible solution of the MP-CIRP by solving a series of assignment problems. The numerical experiments carried out so far show that the proposed Lagrangian relaxation approach nds quite good solutions for the MP-CIRP and in reasonable computation times

On the use of reference points for the biobjective Inventory Routing Problem

The article presents a study on the biobjective inventory routing problem. Contrary to most previous research, the problem is treated as a true multi-objective optimization problem, with the goal of identifying Pareto-optimal solutions. Due to the hardness of the problem at hand, a reference point based optimization approach is presented and implemented into an optimization and decision support system, which allows for the computation of a true subset of the optimal outcomes. Experimental investigation involving local search metaheuristics are conducted on benchmark data, and numerical results are reported and analyzed