M. Grazia Speranza

Deterministic Order-Up-To Level Policies in an Inventory Routing Problem

We consider a distribution problem in which a set of products has to be shipped from a supplier to several retailers in a given time horizon. Shipments from the supplier to the retailers are performed by a vehicle of given capacity and cost. Each retailer determines a minimum and a maximum level of the inventory of each product, and each must be visited before its inventory reaches the minimum level. Every time a retailer is visited, the quantity of each product delivered by the supplier is such that the maximum level of the inventory is reached at the retailer. The problem is to determine for each discrete time instant the retailers to be visited and the route of the vehicle. Various objective functions corresponding to different decision policies, and possibly to different decision makers, are considered. We present a heuristic algorithm and compare the solutions obtained with the different objective functions on a set of randomly generated problem instances.

Conditional value at risk and related linear programming models for portfolio optimization

Many risk measures have been recently introduced which (for discrete random variables) result in Linear Programs (LP). While some LP computable risk measures may be viewed as approximations to the variance (e.g., the mean absolute deviation or the Gini’s mean absolute difference), shortfall or quantile risk measures are recently gaining more popularity in various financial applications. In this paper we study LP solvable portfolio optimization models based on extensions of the Conditional Value at Risk (CVaR) measure. The models use multiple CVaR measures thus allowing for more detailed risk aversion modeling. We study both the theoretical properties of the models and their performance on real-life data.

An Optimization-Based Heuristic for the Split Delivery Vehicle Routing Problem

The split delivery vehicle routing problem is concerned with serving the demand of a set of customers with a fleet of capacitated vehicles at minimum cost. Contrary to what is assumed in the classical vehicle routing problem, a customer can be served by more than one vehicle, if convenient. We present a solution approach that integrates heuristic search with optimization by using an integer program to explore promising parts of the search space identified by a tabu search heuristic. Computational results show that the method improves the solution of the tabu search in all but one instance of a large test set.

Worst-Case Analysis for Split Delivery Vehicle Routing Problems

In the vehicle routing problem (VRP) the objective is to construct a minimum cost set of routes serving all customers where the demand of each customer is less than or equal to the vehicle capacity and where each customer is visited once. In the split delivery vehicle routing problem (SDVRP) the restriction that each customer is visited once is removed. We show that the cost savings that can be realized by allowing split deliveries is at most 50. We also study the variant of the VRP in which the demand of a customer may be larger than the vehicle capacity, but where each customer has to be visited a minimum number of times. We show that the cost savings that can be realized by allowing more than the minimum number of required visits is again at most 50. Furthermore, we analyze the performance of simple heuristics that handle customers with demands larger than the vehicle capacity by employing full load out-and-back trips to these customers until the demands become less than or equal to the vehicle capacity. Finally, we investigate situations in which demands are discrete and vehicle capacities are small.

A Hybrid Heuristic for an Inventory Routing Problem

We consider an inventory routing problem in discrete time where a supplier has to serve a set of customers over a multiperiod horizon. A capacity constraint for the inventory is given for each customer, and the service cannot cause any stockout situation. Two different replenishment policies are considered: the order-up-to-level and the maximum-level policies. A single vehicle with a given capacity is available. The transportation cost is proportional to the distance traveled, whereas the inventory holding cost is proportional to the level of the inventory at the customers and at the supplier. The objective is the minimization of the sum of the inventory and transportation costs. We present a heuristic that combines a tabu search scheme with ad hoc designed mixed-integer programming models. The effectiveness of the heuristic is proved over a set of benchmark instances for which the optimal solution is known.

Hierarchical models for multi-project planning and scheduling

Abstract We propose a model-based approach to nonpreemptive multi-project management problems, based on a hierarchical two-stage decomposition of the planning and scheduling process. Two performance criteria are considered: the net present value, which includes investment costs, operating costs, revenues, penalties for late completion; and the service level, expressed as the agreement between the completion times of the different projects and the customer needs. The resulting hierarchy of integer programming models is aimed at assisting the planners in understanding the interrelations among the allocation of resources, the timing of the activities, the cash flows. A class of branch-and-bound procedures is then proposed for the solution of these integer programming models.

A heuristic algorithm for a portfolio optimization model applied to the Milan stock market

Abstract In this paper we present a model which takes into account characteristics of the portfolio optimization problem which are disregarded in most optimization models. These are transaction costs, minimum transaction units and limits on minimum holdings. The model is a mixed integer linear model which generalizes one of the linear models which recently appeared in the literature as an alternative to the classical Markowitz model. Unfortunately, in order to obtain a greater realism in the problem modelling a set of binary and integer variables needs to be introduced. Computational experiments are carried out to evaluate the complexity of the model, which is applied to the Milan stock market. The results show that the presence of the integer variables dramatically increases the computational complexity of the model compared with the continuous version. For this reason we analyze a heuristic solution procedure based on two phases, which reflect the investors empirical approach to the problem. The computational results show that the heuristic procedure generates an error lower than 4.1 % and that the error decreases when the capital invested increases.

Minimizing the Total Cost in an Integrated Vendor--Managed Inventory System

In this paper we consider a complex production-distribution system, where a facility produces (or orders from an external supplier) several items which are distributed to a set of retailers by a fleet of vehicles. We consider Vendor-Managed Inventory (VMI) policies, in which the facility knows the inventory levels of the retailers and takes care of their replenishment policies. The production (or ordering) policy, the retailers replenishment policies and the transportation policy have to be determined so as to minimize the total system cost. The cost includes the fixed and variable production costs at the facility, the inventory costs at the facility and at the retailers and the transportation costs, that is the fixed costs of the vehicles and the traveling costs. We study two different types of VMI policies: The order-up-to level policy, in which the order-up-to level quantity is shipped to each retailer whenever served (i.e. the quantity delivered to each retailer is such that the maximum level of the inventory at the retailer is reached) and the fill-fill-dump policy, in which the order-up-to level quantity is shipped to all but the last retailer on each delivery route, while the quantity delivered to the last retailer is the minimum between the order-up-to level quantity and the residual transportation capacity of the vehicle. We propose two different decompositions of the problem and optimal or heuristic procedures for the solution of the subproblems. We show that, for reasonable initial values of the variables, the order in which the subproblems are solved does not influence the final solution. We will first solve the distribution subproblem and then the production subproblem. The computational results show that the fill-fill-dump policy reduces the average cost with respect to the order-up-to level policy and that one of the decompositions is more effective. Moreover, we compare the VMI policies with the more traditional Retailer-Managed Inventory (RMI) policy and show that the VMI policies significantly reduce the average cost with respect to the RMI policy.

Twenty years of linear programming based portfolio optimization

Markowitz formulated the portfolio optimization problem through two criteria: the expected return and the risk, as a measure of the variability of the return. The classical Markowitz model uses the variance as the risk measure and is a quadratic programming problem. Many attempts have been made to linearize the portfolio optimization problem. Several different risk measures have been proposed which are computationally attractive as (for discrete random variables) they give rise to linear programming (LP) problems. About twenty years ago, the mean absolute deviation (MAD) model drew a lot of attention resulting in much research and speeding up development of other LP models. Further, the LP models based on the conditional value at risk (CVaR) have a great impact on new developments in portfolio optimization during the first decade of the 21st century. The LP solvability may become relevant for real-life decisions when portfolios have to meet side constraints and take into account transaction costs or when large size instances have to be solved. In this paper we review the variety of LP solvable portfolio optimization models presented in the literature, the real features that have been modeled and the solution approaches to the resulting models, in most of the cases mixed integer linear programming (MILP) models. We also discuss the impact of the inclusion of the real features.

Reoptimizing the traveling salesman problem

In this paper, we study the reoptimization problems which arise when a new node is added to an optimal solution of a traveling salesman problem (TSP) instance or when a node is removed. We show that both reoptimization problems are NP-hard. Moreover, we show that, while the cheapest insertion heuristic has a tight worst-case ratio equal to 2 when applied to a TSP instance, it guarantees, in linear time, a tight worst-case ratio equal to 3/2 when used to add the new node and that also the simplest heuristic to remove a node from the optimal tour guarantees a tight ratio equal to 3/2 in constant time.