M. T. Ortuño

An Approach for Strategic Supply Chain Planning under Uncertainty based on Stochastic 0-1 Programming

We present a two-stage stochastic 0-1 modeling and a related algorithmic approach for Supply Chain Management under uncertainty, whose goal consists of determining the production topology, plant sizing, product selection, product allocation among plants and vendor selection for raw materials. The objective is the maximization of the expected benefit given by the product net profit over the time horizon minus the investment depreciation and operations costs. The main uncertain parameters are the product net price and demand, the raw material supply cost and the production cost. The first stage is included by the strategic decisions. The second stage is included by the tactical decisions. A tight 0-1 model for the deterministic version is presented. A splitting variable mathematical representation via scenario is presented for the stochastic version of the model. A two-stage version of a Branch and Fix Coordination (BFC) algorithmic approach is proposed for stochastic 0-1 program solving, and some computational experience is reported for cases with dozens of thousands of constraints and continuous variables and hundreds of 0-1 variables.

A hierarchical compromise model for the joint optimization of recovery operations and distribution of emergency goods in Humanitarian Logistics

The distribution of emergency goods to a population affected by a disaster is one of the most fundamental operations in Humanitarian Logistics. In the case of a particularly disruptive event, parts of the distribution infrastructure (e.g., bridges, roads) can be damaged. This damage would make it impossible and/or unsafe for the vehicles to reach all the centers of demand (e.g., towns and villages). In this paper, we propose and solve the problem of planning for recovery of damaged elements of the distribution network, so that the consequent distribution planning would benefit the most. We apply the model, called RecHADS, to a case study based on the 2010 Haiti earthquake. We also show empirically the importance of coordinating recovery and distribution operations optimization.

Two alternative models for farm management: Discrete versus continuous time horizon

Crop production entails many decision making processes aimed at improving productivity and achieving the best yield from scarce resources. Assuming that there is a set of tasks to be carried out within a given time horizon, and each task can be performed in different ways, the problem consists of determining how and when to carry out each task, in such a way that the tasks are scheduled in sequence at the minimum cost, taking into account any precedence relationships among them, the time window constraints for performing the tasks and the resources availability. This paper presents two alternative mathematical models to attain the proposed objective. The first model splits the time into discrete units spread throughout the planning horizon; it is presented in connection with flexible manufacturing. The second model keeps a continuous time horizon; a scheduling model is used for which a family of incompatibility conditions is introduced to avoid a certain type of simultaneous usage of resources. This type of conditions require to introduce a new structure so-called conditional disjunction. Computational experience is reported for real-life problems.

On due-date based valid cuts for the sequential ordering problem

The Sequential Ordering Problem with precedence relationships was introduced in Escudero (1988), and extended to cover release and due dates in Escudero and Schiomachen (1993). The problem consists of finding a minimum weight Hamiltonian path on a directed graph with weights on the nodes and the arcs, satisfying precedence relationships among the nodes and lower and upper bounds on the Hamiltonian subpaths. In this paper we introduce valid cuts derived from the due date constraints that can be used in a separation framework for the dual (Lagrangian based) relaxation of the problem. We also provide an heuristic separation algorithm to obtain those cuts.

On surrogating 0–1 knapsack constraints

In this note, we present a scheme for tightening 0–1 knapsack constraints based on other knapsack constraints surrogating.