M. Teresa Ortuño

The double traveling salesman problem with multiple stacks: A variable neighborhood search approach

The double traveling salesman problem with multiple stacks (DTSPMS) is a vehicle routing problem that consists on finding the minimum total length tours in two separated networks, one for pickups and one for deliveries. A set of orders is given, each one consisting of a pickup location and a delivery location, and it is required to send an item from the former location to the latter one. Repacking is not allowed, but collected items can be packed in several rows in such a way that each row must obey the LIFO principle. In this paper, a variable neighborhood search approach using four new neighborhood structures is presented to solve the problem.

A stochastic 0-1 program based approach for the air traffic flow management problem

Abstract We present a model and a robust algorithmic framework for the Air Traffic Flow Management Problem (TFMP) under uncertainty in airport arrival and departure and airspace capacity due to weather conditions. For this purpose we use the state-of-the-art 0–1 deterministic model due to Bertsimas and Stock. We present two 0–1 versions of the stochastic model, depending upon the type of recourse policy to use. A multistage scenario analysis approach based on a simple and full recourse scheme is used. The air traffic scheduling can be implemented for a given set of initial time periods in the full recourse environment and the solution for the other periods does not need to be anticipated and, then, it depends on the scenario to occur. We present a Fix-and-Relax approach to solve the very large-scale 0–1 deterministic equivalent model. Computational results are presented by comparing the deterministic approach (where the stochasticity of the uncertain parameters is reduced to their average) with the recourse based schemes; the optimality gap is within 0.25% of the LP optimal solution for most of the cases with dozens of thousands of constraints and variables.

Using intermediate infeasible solutions to approach vehicle routing problems with precedence and loading constraints

Logistics and transportation issues have been receiving increasing attention during the last decades and their requirements have gradually changed, making it necessary to take into account new situations and conditions. The Double Traveling Salesman Problem with Multiple Stacks (DTSPMS) is a pickup and delivery problem in which some additional precedence and loading constraints are imposed on the vehicle to be used. In this paper we approach the problem using intermediate infeasible solutions to diversify the search process and we develop some fixing procedures and infeasibility measures to deal with this kind of solutions and take advantage of their potential.

A simulation approach to reliability analysis of weapon systems

We report a modeling simulation approach to analyse weapon systems reliability. The introduced functional diagram generalises the logic diagram allowing the replication on the functioning mode of system components. To handle the functional diagram, the availability and connection rules are also introduced. Based on the functional diagram, a simulation model is outlined and a case study, the propulsion system of a Mine Hunter, is included.

Intelligent Decision-Making Models for Disaster Management

ABSTRACT Providing emergency relief to the victims of natural disasters is a hugely complex process fraught with many challenging aspects: multiple uncertainties, little reliable information, scarcity of resources, a variety of involved entities, and so on. Nowadays there is a lot of information that could be used to improve decision-making in disaster management, but usually it is not available at the right moment, in the right way, or it is partially known or vague. In this article we analyze the decision-making process for disaster management from the general view of intelligent decision-making to the specific characteristics of this context. This specificity deals with a new kind of logistics, and it is shown how this humanitarian logistics, specifically designed with the aim of alleviating suffering of vulnerable people, is a growing new research area to develop new decision aid models for disaster management, identifying new and relevant differences with other types of logistics. To illustrate these claims, two models are introduced, one for assessment of consequences in the earlier stage after a disaster (focused on the unknown, one of the main characteristics in disaster management), and another one for last mile distribution of humanitarian aid (focused on the multicriteria nature of decision-making on disaster management).

A GRASP metaheuristic for humanitarian aid distribution

Large scale disasters, natural or human-made, have huge consequences on people and infrastructures. After a disaster strikes, the distribution of humanitarian aid to the population affected is one of the main operations to be carried out, and several crucial decisions must be made in a short time. This paper addresses a last-mile distribution problem in disaster relief operations, under insecure and uncertain conditions. A model is presented that takes into account the cost and time of operation, the security and reliability of the routes, and the equity of aid handed out. The output of the model consists of a detailed set of itineraries that can be used to build an implementable distribution plan. Given its high complexity, the resulting problem is solved using a multi-criteria metaheuristic approach. In particular, a constructive algorithm and a GRASP based metaheuristic are developed, which are tested in a case study based on the 2010 Haiti earthquake.

A lexicographical dynamic flow model for relief operations

Emergency management is a highly relevant area of interest in operations research. Currently the area is undergoing widespread development. Furthermore, recent disasters have highlighted the importance of disaster management, in order to alleviate the suffering of vulnerable people and save lives. In this context, the problem of designing plans for the distribution of humanitarian aid according to the preferences of the decision maker is crucial. In this paper, a lexicographical dynamic flow model to solve this problem is presented, extending a previously introduced static flow model. The new model is validated in a realistic case study and a computational study is performed to compare both models, showing how they can be coordinated to improve their overall performance.

On Dual Based Lower Bounds for the Sequential Ordering Problem with Precedences and Due Dates

The Sequential Ordering Problem (herewith, SOP) with precedence relationships was introduced in Escudero (1988), and extended to cover release and due dates in Escudero and Sciomachen (1993). It has a broad range of applications, mainly in production planning for manufacturing systems. 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 given lower and upper bounds on the weights of the Hamiltonian subpaths. In this paper we present a model for the constrained minimum weight Hamiltonian path problem with precedences and due dates forcing constraints, and introduce related valid cuts that can be used in a separation framework for the dual (Lagrangian based) relaxation of the problem. We also provide an heuristic separation procedure to obtain those cuts, so-called the Lagrangian Relax-and-Cut (LRC) scheme. Computational experience is given for variations of some SOP cases already reported in the literature.

Multi-criteria optimization for last mile distribution of disaster relief aid: Test cases and applications

Abstract Humanitarian organizations transport large quantities of aid for distribution in the aftermath of disasters. Transportation for last mile distribution includes multiple, and often conflicting, performance criteria that include time (deprivation), cost, coverage, equity and security. We build a compromise programming model for multi-criteria optimization in humanitarian last mile distribution. Regarding security, ours is the first multi-criteria model able to produce an actual vehicle schedule while forcing vehicles to form convoys in humanitarian operations research. We illustrate the multi-criteria optimization using a realistic test case based on the Pakistan floods, 2010. We standardize and share this case as well as cases based on the Niger famine, 2005 and the Haiti earthquake, 2010. By sharing test cases, we encourage basic scientific tasks such as replicability and model comparison within the humanitarian operations research community.

Heuristics for the Design of Safe Humanitarian Aid Distribution Itineraries

After a disaster strikes, one of the main activities to be developed from a logistics point of view is the design of humanitarian aid distribution plans. In this work the problem of designing safe feasible itineraries for last-mile distribution under the risk of being assaulted, as well as assuring the equity of the distribution, is addressed. The task of simply finding feasible solutions for this problem is highly complex. In this paper we present a constructive randomized heuristic to generate a variety of solutions within a small computational time, and we also provide some ideas of how to modify this constructive algorithm in order to use it within a metaheuristic framework.