Gregorio Tirado

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.

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.

Decision Aid Models and Systems for Humanitarian Logistics. A Survey

The number and impact of disasters seems to be increasing in the last decades, and their consequences have to be managed in the best possible way. This paper introduces the main concepts used in emergency and disaster management, and presents a literature review on the decision aid models and systems applied to humanitarian logistics in this context.

Heuristics for dynamic and stochastic routing in industrial shipping

Maritime transportation plays a central role in international trade, being responsible for the majority of long-distance shipments in terms of volume. One of the key aspects in the planning of maritime transportation systems is the routing of ships. While static and deterministic vehicle routing problems have been extensively studied in the last decades and can now be solved effectively with metaheuristics, many industrial applications are both dynamic and stochastic. In this spirit, this paper addresses a dynamic and stochastic maritime transportation problem arising in industrial shipping. Three heuristics adapted to this problem are considered and their performance in minimizing transportation costs is assessed. Extensive computational experiments show that the use of stochastic information within the proposed solution methods yields average cost savings of 2.5% on a set of realistic test instances.

Heuristics for the robust vehicle routing problem with time windows

A vehicle routing problem with uncertain travel times and time windows is studied.The problem have direct applications in maritime transportation.An efficient heuristic able to solve large instances of the problem is developed.Computational results show a large benefit from the main novel heuristic components. Uncertainty is frequently present in logistics and transportation, where vehicle routing problems play a crucial role. However, due to the complexity inherent in dealing with uncertainty, most research has been devoted to deterministic problems. This paper considers a robust version of the vehicle routing problem with hard time windows, in which travel times are uncertain. A budget polytope uncertainty set describes the travel times, to limit the maximum number of sailing legs that can be delayed. This makes sure that improbable scenarios are not considered, while making sure that solutions are immune to delays on a given number of sailing legs. Existing exact methods are only able to solve small instances of the problem and can be computationally demanding. With the aim of solving large instances with reduced running times, this paper proposes an efficient heuristic based on adaptive large neighborhood search. The computational study performed on instances with different uncertainty levels compares and analyzes the performance of four versions of the heuristic and shows how good quality solutions can be obtained within short computational times.

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.

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.

The multiple vehicle pickup and delivery problem with LIFO constraints

This paper approaches a pickup and delivery problem with multiple vehicles in which LIFO conditions are imposed when performing loading and unloading operations and the route durations cannot exceed a given limit. We propose two mixed integer formulations of this problem and a heuristic procedure that uses tabu search in a multi-start framework. The first formulation is a compact one, that is, the number of variables and constraints is polynomial in the number of requests, while the second one contains an exponential number of constraints and is used as the basis of a branch-and-cut algorithm. The performances of the proposed solution methods are evaluated through an extensive computational study using instances of different types that were created by adapting existing benchmark instances. The proposed exact methods are able to optimally solve instances with up to 60 nodes.

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.