Problèmes de gestion de flottes de véhicules en temps réel

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal

Relief Distribution Networks: Design and Operations

Logistics area is often recognized as one of the key elements in achieving effective disaster preparedness and response efforts. This chapter presents modeling and solution approaches for both the problem of prepositioning emergency supplies prior to a disaster as well as the problem of their distribution after the disaster onset. Depending on whether uncertainty is taken into account or not, work in these areas will be classified into two major categories: stochastic or deterministic. A distinction will also be made between exact methods and heuristics. In addition, the advantages and limitations of each of these two classes of approaches will be discussed. Am emphasis will be put on the particularities and characteristics of relief distribution networks. More advanced issues in the design and operations of these networks will also be discussed as interesting research avenues

Humanitarian Logistics Network Design for an Effective Disaster Response

In this paper we address the problem of pre-positioning emergency supplies prior to a disaster onset. The goal is to ensure a fast and effective response when the disaster strikes. Pre-positioning of emergency supplies is a strategic decision aimed at determining the number and location of local distribution centers as well as their inventory levels for emergency supplies. These decisions must be made in a highly disruption-prone environment where a timely response is vital and resources are scarce. We present and discuss a scenario-based model that integrates location, inventory and routing decisions

A Collaborative-Distributed Framework for the Long-Haul Truckload Trucking Industry

In this paper, we present a framework for collaborative networks in the long-haul truckload trucking industry. To help preserving members’ privacy, a distributed design is proposed for the network. Moreover, decisions about when to engage a collaboration and how much information to reveal is totally left to the network participants. The proposed framework integrates a dynamic optimization base that intents to achieve a good trade-off between local optimization representing each network participant personal goals and global optimization representing the network global goals. Issues related to the implementation of the proposed solution approaches in a dynamic setting are also discussed

A Stochastic Approach for the Integration of Distributed Energy Resources

Renewable energy sources are widely perceived as a promising solution to help satisfy increasing energy demand with less harm to the environment and human safety. However, most renewable energy resources (e.g. wind and solar) are irregular and hard to control and therefore their ability to ensure enough capacity to satisfy demand is challenging. To overcome this problem, capacity from renewable energies can be increased through the aggregation of Distributed Energy Resources (DER) such as solar panels, micro-CHP, wind turbines and storage. We consider the problem of effectively integrating different DER technologies to minimize energy costs and maximize revenue from renewable energy credits. We propose a scenario-based modeling approach that considers stochastic energy demand, generation costs and energy prices; and takes into account uncertainty in supply from intermittent sources such as wind turbines. Preliminary results are presented to illustrate the proposed approach and discuss further refinements

Production Scheduling for Sustainable Manufacturing

In this paper we investigate the elaboration of an efficient production schedule for sustainable manufacturing systems. Because renewable energies are irregular by nature as they often depend on meteorological conditions (e.g. wind and solar energy), their use in the competitive field of manufacturing production must be addressed with caution. The challenge is to elaborate a reliable production schedule that accommodates energy stochastic fluctuations while satisfying customer and operational constraints. We propose to solve the problem using a meta-heuristic based on Tabu search and discuss major elements that are critical to the success of this approach

Humanitarian and Relief Logistics: Research Issues, Case Studies and Future Trends

This edited volume highlights recent research advances in humanitarian relief logistics. The contributed chapters span the spectrum of key issues and activities from preparedness to mitigation operations (response), planning and execution. The volume also presents state-of-the-art methods and systems through current case studies. Significant issues in planning and execution of humanitarian relief logistics discussed in this volume include the following: • Approaches that tackle realistic relief distribution networks. In addition to large-scale computing issues, heuristics may handle the complexity and particularities of humanitarian supply chains • Methods that integrate real-time information while effectively coping with time pressure and uncertainty, both of which are inherent to a disaster scene • Judicious recourse strategies that allow a quick and effective restoration of pre-planned solutions whenever an unpredictable event occurs • Coordination of multiple parties that are often involved in managing a disaster, including NGOs, local, state and federal agencies. This volume provides robust evidence that research in humanitarian logistics may lead to substantial improvements in effectiveness and efficiency of disaster relief operations. This is quite encouraging, since the unique characteristics of disaster scenes provide significant opportunities for researchers to investigate novel approaches contributing to logistics research while offering a significant service to society

Prepositioning emergency supplies to support disaster relief: a case study using stochastic programming

This paper studies the strategic problem of designing emergency supply networks to support disaster relief over a planning horizon. The problem addresses decisions on the location and number of distribution centres needed, their capacity, and the quantity of each emergency item to keep in stock. It builds on a case study inspired by real-world data obtained from the North Carolina Emergency Management Division (NCEM) and the Federal Emergency Management Agency (FEMA). To tackle the problem, a scenario-based approach is proposed involving three phases: disaster scenario generation, design generation and design evaluation. Disasters are modelled as stochastic processes and a Monte Carlo procedure is derived to generate plausible catastrophic scenarios. Based on this detailed representation of disasters, a multi-phase modelling framework is proposed to design the emergency supply network. The two-stage stochastic programming model proposed is solved using a sample average approximation method. This scenario-based solution approach is applied to the case study to generate plausible scenarios, to produce alternative designs and to evaluate them on a set of performance measures in order to select the best design