A Bi-Objective Fuzzy Credibilistic Chance-Constrained Programming Approach for the Hazardous Materials Road-Rail Multimodal Routing Problem under Uncertainty and Sustainability

Hazardous materials transportation involves extensive risk and cannot be avoided in practice. An advanced routing, however, can help to reduce the risk by planning the best transportation routes for hazardous materials that can make effective tradeoffs between the risk objective and the economic objective. In this study, we explore the hazardous materials routing problem in the road-rail multimodal transportation network with a hub-and-spoke structure, in which the risk is measured by the multiplication of population exposure and the associated volume of hazardous materials, and minimizing the total risk of all the transportation orders of hazardous materials is set as the risk objective. It is difficult to estimate the population exposure exactly during the routing decision-making process, which results in its uncertainty. In this study, we formulate the uncertain population exposure from a fuzzy programming perspective by using triangular fuzzy numbers. Moreover, the carbon dioxide emission constraint is formulated to realize the sustainable transportation of hazardous materials. To optimize the problem under the above framework, we first establish a bi-objective fuzzy mixed integer nonlinear programming model, and then develop a three-stage exact solution strategy that the combines fuzzy credibilistic chance constraint, linearization technique, and the normalized weighting method. Finally, a computational experiment is carried out to verify the feasibility of the proposed method in dealing with the problem. The experimental results indicate that tradeoffs between the two conflicting objectives can be effectively made by using the Pareto frontier to the hazardous materials routing problem. Furthermore, the credibility level and carbon dioxide emission cap significantly influence the hazardous materials routing optimization. Their effects on the optimization result are quantified by using sensitivity analysis, which can draw some useful insights to help decision makers to better organize the hazardous materials road-rail multimodal transportation under uncertainty and sustainability. Document type: Articl

OPTIMIZATION OF RAILWAY TRANSPORTATION HAZMATS AND REGULAR COMMODITIES

Transportation of dangerous goods has been receiving more attention in the realm of academic and scientific research during the last few decades as countries have been increasingly becoming industrialized throughout the world, thereby making Hazmats an integral part of our life style. However, the number of scholarly articles in this field is not as many as those of other areas in SCM. Considering the low-probability-and-high-consequence (LPHC) essence of transportation of Hazmats, on the one hand, and immense volume of shipments accounting for more than hundred tons in North America and Europe, on the other, we can safely state that the number of scholarly articles and dissertations have not been proportional to the significance of the subject of interest. On this ground, we conducted our research to contribute towards further developing the domain of Hazmats transportation, and sustainable supply chain management (SSCM), in general terms. Transportation of Hazmats, from logistical standpoint, may include all modes of transport via air, marine, road and rail, as well as intermodal transportation systems. Although road shipment is predominant in most of the literature, railway transportation of Hazmats has proven to be a potentially significant means of transporting dangerous goods with respect to both economies of scale and risk of transportation; these factors, have not just given rise to more thoroughly investigation of intermodal transportation of Hazmats using road and rail networks, but has encouraged the competition between rail and road companies which may indeed have some inherent advantages compared to the other medium due to their infrastructural and technological backgrounds. Truck shipment has ostensibly proven to be providing more flexibility; trains, per contra, provide more reliability in terms of transport risk for conveying Hazmats in bulks. In this thesis, in consonance with the aforementioned motivation, we provide an introduction into the hazardous commodities shipment through rail network in the first chapter of the thesis. Providing relevant statistics on the volume of Hazmat goods, number of accidents, rate of incidents, and rate of fatalities and injuries due to the incidents involving Hazmats, will shed light onto the significance of the topic under study. As well, we review the most pertinent articles while putting more emphasis on the state-of-the-art papers, in chapter two. Following the discussion in chapter 3 and looking at the problem from carrier company’s perspective, a mixed integer quadratically constraint problem (MIQCP) is developed which seeks for the minimization of transportation cost under a set of constraints including those associating with Hazmats. Due to the complexity of the problem, the risk function has been piecewise linearized using a set of auxiliary variables, thereby resulting in an MIP problem. Further, considering the interests of both carrier companies and regulatory agencies, which are minimization of cost and risk, respectively, a multiobjective MINLP model is developed, which has been reduced to an MILP through piecewise linearization of the risk term in the objective function. For both single-objective and multiobjective formulations, model variants with bifurcated and nonbifurcated flows have been presented. Then, in chapter 4, we carry out experiments considering two main cases where the first case presents smaller instances of the problem and the second case focuses on a larger instance of the problem. Eventually, in chapter five, we conclude the dissertation with a summary of the overall discussion as well as presenting some comments on avenues of future work

Stochastic Service Network Design for Intermodal Freight Transportation

In view of the accelerating climate change, greenhouse gas emissions from freight transportation must be significantly reduced over the next decades. Intermodal transportation can make a significant contribution here. During the transportation process, different modes of transportation are combined, enabling a modal shift to environmentally friendly alternatives such as rail and inland waterway transportation. However, at the same time, the organization of several modes is more complex compared to the unimodal case (where, for example, only trucks are employed). In particular, an efficient management of uncertainties, such as fluctuating transportation demand volumes or delays, is required to realize low costs and transportation times, thereby ensuring the attractiveness of intermodal transportation for a further modal shift. Stochastic service network design can explicitly consider such uncertainities in the planning in order to increase the performance of intermodal transportation. Decisions for the network design as well as for the mode choice are defined by mathematical optimization models, which originate from operations research and include relevant uncertainities by stochastic parameters. As central research gap, this dissertation addresses important operational constraints and decision variables of real-life intermodal networks, which have not been considered in these models so far and, in consequence, strongly limit their application in everyday operations. The resulting research contribution are two new variants of stochastic service network design models: The "stochastic service network design with integrated vehicle routing problem" integrates corresponding routing problems for road vehicles into the planning of intermodal networks. This new variant ensures a cost- and delay-minimal mode choice in the case of uncertain transportation times. The "stochastic service network design with short-term schedule modifications" deals with modifications of intermodal transportation schedules in order to adapt them to fluctuating demand as best as possible. For both new model variants, heuristic solution methods are presented which can efficiently solve even large network instances. Extensive case studies with real-world data demonstrate significant savings potentials compared to deterministic models as well as (simplified) stochastic models that already exist in literature

Modeling dry-port-based freight distribution planning

In this paper we review the dry port concept and its outfalls in terms of optimal design and management of freight distribution. Some optimization challenges arising from the presence of dry ports in intermodal freight transport systems are presented and discussed. Then we consider the tactical planning problem of defining the optimal routes and schedules for the fleet of vehicles providing transportation services between the terminals of a dry-port-based intermodal system. An original service network design model based on a mixed integer programming mathematical formulation is proposed to solve the considered problem. An experimental framework built upon realistic instances inspired by regional cases is described and the computational results of the model are presented and discussed

AIRO 2016. 46th Annual Conference of the Italian Operational Research Society. Emerging Advances in Logistics Systems Trieste, September 6-9, 2016 - Abstracts Book

The AIRO 2016 book of abstract collects the contributions from the conference participants. The AIRO 2016 Conference is a special occasion for the Italian Operations Research community, as AIRO annual conferences turn 46th edition in 2016. To reflect this special occasion, the Programme and Organizing Committee, chaired by Walter Ukovich, prepared a high quality Scientific Programme including the first initiative of AIRO Young, the new AIRO poster section that aims to promote the work of students, PhD students, and Postdocs with an interest in Operations Research. The Scientific Programme of the Conference offers a broad spectrum of contributions covering the variety of OR topics and research areas with an emphasis on “Emerging Advances in Logistics Systems”. The event aims at stimulating integration of existing methods and systems, fostering communication amongst different research groups, and laying the foundations for OR integrated research projects in the next decade. Distinct thematic sections follow the AIRO 2016 days starting by initial presentation of the objectives and features of the Conference. In addition three invited internationally known speakers will present Plenary Lectures, by Gianni Di Pillo, Frédéric Semet e Stefan Nickel, gathering AIRO 2016 participants together to offer key presentations on the latest advances and developments in OR’s research

The Design, Planning and Execution of Sustainable Intermodal Port-hinterland Transport Networks

Globalization has led to a tremendous growth of international trade over the last century amounting to $18.8 trillion in 2014. Approximately 90% of non-bulk cargo is transported in shipping containers. The dominant mode in container transportation is maritime, in which containers are transported from a seaport to another seaport around the globe. Import containers are discharged in seaport container terminals and are destined to inland locations, a reverse process happens for export containers. The inland terminals can be close or far away from the seaport terminals where the containers were discharged. The container transport between the seaport and the inland locations is called port-hinterland transportation. Given the specific physical characteristics and infrastructure of each area this part of the transportation chain can be performed via trucks, trains or river vessels. The sequential use of multiple transport modes in port-hinterland transport is called combined transport. The main aim of this study is to analyze the port-hinterland transportation process and to develop models that support the design, planning and execution of port-hinterland transportation networks with high capacity modes such as barges and trains

Operations Research for Planning and Managing City Logistics Systems

The chapter presents the Operations Research models and methods to plan and manage City Logistics systems, in particular their supply components. It presents the main planning issues and challenges, and reviews the proposed methodologies. The chapter concludes with a discussion on perspectives for City Logistics and decision-support methodological developments

Green intermodal freight transportation: bi-objective modeling and analysis

Efficient planning of freight transportation requires a comprehensive look at wide range of factors in the operation and management of any transportation mode to achieve safe, fast, and environmentally suitable movement of goods. In this regard, a combination of transportation modes offers flexible and environmentally friendly alternatives to transport high volumes of goods over long distances. In order to reflect the advantages of each transportation mode, it is the challenge to develop models and algorithms in Transport Management System software packages. This paper discusses the principles of green logistics required in designing such models and algorithms which truly represent multiple modes and their characteristics. Thus, this research provides a unique practical contribution to green logistics literature by advancing our understanding of the multi-objective planning in intermodal freight transportation. Analysis based on a case study from hinterland intermodal transportation in Europe is therefore intended to make contributions to the literature about the potential benefits from combining economic and environmental criteria in transportation planning. An insight derived from the experiments conducted shows that there is no need to greatly compromise on transportation costs in order to achieve a significant reduction in carbon-related emissions