Collaborative urban transportation : Recent advances in theory and practice

We thank the Leibniz Association for sponsoring the Dagstuhl Seminar 16091, at which the work presented here was initiated. We also thank Leena Suhl for her comments on an early version of this work. Finally, we thank the anonymous reviewers for the constructive comments.Peer reviewedPostprin

Stochastic programming for City Logistics: new models and methods

The need for mobility that emerged in the last decades led to an impressive increase in the number of vehicles as well as to a saturation of transportation infrastructures. Consequently, traffic congestion, accidents, transportation delays, and polluting emissions are some of the most recurrent concerns transportation and city managers have to deal with. However, just building new infrastructures might be not sustainable because of their cost, the land usage, which usually lacks in metropolitan regions, and their negative impact on the environment. Therefore, a different way of improving the performance of transportation systems while enhancing travel safety has to be found in order to make people and good transportation operations more efficient and support their key role in the economic development of either a city or a whole country. The concept of City Logistics (CL) is being developed to answer to this need. Indeed, CL focus on reducing the number of vehicles operating in the city, controlling their dimension and characteristics. CL solutions do not only improve the transportation system but the whole logistics system within an urban area, trying to integrate interests of the several. This global view challenges researchers to develop planning models, methods and decision support tools for the optimization of the structures and the activities of the transportation system. In particular, this leads researchers to the definition of strategic and tactical problems belonging to well-known problem classes, including network design problem, vehicle routing problem (VRP), traveling salesman problem (TSP), bin packing problem (BPP), which typically act as sub-problems of the overall CL system optimization. When long planning horizons are involved, these problems become stochastic and, thus, must explicitly take into account the different sources of uncertainty that can affect the transportation system. Due to these reasons and the large-scale of CL systems, the optimization problems arising in the urban context are very challenging. Their solution requires investigations in mathematical and combinatorial optimization methods as well as the implementation of efficient exact and heuristic algorithms. However, contributions answering these challenges are still limited number. This work contributes in filling this gap in the literature in terms of both modeling framework for new planning problems in CL context and developing new and effective heuristic solving methods for the two-stage formulation of these problems. Three stochastic problems are proposed in the context of CL: the stochastic variable cost and size bin packing problem (SVCSBPP), the multi-handler knapsack problem under uncertainty (MHKPu) and the multi-path traveling salesman problem with stochastic travel times (mpTSPs). The SVCSBPP arises in supply-chain management, in which companies outsource the logistics activities to a third-party logistic firm (3PL). The procurement of sufficient capacity, expressed in terms of vehicles, containers or space in a warehouse for varying periods of time to satisfy the demand plays a crucial role. The SVCSBPP focuses on the relation between a company and its logistics capacity provider and the tactical-planning problem of determining the quantity of capacity units to secure for the next period of activity. The SVCSBPP is the first attempt to introduce a stochastic variant of the variable cost and size bin packing problem (VCSBPP) considering not only the uncertainty on the demand to deliver, but also on the renting cost of the different bins and their availability. A large number of real-life situations can be satisfactorily modeled as a MHKPu, in particular in the last mile delivery. Last mile delivery may involve different sequences of consolidation operations, each handled by different workers with different skill levels and reliability. The improper management of consolidation operations can cause delay in the operations reducing the overall profit of the deliveries. Thus, given a set of potential logistics handlers and a set of items to deliver, characterized by volume and random profit, the MHKPu consists in finding a subset of items which maximizes the expected total profit. The profit is given by the sum of a deterministic profit and a stochastic profit oscillation, with unknown probability distribution, due to the random handling costs of the handlers.The mpTSPs arises mainly in City Logistics applications. Cities offer several services, such as garbage collection, periodic delivery of goods in urban grocery distribution and bike sharing services. These services require the planning of fixed and periodic tours that will be used from one to several weeks. However, the enlarged time horizon as well as strong dynamic changes in travel times due to traffic congestion and other nuisances typical of the urban transportation induce the presence of multiple paths with stochastic travel times. Given a graph characterized by a set of nodes connected by arcs, mpTSPs considers that, for every pair of nodes, multiple paths between the two nodes are present. Each path is characterized by a random travel time. Similarly to the standard TSP, the aim of the problem is to define the Hamiltonian cycle minimizing the expected total cost. These planning problems have been formulated as two-stage integer stochastic programs with recourse. Discretization methods are usually applied to approximate the probability distribution of the random parameters. The resulting approximated program becomes a deterministic linear program with integer decision variables of generally very large dimensions, beyond the reach of exact methods. Therefore, heuristics are required. For the MHKPu, we apply the extreme value theory and derive a deterministic approximation, while for the SVCSBPP and the mpTSPs we introduce effective and accurate heuristics based on the progressive hedging (PH) ideas. The PH mitigates the computational difficulty associated with large problem instances by decomposing the stochastic program by scenario. When effective heuristic techniques exist for solving individual scenario, that is the case of the SVCSBPP and the mpTSPs, the PH further reduces the computational effort of solving scenario subproblems by means of a commercial solver. In particular, we propose a series of specific strategies to accelerate the search and efficiently address the symmetry of solutions, including an aggregated consensual solution, heuristic penalty adjustments, and a bundle fixing technique. Yet, although solution methods become more powerful, combinatorial problems in the CL context are very large and difficult to solve. Thus, in order to significantly enhance the computational efficiency, these heuristics implement parallel schemes. With the aim to make a complete analysis of the problems proposed, we perform extensive numerical experiments on a large set of instances of various dimensions, including realistic setting derived by real applications in the urban area, and combinations of different levels of variability and correlations in the stochastic parameters. The campaign includes the assessment of the efficiency of the meta-heuristic, the evaluation of the interest to explicitly consider uncertainty, an analysis of the impact of problem characteristics, the structure of solutions, as well as an evaluation of the robustness of the solutions when used as decision tool. The numerical analysis indicates that the stochastic programs have significant effects in terms of both the economic impact (e.g. cost reduction) and the operations management (e.g. prediction of the capacity needed by the firm). The proposed methodologies outperform the use of commercial solvers, also when small-size instances are considered. In fact, they find good solutions in manageable computing time. This makes these heuristics a strategic tool that can be incorporated in larger decision support systems for CL

Cargo Logistics Airlift Systems Study (CLASS). Volume 2: Case study approach and results

Models of transportation mode decision making were developed. The user's view of the present and future air cargo systems is discussed. Issues summarized include: (1) organization of the distribution function; (2) mode choice decision making; (3) air freight system; and (4) the future of air freight

The international air cargo transport system: technological evolutions and perspectives in view of traffic trends, network management and energy issues

Air transport industry has gradually increased its share of global passenger and freight traffic, and this trend has accelerated in the last forty years. In the past, air-freight sector offered limited services, with heavy reliance on several intermediaries and a significant dependence on air passenger operations. The sector can now be characterized as a sophisticated, innovative one, relying heavily on new electronic technologies, offering a wide range of transport and logistical products through dedicated specialist freight operators. This project has been developed in two phases. In the first place, it has been examined air freight sector in terms of its growth, structure and organization. It has been also analysed the constraints facing the air cargo movements and possible strategies for accommodating growth in air cargo in future prospects. In the second place, a several simulations of freight aircraft and trains were carried out using the Advanced Emission Model (AEM) software, from Eurocontrol, in order to make a comparison of energy consumption between high speed trains and aircraft, both carrying freight. Finally, the last part of the project is to analyse the results obtained from the simulations and make a conclusion to determine which mode of transport is the best to carry freight in terms of time, cost and energy consumption.La industria del transporte aéreo ha aumentado gradualmente su participación global del tráfico de pasajeros y carga, y esta tendencia se ha acelerado en los últimos cuarenta años. En el pasado, el sector de carga aérea ofrecía servicios limitados, con una fuerte dependencia de varios intermediarios y una dependencia significativa de las operaciones de pasajeros aéreos. El sector se puede caracterizar ahora como sotisficado e innovador, que depende en gran medida de las nuevas tecnologías electrónicas y ofrece una amplia gama de productos de transporte y logística a través de operadores de carga especializados. Este proyecto se ha desarrollado en dos fases. En primer lugar, se ha examinado el sector de carga aérea en términos de crecimiento, estructura y organización. Se ha analizado también las restricciones que se enfrenta los movimientos de la carga aérea y las posibles estrategias para acomodar su crecimiento en las perspectivas futuras. En segundo lugar, se realizaron varias simluaciones de trenes y aviones de carga utilizando el software Advanced Emission Model (AEM), de Eurocontrol, para hacer una comparación del consumo de energía entre trenes de alta velocidad y aviones, ambos con carga. Finalmente, la última parte del proyecto es analizar los resultados obtenidos de las simulaciones y llegar a una conclusión para determinar qué modo de transporte es el mejor para transportar mercancías en términos de tiempo, costo y consumo de energía.Outgoin

Nonstatistical Factors Influencing Predictions of Financial Distress and Managerial Implications in the All-Cargo Airline Industry

All-cargo airlines carry over 50% of global airfreight, yet they are prone to bankruptcy. Many financial models are designed to predict a firms\u27 financial health, but they do not assess many nonstatistical factors that influence the prediction capability of these models. In this study, qualitative grounded theory design was used to identify nonstatistical factors and explore how they influence bankruptcy prediction models in the all-cargo airline industry. In the first phase of the study, financial data from 2005 to 2009 for 17 all-cargo U.S. airlines were used to determine the bankruptcy prediction ability of the Kroeze financial bankruptcy model. A sample of six all-cargo airlines (ABX Air, Arrow Air, Atlas Air, Cargo 360, Gemini Air Cargo, and Kitty Hawk Air Cargo) were selected containing a mixture of airlines for which the Kroeze model correctly and incorrectly predicted bankruptcy. The sample was used as the starting point to explore the nonstatistical factors using grounded theory. Data were obtained on the six airlines from company annual reports, SEC 10K annual reports, reports from professional journals such as Air Transport Intelligence and Traffic World, news reports and company press releases. The data were coded and grouped into conceptual categories, which were used in theory generation to support the emerging theory. Six categories (management, risk, operations, competitive advantage, financial, and external factors) that relate to the financial stability of an all-cargo airline emerged during the research. Three themes emerged that may improve current quantitative bankruptcy prediction models. The three themes are airline fleet type, type of aircraft flown, and aircraft utilization. The three themes relate to the type, use, and make up of an airline’s fleet. These themes influence bankruptcy prediction model and should be incorporated into failure prediction models to improve their overall accuracy. Future research should be conducted to verify these findings on a larger population, such as all-cargo airlines that operate outside the United States. These airlines operate under different financial regimes that may affect the prediction models differently

Cargo Logistics Airlift Systems Study (CLASS). Volume 1: Analysis of current air cargo system

The material presented in this volume is classified into the following sections; (1) analysis of current routes; (2) air eligibility criteria; (3) current direct support infrastructure; (4) comparative mode analysis; (5) political and economic factors; and (6) future potential market areas. An effort was made to keep the observations and findings relating to the current systems as objective as possible in order not to bias the analysis of future air cargo operations reported in Volume 3 of the CLASS final report

Cargo Logistics Airlift Systems Study (CLASS). Volume 3: Cross impact between the 1990 market and the air physical distribution systems, book 1

The interrelations between the infrastructure and the forecast future market are discussed. Also, using forecasts of market growth for a base, future aircraft and air service concepts were evaluated

Book of abstracts of the 24th Euro Working Group on Transportation Meeting

Sem resumo disponível.publishe

Last-mile logistics optimization in the on-demand economy

L'abstract è presente nell'allegato / the abstract is in the attachmen