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We present a multi-ob’jective two-echelon vehicle routing problem with vehicle synchronization and ‘grey zone’ customers arising in the context of urban freight deliveries. Inner-city center deliveries are performed by small vehicles due to access restrictions, while deliveries outside this area are carried out by conventional vehicles for economic reasons. Goods are transferred from the first to the second echelon by synchronized meetings between vehicles of the respective echelons. We investigate the assignment of customers to vehicles, i.e., to the first or second echelon, within a so-called ‘grey zone’ on the border of the inner city and the area around it. While doing this, the economic objective as well as negative external effects of transport, such as emissions and disturbance (negative impact on citizens due to noise and congestion), are taken into account to include objectives of companies as well as of citizens and municipal authorities. Our metaheuristic – a large neighborhood search embedded in a heuristic rectangle/cuboid splitting – addresses this problem efficiently. We investigate the impact of the free assignment of part of the customers (‘grey zone’) to echelons and of three different city layouts on the solution. Computational results show that the impact of a ‘grey zone’ and thus the assignment of these customers to echelons depend significantly on the layout of a city. Potentially pareto-optimal solutions for two and three objectives are illustrated to efficiently support decision makers in sustainable city logistics planning processes.

Classical Lagrangian relaxations for the multicommodity capacitated fixed-charge network design problem are the so-called flow and knapsack relaxations, where the resulting Lagrangian subproblems decompose by commodities and by arcs, respectively. We introduce node-based Lagrangian relaxations, where the resulting Lagrangian subproblem decomposes by nodes. We show that the Lagrangian dual bounds of these relaxations improve upon the linear programming relaxation bound, known to be equal to the Lagrangian dual bounds for the flow and knapsack relaxations. We also develop a Lagrangian matheuristic to compute upper bounds. The computational results on a set of benchmark instances show that the Lagrangian matheuristic is competitive with the state-of-the-art heuristics from the literature.

This paper studies the two-echelon capacitated vehicle routing problem with time windows. The first echelon consists of transferring freight from depots to intermediate facilities (i.e., satellites), whereas the second echelon consists of transferring freight from these facilities to the final customers, within their time windows. We propose two path-based mathematical formulations for our problem: (1) in one formulation, paths are defined over both first- and second-echelon tours, and (2) in the other one, the first- and second-echelon paths are decomposed. Branch-and-price–based algorithms are developed for both formulations. We compare both formulations and solution methods on a comprehensive set of instances and are able to solve instances up to five satellites and 100 customers to optimality. This paper is the first paper in the literature that solves such large instance sizes. The online appendix is available at https://doi.org/10.1287/trsc.2018.0844 .

This paper studies the multi-commodity two-echelon capacitated vehicle routing problem with time windows. Specifically, we take customer-specific origin to destination, non-substitutable demands into account. The main components are: (i) first echelon tours, (ii) second echelon tours and (iii) movement of commodities from first echelon to second echelon, considering route connection and synchronization. Exploiting the structure of the problem, we propose a decomposition scheme which decouples the first and second echelon routing problems, by deleting the third component, and re-couples them by using constraints ensuring movement of commodities between the two echelons. Based on this decomposition, we present (i) a number of model formulations for the MC-2E-VRPTW, and (ii) develop an exact solution approach for this real-life problem. A computational study on a comprehensive set of instances shows the effectiveness in terms of computational effort and solution quality.

When demand for transportation is low or sparse, traditional transit cannot provide efficient and good-quality service, because of its fixed structure. For this reason, mass transit is evolving toward some degree of flexibility. Although the extension of Dial-a-Ride systems to general public meets such need of adaptability, it presents several drawbacks mostly related to the their extreme flexibility. Consequently, new transportation alternatives, such as demand-adaptive systems (DASs), combining characteristics from both the traditional transit and Dial-a-Ride, have been introduced. For their twofold nature, DASs require careful planning. We focus on tactical aspects of the planning process by formalizing the single-line DAS design problem with stationary demand and proposing two alternative hierarchical decomposition approaches for its solution. The main motivation behind this work is to provide a general methodology suitable to be used as a tool to build the tactical DAS plan in real-life conditions. We provide an experimental study where the two proposed decomposition methods are compared and the general behavior of the systems is analyzed when altering some design parameters. Furthermore, we test the versatility of our methods on a variety of situation that may be encountered in real-life conditions.

Benders decomposition is a broadly used exact solution method for stochastic programs, which has been increasingly applied to solve transportation and logistics planning problems under uncertainty. However, this strategy comes with important drawbacks, such as a weak master problem following the relaxation step that confines the dual cuts to the scenario subproblems. In this paper, we propose a partial Benders decomposition methodology, based on the idea of including explicit information from the scenario subproblems in the master. To investigate the benefits of this methodology, we apply it to solve a general class of two-stage stochastic multicommodity network design models. Specifically, we solve the challenging variant of the model where both the demands and the arc capacities are stochastic. Through an extensive experimental campaign, we clearly show that the proposed methodology yields significant benefits in computational efficiency, solution quality, and stability of the solution process.

To improve efficiency and sustainability in synchromodal logistics, it becomes crucial to mitigate the disruptive effects caused by non-synchronized operations. In this work, we study the Synchronized Location-Transshipment Problem in which the decisions on the facilities to select are fundamental to ensure the synchronization of flows and the correct exploiting of just-in-time logistics and cross-docking operations. We propose a Mixed-Integer Linear Programming formulation for the problem and perform an economic analysis to address several realistic situations based on different sizes of the transshipment facilities and location types. The results of the tests allow us to derive managerial insights and to define future research lines.

Blocking constitutes an important rail freight transport operation, by which cars with potentially different origins and destinations are grouped to be moved and handled as a single unit, yielding economies of scale. We address the tactical block-planning problem for intermodal railroads, which has been little studied so far. We propose a new block service network design model explicitly considering the specificity of intermodal rail, and which may be solved using commercial software for realistic sizes. We illustrate the performance and interest of the proposed method through an extensive case study of a major North American railroad.

In this paper, we present a state-of-the-art branch-and-cut (B&C) algorithm for the multicommodity capacitated fixed charge network design problem (MCND). This algorithm combines bounding and branching procedures inspired by the latest developments in mixed-integer programming (MIP) software tools. Several filtering methods that exploit the structure of the MCND are also developed and embedded within the B&C algorithm. These filtering methods apply inference techniques to forbid combinations of values for some variables. This can take the form of adding cuts, reducing the domains of the variables, or fixing the values of the variables. Our experiments on a large set of randomly generated instances show that an appropriate selection of filtering techniques allows the B&C algorithm to perform better than the variant of the algorithm without filtering. These experiments also show that the B&C algorithm, with or without filtering, is competitive with a state-of-the-art MIP solver.

We study the Multi-trip Separate Pickup and Delivery Problem with Time Windows at Customers and Facilities (MT-PDTWCF), arising in two-tiered city logistics systems. The first tier refers to the transportation between the city distribution centers, in the outskirts of the city, and intermediate facilities, while the second tier refers to the transportation of goods between the intermediate facilities and the (pickup and delivery) customers. We focus on the second tier, and consider that customers and facilities have time windows in which they can be visited. Waiting is possible at waiting stations for free or at customers and facilities at a given cost or penalty. Therefore, it is relevant to coordinate the arrivals of vehicles at facilities and customers with the corresponding time windows. The MT-PDTWCF calls for determining minimum (fixed, routing and waiting) cost multi-trip routes, for a given fleet of vehicles, to service separately pickup and delivery customers, while taking into account vehicle capacity and time windows both at customers and facilities. We propose the first exact algorithm for MT-PDTWCF, namely a Branch-and-Cut-and-Price algorithm. It is based on column generation, where the pricing problem is solved by a bi-directional dynamic programming algorithm designed to cope with the features of the problem. Subset-row and rounded capacity inequalities are adapted to deal with MT-PDTWCF and inserted in the Branch-and-Cut-and-Price algorithm. The performance of the proposed algorithm is tested on benchmark instances with up to 200 customers, showing its effectiveness.

International audience

We propose a new ILP formulation for the multi-commodity location-routing problem.The new formulation models the location decisions by a flow intercepting approach.The usage of the proposed formulation for the design of a single-tier freight distribution system is discussed.A branch-and-cut algorithm is proposed and experienced on test networks with different topologies. Research on the location-routing problem (LRP) is very active, producing a good number of effective exact and approximated solution approaches. It is noteworthy that most of the contributions present in the literature address the single-commodity LRP, whereas the multi-commodity case has been scarcely investigated. Yet, this issue assumes an important role in many LRP applications, particularly in the context of designing single-tier freight distribution City Logistics systems. To fill this gap, we define a new multi-commodity LRP, proposing an original integer linear programming model for it. The proposed formulation takes into account the multi-commodity feature of the problem, modeling the strategic location and the tactical routing decisions using the flow intercepting approach. We therefore name this problem the flow intercepting facility location-routing problem. It is solved by a branch-and-cut algorithm which exploits cuts derived and adapted from literature. The proposed method is successfully experienced and validated on test instances reproducing different network topologies and problem settings.

The scope of this paper is to advance the investigation into the importance of introducing uncertainty in service network design (SND) formulations by examining the uncertainty of travel times, a phenomenon that has been little studied up to now. The topic of our research thus is the stochastic scheduled service network design problem with service-quality targets and uncertainty on travel times, an important problem raising in the tactical planning process of consolidation-based freight carriers. Quality-service targets relate to the on-time operation of services and delivery of commodity flows to destinations. The problem is formulated as a two-stage mixed-integer linear stochastic model defined over a space-time network, with service targets modelled through penalties. Its aim is to define a cost-efficient transportation plan such that the chosen quality-service targets are respected as much as possible over time. An extensive experimental campaign is proposed using a large set of random generated instances with the scope of enhancing the understanding of the relations between the characteristics of a service network and its robustness, in terms of respect of the service schedule and delivery due dates, given business-as-usual fluctuations of travel times. Several analyses are reported identifying the features that appear in stochastic solutions to hedge against or, at least, reduce the bad effects of travel time uncertainty on the performance of a service network.

We study the value of deterministic solutions, in particular their quality and upgradability, in addressing stochastic network design problems, by analyzing their time-dependent formulations known as scheduled service network design problems in freight transportation planning. We study several problem variants and models and investigate, for each case, the immediate quality of the deterministic solutions stemming from the 50th and the 75th percentile of the demand distributions. We then show that for all models, but in different ways, we are able to make effective use of parts of the deterministic solution, confirming the value of the deterministic solution in the stochastic environment, even when the deterministic solution itself performs badly. We also investigate what makes the optimal stochastic solution better in the stochastic environment than other feasible solutions, particularly those obtained by addressing deterministic versions of the problem. We do this by quantitatively analyzing the structures of different solutions. A measurement scheme is proposed to evaluate the level of potentially beneficial structural properties (multipath usage and path sharing) in different solutions. We show that these structural properties are important and correlated with the performance of a solution in the stochastic environment. © 2018. This is the authors' accepted and refereed manuscript to the article. The final authenticated version is available online at: https://doi.org/10.1287/ijoc.2018.0819

International audience

We present the symmetric traveling salesman problem with generalized latency (TSP-GL) a new problem arising in the planning of the important class of semiflexible transit systems. The TSP-GL can be seen as a very challenging variant of the symmetric traveling salesman problem (S-TSP), where the objective function combines the usual cost of the circuit with a routing component accounting for the passenger travel times. The main contributions of the paper include the formulation of the problems in terms of multicommodity flows, the study of its mathematical properties, and the introduction of a branch-and-cut approach based on Benders reformulation taking advantage of properties that relate the feasible region of the TSP-GL and the S-TSP polyhedron. An extensive computational experimentation compares a number of variants of the proposed algorithm, as well as a commercial solver. These experiments show that the method we propose significantly outperforms a well-known commercial solver and obtains good-quality solutions to realistically sized instances within short computational times.

International audience; We propose a revenue management (RM) model for the network capacity allocation problem of an intermodal barge transportation system. Accept/reject decisions are made based on a probabilistic mixed integer optimization model maximizing the expected revenue of the carrier over a given time horizon. Probability distribution functions are used to characterize future potential demands. The simulated booking system solves, using a commercial software, the capacity allocation problem for each new transportation request. A conventional model for dynamic capacity allocation considering only the available network capacity and the delivery time constraints is used as alternative when analyzing the results of the proposed model.; We propose a revenue management (RM) model for the network capacity allocation problem of an intermodal barge transportation system. Accept/reject decisions are made based on a probabilistic mixed integer optimization model maximizing the expected revenue of the carrier over a given time horizon. Probability distribution functions are used to characterize future potential demands. The simulated booking system solves, using a commercial software, the capacity allocation problem for each new transportation request. A conventional model for dynamic capacity allocation considering only the available network capacity and the delivery time constraints is used as alternative when analyzing the results of the proposed model.

We consider the complex and not-yet-studied issue of building the tactical plan of a two-tiered city logistics system while explicitly accounting for the uncertainty in the forecast demand. We describe and formally define the problem and then propose a general modeling framework, which takes the form of a two-stage stochastic programming formulation, the first stage selecting the first-tier service network design and the general workloads of the intertier transfer facilities, and the second stage determines the actual vehicle routing on the second tier as well as some limited adjustments of the first-stage service design decisions. Four different strategies of adapting the plan to the observed demand are introduced together with the associated recourse formulations. These strategies are then experimentally compared through an evaluation procedure that, based on Monte Carlo principles, mimics the decision process of a priori planning followed by repetitively applying the adjusted plan to the periods of the planning horizon. The performances of the city logistics system under the adjustment strategies are contrasted through performance measures relative to the costs of operating the system, including those of additional vehicle capacity and movements required when the plan does not provide sufficient transportation means, the utilization of the various types of vehicles, the intensity of the vehicle presence within the city, and the utilization of the intertier transfer facilities. The comparisons are discussed both based on the numerical figures obtained through simulation and from the point of view of managerial insights into the implication for managing city logistics physical and human resources. The analysis emphasizes the interest of flexibility in managing resources and operations for the overall performance of the system, discusses the associated trade-offs, and underlines the benefits of consolidation in terms of system efficiency and impact on the city. The comparisons also show that even when demand variability and management constraints are explicitly taken into account, our approach is still able to build good tactical plans.

International audience; The contribution of infeasible solutions in heuristic searches for vehicle routing problems (VRP) is not a subject of consensus in the metaheuristics community. Infeasible solutions may allow transitioning between structurally different feasible solutions, thus enhancing the search, but they also lead to more complex move-evaluation procedures and wider search spaces. This paper introduces an experimental assessment of the impact of infeasible solutions on heuristic searches, through various empirical studies on local improvement procedures, iterated local searches, and hybrid genetic algorithms for the VRP with time windows and other related variants with fleet mix, backhauls, and multiple periods. Four relaxation schemes are considered, allowing penalized late arrivals to customers, early and late arrivals, returns in time, or a flexible travel time relaxation. For all considered problems and methods, our experiments demonstrate the significant positive impact of penalized infeasible solution. Differences can also be observed between individual relaxation schemes. The “returns in time” and “flexible travel time” relaxations appear as the best options in terms of solution quality, CPU time, and scalability.

Timing problems involve the choice of task execution dates within a predetermined processing sequence, and under various additional constraints or objectives such as time windows, time-dependent costs, or flexible processing times, among others. Their efficient resolution is critical in branch and bound and neighborhood search methods for vehicle routing, project and machine scheduling, as well as in various applications in network optimization, resource allocation, and statistical inference. Timing-related problems have been studied for years, yet research on this subject suffers from a lack of consensus, and most knowledge is scattered among operations research and applied mathematics domains. This article introduces a classification of timing problems and features, as well as a unifying multidisciplinary analysis of timing algorithms. In relation to frequent application cases within branching schemes or neighborhood searches, the efficient resolution of series of similar timing subproblems is also analyzed. A dedicated formalism of reoptimization "by concatenation" is introduced to that extent. The knowledge developed through this analysis is valuable for modeling and algorithmic design, for a wide range of combinatorial optimization problems with time characteristics, including rich vehicle routing settings and emerging nonregular scheduling applications, among others. © 2015 Wiley Periodicals, Inc. NETWORKS, Vol. 652, 102-128 2015

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.

International audience; Vehicle routing variants with multiple depots and mixed fleet present intricate combinatorial aspects related to sequencing choices, vehicle type choices, depot choices, and depots positioning. This paper introduces a dynamic programming methodology for efficiently evaluating compound neighborhoods combining sequence-based moves with an optimal choice of vehicle and depot, and an optimal determination of the first customer to be visited in the route, called rotation. The assignment choices, making the richness of the problem, are thus no more addressed in the solution structure, but implicitly determined during each move evaluation. Two meta-heuristics relying on these concepts, an iterated local search and a hybrid genetic algorithm, are presented. Extensive computational experiments demonstrate the remarkable performance of these methods on classic benchmark instances for multi-depot vehicle routing problems with and without fleet mix, as well as the notable contribution of the implicit depot choice and positioning methods to the search performance. New state-of-the-art results are obtained for multi-depot vehicle routing problems (MDVRP), and multi-depot vehicle fleet mix problems (MDVFMP) with unconstrained fleet size. The proposed concepts are fairly general, and widely applicable to many other vehicle routing variants.

International audience; Vehicle routing attributes are extra characteristics and decisions that complement the academic problem formulations and aim to properly account for real-life application needs. Hundreds of methods have been introduced in recent years for specific attributes, but the development of a single, general-purpose algorithm, which is both efficient and applicable to a wide family of variants remains a considerable challenge. Yet, such a development is critical for understanding the proper impact of attributes on resolution approaches, and to answer the needs of actual applications. This paper contributes towards addressing these challenges with a component-based design for heuristics, targeting multi-attribute vehicle routing problems, and an efficient general-purpose solver. The proposed Unified Hybrid Genetic Search metaheuristic relies on problem-independent unified local search, genetic operators, and advanced diversity management methods. Problem specifics are confined to a limited part of the method and are addressed by means of assignment, sequencing, and route-evaluation components, which are automatically selected and adapted and provide the fundamental operators to manage attribute specificities. Extensive computational experiments on 29 prominent vehicle routing variants, 42 benchmark instance sets and overall 1099 instances, demonstrate the remarkable performance of the method which matches or outperforms the current state-of-the-art problem-tailored algorithms. Thus, generality does not necessarily go against efficiency for these problem classes.

In the Variable Cost and Size Bin Packing Problem with optional items, a set of items characterized by volume and profit and a set of bins of different types characterized by volume and cost are given. The goal consists in selecting those items and bins which optimize an objective function which combines the cost of the used bins and the profit of the selected items. We propose two methods to tackle the problem: branch-and-price as an exact method and beam search as a heuristics, derived from the branch-and-price. Our branch-and-price method is characterized by a two level branching strategy. At the first level the branching is performed on the number of available bins for each bin type. At the second level it consists on pairs of items which can or cannot be loaded together. Exploiting the branch-and-price skeleton, we then present a variegated beam search heuristics, characterized by different beam sizes. We finally present extensive computational results which show a high accuracy of the exact method and a very good efficiency of the proposed heuristics.

In this paper, we deal with the generation of bundles of loads to be submitted by carriers participating in combinatorial auctions in the context of long-haul full truckload transportation services. We develop a probabilistic optimization model that integrates the bid generation and pricing problems together with the routing of the carrier’s fleet. We propose two heuristic procedures that enable us to solve models with up to 400 auctioned loads.

This paper introduces a cooperative parallel metaheuristic for the capacitated vehicle routing problem. The proposed metaheuristic consists of multiple parallel tabu search threads that cooperate by asynchronously exchanging best-found solutions through a common solution pool. The solutions sent to the pool are clustered according to their similarities. The search history information identified from the solution clusters is applied to guide the intensification or diversification of the tabu search threads. Computational experiments on two sets of large-scale benchmark instance sets from the literature demonstrate that the suggested metaheuristic is highly competitive, providing new best solutions to ten of those well-studied instances.

International audience; The attributes of vehicle routing problems are additional characteristics or constraints that aim to better take into account the specificities of real applications. The variants thus formed are supported by a well-developed literature, including a large variety of heuristics. This article first reviews the main classes of attributes, providing a survey of heuristics and meta-heuristics for Multi-Attribute Vehicle Routing Problems (MAVRP). It then takes a closer look at the concepts of 64 remarkable meta-heuristics, selected objectively for their outstanding performance on 15 classic MAVRP with different attributes. This cross-analysis leads to the identification of “winning strategies” in designing effective heuristics for MAVRP. This is an important step in the development of general and efficient solution methods for dealing with the large range of vehicle routing variants.

When demand for transportation is low or sparse, traditional transit cannot provide efficient and good-quality service, due to its fixed structure. New transportation alternatives are therefore increasingly proposed, combining on-demand service adjustment capabilities to the regular route and schedule characteristics of traditional transit. Such so-called semi-flexible systems require careful planning, but no formalization of the corresponding decisions problems, nor any comprehensive methodology has been proposed yet. This paper aims at contributing to fill this gap by presenting a comprehensive literature review, and a general and unifying modeling framework for representing and planning semi-flexible systems. The latter takes the form of the Demand Adaptive Systems, which generalizes the semi-flexible systems described in the literature, and also offers a number of advanced features, the scheduling mechanism, in particular. The paper then provides a classification of planning decisions, which is used to structure a comprehensive and comparative literature review of the field of semi-flexible systems, including methodological contributions as well as a number of particularly significant practical experiences.

International audience; The paper presents an efficient Hybrid Genetic Search with Advanced Diversity Control for a large class of time-constrained vehicle routing problems, introducing several new features to manage the temporal dimension. New move evaluation techniques are proposed, accounting for penalized infeasible solutions with respect to time-window and duration constraints, and allowing to evaluate moves from any classical neighbourhood based on arc or node exchanges in amortized constant time. Furthermore, geometric and structural problem decompositions are developed to address efficiently large problems. The proposed algorithm outperforms all current state-of-the-art approaches on classical literature benchmark instances for any combination of periodic, multi-depot, site-dependent, and duration-constrained vehicle routing problem with time windows.

This paper presents a parallel tabu search algorithm that utilizes several different neighborhood structures for solving the capacitated vehicle routing problem. Single neighborhood or neighborhood combinations are encapsulated in tabu search threads and they cooperate through a solution pool for the purpose of exploiting their joint power. The computational experiments on 32 large scale benchmark instances show that the proposed method is highly effective and competitive, providing new best solutions to four instances while the average deviation of all best solutions found from the collective best results reported in the literature is about 0.22%. We are also able to associate the beneficial use of special neighborhoods with some test instance characteristics and uncover some sources of the collective power of multi-neighborhood cooperation.

In this paper we address the Two-Echelon Vehicle Routing Problem (2E-VRP), the variant of VRP where freight is delivered from depots to intermediate satellites, and then it is delivered to customers while minimizing the global routing costs of the overall two-echelon network. The goal of this work is to address more realistic situations in urban freight delivery where the travel costs are not only given by distances, but also by other components, like fixed costs for using the arcs, operational costs, and environmental costs. We introduce a generalized travel cost that may combine different components, e.g., operational, environmental, congestion, etc. We then analyze how the different components of the generalized cost affect the satellite location in the 2E-VRP and whether and under which conditions the Two-Echelon approach dominates the Single-Echelon one.

We have developed a pattern-identification mechanism that endows cooperative search with capabilities to create new information and guide the global search. The proposed mechanism sends information to independent metaheuristics about promising and unpromising patterns in the solution space. By fixing or prohibiting specific solution attribute values in certain search metaheuristics, we can focus the search on desired regions. The mechanism thus enforces better coordination between individual methods and controls the global search's diversification and intensification. An enhanced cooperative-search mechanism creates new information from exchanged solutions and guides the global search with a pattern-identification mechanism.

City logistics aims to reduce the nuisances associated to freight transportation in urban areas while supporting their economic and social development. The fundamental idea is to view individual stakeholders and decisions as components of an integrated logistics system. This implies the coordination of shippers, carriers, and movements as well as the consolidation of loads of several customers and carriers into the same environment-friendly vehicles. City logistics explicitly aims to optimize such advanced urban transportation systems. We focus on a challenging city logistics planning issue, the integrated short-term scheduling of operations and management of resources, for the general case involving a two-tiered distribution structure. We investigate the main issues related to the problem, introduce a new problem class, propose both a general model and formulations for the main system components, and identify promising solution avenues.

Three-dimensional orthogonal bin packing is a problem NP-hard in the strong sense where a set of boxes must be orthogonally packed into the minimum number of three-dimensional bins. We present a two-level tabu search for this problem. The first-level aims to reduce the number of bins. The second optimizes the packing of the bins. This latter procedure is based on the Interval Graph representation of the packing, proposed by Fekete and Schepers, which reduces the size of the search space. We also introduce a general method to increase the size of the associated neighborhoods, and thus the quality of the search, without increasing the overall complexity of the algorithm. Extensive computational results on benchmark problem instances show the effectiveness of the proposed approach, obtaining better results compared to the existing ones.

Urban freight transportation constitutes both an extremely important and a rather disturbing activity. Increasingly, one observes efforts to measure and control freight movements within city centers. We introduce a possible organizational and technological framework for the integrated management of urban freight transportation and identify important associated planning and operation issues and models. We then describe a formulation for one of these problems, the design of the proposed logistical structure, and discuss algorithmic and implementation issues. Our model city and challenge is Rome.

City Logistics generally addresses inbound movements, from regions outside the city to the zone under City Logistics control. Integrating outbound and intra-city traffic may contribute to make freight transportation more efficient and less intrusive on mobility, the environment, and general quality-of-life conditions in the city. It may also raise significant managerial and methodological challenges, which increase with the degree of integration of fleets and operations. We describe and analyze a number of representative service-integration scenarios, discuss operational and managerial issues, examine corresponding methodological challenges, and identify associated research avenues.

In this paper, we address the Two-Echelon Vehicle Routing Problem (2E-VRP) in which freight is delivered from depots to intermediary satellites, from where it is delivered to customers. Different from other multi-level problems, the focus in 2E-VRP is on the global routing costs of the overall network. 2E-VRP has been recently introduced and, to our knowledge, there are no studies of the impact of instance parameters on the global cost. We present the results of a broad experimental study aimed at analyzing the impact on the total distribution cost of several parameters including customer distribution, satellites-location rules, depot location, number of satellites, mean accessibility of the satellites, and mean transportation cost between the satellites and the customers.

Various indicators are used to qualify the performance of intermodal transportation systems. Some of these are found in public documents, usually providing global measures such as total flow volumes, profits, and share values. While of great interest, such measures are not sufficient to support a fine analysis of different operation strategies, commercial policies, and planning methods. Additional measures are used in the scientific literature to address these issues. Our first goal is to review the performance indicators found in scientific literature and to qualify them with respect to tactical planning of intermodal barge transportation systems. We extend this analysis to include revenue management policies, a topic generally neglected in freight transportation. We also discuss procedures to generate problem instances that provide the means to analyze planning methods and system behavior based on these performance indicators.