Real-world facility planning problems often require to tackle simultaneously network connectivity and zonal requirements, in order to guarantee an equitable provision of services and an efficient flow of goods, people and information among the facilities. Nonetheless, such challenges have not been addressed jointly so far. In this paper we explore the introduction of advanced network connectivity features and spatial-related requirements within Covering Location Problems. We adopt a broad modelling perspective, accounting for structural and economic aspects of connectivity features, while allowing the choice for one or more facilities to serve the facility networks as depots, and containing the maximal distance between any active facility and such depot(s). A novel class of Multi-objective Covering Location problems are proposed, utilising Mixed Integer Linear Programming as a modelling tool. Aiming at obtaining efficiently the arising Pareto Sets and providing actionable decision-making support throughout real planning processes, we adapt to our problem the robust variant of the AUGMEnted ?-CONstraint method (AUGMECON-R). Furthermore, we exploit the mathematical properties of the proposed problems to design tailored Matheuristic algorithms which boost the scalability of the solution method, with particular reference to the case of multiple depots. By conducting a comprehensive computational study on benchmark instances, we provide a thorough proof of concept for the novel problems, highlighting the challenging nature of the advanced connectivity features and the scalability of the proposed Matheuristics. From a managerial standpoint, the suitability of the proposed work in responding effectively to the motivating needs is showcased.

This paper considers a Multiple Objective variant of the Critical Disruption Path problem to extend itssuitability in a range of security operations relying on path-based network interdiction, including flight patternoptimisation for surveillance. Given a pair of nodes s and t from the network to be monitored, the problemseeks for loopless s - t paths such that, within the induced subgraph obtained via deletion of the path, thesize of the largest connected component is minimised, the number of connected components is maximised,while concurrently reducing as much as possible the cost of such disruption path. These three objectives arepossibly in conflict with each other, and the scope of this work is to allow for an efficient and insightfulapproximation of the Pareto front, looking for a trade-off between costs and effectiveness to secure the mostconvenient paths for security and surveillance operations. We first introduce and formulate the Multi-ObjectiveCritical Disruption Path Problem (Multi-Objs-CDP) as a mixed integer programming formulation (MO-CDP),then we propose an original evolutionary metaheuristic algorithm hybridising modified-NSGA-II and VNS forfinding an approximation of the Pareto front, as well as a procedure securing the efficient generation of a highquality pool of initial solutions. The experimental performance of the proposed algorithm, as compared witha variety of competing approaches, proves to be fully satisfactory in terms of time efficiency and quality ofthe solutions obtained on a set of medium to large benchmark instances.

Over the last decade, significant attention has been devoted to Closed-Loop Supply Chain (CLSC) design problems. As such, this review aims at assessing whether the current modelling approaches for CLSC problems can support the transition towards a Circular Economy at a supply chain level. The paper comprehensively assesses the extent to which existing modelling approaches evaluate the performance of supply chains across the complete spectrum of sustainability dimensions. Also, the capability of the current approaches of incorporating strategic, tactical, and operational decisions is considered, along with adopted solution methodologies. As a result, a comprehensive analysis was performed on 254 selected articles. This paper emphasises how most of the current literature in the field is affected by a disconnection between supply chain design and the founding principles of Circular Economy. Specifically, the CLSC literature exhibits a reductionist interpretation of the Circular Economy. CLSC studies focusing on all three dimensions of sustainability are relatively rare, and performance measurement approaches appear to be very much focused on monetary issues. While methodological contributions appear adequate to focus on the non-deterministic nature of CLSC design problems, there is paucity of empirically-grounded research. Coherently, a research agenda is proposed, in order to address the mentioned gaps and increase the relevance of this research field to practice.

Recently, many European local authorities have set up Urban Consolidation Centres (UCC) for dealing with challenges arising from the environmental and social impacts of logistical activities in urban contexts through shipment synchronisation and carrier coordination policies. However, the number of successful UCC projects led by local authorities in Europe is low, with most of the UCCs failing to achieve financial sustainability after the initial experimental phase, which is often heavily supported by public funds. In order to propose mechanisms that could favour the economic and financial sustainability of UCC systems, this research develops an adaptation of game-theoretic approaches to the problems of responsibility and cost allocation among stakeholders participating in a UCC delivery network. A solution based on the Shapley Value concept is employed to derive cost allocations; applications of the model to a real-world scenario are evaluated. An extensive sensitivity analysis shows that the proposed cost allocation rules can provide alternative arrangements, based on extended responsibility concepts, which can alleviate the burden on local authorities for the set up of UCCs. As such, results provide useful policy and practice implications on how to safeguard UCCs' viability under different scenarios, including the outsourcing of the last-mile deliveries.

The relevant role of freight lorry parking facilities as a tool to reduce nuisances and impact of economic activities in densely populated urban areas is widely recognised in the literature. Nevertheless, the literature currently lacks specific contributions addressing the use of a complex Multiple Criteria Decision Analysis (MCDA) approach for coping with an optimal location of freight lorry parking facilities in the urban context. This paper contributes to filling this gap by analysing a real-world case study motivated by the problem of intense freight vehicles traffic around the city of Bradford, Yorkshire (UK). Since it is necessary to include diverse analysis perspectives, reflecting the different classes of involved stakeholders, this study proposes adopting the Analytic Network Process (ANP) approach as a tool to support the selection and evaluation of alternatives for a freight lorry parking facility, followed by the design of software based on this approach. The proposed web Spatial Decision Support System provides a valuable tool to foster extended discussions with experts and facilitate the decision process in this class of location problems.

In the literature on Network Optimization, k-splittable flows were introduced to enhance modeling accuracy in cases where an upper bound on the number of supporting paths for each commodity needs to be imposed, thus extending the suitability of network flow tools for an increased number of practical applications. Such modeling feature has recently been extended to dynamic flows with the introduction of the novel strongly NP-hard Quickest Multicommodity k-splittable Flow Problem (QMCkFP). Such a flows over time problem asks for routing and scheduling of each commodity demand through at most k different paths in a dynamic network with arc capacities per time step, while minimizing the time required by the overall process. In this work, we propose the first exact algorithm for solving the QMCkSFP. The developed technique is a Branch and Price algorithm based on original relaxation, pricing and branching procedures. Linearization and variable substitution are used to obtain the relaxation problem from the path-based formulation of the QMCkSFP. The pricing problem is modeled as a Shortest Path Problem with Forbidden Paths with additional node-set resources on a time expansion of the original digraph and is solved via a tailored dynamic programming algorithm. Two branching rules are designed for restoring feasibility whenever k-splittable or binary variable domain constraints are violated. The results of an extensive batch of computational experiments conducted on small to medium-size reference instances are presented, showing a highly satisfactory performance of the proposed algorithm. The paper concludes with a discussion on further lines of research.

This paper considers a hub location problem where several carriers operate on a shared network to satisfy a given demand represented by a set of commodities. Possible cooperative strategies are studied where carriers can share resources or swap their respective commodities to produce tangible cost savings while fully satisfying the existing demand. Three different collaborative policies are introduced and discussed, and mixed integer programming formulations are provided for each of them. Theoretical analyses are developed in order to assess the potential savings of each model with respect to traditional non-collaborative approaches. An empirical performance comparison on state-of-art sets of instances offers a complementary viewpoint. The influence of several diverse problem parameters on the performance is analyzed to identify those operational settings enabling the highest possible savings for the considered collaborative hub location models. The number of carriers and the number of open hubs have shown to play a key role; depending on the collaborative strategy, savings of up to 50% can be obtained as the number of carriers increases or the number of open hubs decreases.

The literature on k-splittable flows, see Baier et al. (2002) Baier et al. (2005), provides evidence on how controlling the number of used paths enables practical applications of flows optimization in many real-world contexts. Such a modeling feature has never been integrated so far in Quickest Flows, a class of optimization problems suitable to cope with situations such as emergency evacuations, transportation planning and telecommunication systems, where one aims to minimize the makespan, i.e. the overall time needed to complete all the operations, see Pascoal et al. (2006) Pascoal et al. (2006). In order to bridge this gap, a novel optimization problem, the Quickest Multicommodity k-Splittable Flow Problem (QMCkSFP) is introduced in this paper. The problem seeks to minimize the makespan of transshipment operations for given demands of multiple commodities, while imposing restrictions on the maximum number of paths for each single commodity. The computational complexity of this problem is analyzed, showing its NP-hardness in the strong sense, and an original Mixed-Integer Programming formulation is detailed. We propose a matheuristic algorithm based on a hybridized Very Large-Scale Neighborhood Search that, utilizing the presented mathematical formulation, explores multiple search spaces to solve efficiently large instances of the QMCkSFP. High quality computational results obtained on benchmark test sets are presented and discussed, showing how the proposed matheuristic largely outperforms a state-of-the-art heuristic scheme frequently adopted in path-restricted flow problems.

This is the web-site of the workshop 'Mathematical Approach to Climate Change Impacts" held in Rome at INDAM on March 13 - 17, 2017, organized by Piermarco Cannarsa (uni. Tor Vergata), Daniela Mansutti (IAC - CNR) and Antonello Provenzale (IGG - CNR). Beside the program and other practical aspects related to the conference days, it exhibits the book of abstracts and, thanks to the generosity of the lecturers, the slides of each presentation (plenary, contributing and tutorial), which make it very rich of scientific information.

Abstract In this paper the Bi-Objective k-Length-Bounded Critical Disruption Path (BO-kLB-CDP) optimization problem is proposed, aimed at maximizing the interdiction effects provided on a network by removing a simple path connecting a given source and destination whose length does not exceed a certain threshold. The BO-kLB-CDP problem extends the Critical Disruption Path (CDP) problem introduced by Granata et al. in [Granata, D. and Steeger, G. and Rebennack, S., Network interdiction via a Critical Disruption Path: Branch-and-Price algorithms, Computers & Operations Research, Volume 40, Issue 11, November 2013, Pages 2689-2702]. Several real applications of this class of optimization problems arise in the field of security, surveillance, transportation and evacuation operations. In order to overcome some limits of the original {CDP} problem and increase its suitability for practical purposes, first we consider a length limitation for Critical Disruption Paths. Second, we generalize the concept of network interdiction considered in the CDP: beside minimizing the cardinality of the maximal connected component after the removal of the CDP, now we are also interested in maximizing the number of connected components in the residual graph. A Mixed Integer Programming formulation for the BO-kLB-CDP problem is therefore proposed and discussed, presenting the results of a multiple objective analysis performed through a computational experience on a large set of instances.

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.

In transportation networks with limited capacities and travel times on the arcs, a class of problems attracting a growing scientific interest is represented by the optimal routing and scheduling of given amounts of flow to be transshipped from the origin points to the specific destinations in minimum time. Such problems are of particular concern to emergency transportation where evacuation plans seek to minimize the time evacuees need to clear the affected area and reach the safe zones. Flows over time approaches are among the most suitable mathematical tools to provide a modelling representation of these problems from a macroscopic point of view. Among them, the Quickest Path Problem (QPP), requires an origin-destination flow to be routed on a single path while taking into account inflow limits on the arcs and minimizing the makespan, namely, the time instant when the last unit of flow reaches its destination. In the context of emergency transport, the QPP represents a relevant modelling tool, since its solutions are based on unsplittable dynamic flows that can support the development of evacuation plans which are very easy to be correctly implemented, assigning one single evacuation path to a whole population. This way it is possible to prevent interferences, turbulence, and congestions that may affect the transportation process, worsening the overall clearing time. Nevertheless, the current state-of-the-art presents a lack of studies on multicommodity generalizations of the QPP, where network flows refer to various populations, possibly with different origins and destinations. In this paper we provide a contribution to fill this gap, by considering the Multicommodity Quickest Path Problem (MCQPP), where multiple commodities, each with its own origin, destination and demand, must be routed on a capacitated network with travel times on the arcs, while minimizing the overall makespan and allowing the flow associated to each commodity to be routed on a single path. For this optimization problem, we provide the first mathematical formulation in the scientific literature, based on mixed integer programming and encompassing specific features aimed at empowering the suitability of the arising solutions in real emergency transportation plans. A computational experience performed on a set of benchmark instances is then presented to provide a proof-of-concept for our original model and to evaluate the quality and suitability of the provided solutions together with the required computational effort. Most of the instances are solved at the optimum by a commercial MIP solver, fed with a lower bound deriving from the optimal makespan of a splittable-flow relaxation of the MCQPP.

In this paper we introduce the Mathematical Desk for Italian Industry, a project based on applied and industrial mathematics developed by a team of researchers from the Italian National Research Council in collaboration with two major Italian associations for applied mathematics, SIMAI and AIRO. The scope of this paper is to clarify the motivations for this project and to present an overview on the activities, context and organization of the Mathematical Desk, whose mission is to build a concrete bridge of common interests between the Italian scientific community of applied mathematics and the world of the Italian enterprises. Some final considerations on the strategy for the future development of the Mathematical Desk project complete the paper.

This paper focuses on two-tier city logistics systems for advanced management of urban freight activities and, in particular, on the first layer of such systems where freight is moved from distribution centers on the outskirts of the city to satellite platforms by urban vehicles, from where it will be distributed to customers by a different fleet of dedicated vehicles. We address the issue of planning the services of this first tier system, that is, select services, their routes and schedules, and determine the itineraries of the customer-demand flows through these facilities and services. We propose a general scheduled service network design modelling framework that captures the fundamental concepts related to the definition of urban-vehicle tactical plans within a two-tier distribution network. We examine several operational assumptions regarding the management of the urban-vehicle fleet and the flexibility associated with the delivery of goods, and show how the proposed modelling framework can evolve to represent an increasing level of detail. A discussion of algorithmic perspectives completes the paper.

In this paper we propose a multiperiod optimization model based on the maximal covering location problem in order to support safety policies within urban areas. In particular, we focus on the field of car accidents control, by considering the problem of the optimal location of intersection safety cameras (ISC) on an urban traffic network to maximize road control and reduce the number and the impact of car accidents. The effectiveness of accidents prevention programs can be increased by changing periodically the position of the available ISCs on a given time horizon. To this aim, we propose a novel multiperiod maximal covering location approach designed to maximize the overall coverage on the whole discretized time horizon. The results of the application of this methodology on a real dataset concerning road accidents occurred on a portion of the urban traffic network of the city of Rome are presented and discussed.