ABSTRACT. We discuss how omni-channel supply chain networks can be structured in terms of facilities, inventory, transportation and information to be both cost effective and responsive to customer needs. This requires exploiting the complementary strengths of the physical and the online channel to create an omni-channel portfolio that can tailor the fulfillment of each customer request based on product and customer characteristics. To be successful, a firm must target price or service conscious customers using different channels based on the demand uncertainty, value, and information complexity of the product being sold. In general, the physical channel should be used to serve frequent and predictable needs in a cost effective manner. The online channel should be used to provide variety and serve sporadic needs at low cost. The physical channel can also serve as a showroom and pickup location for the online channel.

ABSTRACT. In recent years, online shopping is increasing the flows that transit into the urban areas. An increase in demand corresponds to an increase in operational complexity for logistics operators and in environmental issues. This paper presents the development of a mobile platform integrated with wearable features to foster the collaboration between different actors in freight urban logistics.

ABSTRACT. Two-tier city logistics is a well-established concept to achieve high levels of consolidation in urban freight distribution. With the recent shift towards offering same day delivery, service providers in parcel delivery are looking at new solutions to deal with the resulting challenges. We introduce autonomous vehicles as an upcoming technology to this field of research and consider a mixed fleet in the first tier of city logistics. Because autonomy cannot be ensured on all roads of the network, we handle the heterogeneous infrastructure with manually operated vehicles serving as platoon leaders. In our proposed MILP formulation of service network design for autonomous vehicles in platoons (SNDAVP), we show how platooning can be incorporated into this tactical planning problem. Computational experiments on first instances are conducted using CPLEX.

ABSTRACT. The freight transport in urban areas is fundamental for city life and for satisfying citizens’ needs but, at the same time, produces significant impacts (economic, social and environmental) on city sustainability. Thus, the new challenge facing urban planners is to find solutions that can reduce the impacts of urban goods mobility without penalising the life of the city (sustainable city logistics solutions/measures). The measures implemented traditionally concern freight traffic management (e.g. access time windows, vehicle access constraints and area pricing) and two-tier distribution systems (e.g. transit points and urban consolidation centres). However, in recent years, urban economies have been evolving rapidly towards a higher degree of material intensiveness: store inventory levels have shrunk and businesses are increasing their restocking activities based on the just-in-time concept (Browne et al., 2012). Following such changes, the demand for express transport and couriers is characterized by more frequent and customized deliveries. Therefore, the interactions among the involved actors raise and the methods and models for supporting such city planners’ activities should point out such interactions. Traditionally, methods and models simulate such interaction in aggregate level (Russo and Comi, 2010, Nuzzolo e Comi, 2014, Polimeni and Vitetta, 2014) and, although consider a behavioural approach, not follow the single user (i.e. a carrier) but consider their aggregation (focusing on the mean behaviour). To overtake such limitations, but with a greater computational effort, the Agent-Based Simulation (ABS) (Taniguchi et al., 2012) has been proposed. The ABS allows the representation of the interactions among the actors in urban freight (i.e. administrators, shippers, carriers, intermediaries, end-consumers), that often have conflicting interests, different information, and operate with an individual plan. Each actor can be represented with an entity called agent, where an agent (Weiss, 2000) is an entity that perceives and acts upon its environment and that is independent; its behaviour depends also on its experience. As an intelligent entity, an agent can be operate flexibly and rationally in a variety of environmental. Behavioural flexibility and rationality are achieved by an agent on the basis of key processes such as problem solving, planning, decision making, learning. As an interacting entity, an agent can be affected in its activities by other agents. Bankes (2002) notes that ABS allows the relaxation of several unrealistic assumptions often employed in other approaches (i.e. linearity, homogeneity, stationarity). In logistic field, Roorda et al. (2010) presents an agent based framework to simulate the interactions among stakeholders involved in the freight distribution system. Reis (2014) propose an agent-based model to simulate the transport operations and behavioural reactions of transport agents in relation to medium and long-distance intermodal transport. Baykasoglu and Kaplanoglu (2011) examine the case of load consolidation in freight operations, formulating a multi-agent based decision making approach. Examples of ABS applications in city logistics are reported, as an example, in the works of Tamagawa et al., 2010 (discuss a methodology for evaluating city logistics measures based on a reinforcement learning model and a model for vehicle routing); Wangapisit et al., 2014 (propose a Multi-Agent System (MAS) model for evaluating joint the delivery system and the freight vehicle parking management; the model framework proposed considers on the one hand a vehicle routing problem, on the other the interaction among stakeholders with a reinforcement learning model); Teo el al., 2014 (propose an exact method to solve the vehicle routing problem and a MAS with reinforcement learning to evaluate the impact of distance-based road pricing on the stakeholders). van Heeswijk et al (2016) develop an agent-based simulation framework to evaluate a set of urban logistics policies considering five type of agents (receivers, shippers, carriers, urban consolidation centre, administrator).The solution of the vehicle routing problem, understood as the problem to deliver a set of customers, is investigated in Barbucha (2012), that evaluate the influence in the solution quality varying the cooperation approach (synchronous or asynchronous) among a set of agents that share a central memory (for learning). Sopha et al. (2016) present an ABS to solve dynamic vehicle routing, where the dynamic implies variation in demand. Van Duin et al. (2012) propose a modelling approach based on multi-agent modelling that incorporate the vehicle routing and the use of an urban distribution centre. Some methods for ABS are implemented using extensions of programming languages (i.e. C, Java) where each agent have an objective, interacts with other agents, follows a (modifiable) plan. The toolkit MATSim (Multi-Agent Transport Simulation) is an example of applications of it to freight transport simulation (Zilske et al., 2012). From such a brief literature review, it emerges that the agent-based systems are suitable to be used in urban freight simulation but it seems that so far it is missing a general outline of the subject. ABS appear to be a new element which in turn will improve freight distribution service efficiency and quality. Some tools are under development to capture the new opportunities offered by ABS and to simulate the freight transport and the action to actuate to mitigate the impacts, providing a simulation environment where the stakeholders can interact. However, several theoretical issues remain a matter for in-depth research: better understanding the decisional processes of the agents, better simulate the information effects on the agent actions, specification of more advanced path choice models and improving of their relative parameter learning processes.

References Bankes, S.C. (2002), “Agent-based modeling: a revolution?”. Proceedings of the National Academy of Science, vol. 99, suppl. 3, pp. 7199-7200. Barbucha D., Search modes for the cooperative multi-agent system solving the vehicle routing problem, In Neurocomputing, Volume 88, 2012, Pages 13-23, ISSN 0925-2312. Browne, M., Allen, J., Nemoto, T., Patier, D., Visser, J. (2012). Reducing Social and Environmental Impacts of Urban Freight Transport: A Review of Some Major Cities. Procedia - Social and Behavioral Sciences, 39, 19-33. Baykasoglu A., Kaplanoglu V. (2011). A multi-agent approach to load consolidation in transportation, In Advances in Engineering Software, Volume 42, Issue 7, 2011, Pages 477-490 Nuzzolo, A. Comi, A. (2014). Urban freight demand forecasting: a mixed quantity/delivery/vehicle-based model. In Transportation Research Part E: Logistics and Transportation Review 65 Polimeni A., Vitetta A. (2014). Vehicle routing in urban areas: an optimal approach with cost function calibration. Transportmetrica B: transport dynamics, pp. 1-19. Reis V. (2014). Analysis of mode choice variables in short-distance intermodal freight transport using an agent-based model, In Transportation Research Part A: Policy and Practice, Volume 61, 2014, Pages 100-120 Roorda M. J., Cavalcante R., McCabe S., Kwan H. (2010), A conceptual framework for agent-based modelling of logistics services, In Transportation Research Part E: Logistics and Transportation Review, Volume 46, Issue 1, 2010, Pages 18-31 Russo, F. and Comi, A. (2010) A modelling system to simulate goods movements at an urban scale. In Transportation 37 (6), DOI: 10.1007/s11116-010-9276-y, Springer Science+Business Media, LLC, 987-1009. Sopha B., Siagian A., Asih A., (2016). Simulating Dynamic Vehicle Routing Problem using Agent-Based Modeling and Simulation. 1335-1339. 10.1109/IEEM.2016.7798095. Tamagawa, D., Taniguchi, E., & Yamada, T. (2010). Evaluating city logistics measures using a multi-agent model. Procedia – Social and Behavioral Sciences, 2(3), 6002–6012. Taniguchi E., Thompson R. G., Yamada T. (2012). Emerging Techniques for Enhancing the Practical Application of City Logistics Models, In Procedia - Social and Behavioral Sciences, Volume 39, 2012, Pages 3-18, ISSN 1877-0428 Teo J. S.E., Taniguchi E., Qureshi A. G. (2014). Evaluation of Load Factor Control and Urban Freight Road Pricing Joint Schemes with Multi-agent Systems Learning Models, In Procedia - Social and Behavioral Sciences, Volume 125, Pages 62-74. van Duin J.H.R., van Kolck A., Anand N., Tavasszy L. A., Taniguchi E., (2012) Towards an Agent-Based Modelling Approach for the Evaluation of Dynamic Usage of Urban Distribution Centres, In Procedia - Social and Behavioral Sciences, Volume 39, 2012, Pages 333-348. van Heeswijk W., Mes M., Schutten M. (2016) An Agent-Based Simulation Framework to Evaluate Urban Logistics Schemes. In: Paias A., Ruthmair M., Voß S. (eds) Computational Logistics. ICCL 2016. Wangapisit O., Taniguchi E., Teo J.S.E., Qureshi A.G. (2014). Multi-agent Systems Modelling for Evaluating Joint Delivery Systems, In Procedia - Social and Behavioral Sciences, 125, pages 472-483. Weiss G. (2000), Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. Mit Press, ISBN: 9780262232036. Zilske, M., Schröder, S., Nagel, K. and Liedtke, G. (2012). Adding freight traffic to MATSim. VSP Working Papers. TU Berlin, Transport Systems Planning and Transport Telematics.

ABSTRACT. The paper analyzes a practical case of study related to the distribution of fuels for the Total Erg Oil Company to the service stations located in the Province of Rome (Italy). The problem is formulated as a capacitated vehicle routing problem with time windows, where several heuristic procedures have been tested, considering both static and dynamic travel times. With respect to the standard operational costs used typically, a multivariable objective function has been proposed which takes into account also a new risk index. The risk index proposed is function of the population density of the zones covered by each path and of the estimated number of road accidents on each road link. In such a way, we take into account the population's exposure to the risk associated with an incidental event involving a fuel tank. The obtained output is the set of planned routes with minimum service cost and minimum risk. Results demonstrate how an accurate planning of the service saves up to 3 hours and 30 km on a daily basis compared to a benchmark. Moreover, the configuration of the service can be parameterized by the distribution company, by varying the weight adopted in order to include the risk index. Including the risk index may bring to a higher safety route planning, with an increase of the operating costs of only 2%.

ABSTRACT. At the process of vehicles purchasing, carriers usually choose up between brand new and used vehicles. Essential delays on loading-unloading goods dramatically affect the intensity of long-haul transportation in Russia and in East European countries, stimulating carriers to purchase used vehicles, which generally need replacement on much frequent basis. In case of purchasing long-distance haulage vehicles, it is essential to have evidential information regarding its possibility for sustainable long-term usage, including maintenance constraints and possible financial loses. As an indicator of maximum number of days in operation per year, the potential coefficient of the released vehicles on the line is proposed. The coefficient was performed also in relation with dependence of truck age. As a practical result, for VOLVO tracks in Russia, optimal 5-year exploitation period for vehicles has been determined.

ABSTRACT. This paper studies the Quay Crane Scheduling Problem (QCSP) that aims to find an optimized schedule for Quay Cranes (QCs) to load and unload containers onto and off of vessels. The resulting schedule has to satisfy some technical requirements including crane safety margin and non-crossing as well as vessel stability. We propose a two-stage solution method. In the first stage, we solve the problem without vessel stability using a column generation method and, in the second stage, we adjust the obtained schedule when necessary so that vessel stability is satisfied. Computational study demonstrates the efficiency of the proposed method.

ABSTRACT. See the extended abstract in pdf format attached in the "Uploads" section.

Extended Abstract

Introduction Maritime transport involves many stakeholders, whose decisions and actions affect the whole logistic chain of containerized transport. In this context, ports play a crucial role, since their operation performance determines the quality of containerized transport. Port call processes should be coordinated, and optimized, not only during planning, but also in realizing and evaluating conducted port calls. In fact, nowadays the document exchange in maritime transport is definitely too fragmented. A major reason why is that involved port actors usually try to get access to, and retain control of, information that is valuable with respect to their own goals. As a result, seaport approaches are often uncoordinated. Such a lack of coordination in information exchange among all the involved actors provokes at least a significant waste of time, and an avoidable decrease of maritime transport efficiency. Then, the challenge of designing and implementing an effective coordination in document exchange in maritime transportation should be taken and won, as also European Commission fosters. The main topic of this study focuses on a phase of the port call process and present a way to improve it: the stowage planning. Before starting the operations of discharging and loading, there is an intermediate phase when the ship arrives in the seaport. In fact, ship’s master is responsible for the ship during navigation and he aims that the ship and navigation are safe. So he’s the actor who has to decide how the final loading plan will be. In many cases, when the ship arrives in the port, the terminal planner has to go on board the ship to show the loading planning to the ship’s master. The planner goes on board with a paper printed version of the loading plan or a digital one, and shows it to the ship’s master. The terminal planner spends time analysing the plan face by face with the ship’s master who, sometimes, can modify the position of containers depending on weight stability constraints of the ship in order to improve the ship safety during navigation. This takes time (hours): this additional phase creates more than one step in the port call process and inside the entire chain. The time needed obviously could change time by time, depending on the ship, on the changes in the plan, on the resources and so on. First the terminal planner has to go on board the ship to discuss the plan, then, if there are some changes by the ship’s master, he has to plan again the new final loading list for the ship stowage and for the cranes’ working queues. So the discharging and loading phases for the terminal in these cases are not optimized. If the ship has to discharge a good enough number of containers, the terminal ship planner probably gets enough time to recalculate the final loading plan with the new changes. But if there are not containers to unload or if the discharging phase ends before the terminal ship planner recalculation, there will be waiting time for cranes and all the process will be interrupted until the final loading plan will be ready. Moreover, to guarantee more efficiency usually cranes work on discharging and loading phase at the same time by following an optimized list of moves which meets all the ship’s constraints.

Aim of the paper In this framework, the purpose of this paper is to present an approach to evaluate the benefits of automation in containerized maritime transport, by comparing the current scenario with a different future scenario. In doing so, an ad-hoc model based on Discrete Event Systems has been developed to reproduce the information exchange process among the actors involved. Such a simulation model has been applied to both the current scenario and a possible future scenario resulting from a new real time data sharing implementation. The model presented below shows the loss of time due to this real phase. It’s built to be adaptable to each terminal thanks to the possibility to change the cranes’ moves per hour, the number of container to unload and the number of container to load. It shows a comparison between the nowadays process and a system based on real time data sharing during the navigation. In the figure below the interface of the model is presented. It is built with different hierarchical boxes which contain the “as is” scenario and the one improved thanks to automation implementation. The output shows how thank to this new system the time loss can be avoid.

(Figure 1: model interface)

Methodological aspects The model developed in ExtendSim is presented. As mentioned before, this model is built to set as input data the cranes’ movements per hour, the number of containers to unload, the number of containers to load. This data can be modified to characterize a specific terminal and its efficiency. In fact, the cranes’ movements per hour represent a sensible parameter in terms of efficiency and time saving. Obviously each case could change in function of ship type, number of containers and it also changes depending on the possible ship master changes as said in the introduction. Another input data of the model is the duration of navigation. In fact, sometimes it happens that the previous port is less hours far than the time needed to end the stowage plan. In the example of this scenario the terminal ship planner spends two hours on board the ship with the ship master to plan how to change the placement of some containers; to complete the final loading plan, with all the ship master changes, the planner takes 1,5 hours and we set a time of 30 minutes before the first crane can start the container handling. This time takes into account also the ground resource time needed to pick the container in the specific yard zone and bring it to the crane operational zone. With this input data the model gives out the time loss due to modify the plan linked to the number of containers to unload with an average overall movement rate of the cranes. It’s supposed the use of three cranes at the same time. These results are presented below in figure 2. It shows how the time needed to remake the plan causes time loss which can be balanced out in part only if there is a significant number of containers to unload. This is an example of how the “as is” scenario model works. The future scenario is based on the idea to create the final stowage plan during the navigation thanks to a system that can manage an interactive and real time data sharing among the involved actors. The implementation of this system can avoid the mentioned time loss.

Results and concluding remarks This study presents how a better and well-coordinated data sharing could improve the freight maritime shipping efficiency. Real time data sharing is one of the possible way to generate these benefits. Through a DES simulation tool we built a model which can be adapted to meet the performance rate of each terminal depending on cranes’ movements per hour, duration of navigation, number of containers to unload and number of container to load. Real data examples from terminals have been tested and have shown how the time loss of that phase depends on the lack of coordination and affects money and efficiency loss inside the whole logistic chain regarding maritime transportation. The main cause of this problem is due to an inefficient data sharing in the port call process. The application of automation at this phase and the real time interactive data sharing can solve the problem of losing time and guarantee just in time operations.

(Figure 2: results of time loss in nowadays process - example)

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