Reducing Port-Related Truck Emissions: Coordinated ...

Reducing Port-Related Truck Emissions: Coordinated Truck Appointments to Reduce Empty Truck Trips Frederik Schulte1 , Rosa G. González2 , and Stefan Voß1 1

Institute of Information Systems, University of Hamburg, Germany {frederik.schulte,[email protected]} 2 Faculty of Engineering, University of the Andes, Chile [email protected]

Abstract. Port-related emissions are a growing problem for urban areas often located directly next to ports highly frequented by trucks and vessels. Empty truck trips are responsible for a significant share of these emissions. Truck appointment systems (TASs) allow scheduling of truck arrivals and enable collaboration among truckers. Though, TASs leveraging the potential to reduce avoidable emissions due to empty trips have hardly been studied. We aim to show how a TAS following this idea may be designed and evaluate the approach. We thus review requirements for a collaborative TAS and develop a discrete-event simulation model to assess coordinated truck appointments in a practical case of drayage. The results indicate that the approach effectively reduces port-related truck emissions, but might create congestion in the port. The considered case refers to drayage processes, but may also be transferred to the hinterland. The developed simulation model assumes a generic truck appointment process and may also serve to analyze diverse cases. Keywords: Port Emissions, Truck Appointment System, Empty Trips, Empty Repositioning, Simulation.

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Introduction

Urban port areas increasingly struggle with port-related emissions—mainly caused by trucks moving full and empty containers between port and hinterland. At many ports the empty container truck trips problem is a major cause for those emissions [6], [14], [13]. This relates to the non-utilization of available slot capacities (i.e., the number of empty or non-empty containers a truck can carry) of trucks in import- and export-related trips [14]. Figure 1 illustrates the occurrence of empty repositioning for different truck routes. Both, not fully utilized slot capacity and empty repositioning trips, increase the number of total trips in a port region and thus cause evitable emissions. There are few studies on truck appointment systems (TASs) at ports like, e.g. [8], but these studies mostly do not focus on sharing empty truck capacities. From the perspective of a

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Schulte, Gonz´ alez, and Voß

Fig. 1. Basic empty and loaded container truck movements (adapted from [22])

single carrier, the problem could be modeled mathematically, but this approach would not be as auspicious in cases of collaboration [1]. Simulation models could be a promising alternative, but yet have not been developed for the problem [13]. We aim to introduce and assess collaboration options regarding the number of empty trips, emission savings, capacity utilization, and costs. We develop a discrete-event simulation model and system-based collaborative truck services with real-world data for drayage at the port of San Antonio, Chile. We find that a collaborative TAS may be an effective way to reduce port-related truck emissions, but might have a negative impact on port congestion if not properly integrated in port operations management. In the following, we present relevant literature, the idea of a collaborative TAS, a discrete-event simulation model with a case study, and conclusions drawn from the study. Section 2 reviews collaboration approaches in transport, TASs, and emission-oriented port logistics. Section 3 introduces requirements for a collaborative TAS and options for its application. Section 4 describes a generic simulation model for the evaluation, complemented by a specific case study in Section 5. Section 6 concludes and gives an outlook on future research ideas.

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Literature Review

There is few specific literature available on TAS implementations focusing on collaboration or emission reduction. Thus, subsequently we review collaboration approaches in transport (Section 2.1), TAS literature (Section 2.2), and port logistics incorporating emissions (Section 2.3).

Reducing Port Emissions by Empty Container Trucks Coordination

2.1

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Collaboration in Road Transport

There are various forms of collaboration in road transport applied in industry and discussed in literature. Agarwal et al. [1] have examined different opportunities for collaboration in the business. This includes collaboration set-ups with freight bundling and break-bulk terminals, the formation of alliances to reduce load imbalances, and approaches of carriers to jointly reduce the cost of asset repositioning. From the perspective of a single carrier, the problem could be modeled mathematically and the resulting linear programming model could be solved with reasonable effort. In cases of collaboration, a game theoretic approach has been proposed, but needs to be relaxed significantly for application [26]. Lozano et al. [19] have also proposed a game theoretic approach for a related problem and have conducted numerical experiments, but have also emphasized that further practical requirements need to be incorporated for truly realistic modeling. Besides, few studies [22],[13] have empirically examined challenges and obstacles on the way to consequent collaboration among trucking companies for efficient joint capacity utilization. The study by [13] has analyzed factors enabling and constraining the back-loading of trucks. This work goes back to a large survey to understand the problem of empty running in road transport conducted in Great Britain. Islam and Olsen [13], on the other hand, have provided an exploratory qualitative study by means of interviewing road carriers from the container transport industry. According to them, part of the problem could be addressed with a TAS supporting truck-sharing that yet needs to be evaluated empirically. A successful way to model port-related transport processes with different stakeholders has been introduced by Bielli et al. [5]. The authors make use of discrete-event simulation and an object-oriented modelling approach. 2.2

Truck Appointment Systems

As TASs define when trucks should perform a service (load a container) at ports, these systems naturally have to be considered to plan the point in time when a truck loads its container and, additionally, another container of another trucking company. Zehendner and Feillet [30] have recently demonstrated the multiple benefits a TAS can have on the service quality of trucks, trains, barges and vessels at ports. But generally, there are few studies on TASs, and those existing have hardly considered the impact of a TAS on empty trips at ports and in the hinterland. Among the best described cases in literature are the ports of Los Angeles, Hongkong, and Auckland, as exemplified below. Giuliano and O’Brian [8] have evaluated a terminal gate appointment system at the ports of Los Angeles and Long Beach. This study has focused on queuing and transaction times without incorporating truck sharing options. The authors have not observed any reduction of these figures, and thus have not found any positive impact on truck emissions. Without explicitly considering a TAS, Englert et al. [6] have examined the impact a potential inland port would have on pollution and congestion caused by empty truck repositioning in the Southern California region of the above ports. The authors have demonstrated that empty

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truck trips significantly contribute to port area emissions and could be reduced in a collaboratively planned dry port environment. Morais and Lord [23], on the other hand, have provided first evidence on the quantitative impact of truck appointments on greenhouse gas (GHG) emissions, but did not incorporate collaboration. One of the first documented cases of TASs has been studied by Murty et al. [24] in Hongkong. The authors have aimed for the efficient utilization of scarce terminal space and have demonstrated how a TAS, as part of an integrated decision support system, can contribute to achieve this and related objectives. Islam and Olsen [13] have examined empty container truck trips for the case of Auckland. The authors have conducted an interview-based study with road carriers from the container transportation industry and elaborate truck sharing options for reduced emissions of empty container trucks. Based on this study, Islam and Olsen [14] have proposed a re-engineered container truck hauling process and have derived requirements for a TAS incorporating truck sharing options. Other studies analyzed the impact of truck arrival information on the efficiency of yard and drayage operations [25], [11], [32], [31] or impact of truck announcements on container stacking efficiency [3]. Zhao and Goodchild [32] present a simulation to analyze the impact on container re-handles for different scenarios with partial and perfect information on the truck arrival sequences, observing that partial information is enough to achieve significant benefits with respect to stacking efficiency at the yard. Zhang et al. [31] have developed a model that optimizes the appointment quota of each period optimized subject to the constraints of adjustment quota. The authors have modeled a queuing network of trucks in the terminal and demonstrated that the model can have a positive impact on truck turn times. Van Asperen et al. [3] have focused on the possible effects a TAS could have on stacking operations in the terminal yard. 2.3

Port Logistics Incorporating Emissions

Apart from overall efforts in sustainable port development, there are some dominant issues discussed in the literature of emission-oriented port logistics. Studies with a perspective on logistics operations typically focus on: – Repositioning of (empty) container trucks [6], [13] – Hinterland transport [4], [16], [17], [18] – Terminal operations [12], [20], [28] – Vessel emissions [10], [27], [29] While the various kinds of vessel emissions are the topic of many studies, perspectives and approaches differ quite significantly. For instance, [10] focus on governance methods—such as interactive governance for multiple stakeholders involved in causing maritime emissions—to achieve regulatory compliance. On the other hand, there are works emphasizing operational options to effectively reduce the quantitative emission impact of container shipping like [27]. Villalba


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and Gemechu [29] focus on a single port and derive strategies and policies to reduce emissions in the port area caused by vessels visiting the port. The repositioning and capacity utilization of (empty) container trucks is an often discussed topic. Studies like the aforementioned ones by Englert et al. [6] and Islam and Olsen [13] have examined the emissions impact of empty logistics resources at ports. Generally, the hinterland transport is a major issue for researchers and practitioners in attempts for more sustainable logistics in port areas. Apart from truck emissions, noise and congestion are in the focus of related studies. Bergqvist and Egels-Zandén [4] have examined approaches to internalize external costs related to green port dues in collaborative transport systems of hinterland logistics and ports. In the same realm, Liao et al. [18] have conducted a quantitative activity-based study to analyze emissions from the port of Taipei. They indicate that there are less emission-intensive transshipment routes possible if planners value the emerging port of Taipei as an alternative to the established ports in the region. In the context of hinterland transport routes, L¨ attil¨ a et al. [17] have modeled a hypothetical dry port network in Finland. Utilizing an enhanced dry port concept is a promising approach to decrease both emissions and costs. Lam and Gu [16] have reviewed mathematical models for port hinterland intermodal container flows with sustainability aspects and concluded that models should not only focus on costs, but integrate sustainability aspects by means of multi-objective modeling. Regarding sustainable terminal operations, the studies by Henesey et al. [12] and Lun [20] have evaluated sustainable policy-making, while Rijsenbrij and Wieschemann [28] have examined a terminal design for sustainable operations. This work describes how to balance service requirements, costs, and requirements for sustainability using an appropriate terminal design and respective handling systems.

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A Collaborative Truck Appointment System

TASs basically serve to define time windows in which trucks may arrive at a port or a port area to provide a needed service. Independent from the specific focus of these systems, they offer new opportunities for advanced decision support in and at ports. For the problem of empty truck capacities and resulting emissions a TAS likewise serves as a framework for implementing related planning. There are different options to reduce empty truck capacities, but the most promising are based on the collaboration of trucking companies. Though, the majority of TASs discussed in Section 2.2 do not consider collaboration or capacity sharing among trucking companies. That is, no such system has yet been implemented and evaluated. This section intends to introduce paths for the implementation of TASs incorporating collaboration. The basic components of advanced truck systems for collaboration are user profiles for trucking companies and exporters as well as a matching procedure for specific services [14]. Truckers are thus able to announce empty capacities, and exporters announce their transport requirements for a specific service. A trucker obviously needs to specify the kind of empty capacity that he can offer and

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additionally has the opportunity to add preferences on the possible shared services. Similarly, the exporter specifies a certain transport demand and provides information such as container type, points of destination, estimated times of arrival and departure. A basic matching procedure checks origins, destinations, transport constraints, and time windows to propose possible services for collaboration. That is, we assume elementary truck movements that are defined by origin and destination points. Furthermore, special transport vehicles and time windows may be required by the customers. These criteria need to be considered for all planned and demanded container moves. Thus, the matching procedure can validate combination options for container moves based on these criteria. The procedure is basic in a way that it provides feasible solutions that later on need to be evaluated—either by a heuristic or mathematical optimization approach as discussed in Section 4. As a next step, the generated matching options are evaluated based on specific planning models with respect to objectives, such as reduction of emissions and costs. From literature, at least two economically and ecologically relevant problem classes of empty capacities in road transport can be identified: asset repositioning and empty slot transport. The first basically refers to repositioning trucks or empty containers from a prior destination to a new point of demand or a parking position [6]. The latter covers all kinds of problems related to efficient capacity utilization on similar cargo routes such as establishing break-bulk terminals and cycles for collaboration [1]. The proposed TAS can implicitly or explicitly consider these problems. That is, the TAS can either explicitly search a joint optimum for all participating companies and the related problems or implicitly incorporate sub-problems by means of user preferences. In the implicit set-up, the user could thus, e.g., publish his preference for trips that help him solving his asset relocation problem. In the explicit set-up, on the other hand, user preferences are secondary to the overall objectives such as total costs and total emissions. Obviously, the explicit set-up would create better system-wide solutions, but the implicit set-up may be easier to implement in practice. Similarly, one can choose between different approaches to finally create a match between the users. Probably, best results could be created with a multi-objective mixed integer programming formulation considering costs, emissions, and user preferences to define a fixed plan for a specific time window. As this leads to a complex planning approach that might cause resistance among truckers [13], a more flexible iterative approach could be considered, too. For instance, users could iteratively choose from a list of ranked services. Finally, the system can be implemented in a web-based application enabling users to dynamically interact with their mobile devices. The system then provides all information relevant to its users such as arrival times or navigation data.

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A Generic Simulation Approach For Collaborative Truck Appointment Systems

Based on requirements elaborated in Section 3, we have designed a discreteevent simulation model for a generic truck appointment process. This section


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introduces a generic simulation approach that is applied for a specific case in Section 5. The following subsections discuss scope and objectives (Section 4.1), process definition and assumptions (Section 4.2), input and output data (Section 4.3), and an emission-integrated matching procedure (Section 4.4) for the simulation model. 4.1

Scope and Objectives

The potential of an advanced TAS enabling collaboration between trucking companies has not been evaluated to the knowledge of the authors. That is, there are no specific planning models that have been implemented and evaluated scientifically in practice or in simulation models. With this simulation study we thus aim to build the foundation in this direction. We want to demonstrate how planning models could be incorporated in a respective study and discuss appropriate models. Thus, we aim to assess the current transportation impact on sustainability and efficiency objectives. More specific, we aim to evaluate collaboration options with a respective TAS and their impact on emissions, congestion, and costs. This also allows a better understanding of design requirements for collaborative TASs. For the simulation study, we refer to a conventional set-up of a working day with high utilization. Collaboration based on truck appointments may have a wide impact on port-related logistic processes, but the basic problems of empty capacity sharing and repositioning of empty logistics resources re-occur within the narrow scope of a port and also in a wider scope including the hinterland. We can thus start with a smaller scope and extend in the next step. 4.2

Process Definition and Assumptions

The simulation process depicted in Figure 2 illustrates the basic conditions to be considered for the evaluation of a collaborative TAS at ports. We assume that truck drivers are equipped with mobile devices enabling them to dynamically interact with a TAS and allowing a more flexible truck appointment process. This assumption is made to meet flexibility requirements of truckers. Likewise one could also imagine that the system is operated at a company office and drivers receive orders at transshipment points. The simulated process starts when a truck driver receives a transport order. We assume that this order already contains the truck appointment and potential matches for a collaborative service—both provided by the TAS. That is, as soon as a truck capacity becomes available, a basic matching method (introduced in Section 3) generates feasible solutions for combined trips. This first step already enables a new form of coordinated truck appointments and is likely to improve costs and emissions. In a second step, the generated feasible solutions can now be evaluated for heuristic improvement or mathematical optimization as sketched in Section 4.4. After loading containers at the first demand position, we assume that the trucker could request a new transport and possibly transport additional containers. Finally, the truck needs to be repositioned for a new order or parking. At various steps of this process, the matching method can be called and transport orders updated.

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Fig. 2. Simplified Simulated Process

4.3

Input and Output Data

For the sake of realistic modeling of the above process, various types of data sources need to be considered. These sources involve interviews with trucking companies and port operators, observation, and the analysis of existing secondary data. All these sources serve to generate realistic input data for the simulation model such as probability functions for service or demand rates. Interviews might be especially appropriate to gain insights into the questions of how many trucking companies are actually willing to collaborate, what kind of capacity restrictions might exist and would have to be modeled, and how many locations in port and hinterland need to incorporated. Also, interviews are important to understand if there are any current policies to avoid empty trips and to estimate operational costs. Additional operational information might be collected by observations and existing documentation. This applies to transport, waiting, loading, and documentation times as well as to the number of available trucks and the current percentage of empty trips. The analysis of external documentation by port operators or trucking companies in combination with


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interviews will also serve to gain insight into the distance of truck trips, the number of containers and their destination, operating hours at sea ports and depots as well as the current layout in use at the port. Finally, information gained from trucking companies about truck types in use in combination with external databases will be used for specific calculations of truck emissions at the port and in the hinterland. With this input data the simulation model may provide output data on the percentage of empty trips, emission levels, and time consumption for various sub-processes of the simulated process. 4.4

Emission-integrated Matching Procedure

As discussed in Section 3, port-related empty trips that increase local emissions are caused by the problem classes of asset repositioning and empty slot transport. Respective models that address repositioning [7] and empty slot transport [1] have been reviewed in literature. These models may as well serve as the basis for matching services within a collaborative TAS. Furthermore, synchronization aspects, as reviewed by Mankowska et al. [21], need to be considered. In order to evaluate possible synchronized matches with respect to costs and emissions and independent from a specific problem formulation, these models need to be extended by specific emission objective functions. Considering a basic flow problem, with the set of nodes V and the set of arcs S, a set of order routes O can be introduced to model routes associated with orders during the simulated time period. The set of nodes, V , represents all kinds of locations according to Figure 1. An appropriate bi-criteria formulation has been introduced by Kim et al. [15] and can be adapted for the case of a collaborative TAS. The approach basically applies a cost objective function Z1 and a second objective function incorporating emissions Z2 . Z1 thus adds up the total transportation cost: X X Z1 = min Cij xkij (1) k∈O i,j∈S

Z2 is based on the emissions induced by all considered transport operations: X X XX XX Z2 = min[ Eij xkij + Ei yik + Ej yjk ] (2) k∈O i,j∈S

k∈O i∈V

k∈O j∈V

The functions use the following notation: V = set of all nodes, i.e., origins i and destinations j in the network; S = set of all arcs ij connecting origins and destinations in the network; O = set of all order routes combining sets of arcs in the network; xkij , ij ∈ S, k ∈ O = decision variable for transport flow from point i to j; yik , i ∈ V, k ∈ O = decision variable for transshipment at point i; Cij , ij ∈ S = cost for transport flow from point i to j; Eij , ij ∈ S = emissions for transport flow from i to j; and Ei , i ∈ V = emissions during transshipment at point i.

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For the proposed bi-objective problem formulation, costs and emissions need to be described in an integrated objective function. Therefore, both terms are typically normalized or scaled. That is, the terms of a multi-objective formulation are divided by a factor that enables their dimensionless representation as sum of single objectives. A common approach for this purpose uses the optimal values of single-objective optimization runs as those scaling factors. That is, in this case, Z1 and Z2 as independent objectives first. The resulting objective function can then be implemented in the optimization module of the simulation software for an optimization run based on metaheuristics. Furthermore, additional constraints such as bounds for inventory and vehicle capacities or emission quotas also need to be defined in the optimization module in order to receive near-optimal coordinated order routes. The objective function serves to evaluate potential matching options provided by the basic matching procedure introduced in Section 3. As it evaluates matches with associated order routes, the matching decision implicitly is a routing decision. In the basic set-up with one container type, single-commodity flows are considered. For multi-commodity flows this set-up can easily be extended. xkij serves as decision variable, determining whether an empty or full service is performed, without differentiating transport costs of full and empty movements. Emissions are distinguished during transport and transshipment at point i or j. The emission values at different transshipment points may differ because of different infrastructure and loading devices that are used. For instance, fuel based loading devices might be used at some points, while at others electric devices are used. Truck model specific emission data during transport and transshipment is retrieved from the EMFAC2014 and CARB database, respectively [2].

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Simulation Case Study

As discussed in the previous sections, empty trip problems in a port and in the hinterland have a similar structure. In this section, we model and analyze the the drayage procedures in the port San Antonio, Chile. We first introduce the case (Section 5.1) and then present results of the simulation study (Section 5.2). 5.1

Practical Case

Figure 3 illustrates the basic conditions for the empty container truck trips problem in the case of drayage at the port of San Antonio. There are three major types of transport locations relevant for drayage: depots, custom storage areas (CSAs), and the terminal. In case of San Antonio, five depots, four CSAs, and one terminal are considered in the simulated layout of the port area. Thus, possible truck routes could have all shapes depicted in Figure 1, from bi-lateral to multiple backload. According to these basic route types, a trucker starts at a base (typically the company’s location) and returns to this point. In between the truck could serve the terminal, various depots, and custom storage locations. Depending on each route terminal, CSAs and depots can be understood


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Fig. 3. San Antonio port and hinterland [9]

as delivery, collection, or delivery and collection points. These transport and transshipment processes are usually repeated several times during a day before the trucker returns. Empty trips thus typically occur when repositioning to the trucker’s base point, but are as well likely for movements between delivery and collection points. In the considered case, an empirical analysis has shown that in only 5 % of all drayage transports the potential to combine pick-up and delivery is leveraged in order to reduce empty trips. Coordinated truck appointments require a system that matches empty capacities and demand as introduced in Section 3. Therefore, in the simulation study, we assume a TAS that seeks to leverage this potential by coordinating container pick-ups and deliveries, reducing empty trips and trips with empty slots. For instance, considering a simple case, a truck takes a container from a depot to the terminal and takes another container back to the depot. In comparison to the standard, un-coordinated case, two empty rides are now avoided and respective costs and emissions are saved. In the initial phase, appointments are made directly at the port with a time window of two hours. This set-up follows practical requirements and should reduce acceptance barriers that might exist among truckers if they are urged to accept rigid and inflexible schedules. Also, truckers can decide among options which container to load in a coordinated service and the system does not define a near-optimal plan in the first phase. Since there are no coordinated TASs implemented leveraging any kind of capacity sharing has a significant impact, and more advanced methods should be implemented in the next step after a successful initial phase.

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5.2


Results

The case of San Antonio was implemented in a simulation model built with the software Arena. A simulation run assumed one week of high demand in comparison to one of low demand. Depending on the modeled scenario different working hours are assumed. The modeled scenarios can basically be distinguished by their percentage of successfully coordinated pick-ups and deliveries of containers. That is, in Scenario 1, only 5 % of all drayage trips were successfully coordinated, while the rest of the trips involved repositioning of any empty truck to start the next order. The scenarios thus reflect different levels of collaboration which refer to the motivation of truck drivers as well as to operational restrictions. Next to the number of trips, the major cost drivers are waiting and operational times, added up to the total time in the system. Besides that, we analyzed emissions caused during transport and transshipment. Table 1 provides the average results of 50 simulation runs for the defined scenarios. Table 1. Simulation results averaged over 50 runs

Coordinated Time in Trips [%] System [min.] Scenario 1 Scenario 2 Scenario 3 Scenario 4

5 15 40 50

150,2 162,5 195,1 208,0

Total Emissions [tons/day] 6,70 6,25 5,70 5,36

Number of Empty Trips [-] 504 468 417 393

The values for total time in system and number of empty trucks can be directly obtained from the simulation model. Emissions are calculated according to the formulas introduced in Section 4.4. That is, emissions during transshipment and transport of times of trucks are summed up. The specific emission rates for drayage trucks in the different situations are generated by database query from the EMFAC2014 database [2]. This extensive database is built based on historical data for different kinds of vehicles in different situations. Because most of the trucks used for drayage in San Antonio were already around 15 years old, the emission rates for trucks of that age are considered. These rates furthermore incorporate environmental temperatures and have been derived especially for drayage trucks. Waiting times are derived based on service rates and arrival rates per hour, while emissions for truck transport are derived on rates per kilometer. Service and arrival rates are modeled as Gamma or Weibull distributions based on empirical data. Most reliable data could be obtained for CO2 emissions. Thus, the presented results only refer to CO2 emissions, but could easily be extended when respective data becomes available. The results clearly demonstrate that the approach effectively reduces empty trips and thus port-related emissions. A low number of empty trips itself is no


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quality criterion for the solutions, but may provide insights about the character of good solutions. For Scenario 4 emissions could be reduced by 20 % in comparison to the current situation. This obviously goes along with a reduction of transportation costs. On the other hand, the average time in system per truck increases significantly when more coordinated trips are made. This is intuitive as coordination of trips means that trucks might have to wait for a second operation. In some cases, this waiting may have a significant impact on congestion in the port areas and thus additionally increases the average time in system for a truck. If, e.g., no operational measures allow a truck to leave the queue or the inner port area during waiting, other trucks waiting can obviously not be served. This effect is especially critical when a truck, due to loading restrictions, cannot load the first available container and thus needs to wait until an appropriate container can be provided. More generally, these results indicate that operational costs and emissions may be opposing planning objectives and thus need to be modeled separately (as proposed in Section 4.4). That is, trucking companies may not be willing to accept an emissions reducing plan because it goes along with longer truck times in the system, causing lower cost-efficiency. Furthermore, it becomes clear that the implementation of a collaborative TAS can have a significant positive impact on emission and economic metrics, but needs be complemented by congestion management approaches in order to fully leverage the potential of the approach.

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Conclusion

Prior work, e.g, by Zehendner and Feillet [30], has demonstrated the positive impact of TASs on various operational metrics at ports such as service quality of various transport vehicles. Nevertheless, existing studies on TASs have rarely drawn a connection to the important problem of port-related truck emissions often caused by evitable empty trips. In this study, we propose a collaborative TAS to reduce empty trips and emissions at ports. We evaluate the approach with a simulation model based on a case study with real-world data. We found that a collaborative TAS may be an effective tool to reduce emissions caused by avoidable empty trips, but should be complemented by appropriate congestion management. These findings extend work on a conceptual model for a TAS with collaboration by Islam et al. [14], connecting decision support levels of (truck appointment) systems to mathematical models in empty resource logistics and developing a simulation model to evaluate a basic collaborative TAS in practice. In addition, this study clearly quantifies the emission impact of the evaluated approach. The results also demonstrate that low costs and low emission levels may be opposing planning objectives, providing empirical evidence on the common misconception that operational efficiency improvements naturally go along with a reduction of emissions. Most remarkable, this is the first study to our knowledge to operationally evaluate coordinated truck appointments in a real-world case. The developed simulation model was designed in a generic way for possible application at diverse international ports. Although this practical case considers

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only drayage, the results encourage the development of similar approaches incorporating port hinterland processes because of the related problem structure. However, it has to be noted that this study only considers drayage and does not apply a sophisticated matching procedure. Future work should, therefore, include the application for an extended regional scope and the development of appropriate matching models.

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