Service Network Design Research Articles

Service network design considering multiple types of services

Integrated transportation systems providing multiple types of services are attracting increasing attention due to the diversification of demands. In this paper, we propose both deterministic and two-stage robust optimization formulations for the service network design problem considering multiple types of services. A probability-free uncertainty set is employed to deal with demand uncertainty in the proposed robust model. We implement a column-and-constraint generation approach to solve the robust model, which outperforms the Benders decomposition method. Comparative results indicate that it helps reduce the total cost and the number of vehicles to consider multiple service types in the network.

Intermodal Container Routing: Integrating Long-Haul Routing and Local Drayage Decisions

Intermodal logistics service providers decide on the routing of demand through their service network. Long-haul routing decisions determine the selected departure and arrival terminals for containers and imply corresponding drayage tasks. Traditionally, given these long-haul routes and fixed drayage tasks, drayage operations are planned in a second phase by establishing truck routes to transport containers to and from terminals by truck. In this paper, operational decisions on local drayage routing in large-volume freight regions with multiple terminals on the one hand, and intermodal long-haul routing on the other hand are merged into an integrated intermodal routing problem. Different long-haul routing decisions imply different drayage tasks to be performed and thus impact total trucking costs. The approach aims at reducing the number of road kilometres and increases bundling opportunities by maximising the long-haul capacity utilisation. In this way, it contributes to the modal shift towards intermodal transport and a more sustainable transport system. As a weekly planning horizon is used, a maximum daily active time and a minimum overnight’s rest are included for multi-day drayage routing. A large neighbourhood search heuristic is proposed to solve the integrated intermodal routing problem. This integrated planning approach provides decision support for routing customer orders throughout the intermodal network with the aim of minimising total transport costs and maximising capacity utilisation. Experiments show the added value of the integrated approach, which uses more information to make better-informed decisions and increase the capacity utilisation. The largest savings in trucking costs are obtained for clustered instances with demand characteristics closest to real-life cases. Finally, a real-life case study analyses the impact of tactical service network design decisions on the total operational costs.

Service network design with mixed autonomous fleets

We propose a service network design problem for the tactical planning of parcel delivery with autonomous vehicles in SAE level 4. We consider a heterogeneous infrastructure wherein such vehicles may only drive in feasible zones but need to be guided elsewhere by manually operated vehicles in platoons. Our model decides on the fleet size and mix as well as on the routing of vehicles and goods. We observe cost savings and show that the strategies to coordinate a fleet using platooning depend upon the infrastructure, demand, and fleet mix. We discuss our results and identify areas for future research.

Freight service network design with heterogeneous preferences for transport time and reliability

Value of time and value of reliability are two important user attributes that reflect shippers’ behavioral preferences, and as such influence the design of transport service networks. As shippers preferences will vary widely, it is important to consider these variations between users in the design of service networks. Up to now, network design research has ignored the combined use of time and reliability valuations for heterogeneous user populations. The objective of this paper is to address these attributes in a model for freight service network design targeting service performance improvement. We present a new frequency based service network design model with transshipments, capacity constraints and heterogeneous users. We apply the model to demonstrate that including heterogeneity explicitly in network design pays off in terms of an improved user performance of the network. A case study is conducted for a railway network in China. Values of time and reliability are estimated from a recent Stated Preference survey and used to determine distinct user classes. The proposed optimization problem is solved using an improved Simulated Annealing based heuristic method, for the case of the aggregate user group and the case of two distinct classes. Results show that by taking variations in shippers’ VOT and VOR into account, users’ total generalized cost is reduced while service levels improve. We conclude that incorporating heterogeneous VOT and VOR into the service network design problem is of interest for decisions on network investments.

SemTra: A semi-supervised approach to traffic flow labeling with minimal human effort

Network traffic classification is an essential component for service differentiation, network design and management and security systems. The limitations of traditional port-based and payload methods have been addressed by recent promising studies which rely on the analysis of the statistics of traffic flows and the use of machine learning techniques. However, due to the high cost of manual labeling, it is hard to obtain sufficient, reliable and up-to-date labeled data for effective IP traffic classification. This paper proposes a novel semi-supervised approach, called SemTra, which automatically alleviates the shortage of labeled flows for machine learning by exploiting the advantages of both supervised and unsupervised models. In particular, SemTra involves the following: (i) generating multi-view representations of the original data based on dimensionality reduction methods to have strong discrimination ability, (ii) incorporating the generated representations into the ensemble clustering model to provide a combined clustering output with better quality and stability, (iii) adapting the concept of self-training to iteratively utilize the few labeled data along with unlabeled within local and global viewpoints; and (iiii) obtaining the final class decision by combining the decisions of mapping strategy of clusters, the local self-training and global self-training approaches. Extensive experiments were carried out to compare the effectiveness of SemTra over representative semi-supervised methods using sixteen network traffic datasets.

Stochastic Network Design for Planning Scheduled Transportation Services: The Value of Deterministic Solutions

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. Data and the online appendix are available at https://doi.org/10.1287/ijoc.2018.0819 .

A hybrid algorithm for large-scale service network design considering a heterogeneous fleet

Service network design addresses decisions related to transportation services operation and origin-to-destination commodity flow distribution. In this paper, we consider the usage of a heterogeneous fleet to provide services for a very large transportation network. Apart from decisions on transportation services and commodity flow, the problem also determines the number of vehicles of different types that facilitated on each service link, to better reflect real applications. We propose both arc-based and cycle-path models to formulate the problem. A hybrid algorithm is presented to solve large-scale instances. The method includes pricing and cutting techniques to achieve tight lower bounds, as well as a local search algorithm to obtain high-quality solutions. Computational study indicates the effectiveness and efficiency of the proposed algorithm when compared to the state-of-the-art solver CPLEX. The proposed methodology is applied to a real-world network, which shows the necessity of considering a heterogeneous fleet.

A service network design model for an intermodal rail-road freight forwarder

Intermodal transportation has known a great development recently as a consequence of the development of container transportation services. The development of gateways infrastructures (ports, airports) but also the extension of gateways to dry ports has led to the development of intermodal logistics networks that requires research for the optimisation of their infrastructure design and their services configuration. The service network design problem of a network performing intermodal railroad freight transportation consists of the determination of the services to operate on the network and their frequencies while minimising cost and maximising service level. The purpose of this paper is to present a model for the service network design problem in the case of intermodal rail-road container freight transportation. This model is based on a capacitated fixed charge path formulation. It gives as outcomes shipping flows, service frequency as well as the required capacity and loading plans in the intermodal terminals.

A service network design model for an intermodal rail-road freight forwarder

Intermodal transportation has known a great development recently as a consequence of the development of container transportation services. The development of gateways infrastructures (ports, airports) but also the extension of gateways to dry ports has led to the development of intermodal logistics networks that requires research for the optimisation of their infrastructure design and their services configuration. The service network design problem of a network performing intermodal railroad freight transportation consists of the determination of the services to operate on the network and their frequencies while minimising cost and maximising service level. The purpose of this paper is to present a model for the service network design problem in the case of intermodal rail-road container freight transportation. This model is based on a capacitated fixed charge path formulation. It gives as outcomes shipping flows, service frequency as well as the required capacity and loading plans in the intermodal terminals.

A Two-Stage Chance Constrained Approach with Application to Stochastic Intermodal Service Network Design Problems

Compared with traditional freight transportation, intermodal freight transportation is more competitive which can combine the advantages of different transportation modes. As a consequence, operational research on intermodal freight transportation has received more attention and developed rapidly, but it is still a young research field. In this paper, a stochastic intermodal service network design problem is introduced in a sea-rail transportation system, which considers stochastic travel time, stochastic transfer time, and stochastic container demand. Given candidate train and ship services, we develop a two-stage chance constrained programming model for this problem with the objective of minimising the expected total cost. The first stage allows for the selection of operated services, while the second stage focuses on the determination of intermodal container routes where capacity and on-time delivery chance constraints are presented. A hybrid heuristic algorithm, incorporating sample average approximation and ant colony optimisation, is employed to solve this model. The proposed model is applied to a realistic intermodal sea-rail network, which demonstrates the performance of the model and algorithm as well as the influence of stochasticity on transportation plans. Hence, the proposed methodology can improve effectively the performance of intermodal service network design scheme under stochastic conditions and provide managerial insights for decision-makers.

Network Design for Municipal Solid Waste Collection: A Case Study of the Nanjing Jiangbei New Area.

Garbage collection is an important part of municipal engineering. An effective service network design can help to reduce the municipal operation cost and improve its service level. In this paper, we propose an optimization model for the network design of municipal solid waste (MSW) collection in the Nanjing Jiangbei new area. The problem was formulated as a mixed integer nonlinear programming (MINLP) model with an emphasis on minimizing the annual operation cost. The model simultaneously decides on the optimal number of refuse transfer stations (RTSs), determines the relative size and location for each RTS, allocates each community to a specific RTS, and finally identifies the annual operation cost and service level for the optimal scenario as well as other scenarios. A custom solution procedure which hybrids an enumeration rule and a genetic algorithm was designed to solve the proposed model. A sensitivity analysis was also conducted to illustrate the impact of changes in parameters on the optimality of the proposed model. Test results revealed that our model could provide tangible policy recommendations for managing the MSW collection.

A multi-hub Express Shipment Service Network Design model with flexible hub assignment

The Express Shipment Service Network Design problem consists in designing a network of flights that, at minimum cost, enables the overnight transportation of packages in vast regions. In this paper, we study its multi-hub version and develop a model in which the allocation of packages to hubs is a decision, unlike state-of-the-art approaches that suppose a predetermined allocation. Our formulation is strengthened with three families of valid inequalities and with improved forcing constraints. Extensive numerical experiments using instances provided by FedEx Express Europe show the efficiency of our flexible hub assignment model, particularly compared to fixed hub assignment approaches.

Service network design with equilibrium-driven demands

We study a service network design problem in which the network operator wishes to determine facility locations and sizes in order to satisfy the demand of the customers while balancing the cost of the system with a measure of quality-of-service faced by the customers. We assume customers choose the facilities that meet demand, in order to minimize their total cost, including costs associated with traveling and waiting. When having demand served at a facility, customers face a service delay that depends on the total usage (congestion) of the facility. The total cost of meeting a customer’s demand at a facility includes a facility-specific unit travel cost and a function of the service delay. When customers all minimize their own costs, the resulting distribution of customer demand to facilities is modeled as an equilibrium. This problem is motivated by several applications, including supplier selection in supply chain planning, preventive healthcare services planning, and shelter location-allocation in disaster management. We model the problem as a mixed-integer bilevel program that can be reformulated as a nonconvex mixed-integer nonlinear program. The reformulated problem is difficult to solve by general-purpose solvers. Hence, we propose a Lagrangian relaxation approach that finds a candidate feasible solution along with a lower bound that can be used to validate the solution quality. The computational results indicate that the method can efficiently find feasible solutions, along with bounds on their optimality gap, even for large instances.

One-block train formation in large-scale railway networks: An exact model and a tree-based decomposition algorithm

We investigate the one-block train formation problem (TFP) in the railway freight transportation industry. The input to this problem is a railway network and a set of demands for transportation. Each demand is a request for shipping a number of rail cars from one station to another. The one-block TFP now addresses two subproblems in the tactical level: Which pairs of stations are directly connected via a block (or: train service); and which demands are transported by each block. The aim is to service all demands against minimal costs. Moving beyond current researches on service network design, the unitary rule and the intree rule are taken into account in this study based on the Chinese railway background. We develop a linear binary programming formulation to minimize the total sum of train accumulation costs and car classification costs, subject to limitations on the classification capacity and the number of sort tracks at each station. Furthermore, we propose a novel solution methodology that applies a tree-based decomposition algorithm. Here, we first decompose the whole network into a series of rooted trees for each destination separately. Then, we divide the trees further into sufficiently small subtrees, whose size is regulated by a node size parameter. Finally, we construct a restricted linear binary model for each subtree and solve these models sequentially to find their optimal solutions. The algorithm ensures that an overall feasible solution is obtained if one exists. Our computational results on a realistic network from the Chinese railway system with 83 stations, 158 links and 5700 randomly generated demands show that the proposed algorithm can derive high-quality solutions within a few hours of computation time. These solutions are on average 48.05% better than those obtained after solving the linear binary program with a commercial solver for 1 day.

Market-Oriented Service Network Design When Demand is Sensitive to Congestion

In this paper, we present a market-oriented service network design model in which the seller’s problem is to determine how many facilities to open, where to locate them, and which service capacities and service levels they should have to maximize overall profit. Our model explicitly considers the customers’ facility choice as a function of typical choice determinants, such as travel distance and congestion delays (which are endogenously impacted by the seller’s decisions) as well as other, exogenous factors such as price level and product variety. We relax the assumption adopted in many related works that the service provider has discretion as to the assignment of customers to facilities; instead, we allow customers to self-select based on their preferences for facility attributes according to an attraction-based choice model. Furthermore, we capture not only the effect of congestion on demand but also the reciprocal impacts of demand on congestion and service level by modeling each facility as an M/G/1 queue with service capacity as a decision variable. The resulting model represents a non-linear mixed-integer problem (MIP); however, we show that this problem can be linearized introducing several new continuous variables and constraints. To solve the linearized MIP to proven optimality or approximately, we develop an exact decomposition approach and heuristics. We report the performance testing of our approach with regard to run times and solution quality in an extensive computational experiment. A case study of the selection of locations for new convenience stores in Heidelberg, Germany illustrates the real-world applicability of the model using empirical market research data. An equivalent problem arises in a number of other applications, particularly in service shop industries such as restaurants and retailers. Surprisingly, profit maximization under customer-choice-driven behavior has rarely been considered as an objective in the related literature. The online appendix is available at https://doi.org/10.1287/trsc.2017.0797 .

A simulation-optimization approach for scheduling in stochastic freight transportation

Abstract In this paper, a new Simulation-Based Optimization Model (SBO-Model) is proposed to solve scheduling problem in stochastic multimodal freight transportation systems. The model is applied to find optimal services schedule in a real-world case study. In order to handle demand and travel time inherent variability, the stochastic service network design problem is addressed. Simulation modeling is used to efficiently account for real stochastic behavior with skewed continuous distributions. Such distinctive distribution shapes were commonly reported in transportation research studies that addressed the travel time reliability modeling. Results indicate that the SBO-Model can indeed provide reliable service schedules even under realistic complex stochasticity. The main finding is that, in order to solve efficiently such stochastic optimization problem, we need to go beyond the mean and variance estimates by considering the empirical distributions of uncertain parameters. Specifically, when the data exhibit skewness and/or multimodality, which are commonly found due to the traffic congestion. The originality of this work lies in the integration of stochastic models, commonly used in the transportation research field, for solving logistics planning problem generally addressed by Operations Research community.

Modeling the Service Network Design Problem in Railway Express Shipment Delivery

As air pollution becomes increasingly severe, express trains play a more important role in shifting road freight and reducing carbon emissions. Thus, the design of railway express shipment service networks has become a key issue, which needs to be addressed urgently both in theory and practice. The railway express shipment service network design problem (RESSNDP) not only involves the selection of train services and determination of service frequency, but it is also associated with shipment routing, which can be viewed as a service network design problem (SNDP) with railway characteristics. This paper proposes a non-linear integer programming model (INLP) which aims at finding a service network and shipment routing plan with minimum cost while satisfying the transportation time constraints of shipments, carrying capacity constraints of train services, flow conservation constraint and logical constraints among decision variables. In addition, a linearization technique was adopted to transform our model into a linear one to obtain a global optimal solution. To evaluate the effectiveness and efficiency of our approach, a small trial problem was solved by the state-of-the-art mathematical programming solver Gurobi 7.5.2.

A Bi-Level Programming Model for the Railway Express Cargo Service Network Design Problem

Service network design is fundamentally crucial for railway express cargo transportation. The main challenge is to strike a balance between two conflicting objectives: low network setup costs and high expected operational incomes. Different configurations of these objectives will have different impacts on the quality of freight transportation services. In this paper, a bi-level programming model for the railway express cargo service network design problem is proposed. The upper-level model forms the optimal decisions in terms of the service characteristics, and the low-level model selects the service arcs for each commodity. The rail express cargo is strictly subject to the service commitment, the capacity restriction, flow balance constraints, and logical relationship constraints among the decisions variables. Moreover, linearization techniques are used to convert the lower-level model to a linear one so that it can be directly solved by a standard optimization solver. Finally, a real-world case study based on the Beijing–Guangzhou Railway Line is carried out to demonstrate the effectiveness and efficiency of the proposed solution approach.

The price of discretizing time: a study in service network design

Researchers and practitioners have long recognized that many transportation problems can be naturally and conveniently modeled using time-expanded networks. In such models, nodes represent locations at distinct points in time and arcs represent possible actions, e.g., moving from one location to another at a particular point of time, or staying in the same location for a period of time. To use a time-expanded network, time must be discretized, i.e., the planning horizon is partitioned into discrete time intervals. The length of these intervals, therefore, must be chosen, and the parameters involving time, e.g., travel duration and due times, need to be mapped to these discrete intervals. Short intervals yield a high-quality approximation to the continuous-time problem, but typically induce a computationally intractable model; whereas long intervals can yield a computationally tractable, but low-quality model. The loss of quality is due to the approximation introduced by the mapping of parameters involving time. To guide researchers and practitioners in their use of time-expanded networks, we explore the choice of time discretization and its impact, by means of an extensive computational study on the service network design problem. The empirical results show that in some cases the loss of quality, i.e., the relative gap between the discretized and continuous-time optimal values, can be greater than 20%. We also investigate metrics that characterize and help identify instances that are likely to be sensitive to discretization and could incur a large loss of solution quality.

Integrating long-haul and local transportation planning: the Service Network Design and Routing Problem

We introduce a new optimization problem, the Service Network Design and Routing Problem that integrates long-haul and local transportation planning decisions. Such a problem is particularly important for consolidation carriers that face customer demands for fast delivery and thus must synchronize the different levels of their transportation operations. We present two formulations of the problem: (1) a route-based formulation that allows for the modeling of a rich set of rules governing local delivery routes at the expense of increased instance size and computational solve time, and, (2) an arc-based formulation that can be solved more quickly but has less modeling power. We solve each with a Dynamic Discretization Discovery algorithm that was recently proposed and designed for solving Service Network Design problems that require the precise modeling of time. With an extensive computational study, we examine the benefits of each formulation.