Motor Carrier Service Network Design

This chapter introduces service network design (SND) operations research models and solution methodologies specifically focused on problems that arise in the planning of operations in the trucking, or motor freight, industry. Consolidation carriers such as less-than-truckload and package trucking companies face flow planning problems to decide how to route freight between transfer terminals, and load planning problems to decide how to consolidate shipments into trailerloads and containerloads for dispatch. Integer programming models are introduced for these network design decision problems as well as exact and heuristic solution methods

Service network design for an intermodal container network with flexible due dates/times and the possibility of using subcontracted transport

An intermodal container transportation network is being developed between Rotterdam and several inland terminals in North West Europe: the EUROPEAN GATEWAY SERVICES (EGS) network. This network is developed and operated by the seaports of EUROPE CONTAINER TERMINALS (ECT). To use this network cost-efficiently, a centralized planning of the container transportation is required, to be operated by the seaport. In this paper, a new mathematical model is proposed for the service network design. The model uses a combination of a path-based formulation and a minimum flow network formulation. It introduces two new features to the intermodal network-planning problem. Firstly, overdue deliveries are penalized instead of prohibited. Secondly, the model combines self-operated and subcontracted services. The service network design considers the network-planning problem at a tactical level: the optimal service schedule between the given network terminals is determined. The model considers self-operated or subcontracted barge and rail services as well as transport by truck. The model is used for the service network design of the EGS network. For this case, the benefit of using container transportation with multiple legs and intermediate transfers is studied. Also, a preliminary test of the influence of the new aspects of the model is done. The preliminary results indicate that the proposed model is suitable for the service network design in modern intermodal container transport networks. Also, the results suggest that a combined business model for the network transport and terminals is worth investigating further, as the transit costs can be reduced with lower transfer costs

Fuzzy C-means-based scenario bundling for stochastic service network design

Stochastic service network designs with uncertain demand represented by a set of scenarios can be modelled as a large-scale two-stage stochastic mixed-integer program (SMIP). The progressive hedging algorithm (PHA) is a decomposition method for solving the resulting SMIP. The computational performance of the PHA can be greatly enhanced by decomposing according to scenario bundles instead of individual scenarios. At the heart of bundle-based decomposition is the method for grouping the scenarios into bundles. In this paper, we present a fuzzy c-means-based scenario bundling method to address this problem. Rather than full membership of a bundle, which is typically the case in existing scenario bundling strategies such as k-means, a scenario has partial membership in each of the bundles and can be assigned to more than one bundle in our method. Since the multiple bundle membership of a scenario induces overlap between the bundles, we empirically investigate whether and how the amount of overlap controlled by a fuzzy exponent would affect the performance of the PHA. Experimental results for a less-than-truckload transportation network optimization problem show that the number of iterations required by the PHA to achieve convergence reduces dramatically with large fuzzy exponents, whereas the computation time increases significantly. Experimental studies were conducted to find out a good fuzzy exponent to strike a trade-off between the solution quality and the computational time

A Choice-Driven Service Network Design and Pricing Including Heterogeneous Behaviors

The design and pricing of services are two of the most important decisions faced by any intermodal transport operator. The key success factor lies in the ability of meeting the needs of the shippers. Therefore, making full use of the available information about the demand helps to come up with good design and pricing decisions. With this in mind, we propose a Choice-Driven approach, incorporating advanced choice models directly into a Service Network Design and Pricing problem. We evaluate this approach considering three different mode choice models: one deterministic with 4 attributes (cost, time, frequency and accessibility); and two stochastic also accounting for unobserved attributes and shippers heterogeneity respectively. To reduce the computational time for the stochastic instances, we propose a predetermination heuristic. These models are compared to a benchmark, where shippers are solely cost-minimizers. Results show that the operator profits can be significantly improved, even with the deterministic version. The two stochastic versions further increase the realized profits, but considering heterogeneity allows a better estimation of the demand

Service Network Design for Parcel Trucking

We develop a large-scale package express service network design methods using integer programming optimization models specified on flat network models that capture important timing constraints to ensure that package flows meet service constraints. In the first part, we focus on shuttle activities and develop optimization technology for the design of shuttle services using novel rate-based models to determine package flow paths as well as vehicle routes. A computational study using data from a large Chinese package company demonstrates that the technology produces a cost-effective service network design for shuttle schedules with excellent on-time performance. The second part presents a strategic hub selection problem developing a cost-effective greedy heuristic approach that solves tractable integer programming models to add a single intermediate hub on each iteration. A computational study shows that the greedy approach selects geographically-distributed and cost-effective hubs for package transfer, and moreover, the heuristic outperforms the full optimization model by a 20% gap difference for the relevant test instances. In the last part, we develop a new approach for solving the flow planning problem of service network design for large-scale networks with timing constraints. We introduce a so-called generalized in-tree, referred to as GIT, which has useful operational benefits. We demonstrate, via a computational study, that imposing a discretized GIT structure that groups remaining times into fixed-width buckets of 2 hours or 4 hours leads to solutions that are only 2% to 4% more costly than those that do not require GIT structure but significantly simpler to operationalize.Ph.D