The Flexible Scheduled Service Network Design Problem

The scheduled service network design problem (SSNDP) can support planning the transportation operations of consolidation carriers given shipment-level service commitments regarding available and due times. These available and due times impact transportation costs by constraining potential consolidation opportunities. However, such available and due times may be changed, either because of negotiations with customers or redesigned internal operations to increase shipment consolidation and reduce transportation costs. As changing these times can lead to customer service and operational issues, we presume a carrier seeks to do so for a limited number of shipments. We propose a new variant of the SSNDP, the flexible scheduled service network design problem, that identifies the shipments for which these times should be changed to minimize total transportation and handling costs. We present a solution approach for this problem that outperforms a commercial optimization solver on instances derived from the operations of a U.S. less-than-truckload freight transportation carrier. With an extensive computational study, we study the savings potential of leveraging flexibility and the operational settings that are fertile ground for doing so.

Designing of Container Feeder Service Networks Under Unstable Demand Conditions

The COVID-19 pandemic has greatly magnified supply challenges in all industries, and virus waves continue to cause an extraordinary amount of variation in both the demand for and the availability of necessary products. This uncertainty has also forced many organizations including container liner shipping to redesign their supply chain. Feeder services from hub ports are essential chain of shipping networks. This chapter addresses the design of feeder networks under consideration of demand fluctuations over the year. For this purpose, a perturbation-based variable neighbourhood search approach is developed in order to determine the feeder ship fleet size and mix, the fleet deployment, service routes, and voyage schedules to minimize operational costs. In the case study investigation, the authors consider the feeder network design problem faced by a feeder shipping company as a sample application. The performance of alternate network configurations is compared under dynamic demand conditions. Numerical results highlight the advantage of dynamic and flexible design of feeder service networks.

Toward Baggage-Free Airport Terminals: A Case Study of London City Airport

Nowadays, the aviation industry pays more attention to emission reduction toward the net-zero carbon goals. However, the volume of global passengers and baggage is exponentially increasing, which leads to challenges for sustainable airports. A baggage-free airport terminal is considered a potential solution in solving this issue. Removing the baggage operation away from the passenger terminals will reduce workload for airport operators and promote passengers to use public transport to airport terminals. As a result, it will bring a significant impact on energy and the environment, leading to a reduction of fuel consumption and mitigation of carbon emission. This paper studies a baggage collection network design problem using vehicle routing strategies and augmented reality for baggage-free airport terminals. We use a spreadsheet solver tool, based on the integration of the modified Clark and Wright savings heuristic and density-based clustering algorithm, for optimizing the location of logistic hubs and planning the vehicle routes for baggage collection. This tool is applied for the case study at London City Airport to analyze the impacts of the strategies on carbon emission quantitatively. The result indicates that the proposed baggage collection network can significantly reduce 290.10 tonnes of carbon emissions annually.

Quasi-Polynomial Algorithms for Submodular Tree Orienteering and Directed Network Design Problems

We consider the following general network design problem. The input is an asymmetric metric (V, c), root [Formula: see text], monotone submodular function [Formula: see text], and budget B. The goal is to find an r-rooted arborescence T of cost at most B that maximizes f(T). Our main result is a simple quasi-polynomial time [Formula: see text]-approximation algorithm for this problem, in which [Formula: see text] is the number of vertices in an optimal solution. As a consequence, we obtain an [Formula: see text]-approximation algorithm for directed (polymatroid) Steiner tree in quasi-polynomial time. We also extend our main result to a setting with additional length bounds at vertices, which leads to improved [Formula: see text]-approximation algorithms for the single-source buy-at-bulk and priority Steiner tree problems. For the usual directed Steiner tree problem, our result matches the best previous approximation ratio but improves significantly on the running time. For polymatroid Steiner tree and single-source buy-at-bulk, our result improves prior approximation ratios by a logarithmic factor. For directed priority Steiner tree, our result seems to be the first nontrivial approximation ratio. Under certain complexity assumptions, our approximation ratios are the best possible (up to constant factors).

The Euclidean Steiner Tree Problem: Simulated Annealing and Other Heuristics

<p>In this thesis the Euclidean Steiner tree problem and the optimisation technique called simulated annealing are studied. In particular, there is an investigation of whether simulated annealing is a viable solution method for the problem. The Euclidean Steiner tree problem is a topological network design problem and is relevant to the design of communication, transportation and distribution networks. The problem is to find the shortest connection of a set of points in the Euclidean plane. Simulated annealing is a generally applicable method of finding solutions of combinatorial optimisation problems. The results of the investigation are very satisfactory. The quality of simulated annealing solutions compare favourably with those of the best known tailored heuristic method for the Euclidean Steiner tree problem</p>

The Euclidean Steiner Tree Problem: Simulated Annealing and Other Heuristics

<p>In this thesis the Euclidean Steiner tree problem and the optimisation technique called simulated annealing are studied. In particular, there is an investigation of whether simulated annealing is a viable solution method for the problem. The Euclidean Steiner tree problem is a topological network design problem and is relevant to the design of communication, transportation and distribution networks. The problem is to find the shortest connection of a set of points in the Euclidean plane. Simulated annealing is a generally applicable method of finding solutions of combinatorial optimisation problems. The results of the investigation are very satisfactory. The quality of simulated annealing solutions compare favourably with those of the best known tailored heuristic method for the Euclidean Steiner tree problem</p>