Duc Thien Nguyen

An auction mechanism for the last-mile deliveries via urban consolidation centre

A number of cities around the world have adopted urban consolidation centres (UCCs) to address some challenges of their last-mile deliveries. At the UCC, goods are consolidated based on their destinations prior to their deliveries into the city centre. In many examples, the UCC owns a fleet of eco-friendly vehicles to carry out the deliveries. A carrier/shipper who buys the UCCs service hence no longer needs to enter the city centre where there might be time-window and vehicle-type restrictions. As a result, it becomes possible to retain the use of large trucks for the economies of scale outside the city centre. Furthermore, time which would otherwise be spent in the city centre can then be used to deliver more orders. With possibly tighter regulation and thinning profit margin in near future, requests for the use of the UCCs service shall become more and more common. In this paper, we propose a profit-maximizing auction mechanism for the use of the UCCs service. We first formulate the winner determination problem as mixed-integer program (MIP). Then, we provide a greedy approximation algorithm to solve the MIP in reasonable time. Our experiments indicate that the proposed auction along with the greedy approximation algorithm is able to maximize the UCCs profit to near optimality with reasonable computational budget.

A Mechanism for Organizing Last-Mile Service Using Non-dedicated Fleet

Unprecedented pace of urbanization and rising income levels have fueled the growth of car ownership in almost all newly formed mega cities. Such growth has congested the limited road space and significantly affected the quality of life in these mega cities. Convincing residents to give up their cars and use public transport is the most effective way in reducing congestion, however, even with sufficient public transport capacity, the lack of last-mile (from the transport hub to the destination) travel services is the major deterrent for the adoption of public transport. Due to the dynamic nature of such travel demands, fixed-size fleets will not be a cost-effective approach in addressing last-mile demands. Instead, we propose a dynamic, incentive-based mechanism that enables taxi ride-sharing for satisfying last-mile travel demands. On the demand side, travelers would register their last-mile travel demands in real-time, and they are expected to receive ride arrangements before they reach the hub, on the supply side, depending on the real-time demands, proper incentives will be computed and provided to taxi drivers willing to commit to the last-mile service. Multiple travelers will be clustered into groups according to their destinations, and travelers belonging to the same group will be assigned to a taxi, while each of them paying fares considering their destinations and also their orders in reaching destinations. In this paper, we provide mathematical formulations for demand clustering and fare distribution. If the model returns a solution, it is guaranteed to be implement able. For cases where it is not possible to satisfy all demands despite having enough capacity, we propose a two-phase approach that identifies the maximal subset of riders that can be feasibly served. Finally, we use a series of numerical examples to demonstrate the effectiveness of our approach.

Reinforcement learning for adaptive operator selection in memetic search applied to quadratic assignment problem

Memetic search is well known as one of the state-of-the-art metaheuristics for finding high-quality solutions to NP-hard problems. Its performance is often attributable to appropriate design, including the choice of its operators. In this paper, we propose a Markov Decision Process model for the selection of crossover operators in the course of the evolutionary search. We solve the proposed model by a Q-learning method. We experimentally verify the efficacy of our proposed approach on the benchmark instances of Quadratic Assignment Problem.

An empirical study of off-line configuration and on-line adaptation in operator selection

Automating the process of finding good parameter settings is important in the design of high-performing algorithms. These automatic processes can generally be categorized into off-line and on-line methods. Off-line configuration consists in learning and selecting the best setting in a training phase, and usually fixes it while solving an instance. On-line adaptation methods on the contrary vary the parameter setting adaptively during each algorithm run. In this work, we provide an empirical study of both approaches on the operator selection problem, explore the possibility of varying parameter value by a non-adaptive distribution tuned off-line, and incorporate the off-line with on-line approaches. In particular, using an off-line tuned distribution to vary parameter values at runtime appears to be a promising idea for automatic configuration.

Decomposition techniques for urban consolidation problems

Less-than-truckload deliveries is known to be a source of inefficiency in last-mile logistics leading to high transport costs, environmental pollution, traffic jam, particularly in urban settings. An Urban Consolidation Center (UCC) provides a platform to consolidate freights from various sources before delivering into the city. The operations of UCCs consist of 2 interrelated phases, consolidating freights and scheduling trucks into the city center. This problem is computationally challenging because of large urban freight volumes, which prohibits optimal solutions of conventional integer programming models to be found efficiently. In this paper, we propose two novel decomposition schemes: a vertical decomposition based on dynamic programming can achieve optimal consolidation for the single-period problem, and the horizontal decomposition based on a Lagrangian Relaxation can achieve good approximate solution for the multi-period problem. The combination of these two decompositions yield an efficient approach for dealing with large-scale problems.