ThuBa Nguyen

An efficient heuristic algorithm for the alternative-fuel station location problem

We have developed an efficient heuristic algorithm for location of alternative-fuel stations. The algorithm is constructed based on solving the sequence of subproblems restricted on a set of promising station candidates, and fixing a number of the best promising station locations. The set of candidates is initially determined by solving a relaxation model, and then modified by exchanging some stations between the promising candidate set and the remaining station set. A number of the best station candidates in the promising candidate set can be fixed to improve computation time. In addition, a parallel computing strategy is integrated into solving simultaneously the set of subproblems to speed up computation time. Experimental results carried out on the benchmark instances show that our algorithm outperforms genetic algorithm and greedy algorithm. As compared with CPLEX solver, our algorithm can obtain all the optimal solutions on the tested instances with less computation time.

Transforming Last-mile Logistics: Opportunities for more Sustainable Deliveries

Road congestion, air pollution and sustainability are increasingly important in major cities. We look to understand how last-mile deliveries in the parcel sector are impacting our roads. Using formative field work and quantitative analysis of consignment manifests and location data, we identify how the effectiveness of life-style couriers is contributing to both environmental and non-environmental externalities. This paper presents an analysis of delivery performances and practices in last-mile logistics in central London, quantifying the impacts differing levels of experience have on overall round efficiency. We identify eleven key opportunities for technological support for last-mile parcel deliveries that could contribute to both driver effectiveness and sustainability. We finish by examining how HCI can lead to improved environmental and social justice by re-considering and realizing future collaborative visions in last-mile logistics.

Optimising parcel deliveries in London using dual-mode routing

Abstract Last-mile delivery operations are complex, and the conventional way of using a single mode of delivery (e.g. driving) is not necessarily an efficient strategy. This paper describes a two-level parcel distribution model that combines walking and driving for a single driver. The model aims to minimise the total travelling time by scheduling a vehicle’s routing and the driver’s walking sequence when making deliveries, taking decisions on parking locations into consideration. The model is a variant of the Clustered Travelling Salesman Problem with Time Windows, in which the sequence of visits within each cluster is required to form a closed tour. When applied to a case study of an actual vehicle round from a parcel carrier operating in London, savings of over 20% in the total operation time were returned over the current situation where 144 parcels were being delivered to 57 delivery locations.

Capacity planning with demand uncertainty for outpatient clinics

In this study, we develop a capacity planning model to determine the required number of physicians for an outpatient system with patient reentry. First-visit (FV) patients are assumed to arrive randomly to the system. After their first appointment, the FV patient may require additional appointments, and will then become a re-visit (RV) patient; after each appointment an RV patient will require subsequent visits with a given probability. The system must achieve a set of targets on the appointment lead-times for both FV and RV patients. Furthermore, the system must have sufficient capacity to assure that a given percentile of FV patients is admitted. We develop a deterministic model that finds the required capacity over a finite horizon. We establish the tractability of the deterministic model and show that it provides a reasonable approximation to the stochastic model. We also demonstrate the value from knowing the demands in terms of the required resources. These conclusions are numerically illustrated using real data from the Urology outpatient clinic of the studied hospital.