Dynamic Collection Scheduling Using Remote Asset Monitoring: Case Study in the UK Charity Sector

Remote sensing technology is now coming onto the market in the waste collection sector. This technology allows waste and recycling receptacles to report their fill levels at regular intervals. This reporting enables collection schedules to be optimized dynamically to meet true servicing needs in a better way and so reduce transport costs and ensure that visits to clients are made in a timely fashion. This paper describes a real-life logistics problem faced by a leading UK charity that services its textile and book donation banks and its high street stores by using a common fleet of vehicles with various carrying capacities. Use of a common fleet gives rise to a vehicle routing problem in which visits to stores are on fixed days of the week with time window constraints and visits to banks (fitted with remote fill-monitoring technology) are made in a timely fashion so that the banks do not become full before collection. A tabu search algorithm was developed to provide vehicle routes for the next day of operation on the basis of the maximization of profit. A longer look-ahead period was not considered because donation rates to banks are highly variable. The algorithm included parameters that specified the minimum fill level (e.g., 50%) required to allow a visit to a bank and a penalty function used to encourage visits to banks that are becoming full. The results showed that the algorithm significantly reduced visits to banks and increased profit by up to 2.4%, with the best performance obtained when the donation rates were more variable

Effect of simplicity and attractiveness on route selection for different journey types

This study investigated the effects of six attributes, associated with simplicity or attractiveness, on route preference for three pedestrian journey types (everyday, leisure and tourist). Using stated choice preference experiments with computer generated scenes, participants were asked to choose one of a pair of routes showing either two levels of the same attribute (experiment 1) or different attributes (experiment 2). Contrary to predictions, vegetation was the most influential for both everyday and leisure journeys, and land use ranked much lower than expected in both cases. Turns ranked higher than decision points for everyday journeys as predicted, but the positions of both were lowered by initially unranked attributes. As anticipated, points of interest were most important for tourist trips, with the initially unranked attributes having less influence. This is the first time so many attributes have been compared directly, providing new information about the importance of the attributes for different journeys. © 2014 Springer International Publishing

Personalized Tourist Route Generation

When tourists are at a destination, they typically search for information in the Local Tourist Organizations. There, the staff determines the profile of the tourists and their restrictions. Combining this information with their up-to-date knowledge about the local attractions and public transportation, they suggest a personalized route for the tourist agenda. Finally, they fine tune up this route to better fit tourists' needs. We present an intelligent routing system to fulfil the same task. We divide this process in three steps: recommendation, route generation and route customization. We focus on the last two steps and analyze them. We model the tourist planning problem, integrating public transportation, as the Time Dependent Team Orienteering Problem with Time Windows (TDTOPTW) and we present an heuristic able to solve it on real-time. Finally, we show the prototype which generates and customizes routes in real-time