A Heuristic Method for Vehicle Scheduling Problems

This paper presents a sequential optimum algorithm for vehicle schedulingproblem, which includes obtaining initial theoretical solution, adjustingsolution, forming initial routes and adjustins routes. This method can beapplied to general transportation problems with multiple depots and multiplevehicle types on complex network. In comparison with manual scheduling ofChengdu Transportation Company II, the result shows that this method isreasonable, feasible and applicable.

ScenicPlanner： planning scenic travel routes leveraging heterogeneous user-generated digital footprints

To facilitate the travel preparation process to a city, a lot of work has been done to recommend a POI or a sequence of POIs automatically to satisfy users＇ needs. How- ever, most of the existing work ignores the issue of planning the detailed travel routes between POIs, leaving the task to online map services or commercial GPS navigators. Such a service or navigator in terms of suggesting the shortest travel distance or time, which cannot meet the diverse requirements of users. For instance, in the case of traveling by driving for leisure purpose, the scenic view along the travel routes would be of great importance to users, and a good planning ser- vice should put the sceneries of the route in higher priority rather than the distance or time taken. To this end, in this paper, we propose a novel framework called ScenicPlanner for route recommendation, leveraging a combination of get- tagged image and check-in digital footprints from location- based social networks （LBSNs）. First, we enrich the road net- work and assign a proper scenic view score to each road seg- ment to model the scenic road network, by extracting relevant information from get-tagged images and check-ins. Then, we apply heuristic algorithms to iteratively add road segment and determine the travelling order of added road segments with the objective of maximizing the total scenic view score while satisfying the user-specified constraints （i.e., origin, desti- nation and the total travel distance）. Finally, to validate the efficiency and effectiveness of the proposed framework, we conduct extensive experiments on three real-world data sets from the Bay Area in the city of San Francisco, which con- tain a road network crawled from OpenStreetMap, more than 31 000 geo-tagged images generated by 1 571 Flickr users in one year, and 110 214 check-ins left by 15 680 Foursquare users in six months.

Personalized travel route recommendation using collaborative filtering based on GPS trajectories

Travelling is a critical component of daily life.With new technology,personalized travel route recommendations are possible and have become a new research area.A personalized travel route recommendation refers to plan an optimal travel route between two geographical locations,based on the road networks and users’travel preferences.In this paper,we define users’travel behaviours from their historical Global Positioning System(GPS)trajectories and propose two personalized travel route recommendation methods–collaborative travel route recommendation(CTRR)and an extended version of CTRR(CTRR+).Both methods consider users’personal travel preferences based on their historical GPS trajectories.In this paper,we first estimate users’travel behaviour frequencies by using collaborative filtering technique.A route with the maximum probability of a user’s travel behaviour is then generated based on the naïve Bayes model.The CTRR+method improves the performances of CTRR by taking into account cold start users and integrating distance with the user travel behaviour probability.This paper also conducts some case studies based on a real GPS trajectory data set from Beijing,China.The experimental results show that the proposed CTRR and CTRR+methods achieve better results for travel route recommendations compared with the shortest distance path method.

A Survey of Truck-Drone Routing Problem:Literature Review and Research Prospects

The vehicle routing problem(VRP)has been an important research topic in operations research for decades.The major applications of the VRP arise in transportation,especially the last-mile delivery.In recent years,a growing number of logistic companies introduce drones or unmanned aerial vehicles in the delivery operations.Therefore,the truck-drone routing problem(TDRP),where trucks and drones are scheduled and coordinated to serve customers,vitalizes a new research stream in the literature.In this paper,we provide a comprehensive review on the TDRP.First,two basic models for the traveling salesman problem with drones and vehicle routing problem with drones are presented.Second,researches devoted to the TDRP are classified according to their addressed constraints and features.Third,prevalent algorithms that have been widely used in the existing literature are reviewed and described.Last,potential research opportunities are identified for future study.