A dynamic ridesharing dispatch and idle vehicle repositioning strategy with integrated transit transfers

We propose a ridesharing strategy with integrated transit in which a private on-demand mobility service operator may drop off a passenger directly door-to-door, commit to dropping them at a transit station or picking up from a transit station, or to both pickup and drop off at two different stations with different vehicles. We study the effectiveness of online solution algorithms for this proposed strategy. Queueing-theoretic vehicle dispatch and idle vehicle relocation algorithms are customized for the problem. Several experiments are conducted first with a synthetic instance to design and test the effectiveness of this integrated solution method, the influence of different model parameters, and measure the benefit of such cooperation. Results suggest that rideshare vehicle travel time can drop by 40-60% consistently while passenger journey times can be reduced by 50-60% when demand is high. A case study of Long Island commuters to New York City (NYC) suggests having the proposed operating strategy can substantially cut user journey times and operating costs by up to 54% and 60% each for a range of 10-30 taxis initiated per zone. This result shows that there are settings where such service is highly warranted

The Position-Aware-Market: Optimizing Freight Delivery for Less-Than-Truckload Transportation

The increasing competition faced by logistics carriers requires them to ship at lower cost and higher efficiency. In reality, however, many trucks are running empty or with a partial load. Bridging such residual capacity with real time transportation demand enhances the efficiency of the carriers. We therefore introduce the Position-Aware-Market (PAM), where transportation requests are traded in real time to utilize transportation capacities optimally. In this paper we mainly focus on the decision support system for the truck driver, which solves a profit- maximizing Pickup and Delivery Problem with Time Windows (PM-PDPTW). We propose a novel Recursive Branch-and-Bound algorithm that solves the problem optimally, and apply it to a Tabu-Search heuristic for larger problem instances. Simulations show that problems with up to 50 requests can be solved optimally within seconds. Larger problems with 200 requests can be solved approximately by Tabu-Search in seconds, retaining 60% of the optimal profit

A new VRPPD model and a hybrid heuristic solution approach for e-tailing

We analyze a business model for e-supermarkets to enable multi-product sourcing capacity through co-opetition (collaborative competition). The logistics aspect of our approach is to design and execute a network system where “premium” goods are acquired from vendors at multiple locations in the supply network and delivered to customers. Our specific goals are to: (i) investigate the role of premium product offerings in creating critical mass and profit; (ii) develop a model for the multiple-pickup single-delivery vehicle routing problem in the presence of multiple vendors; and (iii) propose a hybrid solution approach. To solve the problem introduced in this paper, we develop a hybrid metaheuristic approach that uses a Genetic Algorithm for vendor selection and allocation, and a modified savings algorithm for the capacitated VRP with multiple pickup, single delivery and time windows (CVRPMPDTW). The proposed Genetic Algorithm guides the search for optimal vendor pickup location decisions, and for each generated solution in the genetic population, a corresponding CVRPMPDTW is solved using the savings algorithm. We validate our solution approach against published VRPTW solutions and also test our algorithm with Solomon instances modified for CVRPMPDTW

The traveling salesman problem with pickups, deliveries, and draft limits

open3siResearch supported by Air Force Office of Scientific Research (Grants FA9550-17-1-0025 and FA9550-17-1-0067 ) and by MIUR- Italy (Grant PRIN 2015 ).We introduce a new generalization of the traveling salesman problem with pickup and delivery, that stems from applications in maritime logistics, in which each node represents a port and has a known draft limit. Each customer has a demand, characterized by a weight, and pickups and deliveries are performed by a single ship of given weight capacity. The ship is able to visit a port only if the amount of cargo it carries is compatible with the draft limit of the port. We present an integer linear programming formulation and we show how classical valid inequalities from the literature can be adapted to the considered problem. We introduce heuristic procedures and a branch-and-cut exact algorithm. We examine, through extensive computational experiments, the impact of the various cuts and the performance of the proposed algorithms.openMalaguti, Enrico; Martello, Silvano*; Santini, AlbertoMalaguti, Enrico; Martello, Silvano*; Santini, Albert

Dynamic vehicle routing problems: Three decades and counting

Since the late 70s, much research activity has taken place on the class of dynamic vehicle routing problems (DVRP), with the time period after year 2000 witnessing a real explosion in related papers. Our paper sheds more light into work in this area over more than 3 decades by developing a taxonomy of DVRP papers according to 11 criteria. These are (1) type of problem, (2) logistical context, (3) transportation mode, (4) objective function, (5) fleet size, (6) time constraints, (7) vehicle capacity constraints, (8) the ability to reject customers, (9) the nature of the dynamic element, (10) the nature of the stochasticity (if any), and (11) the solution method. We comment on technological vis-à-vis methodological advances for this class of problems and suggest directions for further research. The latter include alternative objective functions, vehicle speed as decision variable, more explicit linkages of methodology to technological advances and analysis of worst case or average case performance of heuristics.© 2015 Wiley Periodicals, Inc

Multi-Level Route-Optimization Computer Application

This report provides a detailed analysis on how to optimize driving routes by creating a computer application. There are many different route-optimization issues that logistical companies consistently face, as well as many different solutions and algorithms. With technology on the rise, pick-up, delivery, and transportation services are become a huge part of our everyday lives. When optimizing routes, reducing transportation costs by minimizing travel distance is always ideal, but other factors must be considered such as arriving at a location before or after a certain time. Our objective is to optimize driving routes based on travel distance and priorities. We approached this project by using SQL as our main source of determining the route order based on the given distances and priorities of each destination in the projected route. We also used VBA as a tool to support the calculations and ASP.net to insert Javascript and HTML code, which allows us to visualize the Google Maps route once the order has been determined