Jill Hardin Wilson

Bio: Jill Hardin Wilson is an academic researcher from Northwestern University. The author has contributed to research in topics: Vehicle routing problem & Garbage collection. The author has an hindex of 2, co-authored 3 publications receiving 112 citations.

Forty years of periodic vehicle routing

Abstract: The periodic vehicle routing problem PVRP first appeared in 1974 in a paper about garbage collection Beltrami and Bodin, Networks 4 1974, 65-74. The wide applicability and versatility of the problem has led to a vast body of literature addressing both novel applications and solution methods. This article discusses the wide array of circumstances and settings in which the PVRP has been applied and describes the development of solution methods, both exact and heuristic, for the PVRP. As with many core research problems, many variants have been proposed. We will describe additional problem variants and extensions, as well as discuss the future of research for the PVRP. © 2013 Wiley Periodicals, Inc. NETWORKS, Vol. 631, 2-15 2014

Batching-Based Approaches for Optimized Packing of Jobs in the Spatial Scheduling Problem

Abstract: Spatial resources are often an important consideration in shipbuilding and large-scale manufacturing industries. Spatial scheduling problems (SSP) involve the non-overlapping arrangement of jobs within a limited physical workspace such that some scheduling objective is optimized. The jobs are typically heavy and occupy large areas, requiring that the same contiguous units of space be assigned throughout the duration of their processing time. This adds an additional level of complexity to the general scheduling problem. Since solving large instances using exact methods becomes computationally intractable, there is a need to develop alternate solution methodologies to provide near optimal solutions for these problems. Much of the literature focuses on minimizing the makespan of the schedule. We propose two heuristic methods for the minimum sum of completion times objective. Our approach is to group jobs into a batch and then apply a scheduling heuristic to the batches. We show that grouping jobs earlier in the schedule, although intuitive, can result in poor performance when jobs have sufficiently large differences in processing times. We provide bounds on the performance of the algorithms and also present computational results comparing the solutions to the optimal objective obtained from the integer programming formulation for SSP. With a smaller number of jobs, both algorithms produce comparable solutions. For instances with a larger number of jobs and a higher variability in spatial dimensions, we observe that the efficient area model outperforms the iterative model both in terms of solution quality and run time.

Vehicle routing problems in which consistency considerations are important: A survey

Abstract: An increasing number of companies focus on customer satisfaction to increase the lifetime value of each customer. In vehicle routing, customer satisfaction is often a result of consistent service. Customers appreciate service at regular times of the day provided by the same driver each time. Additionally, drivers become more familiar with their tasks if they visit the same customers and service regions repeatedly. In this article, we survey literature that addresses service consistency in vehicle routing. We present early solution approaches, starting from the 1970s, that focus on reducing the operational complexity resulting from planning and executing new routes each day. One side benefit of these approaches is service consistency; therefore, many recent solution approaches devised for improving customer satisfaction are based on previous achievements. We classify the literature according to three consistency features: arrival time consistency, person-oriented consistency, and delivery consistency. For each feature, we survey different modeling concepts and measurements, demonstrate solution approaches, and examine the increase in cost of improving service consistency. We close the article by presenting challenging ideas for future research. © 2014 The Authors Networks Published by Wiley Periodicals, Inc. NETWORKS, Vol. 643, 192-213 2014

Solving the Multivariant EV Routing Problem Incorporating V2G and G2V Options

Abstract: In the near future, gasoline-fueled vehicles are expected to be replaced by electrical vehicles (EVs) to save energy and reduce carbon emissions. A large penetration of EVs threatens the stability of the electric grid but also provides a potential for grid ancillary services, which strengthens the grid, if well managed. This paper incorporates grid-to-vehicle (G2V) and vehicle-to-grid (V2G) options in the travel path of logistics sector EVs. The paper offers a complete solution methodology to the multivariant EV routing problem rather than considering only one or two variants of the problem like in previous research. The variants considered include a stochastic environment, multiple dispatchers, time window constraints, simultaneous and nonsimultaneous pickup and delivery, and G2V and V2G service options. Stochastic demand forecasts of the G2V and V2G services at charging stations are modeled using hidden Markov model. The developed solver is based on a modified custom genetic algorithm incorporated with embedded Markov decision process and trust region optimization methods. An agent-based communication architecture is adopted to ensure peer-to-peer correspondence capability of the EV, customer, charging station, and dispatcher entities. The results indicate that optimal route for EVs can be achieved while satisfying all constraints and providing V2G ancillary grid service.