Mike Hewitt

Bio: Mike Hewitt is an academic researcher from Loyola University Chicago. The author has contributed to research in topics: Network planning and design & Integer programming. The author has an hindex of 17, co-authored 60 publications receiving 968 citations. Previous affiliations of Mike Hewitt include Georgia Institute of Technology & Rochester Institute of Technology.

Combining Exact and Heuristic Approaches for the Capacitated Fixed-Charge Network Flow Problem

Abstract: We develop a solution approach for the fixed-charge network flow (FCNF) problem that produces provably high-quality solutions quickly. The solution approach combines mathematical programming algorithms with heuristic search techniques. To obtain high-quality solutions, it relies on neighborhood search with neighborhoods that involve solving carefully chosen integer programs derived from the arc-based formulation of FCNF. To obtain lower bounds, the linear programming relaxation of the path-based formulation of FCNF is used and strengthened with cuts discovered during the neighborhood search. The solution approach incorporates randomization to diversify the search and learning to intensify the search. Computational experiments demonstrate the efficacy of the proposed approach.

The Continuous-Time Service Network Design Problem.

Abstract: Consolidation carriers transport shipments that are small relative to trailer capacity. To be cost effective, the carrier must consolidate shipments, which requires coordinating their paths in both space and time; i.e., the carrier must solve a service network design problem. Most service network design models rely on discretization of time—i.e., instead of determining the exact time at which a dispatch should occur, the model determines a time interval during which a dispatch should occur. While the use of time discretization is widespread in service network design models, a fundamental question related to its use has never been answered: Is it possible to produce an optimal continuous-time solution without explicitly modeling each point in time? We answer this question in the affirmative. We develop an iterative refinement algorithm using partially time-expanded networks that solves continuous-time service network design problems. An extensive computational study demonstrates that the algorithm not only...

Service Network Design with Resource Constraints

Abstract: We first present a new service network design model for freight consolidation carriers, one that selects services and routes both commodities and resources needed to support the services that transport them, while explicitly recognizing that there are limits on how many resources are available at each terminal. We next present a solution approach that combines column generation, meta-heuristic, and exact optimization techniques to produce high-quality solutions. We demonstrate the efficacy of the approach with an extensive computational study and benchmark its performance against both a leading commercial solver and a column generation-based heuristic.

Improved Load Plan Design Through Integer Programming Based Local Search

Abstract: We present integer programming models of the service network design problem faced by less-than-truckload LTL freight transportation carriers and a solution approach for the large-scale instances that result in practical applications. To accurately represent freight consolidation opportunities, the models use a fine discretization of time. Furthermore, the models simultaneously route freight and empty trailers and thus explicitly recognize the efficiencies presented by backhaul lanes. The solution approach can generate the traditional service network designs commonly used by LTL carriers but also enables the construction of designs that allow more flexibility, e.g., that allow freight routes to vary by day of week. An iterative improvement scheme is employed that searches a large neighborhood, each iteration using an integer program. Computational experiments using data from a large U.S. carrier demonstrate that the proposed modeling and solution approach has the potential to generate significant cost savings.

Thirty Years of Inventory Routing

Abstract: The inventory-routing problem (IRP) dates back 30 years. It can be described as the combination of vehicle-routing and inventory management problems, in which a supplier has to deliver products to a number of geographically dispersed customers, subject to side constraints. It provides integrated logistics solutions by simultaneously optimizing inventory management, vehicle routing, and delivery scheduling. Some exact algorithms and several powerful metaheuristic and matheuristic approaches have been developed for this class of problems, especially in recent years. The purpose of this article is to provide a comprehensive review of this literature, based on a new classification of the problem. We categorize IRPs with respect to their structural variants and the availability of information on customer demand.

Impacts of epidemic outbreaks on supply chains: mapping a research agenda amid the COVID-19 pandemic through a structured literature review.

Abstract: The coronavirus (COVID-19) outbreak shows that pandemics and epidemics can seriously wreak havoc on supply chains (SC) around the globe Humanitarian logistics literature has extensively studied epidemic impacts; however, there exists a research gap in understanding of pandemic impacts in commercial SCs To progress in this direction, we present a systematic analysis of the impacts of epidemic outbreaks on SCs guided by a structured literature review that collated a unique set of publications The literature review findings suggest that influenza was the most visible epidemic outbreak reported, and that optimization of resource allocation and distribution emerged as the most popular topic The streamlining of the literature helps us to reveal several new research tensions and novel categorizations/classifications Most centrally, we propose a framework for operations and supply chain management at the times of COVID-19 pandemic spanning six perspectives, ie, adaptation, digitalization, preparedness, recovery, ripple effect, and sustainability Utilizing the outcomes of our analysis, we tease out a series of open research questions that would not be observed otherwise Our study also emphasizes the need and offers directions to advance the literature on the impacts of the epidemic outbreaks on SCs framing a research agenda for scholars and practitioners working on this emerging research stream

Dynamic passenger demand oriented metro train scheduling with energy-efficiency and waiting time minimization: Mixed-integer linear programming approaches

Abstract: In the daily operation of metro systems, the train scheduling problem aims to find a set of space-time paths for multiple trains that determine their departure and arrival times at metro stations, while train operations are in charge of selecting the best operational speed to satisfy the punctuality and operation costs. Different from the most existing researches that treat these two problems separately, this paper proposes an integrated approach for the train scheduling problem on a bi-direction urban metro line in order to minimize the operational costs (i.e., energy consumption) and passenger waiting time. More specifically, we simultaneously consider (1) the train operational velocity choices that correspond to the energy consumption of trains on each travelling arc, and (2) the dynamic passenger demands at each station for the calculation of total passenger waiting time in the planning horizon. By employing a space-time network representation in the formulations, this complex train scheduling and control problem with dynamic passenger demands is rigorously formulated into two optimization models with linear forms. The first model is an integer programming model that jointly minimizes train traction energy consumption and passenger waiting time. The second model, which is formulated as a mixed-integer programming model, further considers the utilization of regenerative braking energy on the basis of the first model. Due to the computational complexity of these two models, especially for large-scale real-world instances, we develop a Lagrangian relaxation (LR)-based heuristic algorithm that decomposes the primal problem into two sets of subproblems and thus enables to find a good solution in short computational time. Finally, two sets of numerical experiments, involving a relatively small-scale case and a real-world instance based on the operation data of Beijing metro are implemented to verify the effectiveness of the proposed approaches.

Balancing the economic and environmental performance of maritime transportation (vol 15, pg 458, 2010)

Abstract: This paper looks at the implications of various maritime emissions reductions policies for maritime logistics. There can be important trade-offs that have to be made between the environmental benefits associated with such measures as reduction in steaming speed and change in the number of vessels in the fleet, and more conventional logistics attributes such as in-transit inventory holdings.