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Management Science and Engineering

Permanent URI for this collectionhttps://uwspace.uwaterloo.ca/handle/10012/9910

This is the collection for the University of Waterloo's Department of Management Science and Engineering.

Research outputs are organized by type (eg. Master Thesis, Article, Conference Paper).

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Browsing Management Science and Engineering by Author "Alumur Alev, Sibel"

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Now showing 1 - 5 of 5
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    Bi-objective p-hub Location Problems
    (University of Waterloo, 2017-05-09) Bilen, Fatih; Alumur Alev, Sibel
    In this thesis, we introduce, model, and solve bi-objective hub location problems. The two well-known hub location problems from the literature, the p-hub median and p-hub center problems, are uni ed under a bi-objective setting considering the single, multiple, and r-allocation strategies. We developed a 3-index and a 4-index mixed-integer programming formulation for each of the allocation strategies. All the formulations are tested on the CAB dataset from the literature using a commercial optimization software. We observe the effect of different priorities given to the objectives on the locations of hub nodes, allocations, and the CPU time requirements with different allocation strategies under different values of problem parameters.
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    Data-Driven Analysis of Optimal Repositioning in Dockless Bike-Sharing Systems
    (University of Waterloo, 2022-08-29) Unsal, Emre Berk; Alumur Alev, Sibel
    Bike-sharing systems provide sustainable and convenient mobility services for short-distance transportation in urban areas. The dockless or free-floating bike-sharing systems allow users to leave vehicles at any location in the service zones which leads to an imbalance of inventory between different areas across a city. Hence, vehicles in such dockless bike-sharing systems need to be repositioned throughout the day to be able to capture and serve more demand. In this study, we analyze the impact of optimal repositioning on the efficiency of dockless bike-sharing systems under several performance measures. We first develop a multi-period network flow model to find the optimal repositioning decisions which consist of the origin, destination, and the time of the repositioning that maximize the total profit of the bike-sharing system. The proposed model is then implemented on the real-world bike-sharing data of New York, Toronto, and Vancouver. After finding the optimal repositioning actions, we analyze the effect of repositioning on the fulfilled demand, the number of required vehicles, and the utilization rates of the vehicles. Through computational experiments, we show that repositioning significantly increases the efficiency of bike-sharing systems under these performance measures. In particular, our analyses show that up to 41\% more demand can be satisfied with repositioning. Moreover, it is possible to reduce the required fleet size up to 61\% and increase the average utilization rate of the vehicles up to 21\% by employing repositioning. We also demonstrate that the effect of optimal repositioning is robust against the uncertainty of demand.
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    Hub Location Problems with Profit Considerations
    (University of Waterloo, 2019-04-23) Taherkhani, Gita; Alumur Alev, Sibel
    This thesis studies profit maximizing hub location problems. These problems seek to find the optimal number and locations of hubs, allocations of demand nodes to these hubs, and routes of flows through the network to serve a given set of demands between origin-destination pairs while maximizing total profit. Taking revenue into consideration, it is assumed that a portion of the demand can remain unserved when it is not profitable to be served. Potential applications of these problems arise in the design of airline passenger and freight transportation networks, truckload and less-than-truckload transportation, and express shipment and postal delivery. Firstly, mathematical formulations for different versions of profit maximizing hub location problems are developed. Alternative allocation strategies are modeled including multiple allocation, single allocation, and $r$-allocation, as well as allowing for the possibility of direct connections between non-hub nodes. Extensive computational analyses are performed to compare the resulting hub networks under different models, and also to evaluate the solution potential of the proposed models on commercial solvers with emphasis on the effect of the choice of parameters. Secondly, revenue management decisions are incorporated into the profit maximizing hub location problems by considering capacities of hubs. In this setting, the demand of commodities are segmented into different classes and there is available capacity at hubs which is to be allocated to these different demand segments. The decision maker needs to determine the proportion of each class of demand to serve between origin-destination pairs based on the profit to be obtained from satisfying this demand. A strong mixed-integer programming formulation of the problem is presented and Benders-based algorithms are proposed to optimally solve large-scale instances of the problem. A new methodology is developed to strengthen the Benders optimality cuts by decomposing the subproblem in a two-phase fashion. The algorithms are enhanced by the integration of improved variable fixing techniques. Computational results show that large-scale instances with up to 500 nodes and 750,000 commodities of different demand segments can be solved to optimality, and that the proposed algorithms generate cuts that provide significant speedups compared to using Pareto-optimal cuts. As precise information on demand may not be known in advance, demand uncertainty is then incorporated into the profit maximizing hub location problems with capacity allocation, and a two-stage stochastic program is developed. The first stage decision is the locations of hubs, while the assignment of demand nodes to hubs, optimal routes of flows, and capacity allocation decisions are made in the second stage. A Monte-Carlo simulation-based algorithm is developed that integrates a sample average approximation scheme with the proposed Benders decomposition algorithm. Novel acceleration techniques are presented to improve the convergence of the algorithm. The efficiency and robustness of the algorithm are evaluated through extensive computational experiments. Instances with up to 75 nodes and 16,875 commodities are optimally solved, which is the largest set of instances that have been solved exactly to date for any type of stochastic hub location problems. Lastly, robust-stochastic models are developed in which two different types of uncertainty including stochastic demand and uncertain revenue are simultaneously incorporated into the capacitated problem. To embed uncertain revenues into the problem, robust optimization techniques are employed and two particular cases are investigated: interval uncertainty with a max-min criterion and discrete scenarios with a min-max regret objective. Mixed integer programming formulations for each of these cases are presented and Benders-based algorithms coupled with sample average approximation scheme are developed. Inspired by the repetitive nature of sample average approximation scheme, general techniques for accelerating the algorithms are proposed and instances involving up to 75 nodes and 16,875 commodities are solved to optimality. The effects of uncertainty on optimal hub network designs are investigated and the quality of the solutions obtained from different modeling approaches are compared under various parameter settings. Computational results justify the need for embedding both sources of uncertainty in decision making to provide robust solutions.
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    Infrastructure Design for Electric and Autonomous Vehicles
    (University of Waterloo, 2022-04-29) Kinay, Omer Burak; Alumur Alev, Sibel; Gzara, Fatma
    This thesis focuses on infrastructure design for the disruptive transportation technologies of electric vehicles (EVs) and autonomous vehicles (AVs) to enable their adoption at large scale. Particularly, two EV-related problem frameworks concerning the spatial distribution of charging stations and their respective capacity levels are studied, and a new problem is introduced to determine the optimal deployment of AV lanes and staging facilities to enable shared autonomous transportation in urban areas. The first problem is centered around determining optimal locations of fast-charging stations to enable long-distance transportation with EVs. A new mathematical model is developed to address this problem. This model not only determines optimal facility locations but also finds optimal routes for every origin-destination (OD) trip which follows the path that leads to the minimum total en route recharging. Through computational experiments, this model is shown to outperform the widely used maximum and set cover problem settings in the literature in terms of several routing-related performance measures. A Benders decomposition algorithm is developed to solve large-scale instances of the problem. Within this algorithm, a novel subproblem solution methodology is developed to accelerate the performance of the classical Benders implementation. Computational experiments on real-world transportation networks demonstrate the value of this methodology as it turns out to speed the classical Benders up to 900 times and allows solving instances with up to 1397 nodes. The second problem extends the previous one by seeking to determine EV charging station locations and capacities under stochastic vehicle flows and charging times. It also considers the route choice behavior of EV users by means of a bilevel optimization model. This model incorporates a probabilistic service requirement on the waiting time to charge, and it is studied under a framework where charging stations operate as M/M/c queuing systems. A decomposition-based solution methodology, that uses a logic-based Benders algorithm for the location-only problem, is developed to solve the proposed bilevel model. This methodology is designed to be versatile enough to be tailored for the cooperative or uncooperative EV user behavior. Computational experiments are conducted on real-life highway networks to evaluate how service level requirements, deviation tolerance levels, and route choice behavior affect the location and sizing decisions of charging stations. The third problem entails the staging facility location and AV lane deployment problem for shared autonomous transportation. The proposed problem aims to find the optimal locations of staging facilities utilizing a bi-objective model that minimizes total travel distance and the total AV travel not occurring on AV lanes with respect to a given AV lane deployment budget and a number of staging facilities to locate. A Benders decomposition algorithm with Pareto-optimal cuts is developed and the trade-offs with optimal solutions on benchmark instances are evaluated. Computational experiments are performed to analyze the effects of AV lane budget, staging facility count, and the objective preferences of decision makers on optimal solutions.
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    Shipment Scheduling In Hub Location Problems
    (University of Waterloo, 2017-07-26) Masaeli, Mobina; Bookbinder, James H.; Alumur Alev, Sibel
    In this thesis, we incorporate shipment scheduling decisions into hub location problems. Our aim is to determine optimal locations of hubs, hub network structure, and the number of vehicles to operate on the hub network as well as the time period of dispatching each vehicle from a hub. We develop mathematical models for di erent versions of this problem. We initially propose a mixed-integer shipment scheduling hub network design model where the costs of holding freight are negligible. We then expand the model to keep track of the holding decisions where the holding costs are not negligible. We further analyze the shipment scheduling model with holding costs when di erent types of vehicles are available to operate on the inter-hub links. We investigate the impact of shipment scheduling decisions and holding costs on hub network con gurations, routing decisions, and total cost of the network. We solve the models on instances from a new USAF dataset with real data.