Browsing by Author "Alp, Osman"

Now showing 1 - 11 of 11
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    Adoption of Electric Trucks in Freight Transportation
    (2019-10-15) Alp, Osman; Tan, Tarkan; Udenio, Maximiliano
    Transportation sector is the largest contributor of global greenhouse gas emissions in the USA. Disruptive technological changes in this sector, such as alternative fuel vehicles, are crucial for emission reduction. We analyze how a cost-minimizing strategic transition plan can be developed for a transportation firm that aims to adopt electric trucks in their fully diesel fleet, over time. We consider the case in which the firm needs to invest in charging infrastructure required to support this transition, as the public charging infrastructure is currently inadequate. The congestion effect at the charging stations, the charging times, and the potential loss of productive driving time due to detours to reach charging stations are explicitly considered. By developing an independence property, we are able to model this problem as a linear integer program without any need to explicitly specify origins and destinations. We illustrate the resulting transition plan with a realistic data set. Our results indicate that a transportation firm that operates with high demand density over a given service region significantly benefits from adoption of electric trucks, while also enjoying substantial carbon emissions savings. High demand density also favors smaller battery capacity with shorter ranges under the optimized charging network capacity, even though larger battery capacity would increase productivity with extended ranges. Our analysis also offers insights for governments and regulators regarding the impact of several influential factors such as carbon cost, content of renewable energy in electricity mix, diesel engine efficiency, and subsidizing the charging infrastructure.
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    Bullwhip Effect in the Oil and Gas Supply Chain
    (2021-07-06) Zhu, Tianyuan; Balakrishnan, Jaydeep; da Silveira, Giovani JC; Alp, Osman; Zhao, Rong; Beaulieu, Eugene C; Peng, Xiaosong (David)
    The bullwhip effect refers to the phenomenon where demand variability is amplified from downstream to upstream in the supply chain. My thesis consists of three studies investigating the bullwhip effect in different types of companies in the North American oil and gas supply chain. The first study of my thesis investigates the factors that impact the bullwhip effect in the oil and gas supply chain using case study evidence from six companies, covering refining and marketing, exploration and production, integrated oil and gas, and drilling. It is found that the existing theories of the bullwhip effect have limitations in explaining the phenomenon in the oil and gas industry. Information sharing, a widely advocated countermeasure of the bullwhip effect, may not be relevant in the integrated oil and gas company. The analysis also suggests that the bullwhip effect in companies involved in the oil and gas exploration and development activities mainly exists in orders of capital items or services related to capital investments. Financial factors considered in the capital budgeting process, such as the oil price and cash flow, play an important role in determining order quantities in these companies. Therefore, the second study of my thesis quantifies the firm-level bullwhip effect in orders of capital items or services related to capital investments, and examines the impacts of oil price and cash flow variabilities on the bullwhip effect in exploration and production, drilling, and oilfield service companies. The results show that the relationships between oil price variability and bullwhip effect and between cash flow variability and bullwhip effect are both non-linear and more complicated than previously proposed. The third paper of my thesis explores the relationship between the firm-level bullwhip effect in orders of capital items or services related to capital investments and firm performance in exploration and production, drilling, and oilfield service companies. Different from the previous results obtained in the manufacturing industries, it is found that the highest firm performance is not achieved when the bullwhip effect is largely smoothed, and the bullwhip effect influences the firm performance on the income side rather than the asset side.
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    Capacity and Operational Performance Optimization under External Constraints with Uncertainty
    (2016) Sabzevar, Nikoo; Enns, Van; Bergerson, Joule; Freiheit, Theodor; Silva, Emilson; Tu, Paul; Alp, Osman
    This thesis presents novel models to investigate the significant impacts of globalization, demand uncertainty, competition, and external emissions regulations on firms' profitability, operational performance, and decision-making processes. The models relax common simplifying assumptions (i.e., deterministic lead-times, exogenous emissions permit price) to better represent real market characteristics and applications. Specifically, these models are developed in the context of maritime shipments of fuels, such as Liquefied Natural Gas (LNG), to supply global energy markets. The transport capacity investment problem is studied in terms of a mixed integer nonlinear model to maximize a single firm's profit. Using a proposed heuristic algorithm, the model determines the optimal number and size of tankers to deliver a single product under demand uncertainty. The model incorporates economies of scale, lost sales penalties, and the possibility of leasing extra capacity. Results show that tanker size and capacity utilization decrease if demand uncertainty increases. Moreover, tanker sizes are found to decrease as economies of scale decrease. This model is extended to a duopoly setting under competition constrained by the cap-and-trade policy. It is treated as a nonlinear Cournot game and solved analytically to maximize firms' individual profits by determining their optimal production volumes. Furthermore, it provides a set of cap-and-trade policy characteristics (i.e., market cap, cap allocation rate, permit price) leading to the maximum total profit and increasing the trading possibility. Moreover, individual bounds on the ranges of these characteristics are determined within which trading occurs. This model is modified for the carbon tax policy and is further extended to an oligopoly natural gas (NG) market to satisfy price-sensitive NG demand in the Chinese power sector. The supplied NG volume replaces a fraction of the coal consumption for electricity generation to investigate the economic and environmental implications of replacing coal with imported NG in its life cycle through an integrated Cournot game and life cycle assessment approach. The model is able to find a carbon price range under various sources of uncertainty involved in the market at which the import of NG to generate electricity is economically preferred compared to the coal-dominant Chinese power mix outlook.
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    COM-Poisson Clustering of Correlated Bivariate Over- and Under-Dispersed Counts
    (2016) Raz, Saifa; de Leon, Alexander R.; Alp, Osman; Wu, Jingjing; Qiu, Chao
    Ruan (2015) recently proposed using a finite mixture model with components modelled by Gaussian copula with Poisson margins (BP-GCD) as the basis for model-based clustering of bivariate correlated counts. Although the Poisson distribution is a useful model for modelling count data, the distribution is constrained by its equi-dispersion assumption. Motivated by this limitation, the thesis introduces a more flexible model, one with Gaussian copula models as components but with Conway-Maxwell Poisson (COM-Poisson) margins (BCOM-GCD) which allows the accounting of under- and over-dispersion in the correlated count data. We test our proposed method on a variety of simulated settings and on data from the Australian National Health Survey to explore the impact of ignoring the non-equidispersion. Our simulations and real-life data analysis indicate that using BCOM-GCDs as mixture components instead of BP-GCDs provides a better and more flexible approach for performing model-based clustering for under- or over-dispersed counts.
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    Delegation of Stocking Decisions under Asymmetric Demand Information
    (2017-11-22) Sen, Alper; Alp, Osman
    Headquarters of a retailer delegates stocking decisions to store managers at its various stores. Store managers have complete information of the local demand process, whereas headquarters has partial information. The problem is how to incentivize the managers to make stocking decisions that minimizes headquarters' expected overage and underage costs. We investigate performance measurement schemes in which headquarters incite store managers make stocking decisions in headquarters' best interests using their private information. Adopting such schemes helps retailers reduce stock-outs and attain desired service levels across their stores. Headquarters knows that the underlying demand process at a store for a product over a replenishment cycle is one of J possible Wiener demand processes, whereas store manager knows the specific process with certainty. Store manager creates a single order before the cycle. Headquarters use an incentive scheme which is based on the end-of-period leftover inventory and on a stock-out occasion at a pre-specified inspection time {\it before} the end of a period. Headquarters' problem to determine the inspection time and relative importance of stock-outs to leftover stock is formulated as a constrained non-linear optimization problem in a single period setting and a dynamic program in a multi-period setting. The proposed ``early inspection'' scheme leads to perfect alignment under certain conditions. Under more general conditions, it provides a near-perfect alignment and performs strictly better than stock-out inspection at the end. Using historical sales data of a retailer, we show that this scheme can lead to considerable cost reduction. Even though attaining high on-shelf availability is crucial in retail, stock-out related measures are not reflected in store managers' performance scorecards. We prescribe a novel, easy and practical method for headquarters with which they can increase on-shelf-availability by relying on their store managers' private demand information. The proposed method decidedly outperforms the computer aided ordering systems that are commonly used in practice.
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    Dynamic Shared Autonomous Vehicle Fleet Operations with Consideration of Fairness
    (2021-04-27) Habib, Nouran; Kattan, Lina; Alp, Osman; Waters, Nigel; Saidi, Saeid
    The future of urban transportation has arrived, and it is moving in the direction of enabling urban mobility platforms to provide shared mobility services, accelerating the shift away from personal vehicle ownership. New companies, like Uber and DiDi, are heavily investing in developing and testing emerging mobility technologies, including shared autonomous vehicles (SAVs). The full implementation of emerging mobility technologies is expected to deliver a transformative wave of urban reform. Besides, emerging mobility technologies could offer promising sustainable solutions that would optimize the usage of limited mobility resources. For instance, shared mobility services are convenient, flexible, cost- and time-efficient, and environment-friendly. Further, fully-autonomous vehicle (AV) technology surpasses human drivers in terms of costs, driving behavior, hours of service, and compliance with the plans of fleet operators. Currently, researchers are extensively studying the operations of SAV fleets that provide on-demand curb-to-curb mobility services. Specifically, they develop traveler assignment and scheduling algorithms that aim to match each traveler with a proper vehicle and plan the schedule of the vehicle simultaneously, including picking-up and dropping-off other travelers, based on a specific fleet objective. This thesis aims to fill an existing gap in the literature regarding introducing “equitable” methods to dynamic ride-sharing (DRS) systems. Thus, to meet the rising concerns of social justice, equity, and fairness in transportation systems, this thesis introduces the proportional fairness concept to DRS systems while considering the passenger heterogeneity in terms of their valuation of in-vehicle travel time. The proportional fairness formulation seeks to balance efficiency and fairness in resource allocation problems. The proportional fairness approach is then compared to two other approaches in a simulation-based environment implemented in MATSim (i.e., an agent-based transport simulator). In a centralized-fleet setting, the first approach aims to maximize traveler utility/satisfaction, while the second approach aims to maximize the total travelers’ utility. Simulation scenarios are tested to quantify the trade-offs between fleet size and vehicle maximum allowable occupancy. The performance of the three approaches is evaluated based on various performance measures from a fleet management perspective [e.g., the ratio of zero-occupant (i.e., empty-vehicle) fleet kilometers traveled to total fleet kilometers traveled], a traveler perspective (e.g., the average traveler wait time), and equity in resource allocation perspective (i.e., the Gini coefficient).
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    Improving Emergency Department Efficiency: A Study of Physician Scheduling Strategies to Reduce Patient Wait Times
    (2024-06-17) Ganjouhaghighi, Negar; Bijvank, Marco; Sabouri Bagh Abbas, Alireza; Alp, Osman; da Silveria, Giovani Jose Caetano; Lang, Eddy S.; Lahrichi, Nadia; Weinhardt, Justin
    Prolonged wait times and overcrowding in emergency departments (EDs) represent significant national challenges in Canada. Within this thesis, I examine three distinct approaches aimed at assisting managers, decision-makers, and schedulers within EDs in addressing these pressing issues. Emergency departments serve as the initial point of contact for patients within the healthcare system, constituting a crucial yet interconnected component of healthcare provision. This study narrows its focus to the aspect of physician scheduling within EDs, recognizing its pivotal role in mitigating wait times and improving efficiency. The initial focus of my investigation lies in optimizing physician schedules within EDs to align with the fluctuating supply and demand dynamics. Extensive literature review and our own dataset reveal the variable productivity levels among physicians in these settings. In this thesis, productivity primarily refers to the number of new patients seen (or treated) by a physician per hour of their shift—a crucial metric in our pursuit of reducing patient wait times. Acknowledging this variability, I first delve into incorporating physician productivity (measured in Patients Per Hour, or PPH rate) into a staffing and shift scheduling problem for physicians. Numerical results show that significant improvements can be obtained in terms of average wait times for patients if we consider the variable productivity if physicians in the staffing and shift scheduling problem. Despite the optimization of schedules, the inherent stochastic nature of ED operations implies occasions where patient volumes exceed expectations, which lead to increased wait times. In response to these fluctuations, in the second and third studies, I propose and assess two distinct strategies aimed at managing ED crowding levels. Particularly, I derive optimal policies for EDs on when and how to extend physicians' shifts or call in physicians in response to a surge in demand. Using the simulation model to evaluate these strategies, I show the effectiveness of having flexibility in physicians schedules in reducing the average wait time of patients.
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    Optimal Configurations for Urban Transit: A Study on Bus and Express Network Design via Analytical Approaches and Mathematical Programming
    (2024-04-24) Mahmoudi, Reza; Saidi, Saeid; Wirasinghe, Sumedha Chandana; Waters, Nigel Michael; Kattan, Lina; Wilson, Nigel H. M.; Alp, Osman
    The Public Transit Network Design Problem (PTNDP), particularly the Public Bus Transit Network Design Problem (PBTNDP) and Express Transit Network Design Problem (ETNDP), is a key area in transportation research. Analytical approaches and mathematical programming are widely used methodologies in this field. Mathematical programming is particularly effective in handling complex problems and capturing real-world conditions. However, mathematical programming often acts as "black boxes" and offers limited insights into the problem. Conversely, analytical approaches provide deeper insights into the relationships between decision variables and problem parameters, though they sometimes necessitate simplifications that overlook certain real-world nuances. A comprehensive review of the applications of both analytical approaches and mathematical programming in the problems considered in this thesis is presented in Chapter 2. This thesis endeavors to explore three major problems related to the PTNDP by employing both analytical approaches and mathematical programming. In Chapter 3, the focus is on a single many-to-many public bus transit line and analytical approaches are used to model headway setting and vehicle sizing problems under varying demand and crowding conditions and in three different scenarios. In Chapter 4, the focus shifts to joint transit network design and headway setting problems for Public Bus Transit Systems (PBTSs) at the network level. Analyzing single-mode and bi-modal transit networks in a rectangular city, this chapter explores various routing schemes, including local buses and integrated local-express services. Chapter 5 delves into the ETNDP within a real-world urban framework, employing a hybrid approach of analytical methods and mathematical programming. A two-stage approach is suggested for the bi-modal surface ETNDP. The first stage employs analytical approaches to establish a methodology for identifying optimal station locations for different transit systems. In the second stage, first a mathematical programming is proposed to determine the optimal transit express routes and the transit technology of each transit route. Then, a metaheuristic algorithm based on the Genetic Algorithm is introduced to solve the proposed mathematical programming for real size transit networks. A case study of Calgary, Canada, demonstrates the applicability of the proposed methodologies in Chapter 6.
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    Optimal Policy for Blood Inventory Management Problem
    (2018-08-22) Grushevska, Iaryna; Braverman, Elena; Sabouri, Alireza; Zinchenko, Yuriy; Alp, Osman
    Blood units that are used for transfusion can be stored for a limited amount of time. The blood that is older than 42 days must be discarded. In order not to face shortage usually the oldest blood is used. But the risk of complications after surgery is growing as the age of used blood is growing as well. In this work we find the optimal policy to use blood for transfusion for two blood types. The main goal is to find the policy that will reduce the shortage and minimize the risk of complications at the same time. For this purpose, we use two methods (Linear Programming and Approximate Dynamic Programming) and compare the results of two approaches.
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    Store Incentives and Retailer Inventory Performance under Asymmetric Demand Information and Unobservable Lost Sales*
    (2017) Alp, Osman; Sen, Alper
    We study incentive issues in an inventory management setting in which high on-shelf availability is crucial. Headquarters of a retailer delegates inventory replenishment decisions to store managers in its various stores. Store manager has complete information of the local demand process, whereas headquarters has partial information and cannot observe unsatisfied demand. The problem is how to incentivize the manager to make an order quantity decision that minimizes the sum of headquarters’ expected overage and underage costs. We propose two incentive schemes that explicitly incorporate excess inventory and stock-outs into the store manager’s performance measurement.We prove that a perfect alignment of incentives is possible under certain conditions. Interestingly, perfect or near-perfect alignment requires the stock-out inspection before the end of the replenishment cycle. We validate our approach and assumptions on a retailer’s actual data and show that the retailer may improve its profitability by using the proposed incentive scheme.
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    Three Essays on Assortment Planning in Omni-Channel Retail Supply Chains
    (2023-08) Aslani, Amin; Alp, Osman; Alp, Osman; Bijvank, Marco; Sabouri Bagh Abbas, Alireza; Bagga, Charan Kamal; Pun, Hubert
    In omni-channel retail systems, comprising an online sales website and brick-and-mortar (physical) stores, a physical store typically faces limited shelf-space capacity, while capacity is not an issue for the online channel. Consequently, a crucial aspect of such retail systems is to choose a subset of products present online for showcasing in the physical store (i.e., assortment planning). In my first research stream, I investigate the omni-channel assortment problem when product returns are allowed. Assortment decisions influence product returns, as showcased products provide information to online shoppers who visit the physical store. Therefore, although product returns can be a factor for profit loss, effective assortment planning can mitigate the returns’ adverse impact and optimize profitability. My results indicate that even with sufficient capacity, showcasing all products in the physical store may not be optimal. Additionally, retailers generally fare better when customers undervalue hidden attribute levels. In my second research stream, I explore a decentralized retail supply chain (RSC) comprising an online channel managed by a manufacturer setting wholesale prices, and an independent retailer managing the physical store and making assortment decisions. As a benchmark, I examine a centralized setting where both channels are under a central authority aiming to maximize overall profit. My findings show fundamental differences in optimal centralized and decentralized assortments, indicating inefficiency in the decentralized approach. I propose scope contracts for coordination, wherein the manufacturer offers discounts on wholesale prices for products with specific attribute levels, incentivizing the retailer to adopt the centralized assortment. The scope contracts ensure both parties' profitability and coordinate the RSC. In the third stream, I suppose that the magnitude of inaccuracy in online assessment of products due to the lack of physical encounter is unknown to the RSC parties, and they make decisions with asymmetric information. I investigate the assortment and wholesale price decisions along with profit regrets. My findings under the decentralized setting indicate that while both parties cannot fare better simultaneously, each party can be advantaged under certain conditions. Under the centralized setting, when supposing accurate online assessments, showcasing an assortment of the highest utility attribute levels possibly minimizes system-wide regret.