Browsing by Author "Alp, Osman"
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- ItemOpen AccessAdoption of Electric Trucks in Freight Transportation(2019-10-15) Alp, Osman; Tan, Tarkan; Udenio, MaximilianoTransportation 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.
- ItemOpen AccessCapacity and Operational Performance Optimization under External Constraints with Uncertainty(2016) Sabzevar, Nikoo; Enns, Van; Bergerson, Joule; Freiheit, Theodor; Silva, Emilson; Tu, Paul; Alp, OsmanThis 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.
- ItemOpen AccessCOM-Poisson Clustering of Correlated Bivariate Over- and Under-Dispersed Counts(2016) Raz, Saifa; de Leon, Alexander R.; Alp, Osman; Wu, Jingjing; Qiu, ChaoRuan (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.
- ItemRestrictedDelegation of Stocking Decisions under Asymmetric Demand Information(2017-11-22) Sen, Alper; Alp, OsmanHeadquarters 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.
- ItemOpen AccessDynamic Shared Autonomous Vehicle Fleet Operations with Consideration of Fairness(2021-04-27) Habib, Nouran; Kattan, Lina; Alp, Osman; Waters, Nigel; Saidi, SaeidThe 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).
- ItemOpen AccessOptimal Policy for Blood Inventory Management Problem(2018-08-22) Grushevska, Iaryna; Braverman, Elena; Sabouri, Alireza; Zinchenko, Yuriy; Alp, OsmanBlood 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.
- ItemOpen AccessStore Incentives and Retailer Inventory Performance under Asymmetric Demand Information and Unobservable Lost Sales*(2017) Alp, Osman; Sen, AlperWe 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.
- ItemOpen AccessThree 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, HubertIn 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.