Researcher:
Yıldız, Barış

Profile Picture
ORCID

Job Title

Faculty Member

First Name

Barış

Last Name

Yıldız

Name

Name Variants

Yıldız, Barış

Email Address

Birth Date

Filters

2017 - 201952020 - 202211

Advanced Search

Filter by

Settings

Researcher Of Publications: search.filters.isAuthorOfPublication.a2f7d58d-b6f4-489a-b6bf-71170c6d7def

Search Results

Now showing 1 - 10 of 16
  • Placeholder
    PublicationMetadata only
    Optimizing package express operations in China
    (Elsevier Ltd, 2022) Savelsbergh, M.; Department of Industrial Engineering; Yıldız, Barış; Faculty Member; Department of Industrial Engineering; College of Engineering; 258791
    We explore a novel optimization model to support the planning and operation of an air service network at a package express carrier in China. The model simultaneously considers ground and air transportation, company-owned aircraft capacity and belly capacity purchased on commercial aircraft, multiple service classes for packages, and packages becoming available throughout the day. A column generation based algorithm is devised to solve real-life problems with more than 650,000 timed origin-destination demands (packages), a service network with 150 hubs at airports, and an air cargo fleet of 60 aircraft of varying capacities. An extensive computational study shows the efficacy of the model and algorithm, and provides managerial insight. In particular, we see that considering ground transportation when designing an air service network is critical to improving operational efficiency and company profits as well as to extending service coverage. Furthermore, we find that in an environment with ample and reasonably-priced capacity on commercial aircraft, an air service network design based on single-flight shipments can be more effective than the commonly used star network design with multi-flight shipments.
  • Placeholder
    PublicationMetadata only
    A learning based algorithm for drone routing
    (Pergamon-Elsevier Science Ltd, 2022) N/A; N/A; Department of Industrial Engineering; Department of Industrial Engineering; Ermağan, Umut; Yıldız, Barış; Salman, Fatma Sibel; Master Student; Faculty Member; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; 258791; 178838
    We introduce a learning-based algorithm to solve the drone routing problem with recharging stops that arises in many applications such as precision agriculture, search and rescue, and military surveillance. The heuristic algorithm, namely Learn and Fly (L&F), learns from the features of high-quality solutions to optimize recharging visits, starting from a given Hamiltonian tour that ignores the recharging needs of the drone. We propose a novel integer program to formulate the problem and devise a column generation approach to obtain provably high-quality solutions that are used to train the learning algorithm. Results of our numerical experiments with four groups of instances show that the classification algorithms can effectively identify the features that determine the timing and location of the recharging visits, and L&F generates energy feasible routes in a few seconds with around 5% optimality gap on the average.
  • Placeholder
    PublicationMetadata only
    The urban recharging infrastructure design problem with stochastic demands and capacitated charging stations
    (Pergamon-Elsevier Science Ltd, 2019) Olcaytu, Evren; Sen, Ahmet; Department of Industrial Engineering; Yıldız, Barış; Faculty Member; Department of Industrial Engineering; College of Engineering; 258791
    In this study we develop an exact solution method to optimize the location and capacity of charging stations to satisfy the fast charging needs of electric vehicles in urban areas. Stochastic recharge demands, capacity limitations of charging stations and drivers' route preferences (deviation tolerances) are simultaneously considered to address this challenging problem faced by recharging infrastructure planners or investors. Taking a scenario based approach to model demand uncertainty, we first propose a compact two stage stochastic programming formulation. We then project out the second stage decision variables from the compact formulation by describing the extreme rays of its polyhedral cone and obtain (1) a cut formulation that enables an efficient branch and cut algorithm to solve large problem instances (2) a novel characterization for feasible solutions to the capacitated covering problems. We test our algorithm on the Chicago metropolitan area network, by considering real world origin-destination trip data to model charging demands. Our results attest the efficiency of the proposed branch and cut algorithm and provide significant managerial insights.
  • Placeholder
    PublicationMetadata only
    Service and capacity planning in crowd-sourced delivery
    (Pergamon-Elsevier Science Ltd, 2019) Savelsbergh, Martin; Department of Industrial Engineering; Yıldız, Barış; Faculty Member; Department of Industrial Engineering; College of Engineering; 258791
    The success of on-demand service platforms, e.g., Uber and Lyft to obtain a ride and Grubhub and Eat24 to get a meal, which rely on crowd-sourced transportation capacity, has radically changed the view on the potential and benefits of crowd-sourced transportation and delivery. Many retail stores, for example, are examining the pros and cons of introducing crowd-sourced delivery in their omni-channel strategies. However, few models exist to support the analysis of service area, service quality, and delivery capacity planning, and their interaction, in such environments. We introduce a model that seeks to do exactly that and can answer many fundamental questions arising in these settings. Using on-demand meal delivery platforms as an example, we investigate, among others, the relation between service area and profit and delivery offer acceptance probability and profit, and the benefits of integrating delivery service of multiple restaurants, and generate many valuable insights.
  • Placeholder
    PublicationMetadata only
    Express package routing problem with occasional couriers
    (Pergamon-Elsevier Science Ltd, 2021) Department of Industrial Engineering; Yıldız, Barış; Faculty Member; Department of Industrial Engineering; College of Engineering; 258791
    This study investigates the viability of a "courier friendly" crowd-shipping (CS) model to carry out express package deliveries in an urban area. Imposing almost no managerial control over the occasional couriers (OC) to maximize their willingness to participate, the envisioned model suggests using transshipment points to enhance operational efficiency and a company-controlled backup delivery capacity to account for the uncertainty in the crowd-provided delivery capacity. A dynamic programming (DP) approach is developed to address the package routing problem that needs to be solved for the real-time management of the CS network. The suggested methodology does not assume any specific distributions for the courier and demand arrivals and provides the optimal package routing policy when the courier and service point capacities are not binding. Several extensions of the basic DP approach are studied to consider limited courier and service point capacities and take advantage of the extra information provided by those couriers that declare their arrivals in advance. To the best of the author's knowledge, this study presents the first example of using Monte-Carlo simulations to determine "shadow costs" of capacity utilizations and use them in making the assignment (matching) decisions. An extensive computational study demonstrates the efficacy of the solution approach. It provides valuable insights into, among others, the potential benefits of delivering packages with a coordinated effort of OCs (with transshipments) under short delivery lead time restrictions, the impact of the spatial and temporal distribution of the demand and courier arrivals on the system performance, and the importance of the notice times for OC arrivals.
  • Placeholder
    PublicationMetadata only
    Electric bus fleet composition and scheduling
    (Pergamon-Elsevier Science Ltd, 2021) Department of Industrial Engineering; Department of Industrial Engineering; Yıldırım, Şule; Yıldız, Barış; Master Student; Faculty Member; Department of Industrial Engineering; College of Engineering; College of Engineering; N/A; 258791
    The low energy density of batteries and the long recharging times constitute a significant barrier for electrification of public transportation (PT) systems since electric buses (EB) require too heavy and expensive batteries to achieve the operational availability of their combustion engine counterparts. New recharging technologies such as fast chargers and dynamic wireless power transfer (DWPT) emerge as promising solutions to overcome these challenges. Optimizing the bus fleet composition and the schedules is essential to take advantage of these emerging technologies and achieve electrification of PT in a cost-efficient way. To address this challenge, this paper proposes an integer (binary) programming formulation to find the optimal electric bus fleet composition and scheduling that minimizes the total procurement cost of the buses and the operating cost of the schedules. A column generation (CG) approach is devised to obtain provably high-quality solutions, for large problem instances. The success of the approach is due to a novel dynamic programming algorithm we develop to solve the generalized resource-constrained shortest path problem that needs to be solved in each CG iteration to find out new schedules to include in the model. Extensive computational studies on large real-world PT networks attest to the efficacy of the suggested methodology and reveal valuable managerial insights from a systemwide perspective.
  • Placeholder
    PublicationMetadata only
    Managing consumer returns with technology-enabled countermeasures
    (Pergamon-Elsevier Science Ltd, 2021) Aktürk, M. Serkan; Ketzenberg, Michael; Department of Industrial Engineering; Yıldız, Barış; Faculty Member; Department of Industrial Engineering; College of Engineering; 258791
    This paper examines retail return abuse with respect to both opportunistic and fraudulent consumer behavior. The decisions of interest are the retailer's price for purchases and refund amount for returns. Our analysis provides managerial insight into how a retailer makes these decisions to mitigate return abuse. Including both forms of return abuse in a base model, we find that there is an interaction effect that meaningfully changes a retailer's optimal decisions and profit, from what would be observed if only opportunism was present. We also evaluate two innovative technology-enabled countermeasures designed to mitigate return abuse: customer profiling and product tracking. A customer profiling system identifies opportunistic customers by using their personal identification and transaction history. In contrast, a product tracking system identifies fraudulent returns by recording each transaction of a product through the use of unique identifiers. We develop prescriptive models for these technologies and investigate the value of making such investments. Our analyses demonstrate the conditions in which it is advantageous to adopt these technologies and when such investments should be avoided. We find that when a retailer is able to charge restocking fees, it is well equipped to mitigate opportunism and fraud with only its price and refund decisions. Hence, the value of consumer profiling and product tracking technologies is limited. However, when a retailer is constrained to offer a full refund due to market dynamics, employing these technologies may hold significant value. Our analysis details the determinants of value, model sensitivity, and comparisons between models. We also address how these countermeasures impact a retailer's profitability, demand structure, and decisions with respect to price and refund amount.
  • Placeholder
    PublicationMetadata only
    Public transport-based crowd-shipping with backup transfers
    (Informs, 2022) N/A; N/A; Department of Industrial Engineering; Kızıl, Kerim Uygur; Yıldız, Barış; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 258791
    With the rising urbanization and booming e-commerce, traditional last-mile delivery systems fail to satisfy the need for faster, cheaper, and more environmentally friendly deliveries. Several new approaches are put forward as an alternative to classical delivery systems in this regard, yet none of them offers the same level of flexibility, capacity, reliability, and managerial control by itself. This paper proposes a new last-mile delivery model that combines several new approaches and technologies to address this issue. More precisely, we suggest using public transit as a backbone network completed by automated service points, crowd-shipping, and backup transfers with zero-emission vehicles to provide a low-cost and environmentally friendly express delivery service. The design problem for the envisioned system is formulated as a two-stage stochastic program, and a branch-and-price (BP) algorithm is devised to solve it. Taking advantage of the nearly decomposable structure that would emerge in possible real-world applications, our study presents the first example of using decomposition branching in a BP framework to enhance computational efficiency. Extensive computational studies and simulations with real-world data reveal valuable managerial insights for the proposed system and attest to the efficacy of the suggested methodology.
  • Placeholder
    PublicationMetadata only
    Package routing problem with registered couriers and stochastic demand
    (Pergamon-Elsevier Science Ltd, 2021) Department of Industrial Engineering; Yıldız, Barış; Faculty Member; Department of Industrial Engineering; College of Engineering; 258791
    Providing the crowd-sourced delivery capacity and hence enabling the practice, occasional couriers (OC) are the most critical resource in crowd-shipping (CS). therefore, As well as establishing and retaining a solid OC base, using the OC trips efficiently is of utmost importance for the viability of the CS applications. one auspicious idea to enhance the efficiency, i.e., cover a larger demand set with the available OC trips, is to use transshipments (deliver packages with a coordinated effort of OCs) and collect OC trip information in advance to efficiently coordinate them, which gives rise to the package routing problem with registered couriers (PRP-R) we introduce in this paper. in particular, we study a CS model in which the couriers register their trips in advance while the express shipping demands arrive through a stochastic process, and the network management needs to dynamically decide package-courier assignments to carry out deliveries in the most efficient way. We develop a novel rolling horizon algorithm to solve this challenging problem in real-time, which explicitly considers the limited OC capacities and use of a back-up delivery capacity (company-owned or third party provided) to ensure the service quality. Beyond the classical rolling horizon approaches, the suggested methodology uses a novel Monte Carlo procedure to take anticipated future system conditions into account, and thus can provide package-courier assignments that have almost the same cost with the optimal solution of the static version of the problem where all demand arrivals are known a-priory. the comprehensive numerical experiments attest to the efficacy of our methodology for the real-time management of the CS operations and provide significant managerial insights about the design of CS networks.
  • Thumbnail Image
    PublicationOpen Access
    Pricing for delivery time flexibility
    (Elsevier, 2020) Savelsbergh, Martin; Department of Industrial Engineering; Yıldız, Barış; Faculty Member; Department of Industrial Engineering; College of Engineering; 258791
    We study a variant of the multi-period vehicle routing problem, in which a service provider offers a discount to customers in exchange for delivery flexibility. We establish theoretical properties and empirical insights regarding the intricate and complex relation between the benefit from additional delivery flexibility, the discounts offered to customers to gain additional delivery flexibility, and the likelihood of acceptance of discount offers by customers. Computational experiments, using an exact dynamic programming algorithm show that, depending on the setting, cost savings exceeding 30% can be achieved.