Research Outputs
Permanent URI for this communityhttps://hdl.handle.net/20.500.14288/2
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Publication Metadata only A binarization strategy for modelling mixed data in multigroup classification(Institute of Electrical and Electronics Engineers (IEEE), 2013) Masmoudi, Youssef; Chabchoub, Habib; Department of Industrial Engineering; Türkay, Metin; Faculty Member; Department of Industrial Engineering; College of Engineering; 24956This paper presents a binarization pre-processing strategy for mixed datasets. We propose that the use of binary attributes for representing nominal and integer data is beneficial for classification accuracy. We also describe a procedure to convert integer and nominal data into binary attributes. Expectation-Maximization (EM) clustering algorithms was applied to classify the values of the attributes with a wide range to use a small number of binary attributes. Once the data set is pre-processed, we use the Support Vector Machine (LibSVM) for classification. The proposed method was tested on datasets from the literature. We demonstrate the improved accuracy and efficiency of presented binarization strategy for modelling mixed and complex data in comparison to the classification of the original dataset, nominal dataset and binary dataset.Publication Metadata only An exact algorithm for integrated planning of operations in dry bulk terminals(Pergamon-Elsevier Science Ltd, 2019) N/A; Department of Industrial Engineering; Ünsal, Özgür; Oğuz, Ceyda; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; 328856; 6033We consider integrated planning problem of export dry bulk terminals. This problem consists of three important operations: (i) berth allocation, (ii) reclaimer scheduling, and (iii) stockyard allocation, and includes tidal time windows, multiple stocking pads and non-crossing of reclaimers. We exploit relationships among these operations to decompose this complex problem and propose a logic-based Benders decomposition algorithm. Master and subproblems are modeled with mixed-integer programming and constraint programming, respectively, such that complementary strengths of these programming paradigms are utilized. Computational experiments show that the proposed method can effectively solve the integrated problem for up to two weeks of planning horizon.Publication Metadata only An online optimization approach to post-disaster road restoration(Pergamon-Elsevier Science Ltd, 2021) Akbari, Vahid; Shiri, Davood; Department of Industrial Engineering; Salman, Fatma Sibel; Faculty Member; Department of Industrial Engineering; College of Engineering; 178838Natural disasters impact transportation networks adversely and cause road sections to be damaged or blocked. The road network may even become disconnected, impeding accessibility between disaster-stricken areas and critical locations such as hospitals, relief aid depots and transportation hubs. In the immediate response phase, a set of blocked edges should be selected and restored to reconnect the transportation network. While locations of the disrupted roads can be identified using drone or satellite images, an accurate estimation of time to restore a road segment can be carried out only after expert observations on the field. In this article, we study a post-disaster road restoration problem modeled on an undirected edge-weighted graph with k blocked edges, where the unblocking time of a blocked edge is revealed online once the road restoration team visits an end-node of that blocked edge. The objective is to minimize the time at which the road network is reconnected. We first investigate the worst-case performance of online algorithms against offline optimal solutions by means of the competitive ratio. We prove that any online deterministic algorithm cannot achieve a competitive ratio better than 2k-1. We also provide an optimal online algorithm that is proven to achieve this lower bound. In addition, to achieve good performance on realistic instances, we implement an algorithm that solves a mixed integer programming model each time new information is revealed. Since model solution is prohibitively time-consuming, we also propose a novel polynomial time online algorithm. We compare these two algorithms with two other benchmark online algorithms on both Istanbul road network instances and several other city instances from the literature. Our experiments show that the proposed polynomial time online algorithm performs superior to the benchmark ones and obtains solutions close to the offline optimum on all the tested instances.Publication Metadata only Arc routing problems to restore connectivity of a road network(Pergamon-Elsevier Science Ltd, 2016) Kasaei, Maziar; Department of Industrial Engineering; Salman, Fatma Sibel; Faculty Member; Department of Industrial Engineering; College of Engineering; 178838After a disaster, restoring accessibility in the affected area is critical for response operations. We study two arc routing problems for clearing blocked roads. The first problem minimizes the time to reconnect the road network, while the second maximizes the total benefit gained by reconnecting network components within a time limit. For each problem, we develop a mixed integer programming formulation and two versions of a heuristic algorithm. We conduct computational experiments on Istanbul data and instances adapted from the literature. The heuristics achieve near-optimal or optimal solutions quickly in most of the tested instances.Publication Metadata only Constraint programming approach to quay crane scheduling problem(Pergamon-Elsevier Science Ltd, 2013) N/A; N/A; Department of Industrial Engineering; Ünsal, Özgür; Oğuz, Ceyda; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; 328856; 6033This study presents a constraint programming (CP) model for the quay crane scheduling problem (QCSP), which occurs at container terminals, with realistic constraints such as safety margins, travel times and precedence relations. Next, QCSP with time windows and integrated crane assignment and scheduling problem, are discussed. The performance of the CP model is compared with that of algorithms presented in QCSP literature. The results of the computational experiments indicate that the CP model is able to produce good results while reducing the computational time, and is a robust and flexible alternative for different types of crane scheduling problems.Publication Metadata only Design and operation of intermodal transportation network in the Marmara region of Turkey(Pergamon-Elsevier Science Ltd, 2015) N/A; N/A; Department of Industrial Engineering; Reşat, Hamdi Giray; Türkay, Metin; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 24956This paper presents a multi-objective optimization model for integrating different transportation modes in the design and operation of an intermodal transportation network in a geographical region. The problem is formulated as a mixed-integer optimization problem that accounts for time and congestion dependent vehicle speeds. We present modeling approach, data analysis and outline the important characteristics of the mathematical programming problem for minimization of transportation cost and time simultaneously by using the augmented epsilon-constraint method. The proposed approach is illustrated on a real world case using data from Marmara region where approximately 50% industrial goods and services in Turkey are produced.Publication Open Access Hub location, routing, and route dimensioning: strategic and tactical intermodal transportation hub network design(The Institute for Operations Research and the Management Sciences (INFORMS), 2021) Yaman Hande; Karaşan Oya Ekin; Department of Industrial Engineering; Yıldız, Barış; Faculty Member; Department of Industrial Engineering; College of Engineering; 258791We propose a novel hub location model that jointly eliminates some of the traditional assumptions on the structure of the network and on the discount as a result of economies of scale in an effort to better reflect real-world logistics and transportation systems. Our model extends the hub literature in various facets: instead of connecting nonhub nodes directly to hub nodes, we consider routes with stopovers; instead of connecting pairs of hubs directly, we design routes that can visit several hub nodes; rather than dimensioning pairwise connections, we dimension routes of vehicles; and rather than working with a homogeneous fleet, we use intermodal transportation. Decisions pertinent to strategic and tactical hub location and transportation network design are concurrently made through the proposed optimization scheme. An effective branch-and-cut algorithm is developed to solve realistically sized problem instances and to provide managerial insights.Publication Metadata only Hub network design problem with capacity, congestion, and stochastic demand considerations(Informs, 2023) Bayram, Vedat; Farham, M. Saleh; Department of Industrial Engineering; Yıldız, Barış; Department of Industrial Engineering; College of EngineeringOur study introduces the hub network design problem with congestion, capacity, and stochastic demand considerations (HNDC), which generalizes the classical hub location problem in several directions. In particular, we extend state-of-the-art by integrating capacity acquisition decisions and congestion cost effect into the problem and allowing dynamic routing for origin-destination (OD) pairs. Connecting strategic and operational level decisions, HNDC jointly decides hub locations and capacity acquisitions by considering the expected routing and congestion costs. A path-based mixed-integer second-order cone programming (SOCP) formulation of the HNDC is proposed. We exploit SOCP duality results and propose an exact algorithm based on Benders decomposition and column generation to solve this challenging problem. We use a specific characterization of the capacity-feasible solutions to speed up the solution procedure and develop an efficient branch-and-cut algorithm to solve the master problem. We conduct extensive computational experiments to test the proposed approach's performance and derive managerial insights based on realistic problem instances adapted from the literature. In particular, we found that including hub congestion costs, accounting for the uncertainty in demand, and whether the underlying network is complete or incomplete have a significant impact on hub network design and the resulting performance of the system.Publication Metadata only Optimal locations of electric public charging stations using real world vehicle travel patterns(Pergamon-Elsevier Science Ltd, 2015) Cai, Hua; Xu, Ming; N/A; Department of Industrial Engineering; Shahraki, Narges; Türkay, Metin; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 24956We propose an optimization model based on vehicle travel patterns to capture public charging demand and select the locations of public charging stations to maximize the amount of vehicle-miles-traveled (VMT) being electrified. The formulated model is applied to Beijing, China as a case study using vehicle trajectory data of 11,880 taxis over a period of three weeks. The mathematical problem is formulated in GAMS modeling environment and Cplex optimizer is used to find the optimal solutions. Formulating mathematical model properly, input data transformation, and Cplex option adjustment are considered for accommodating large-scale data. We show that, compared to the 40 existing public charging stations, the 40 optimal ones selected by the model can increase electrified fleet VMT by 59% and 88% for slow and fast charging, respectively. Charging demand for the taxi fleet concentrates in the inner city. When the total number of charging stations increase, the locations of the optimal stations expand outward from the inner city. While more charging stations increase the electrified fleet VMT, the marginal gain diminishes quicldy regardless of charging speed.Publication Metadata 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; 258791Providing 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.