Publications without Fulltext
Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
Browse
130 results
Search Results
Publication Metadata only Transshipment network design for express air cargo operations in China(Elsevier B.V., 2023) Savelsbergh, Martin; Dogru, Ali K.; Department of Industrial Engineering; Yıldız, Barış; Department of Industrial Engineering; College of EngineeringWe introduce a novel multimodal (ground and air transportation) network design model with transshipments for the transport of express cargo with heterogeneous service classes (i.e., next morning delivery, and next day delivery). We formulate this problem using a novel path-based mixed-integer program which seeks to maximize the demand (weight) served. We investigate the value of the proposed transshipment network under various operational conditions and by benchmarking against a direct shipment network and a network with a single transshipment point which mimics a classical star-shaped hub-and-spoke network. Our extensive computational study with real-world data from ShunFeng (SF) Express reveals that the integration of ground and air transportation improves the coverage and that transshipment enables serving a large number of origin–destination pairs with a small number of cargo planes. Importantly, we show that by simplifying handling, i.e., employing cross-docking rather than time-consuming sortation, a transshipment network can transport express cargo fast enough to meet demanding delivery deadlines. Finally, we find that increasing the efficiency of intra-city operations and extending the nightly operating time window are the most effective operational adjustments for further improving the performance of the proposed transshipment network.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 Sustainability analysis of cement supply chains considering economic, environmental and social effects(Elsevier, 2023) Suhaib, Seyyed Amir Babak; Rasmi, Seyyed Amir Babak; Department of Industrial Engineering; Türkay, Metin; Department of Industrial Engineering; College of EngineeringCement is a fundamental ingredient in the construction industry and infrastructure development; these sectors depend on this raw material and the demand proportionally increases as the population of the world grows and the urbanization rate accelerates. Despite being a vital element of the development, cement manufacturing sector is a major source of GHG emissions and depletes the natural capital. In this paper we examine the effects of incorporating sustainability indicators in cement supply chains under the Triple Bottom Line (TBL) accounting of sustainability using multi-Objective optimization. We implement a tailored multi-objective optimization algorithm that generates unique optimal solutions hence giving an accurate and well-defined Pareto front to decision makers. Our model shows that even by including additional environmental and social considerations cement manufacturing is economically feasible.Publication Metadata only Operational research: methods and applications(Taylor and Francis Ltd., 2024) Petropoulos, Fotios; Laporte, Gilbert; Aktas, Emel; Alumur, Sibel A.; Archetti, Claudia; Ayhan, Hayriye; Battarra, Maria; Bennell, Julia A.; Bourjolly, Jean-Marie; Boylan, John E.; Breton, Michèle; Canca, David; Charlin, Laurent; Chen, Bo; Cicek, Cihan Tugrul; Cox, Louis Anthony; Currie, Christine S.M.; Demeulemeester, Erik; Ding, Li; Disney, Stephen M.; Ehrgott, Matthias; Eppler, Martin J.; Erdoğan, Güneş; Fortz, Bernard; Franco, L. Alberto; Frische, Jens; Greco, Salvatore; Gregory, Amanda J.; Hämäläinen, Raimo P.; Herroelen, Willy; Hewitt, Mike; Holmström, Jan; Hooker, John N.; Işık, Tuğçe; Johnes, Jill; Kara, Bahar Y.; Karsu, Özlem; Kent, Katherine; Köhler, Charlotte; Kunc, Martin; Kuo, Yong-Hong; Letchford, Adam N.; Leung, Janny; Li, Dong; Li, Haitao; Lienert, Judit; Ljubić, Ivana; Lodi, Andrea; Lozano, Sebastián; Lurkin, Virginie; Martello, Silvano; McHale, Ian G.; Midgley, Gerald; Morecroft, John D.W.; Mutha, Akshay; Petrovic, Sanja; Pferschy, Ulrich; Psaraftis, Harilaos N.; Rose, Sam; Saarinen, Lauri; Salhi, Said; Song, Jing-Sheng; Sotiros, Dimitrios; Stecke, Kathryn E.; Strauss, Arne K.; Tarhan, İstenç; Thielen, Clemens; Toth, Paolo; Van Woensel, Tom; Berghe, Greet Vanden; Vasilakis, Christos; Vaze, Vikrant; Vigo, Daniele; Virtanen, Kai; Wang, Xun; Weron, Rafał; White, Leroy; Yearworth, Mike; Yıldırım, E. Alper; Zaccour, Georges; Zhao, Xuying; Department of Industrial Engineering; Oğuz, Ceyda; Department of Industrial Engineering; College of EngineeringThroughout its history, Operational Research has evolved to include methods, models and algorithms that have been applied to a wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first summarises the up-to-date knowledge and provides an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion and used as a point of reference by a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes.Publication Metadata only An adaptive and diversified vehicle routing approach to reducing the security risk of cash-in-transit operations(Wiley, 2017) Bozkaya, Burçin; Department of Industrial Engineering; N/A; Salman, Fatma Sibel; Telciler, Kaan; Faculty Member; Master Student; Department of Industrial Engineering; College of Engineering; Graduate School of Sciences and Engineering; 178838; N/AWe consider the route optimization problem of transporting valuables in cash-in-transit (CIT) operations. The problem arises as a rich variant of the capacitated vehicle routing problem (CVRP) with time windows and pickup and deliveries. Due to the high-risk nature of this operation (e.g., robberies) we consider a bi-objective function where we attempt to minimize the total transportation cost and the security risk of transporting valuables along the designed routes. For risk minimization, we propose a composite risk measure that is a weighted sum of two risk components: (i) following the same or very similar routes, and (ii) visiting neighborhoods with low socioeconomic status along the routes. We also consider vehicle capacities in terms of monetary value carried as per insurance regulations. We develop an adaptive randomized bi-objective path selection algorithm that uses the composite risk measure in choosing alternative paths between origin-destination pairs over a sequence of days. We solve the rich CVRP approximately for each day with updated costs. We test our solution approach on a data set from a CIT delivery service provider and provide insights on how the routes diversify daily. Our approach generates a spectrum of solutions with costrisk trade-off to support decision making.Publication Metadata only Multi-vehicle synchronized arc routing problem to restore post-disaster network connectivity(Elsevier Science Bv, 2017) Department of Industrial Engineering; Department of Industrial Engineering; Akbari, Vahid; Salman, Fatma Sibel; Teaching Faculty; Faculty Member; Department of Industrial Engineering; College of Engineering; College of Engineering; N/A; 178838After a natural disaster roads can be damaged or blocked by debris, while bridges and viaducts may collapse. This commonly observed hazard causes some road sections to be closed and may even disconnect the road network. In the immediate disaster response phase work teams are dispatched to open a subset of roads to reconnect the network. Closed roads are traversable only after they are unblocked/cleared by one of the teams. The main objective of this research is to provide an efficient solution method to generate a synchronized work schedule for the road clearing teams. The solution should specify the synchronized routes of each clearing team so that: 1) connectivity of the network is regained, and 2) none of the closed roads are traversed unless their unblocking/clearing procedure is finished. In this study we develop an exact Mixed Integer Programming (MIP) formulation to solve this problem. Furthermore, we propose a matheuristic that is based on an MIP-relaxation and a local search algorithm. We prove that the optimality gap of the relaxation solution is bounded by K times the lower bound obtained from the relaxed model, where K is the number of teams. We show computationally that the matheuristic obtains optimal or near-optimal solutions. (C) 2016 Elsevier B.V. All rights reserved.Publication Metadata only Bounded rationality in clearing service systems(Elsevier, 2020) Department of Industrial Engineering; Canbolat, Pelin Gülşah; Faculty Member; Department of Industrial Engineering; College of Engineering; 108242This paper considers a clearing service system where customers arrive according to a Poisson process, and decide to join the system or to balk in a boundedly rational manner. It assumes that all customers in the system are served at once when the server is available and times between consecutive services are independently and identically distributed random variables. Using logistic quantal-response functions to model bounded rationality, it first characterizes customer utility and system revenue for fixed price and degree of rationality, then solves the pricing problem of a revenue-maximizing system administrator. The analysis of the resulting expressions as functions of the degree of rationality yields several insights including: (i) for an individual customer, it is best to be perfectly rational if the price is fixed; however, when customers have the same degree of rationality and the administrator prices the service accordingly, a finite nonzero degree of rationality uniquely maximizes customer utility, (ii) system revenue grows arbitrarily large as customers tend to being irrational, (iii) social welfare is maximized when customers are perfectly rational, (iv) in all cases, at least 78% of social welfare goes to the administrator. The paper also considers a model where customers are heterogeneous with respect to their degree of rationality, explores the effect of changes in distributional parameters of the degree of rationality for fixed service price, provides a characterization for the revenue-maximizing price, and discusses the analytical difficulties arising from heterogeneity in the degree of bounded rationality. (C) 2019 Elsevier B.V. All rights reserved.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 Bunkering policies for a fuel bunker management problem for liner shipping networks(Elsevier, 2021) De, Arijit; Choudhary, Alok; Tiwari, Manoj K.; Department of Industrial Engineering; Türkay, Metin; Faculty Member; Department of Industrial Engineering; College of Engineering; 24956This paper investigates the problem of bunker fuel management for liner shipping networks under different fuel pricing scenarios and taking into consideration different fuel bunkering policies. The fuel consumption of a vessel on a sailing leg may fluctuate as the real vessel speed deviates from the planned vessel speed. Furthermore, fluctuation of fuel prices at various ports increases the complexity of bunkering decisions related to the selection of the bunkering ports and the estimation of bunkered fuel cost. We have developed a mixed integer non-linear programming model to minimize the total expected cost consisting of inventory cost related to container transportation, operating cost associated with ship hiring, as well as bunkering cost and fuel consumption cost at the port. The novelty of our research lies in its consideration of stochastic fuel consumption for different sailing legs, stochastic fuel prices at each port and different fuel bunkering policies to determine optimal bunker fuel management strategies for the selection of bunkering ports and for the estimation of the amount of bunkered fuel required. We have proposed a novel approximate algorithm based on mathematical formulation and the fuel bunkering policies to calculate the total expected cost; the fuel inventory while arriving at and departing from the port; the number of vessels hired for weekly service; the arrival and departure time of the ship; and the amount of fuel bunkered at a port. We have performed extensive computational experiments on the practical routes to demonstrate the applicability, efficacy and robustness of the proposed novel methodology.Publication Metadata only Optimal threshold levels in stochastic fluid models via simulation-based optimization(Springer, 2007) Gurkan, Gul; Ozdemir, Ozge; Department of Industrial Engineering; Karaesmen, Fikri; Faculty Member; Department of Industrial Engineering; College of Engineering; 3579A number of important problems in production and inventory control involve optimization of multiple threshold levels or hedging points. We address the problem of finding such levels in a stochastic system whose dynamics can be modelled using generalized semi-Markov processes (GSMP). The GSMP framework enables us to compute several performance measures and their sensitivities from a single simulation run for a general system with several states and fairly general state transitions. We then use a simulation-based optimization method, sample-path optimization, for finding optimal hedging points. We report numerical results for systems with more than twenty hedging points and service-level type probabilistic constraints. In these numerical studies, our method performed quite well on problems which are considered very difficult by current standards. Some applications falling into this framework include designing manufacturing flow controllers, using capacity options and subcontracting strategies, and coordinating production and marketing activities under demand uncertainty.