Joe Naoum-Sawaya

• Naoum-Sawaya, J.; Elhedhli, S.; De Carvalho, P., (Forthcoming), "Strategic blockchain adoption to deter deceptive counterfeiters", European Journal of Operational Research, November 311(1): 373 - 386.
• Manias, D. M.; Naoum-Sawaya, J.; Shami, A.; Javadtalab, A.; Hemmati, M.; You, Y., 2023, "Robust traffic grooming and infrastructure placement in OTN-over-DWDM networks", Journal of Optical Communications and Networking, August 15(8): 553 - 553.
  Abstract: The advent of next-generation networks has revolutionized modern networking practices through its improved service capability as well as its numerous emerging use cases. Coupled with the increasing number of connected devices, 5G and beyond (5G+) network traffic is expected to be increasingly diverse and high in volume. To address the large amount of data exchanged between the 5G+ core and external data networks, optical transport networks (OTNs) with dense wavelength-division multiplexing (DWDM) will be leveraged. In order to prepare for this increase in traffic, network operators (NOs) must develop and expand their existing backbone networks, requiring significant levels of capital expenditures. To this end, the traffic grooming and infrastructure placement problem is critical to supporting NO decisions. The work presented in this paper considers the traffic grooming and infrastructure placement problem for OTN-over-DWDM networks. The dynamicity and diversity of 5G+ network traffic are addressed through the use of robust optimization, allowing for increasing levels of solution conservativeness to protect against various levels of demand uncertainty. Furthermore, a robust traffic grooming and infrastructure placement heuristic (RGIP-H) solution capable of addressing the scalability concerns of the optimization problem formulation is presented. The results presented in this work demonstrate how the tuning of the robust parameters affects the cost of the objective function. Additionally, the ability of the robust solution to protect the solution under demand uncertainty is highlighted when the robust and deterministic solutions are compared during parameter deviation trials. Finally, the performance of the RGIP-H is compared to the optimization models when applied to larger network sizes.

  Link(s) to publication:

  http://dx.doi.org/10.1364/jocn.486838

• Manias, D. M.; Javadtalab, A.; Naoum-Sawaya, J.; Shami, A., 2023, "The Role of Optical Transport Networks in 6G and Beyond: A Vision and Call to Action", Journal of Sensor and Actuator Networks, May 12(3): 43 - 43.
  Abstract: As next-generation networks begin to take shape, the necessity of Optical Transport Networks (OTNs) in helping achieve the performance requirements of future networks is evident. Future networks are characterized as being data-centric and are expected to have ubiquitous artificial intelligence integration and deployment. To this end, the efficient and timely transportation of fresh data from producer to consumer is critical. The work presented in this paper outlines the role of OTNs in future networking generations. Furthermore, key emerging OTN technologies are discussed. Additionally, the role intelligence will play in the Management and Orchestration (MANO) of next-generation OTNs is discussed. Moreover, a set of challenges and opportunities for innovation to guide the development of future OTNs is considered. Finally, a use case illustrating the impact of network dynamicity and demand uncertainty on OTN MANO decisions is presented.

  Link(s) to publication:

  http://dx.doi.org/10.3390/jsan12030043

• Manias, D. M.; Shaer, I.; Naoum-Sawaya, J.; Shami, A., 2023, "Robust and Reliable SFC Placement in Resource-Constrained Multi-Tenant MEC-Enabled Networks", IEEE Transactions on Network and Service Management: 1 - 1.
• Manias, D. M.; Chouman, A.; Naoum-Sawaya, J.; Shami, A., 2023, "Resilient and Robust QoS-Preserving Post-Fault VNF Placement", IEEE Networking Letters: 1 - 1.
• de Carvalho, P. R. V.; Naoum-Sawaya, J.; Elhedhli, S., 2022, "Blockchain-Enabled supply chains: An application in fresh-cut flowers", Applied Mathematical Modelling, October 110: 841 - 858.
  Abstract: Supply chains have often benefited from breakthroughs in information technology. Blockchain, in particular, is a recent technology that is promising to revolutionize the way supply chains are designed and operated. This paper proposes a framework to optimize the adoption of blockchain jointly with the design of the supply chain network. A new form of product differentiation is enabled through blockchain adoption where blockchain-certified products are sold at a premium price to a growing segment of customers. The proposed framework allows supply chain managers to monetize data which has traditionally been used to improve supply chain efficiency. The framework is evaluated using a realistic case study inspired by the global supply chain of fresh-cut flowers. The results show that by strategically using blockchain at certain locations in the supply chain network, significant cost savings are realized compared to fully deploying blockchain throughout the supply chain. These cost savings lead to increased demand, better consumer surplus, and higher supply chain profit. Furthermore, the proposed data-enabled product differentiation leads to fresher products in the market.

  Link(s) to publication:

  http://dx.doi.org/10.1016/j.apm.2022.06.011

• Gambella, G.; Ghaddar, B.; Naoum-Sawaya, J., 2021, "Optimization Problems for Machine Learning: A Survey", European Journal of Operational Research, May 290(3): 807 - 828.
  Abstract: This paper surveys the machine learning literature and presents in an optimization framework several commonly used machine learning approaches. Particularly, mathematical optimization models are presented for regression, classification, clustering, deep learning, and adversarial learning, as well as new emerging applications in machine teaching, empirical model learning, and Bayesian network structure learning. Such models can benefit from the advancement of numerical optimization techniques which have already played a distinctive role in several machine learning settings. The strengths and the shortcomings of these models are discussed and potential research directions and open problems are highlighted.

  Link(s) to publication:

  http://dx.doi.org/10.1016/j.ejor.2020.08.045

• Rostami, B.; Kammerling, N.; Naoum-Sawaya, J.; Buchheim, C.; Clausen, U., 2021, "Stochastic single-allocation hub location", EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, March 289(3): 1087 - 1106.
  Abstract: This paper considers the single allocation hub location problem under demand uncertainty where the allocation of the spokes to the hubs is optimized as second stage decision after the uncertainty in the demand is realized. We refer to this case as the variable allocation case, meaning that the allocation of the spokes to the hubs can be altered after the uncertainty is realized. This is in contrast to the fixed allocation case that is addressed in the literature, where the spokes are allocated to the chosen hubs before the uncertainty is realized. As shown in the paper, the fixed allocation case can be solved as a deterministic problem using the expected values of the random variables. However, the variable allocation model is a two-stage stochastic program that is challenging to solve. An alternative convex mixed-integer nonlinear formulation is presented for the variable allocation and a customized solution approach based on cutting planes is proposed to address the computational challenges. The proposed solution approach is implemented in a branch-and-cut framework where the cut-generating subproblems are solved combinatorially, i.e. without an optimization solver. Extensive computational results on the single allocation hub location problem and two of its variants, the capacitated case and the single allocation p-median problem are presented. The proposed cutting plane approach outperforms the direct solution of the problem using the state-of-the-art solver GUROBI as well the L-shaped decomposition, which is a common approach for addressing two-stage stochastic programs with recourse.

  Link(s) to publication:

  http://dx.doi.org/10.1016/j.ejor.2020.07.051

• Sarhadi, H.; Naoum-Sawaya, J.; Verma, M., 2020, "A robust optimization approach to locating and stockpiling marine oil-spill response facilities", Transportation Research Part E: Logistics and Transportation Review, September 102005(141): 1 - 19.
  Abstract: In this research, a robust optimization approach is proposed to the problem of designing emergency response networks for marine oil-spills given uncertainty in the location, size and type of the spill. In this regard, we formulate two robust models (Gamma and Ellipsoidal) to optimize the allocation of response equipment while considering the underlying uncertainty in each oil-spill scenario. An efficient Branch-and-Cut algorithm is then designed to improve the computational performance. The benefits of applying the robust formulations are illustrated and compared to the non-robust model using a realistic case study from Newfoundland (Canada).

  Link(s) to publication:

  http://dx.doi.org/10.1016/j.tre.2020.102005

• Kuang, X.; Ghaddar, B.; Naoum-Sawaya, J.; Zuluaga, L. F., 2019, "Alternative SDP and SOCP approximations for polynomial optimization", EURO Journal on Computational Optimization, June 7(2): 153 - 175.
  Abstract: In theory, hierarchies of semidefinite programming (SDP) relaxations based on sum of squares (SOS) polynomials have been shown to provide arbitrarily close approximations for a general polynomial optimization problem (POP). However, due to the computational challenge of solving SDPs, it becomes difficult to use SDP hierarchies for large-scale problems. To address this, hierarchies of second-order cone programming (SOCP) relaxations resulting from a restriction of the SOS polynomial condition have been recently proposed to approximate POPs. Here, we consider alternative ways to use the SOCP restrictions of the SOS condition. In particular, we show that SOCP hierarchies can be effectively used to strengthen hierarchies of linear programming relaxations for POPs. Specifically, we show that this solution approach is substantially more effective in finding solutions of certain POPs for which the more common hierarchies of SDP relaxations are known to perform poorly. Furthermore, when the feasible set of the POP is compact, these SOCP hierarchies converge to the POP’s optimal value.

  Link(s) to publication:

  http://dx.doi.org/10.1007/s13675-018-0101-2

• Liu, M.; Naoum-Sawaya, J.; Gu, Y.; Leccue, F.; Shorten, R., 2019, "A Distributed Markovian Parking Assist System", IEEE Transactions on Intelligent Transportation Systems, June 20(6): 2230 - 2240.
  Abstract: This paper proposes a congestion balancing parking guidance system that suggests to a driver a sequence of streets to follow around the desired destination with the aim to reduce the total distance that is travelled while searching for a free parking spot. The system requires only limited infrastructure information, and neither requires parking spaces to be instrumented, nor vehicles to communicate with each other. Specifically, the system utilizes parking vacancy information on each street. The system also accounts for the added cost of not finding a free space, which is typically expressed as the additional distance that needs to be travelled to find an available parking spot. To avoid local congestion, different drivers respond to different suggestions based on a probability distribution that considers the total distance that needs to be travelled. A mobility simulator is used to model the searching behaviors of vehicles for parking spaces with and without the smart parking algorithm and experimental results are provided using the road network of the city of Dublin, Ireland.

  Link(s) to publication:

  http://dx.doi.org/10.1109/TITS.2018.2865648

• Kuang, X.; Ghaddar, B.; Naoum-Sawaya, J.; Zuluaga, K., 2019, "Alternative SDP and SOCP Approximations for Polynomial Optimization", EURO Journal on Computational Optimization, June 7(2): 153 - 175.
  Abstract: In theory, hierarchies of semidefinite programming (SDP) relaxations based on sum of squares (SOS) polynomials have been shown to provide arbitrarily close approximations to general polynomial optimization problems (POP). However, due to the computational challenge of solving SDPs, it becomes difficult to use SDP hierarchies for large-scale problems. To address this, hierarchies of second-order cone programming (SOCP) relaxations resulting from a restriction of the SOS polynomial condition have been recently proposed to approximate POPs. Here, we consider alternative ways to use these SOCP restriction of the SOS condition. In particular, we show that SOCP hierarchies can be effectively used to strengthen hierarchies of linear programming (LP) relaxations for POPs. Specifically, we show that this solution approach is substantially more effective in finding solutions of certain POPs for which the more common hierarchies of SDP relaxations are known to perform poorly. Also, we use hierarchies of LP relaxations for POPs that allows us to show that the SOCP approach can be used to obtain hierarchies of SOCPs that converge to the optimal value of the POP when its feasible set is compact. Additionally, we show that the SOCP approach can be used to address the solution of the fundamental alternating current optimal power flow (ACOPF) problem. In particular, we show that the first-order SOCP hierarchy obtained by weakening the more common hierarchy of SDP relaxations for this problem is equivalent to solving the conic dual of the SOCP approximations recently proposed to address the ACOPF problem. Through out the article, we illustrate our findings with relevant experimental results. In the case of the ACOPF problem, we use well-known instances of the problem that appear in the related literature.

  Link(s) to publication:

  https://www.researchgate.net/publication/283247186_Alternative_Linear_and_Second-order_Cone_Approximation_Approaches_for_Polynomial_Optimization

  http://dx.doi.org/10.1007/s13675-018-0101-2

• Griggs, W. M.; Verago, R.; Naoum-Sawaya, J.; Ordonez-Hurtado, R. H.; Gilmore, R.; Shorten, R. N., 2018, "Localizing Missing Entities Using Parked Vehicles: An RFID-Based System", IEEE Internet of Things Journal, October 5(5): 4018 - 4030.
  Abstract: In this paper, we demonstrate a system for locating missing entities using radio-frequency identification (RFID)-based techniques. A key feature of our system is that we utilize the large, high-density networks of parked vehicles incident to urban areas for the detection and reporting process. RFID readers and antennas are placed within the vehicles, while RFID passive tags are carried on the entity of interest via some means, e.g., a wrist band. If an entity is reported as missing, then the application on board each of the parked vehicles is awoken by an administrative center. The technology on board the vehicles enables those participating in the service to attempt to locate the missing entity, sending useful information back to the administration center, which could be tied to an organization like the police. We demonstrate our system via a use case of a missing Alzheimer's patient in inner-city Dublin, Ireland. One of the key challenges in validating our system is being able to replicate a large-scale, real-world setting. Our technique for obtaining an early evaluation of our system thus employs the use of the microscopic traffic simulation package Simulation of Urban MObility (SUMO). SUMO permits multiple emulations of hundreds or thousands of parked vehicles participating in the service to be carried out, while simulated pedestrians walk random routes. Our results show that a simulated wandering person in need can be detected within a 30-min time frame, in the heart of Dublin city center, during a typical weekday, up to approximately 98% of the time, depending on how various parameters of the system are set.

  Link(s) to publication:

  http://dx.doi.org/10.1109/JIOT.2018.2864590

• Gambella,, C.; Naoum-Sawaya, J.; Ghaddar, B., 2018, "The Vehicle Routing Problem with Floating Targets: Formulation and Solution Approaches", INFORMS Journal on Computing, August 30(3): 554 - 569.
  Abstract: This paper addresses a generalization of the vehicle routing problem in which the pick-up locations of the targets are nonstationary. We refer to this problem as the vehicle routing problem with floating targets and the main characteristic is that targets are allowed to move from their initial home locations while waiting for a vehicle. This problem models new applications in drone routing, ridesharing, and logistics where a vehicle agrees to meet another vehicle or a customer at a location that is away from the designated home location. We propose a Mixed Integer Second Order Cone Program (MISOCP) formulation for the problem, along with valid inequalities for strengthening the continuous relaxation. We further exploit the problem structure using a Lagrangian decomposition and propose an exact branch-and-price algorithm. Computational results on instances with varying characteristics are presented and the results are compared to the solution of the full problem using CPLEX. The proposed valid inequalities reduce the computational time of CPLEX by up to 30% on average while the proposed branch and price is capable of solving instances where CPLEX fails in finding the optimal solution within the imposed time limit.

  Link(s) to publication:

  http://dx.doi.org/10.1287/ijoc.2017.0800

• Ghaddar, B.; Naoum-Sawaya, J., 2018, "High Dimensional Data Classification and Feature Selection using Support Vector Machines", European Journal of Operational Research, March 265(3): 993 - 1004.
  Abstract: In many big-data systems, large amounts of information are recorded and stored for analytics purposes. Often however, this vast amount of information does not offer additional benefits for optimal decision making, but may rather be complicating and too costly for collection, storage, and processing. For instance, tumor classification using high-throughput microarray data is challenging due to the presence of a large number of noisy features that do not contribute to the reduction of classification errors. For such problems, the general aim is to find a limited number of genes that highly differentiate among the classes. Thus in this paper, we address a specific class of machine learning, namely the problem of feature selection within support vector machine classification that deals with finding an accurate binary classifier that uses a minimal number of features. We introduce a new approach based on iteratively adjusting a bound on the l1-norm of the classifier vector in order to force the number of selected features to converge towards the desired maximum limit. We analyze two real-life classification problems with high dimensional features. The first case is the medical diagnosis of tumors based on microarray data where we present a generic approach for cancer classification based on gene expression. The second case deals with sentiment classification of on-line reviews from Amazon, Yelp, and IMDb. The results show that the proposed classification and feature selection approach is simple, computationally tractable, and achieves low error rates which are key for the construction of advanced decision-support systems.

  Link(s) to publication:

  https://doi.org/10.1016/j.ejor.2017.08.040

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