Joe Naoum-Sawaya

• Lotfi, A.; Naoum-Sawaya, J.; Lotfi, A.; Jiang, Z., 2024, "To Skim or not to Skim: Studying the Optimal Pricing Strategy for Technology Products", Omega, September 127: 103079 - 103079.
  Abstract: Pricing of technology products often follows a skimming strategy in which a firm charges the highest price at product launch then reduces the price over time to attract additional buyers. Price-skimming has not been adequately examined in the literature related to technology products. Most pricing models proposed in the literature consider only initial product purchases while ignoring repeat purchases. However, for technology products, frequent upgrades often result in substantial repeat purchases. To fill this void, we develop an optimization model that accounts for both initial and repeat purchases. We find that the effectiveness of price skimming is highly dependent on the rate of repeat purchases. When the repeat purchase rate is low, firms may be better off delaying price reduction to accumulate more profit earlier in the product lifecycle, thus compensating for the decline in sales that occurs later in the lifecycle. For example, price skimming may not be the optimal pricing strategy for fad products with low repeat-purchase rates due to the product losing popularity in a short time period. Furthermore, contrary to common expectations, in markets highly sensitive to price changes firms may get away with charging a higher introductory price but must decrease the price over time to motivate sales.

  Link(s) to publication:

  http://dx.doi.org/10.1016/j.omega.2024.103079

• Amirnequiee, S.; Pun, H.; Naoum-Sawaya, J., (Forthcoming), "Navigating supplier encroachment: Game-theoretic insights for outsourcing strategies", European Journal Of Operational Research
  Abstract: It has become increasingly typical for the upstream suppliers to invest in direct sales channels and compete with the downstream manufacturer. The oft-called phenomenon of supplier encroachment allows the supplier to benefit from both the wholesale to the manufacturer and the sale to end customers. However, from the manufacturer’s perspective, encroachment may suggest the supplier’s lack of reliability, which can contribute to the breakdown of supplier-manufacturer collaboration. In response to the supplier’s encroachment, the manufacturer can change its supplier(s); while the encroaching supplier might face consequences (e.g., the manufacturer dropping the supplier to seek new partnerships). The existence of future outsourcing alternatives for the manufacturer and the future consequences for the supplier has not been studied in the extant literature. In this paper we propose a two-period game-theoretic approach to supplier encroachment; where the downstream manufacturer outsources the production to a group of suppliers that are characterized by a low-quality supplier without encroachment capabilities, and a high-quality supplier with encroachment capabilities, i.e., capable of launching its own independent product. We show that (a) an increase in the quality of the encroaching supplier’s independent product can convince the manufacturer to redirect its wholesale order from the non-encroaching supplier to the encroaching supplier and simultaneously boost the manufacturer’s profits, (b) as the quality of the non-encroaching supplier is improved, the manufacturer may opt to drop the non-encroaching supplier and redirect its wholesale order to the encroaching supplier instead, and (c) an improvement in the qualities of the encroaching and non-encroaching suppliers might decrease their corresponding profits. Accordingly, we offer actionable guidelines for practitioners; in particular, we help practitioners navigate the competitive outsourcing landscape under threat of encroachment and advise them on the counter-productive impacts of quality improvements.

  Link(s) to publication:

  http://dx.doi.org/10.1016/j.ejor.2024.07.003

• Lofti, A.; Jiang, Z.; Naoum-Sawaya, J.; Begen, M. A., 2024, "Modeling Sales of Multigeneration Technology Products in the Presence of Frequent Repeat Purchases: A Fractional Calculus-Based Approach", Production and Operations Management, May 33(5)
  Abstract: Frequently releasing a new product generation has become a common practice to sustain sales over time, thus accurately forecasting the sales trajectory of each product generation plays a vital role in the short-, medium-, and long-term planning of a firm. Classic multigeneration diffusion models do not incorporate within-generation repeat purchases, making them unusable for product lines with high rates of such purchases. Concentrating on technology products, we develop a multigeneration sales model to fill this void. We demonstrate that the new model can be used for predictive and prescriptive analytics. Our empirical results show that the new model estimates and forecasts sales more accurately than a state-of-the-art benchmark model that does not account for within-generation repeat purchases, underscoring the importance of incorporating repeat purchases. Furthermore, we use two different versions of our model to examine market entry timing under two main strategies, i.e., (i) a phase-out transition strategy in which firms continue to sell the old generation after the release of the new generation, and (ii) a total transition strategy in which firms discontinue the old generation after the introduction of the new generation. Our results indicate that the repeat purchases rate determines whether it is optimal to expedite or delay the new product launch, underscoring the importance of incorporating repeat purchases in market entry strategies.

  Link(s) to publication:

  http://dx.doi.org/10.1177/10591478241240744

• Fukasawa, R.; Naoum-Sawaya, J.; Oliveira, D., 2024, "The Price-elastic Knapsack Problem", Omega (Oxford), April 124: 103003 - 103003.
  Abstract: This paper introduces the price-elastic knapsack problem (PEKP), an extension of the classic knapsack problem where instead of fixed item characteristics, the weight of each item and the profit from including an item in the knapsack are a function of a parameter, namely the price. PEKP is first formulated as a generic nonlinear optimization problem and three special cases are investigated. First, we show a polynomial-time solvable case. Next, we formulate the case where the item weights are affine-linear functions as a quadratic program. The computational results show that solving the quadratic program to optimality is computationally challenging, and thus, an approach that decomposes the problem into three mixed integer programs is proposed. Similarly, the case where the weights of the items are piecewise-linear functions is investigated and a quadratic formulation is proposed. A solution approach based on decomposing the problem into three mixed integer programs that are solved independently is also proposed. Using randomly generated instances of varying sizes, the computational results show that the proposed decomposition leads to significant computational advantages compared to solving the quadratic program in the cases of affine-linear and piecewise-linear functions.

  Link(s) to publication:

  http://dx.doi.org/10.1016/j.omega.2023.103003

• Manias, D. M.; Shaer, I.; Naoum-Sawaya, J.; Shami, A., 2024, "Robust and Reliable SFC Placement in Resource-Constrained Multi-Tenant MEC-Enabled Networks", IEEE Transactions on Network and Service Management, February 21(1): 187 - 199.
  Abstract: With the rapid development and incoming implementation of 5G networks, many use cases, such as Intelligent Transportation Systems (ITS), are being realized. Utilizing networking technologies, including Network Function Virtualization and Mobile Edge Computing, along with 5G network slicing, the Next-Generation Service Placement Problem (NGSPP) is gaining significant attention due to the criticality of its services and its resource-constrained network nodes. The placement of services on Next-Generation (NG) networks has inherent challenges, mainly ultra-low latency requirements and the complexity of NG network management and orchestration. A candidate solution to the NGSPP should provide a placement that adheres to the strict Quality of Service (QoS) requirements. This work presents the formulation of a robust optimization problem that optimizes the high-availability placement of applications in resource-constrained and multi-tenant NG networks, which complies with QoS requirements and is capable of protecting the performance of the solution under adverse conditions. Finally, a set of hierarchical clustering-based heuristic algorithms, which reduce the time-complexity of the solution are proposed. Results demonstrate that formulating the robust solution is a proactive method of injecting resilience into the system and can preserve performance across various levels of system uncertainty.

  Link(s) to publication:

  http://dx.doi.org/10.1109/tnsm.2023.3293027

• D. M. Manias, .; A. Chouman, .; Naoum-Sawaya, J.; A. Shami, ., 2023, "Resilient and Robust QoS-Preserving Post-Fault VNF Placement", IEEE Networking Letters, December 5(4): 270 - 274.
  Abstract: In the realm of network management and orchestration, such as Virtual Network Function (VNF) lifecycle management, the dynamicity of 5G networks raises the importance of reliability and robustness when determining optimal VNF placement. Specifically, after a fault has occurred, the set of services that must maintain a certain level of performance and quality depends on the interaction between VNFs. This letter proposes a novel robust optimization model for VNF placement during post-fault status, while addressing the resilience and reliability of the 5G network in testing. The model results are compared with a deterministic placement solution with varying demand uncertainties.

  Link(s) to publication:

  http://dx.doi.org/10.1109/LNET.2023.3286104

• Naoum-Sawaya, J.; Elhedhli, S.; De Carvalho, P., 2023, "Strategic Blockchain Adoption to Deter Deceptive Counterfeiters", European Journal of Operational Research, November 311(1): 373 - 386.
  Abstract: Counterfeiting is an ever growing problem worldwide which is exacerbated by the ease of access through e-commerce and online shopping. This calls for innovative technologies, such as blockchain, to identify, track, and prevent fake products from reaching consumers, especially for vital sectors such as the drug industry, which is the main motivation for this work. We investigate the strategic implications of using blockchain technology to deter counterfeiters. We particularly focus on the case of deceptive counterfeits that infiltrate legitimate distribution channels. Deceptive counterfeits lack the quality of genuine products and may pose immense health and safety risks to consumers who are unable to distinguish them from genuine products at the time of purchase. In contrast to prior literature that assumes that blockchain eliminates deceptive counterfeiting, we present a model that realistically considers blockchain as a technology that increases the capability of detecting counterfeits. This capability nonetheless comes at an increasing cost that may financially discourage genuine manufacturers from adopting the technology. The presented model shows that blockchain is not always financially beneficial and demonstrates that manufacturers can strategically balance between product quality and investment in blockchain to combat counterfeiting. Furthermore, our results demonstrate that, with the availability of blockchain, genuine manufacturers may be less interested to differentiate products based on quality, but rather rely on blockchain to block counterfeits.

  Link(s) to publication:

  http://dx.doi.org/10.1016/j.ejor.2023.04.031

• 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)
  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

• 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

• Shaheen, S.; Naoum-Sawaya, J.; Crisostomi, E., 2022, "Editorial: Smart Mobility", Frontiers in Sustainable Cities, April 4
  Abstract: Urban mobility is experiencing a number of disruptive forces that are changing how individuals interact with cities. Many younger travelers are seeking on-demand mobility strategies to avoid the hassles of car ownership, while older travelers want the freedoms guaranteed by long-term access to personal mobility. Regulators, driven by concerns about climate change, air quality, noise pollution, and the economic and societal cost of congestion, are placing more stringent requirements on mobility strategies to manage their integration into urban settings. In addition, advances in areas such as communication networks, the internet of things (IoT), distributed ledger technology (blockchain), smart cities, and cyber physics, are placing increased expectations on the performance of urban mobility strategies. This is happening at a time when the workhorse of urban mobility—the motor vehicle—is undergoing a technological transformation. Cars have basically retained the same form, with the same functionalities, since the invention of the diesel engine over 100 years ago. More recently innovation is coming in every direction. The future of mobility is no longer dominated by internal combustion engine automobiles but rather automated, electric, connected, shared, and deliverable mobility options ranging from bikes and scooters to cars, shuttles, and more.

  Link(s) to publication:

  http://dx.doi.org/10.3389/frsc.2022.880968

• 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

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