Yang Li

• Baron, O.; Chen, X.; Li, Y., 2023, "Omnichannel Services: The False Premise and Operational Remedies", Management Science, June 69(2)
  Abstract: The notion of omnichannel, an integration of brick-and-mortar stores with online channels, has been thriving in recent years and is reforming the traditional service industry. Many service chains, such as Starbucks and McDonald’s, established omnichannel capability by allowing customers to order online in advance before visiting stores for pickup. The premise of omnichannel services is that when customers take advantage of the low-cost-of-waiting online channel, both their utility and the provider’s revenue will increase. Although simply adding an online-ordering option to the conventional walk-in model stimulates revenue, it also inflicts interference on the walk-in channel. We show that online ordering inadvertently reduces customers’ individual utility and social welfare when both channels are used in equilibrium. Moreover, the less it costs to order and wait online, the more the social welfare is reduced. We then evaluate two industry state-of-the-art operational remedies: regulating the use of the online channel and establishing channel-dedicated capacities. Although both remedies may improve the throughput over the walk-in-only service or even the first-come-first-served omnichannel service, they are unlikely to achieve this without jeopardizing the social welfare. We thus propose prioritizing walk-in customers and show that such prioritization can deliver this premise—that is, simultaneously benefiting the service provider and customers in comparison with the conventional walk-in-only service when both channels are used in equilibrium. Our results highlight that creating an efficient marketplace requires synergy between innovative technology and effective operational strategies.

  Link(s) to publication:

  https://ocul-uwo.primo.exlibrisgroup.com/permalink/01OCUL_UWO/t54l2v/cdi_proquest_journals_2778061031

  http://dx.doi.org/10.1287/mnsc.2022.4416

• Hu, M.; Li, Y.; Wang, J., 2018, "Efficient Ignorance: Information Heterogeneity in a Queue", Management Science, June 64(6): 2650 - 2671.
  Abstract: How would the growing prevalence of real-time delay information affect a service system? We consider a single-server queueing system where customers arrive according to a Poisson process and the service time follows an exponential distribution. There are two streams of customers, one informed about real-time delay and the other uninformed. The customers’ uninformed behavior may be due to information ignorance or rational behavior in the presence of an information fee. We characterize the equilibrium behavior of customers with information heterogeneity and investigate how the presence of a larger fraction of informed customers affects the system performance measures, i.e., throughput and social welfare. We show that the effects of growing information prevalence on system performance measures are determined by the equilibrium joining behavior of uninformed customers. Perhaps surprisingly, we find that throughput and social welfare can be unimodal in the fraction of informed customers. In other words, some amount of information heterogeneity in the population can lead to more efficient outcomes, in terms of the system throughput or social welfare, than information homogeneity. For example, under a very mild condition, throughput in a system with an offered load of 1 will always suffer if there are more than 58% of informed customers in the population. Moreover, it is shown that for an overloaded system with offered load sufficiently higher than 1, social welfare always reaches its maximum when some fraction of customers is uninformed of the congestion level in real time.

  Link(s) to publication:

  http://dx.doi.org/10.1287/mnsc.2017.2747

• Jewell, S. W.; Li, Y.; Pirvu, T. A., 2013, "Non-linear equity portfolio variance reduction under a mean–variance framework—A delta–gamma approach", Operations Research Letters, November 41(6): 694 - 700.
  Abstract: To examine the variance reduction from portfolios with both primary and derivative assets we develop a mean–variance Markovitz portfolio management problem. By invoking the delta–gamma approximation we reduce the problem to a well-posed quadratic programming problem. From a practitioner’s perspective, the primary goal is to understand the benefits of adding derivative securities to portfolios of primary assets. Our numerical experiments quantify this variance reduction from sample equity portfolios to mixed portfolios (containing both equities and equity derivatives).

  Link(s) to publication:

  http://dx.doi.org/10.1016/j.orl.2013.09.013

• Li, Y.; Terlaky, T., 2010, "A New Class of Large Neighborhood Path-Following Interior Point Algorithms for Semidefinite Optimization with $O(\sqrt{n}\log\frac{\mathrm{Tr}(X^0S^0)}{\epsilon})$ Iteration Complexity", Siam Journal On Optimization, January 20(6): 2853 - 2875.
  Abstract: In this paper, we extend the Ai–Zhang direction to the class of semidefinite optimization problems. We define a new wide neighborhood $\mathcal{N}(\tau_1,\tau_2,\eta)$, and, as usual but with a small change, we make use of the scaled Newton equations for symmetric search directions. After defining the “positive part” and the “negative part” of a symmetric matrix, we recommend solving the Newton equation with its right-hand side replaced first by its positive part and then by its negative part, respectively. In such a way, we obtain a decomposition of the classical Newton direction and use different step lengths for each of them. Starting with a feasible point $(X^0,y^0,S^0)$ in $\mathcal{N}(\tau_1,\tau_2,\eta)$, the algorithm terminates in at most $O(\eta\sqrt{\kappa_{\infty}n}\log({Tr}(X^0S^0)/\epsilon))$ iterations, where $\kappa_{\infty}$ is a parameter associated with the scaling matrix P and $\epsilon$ is the required precision. To our best knowledge, when the parameter $\eta$ is a constant, this is the first large neighborhood path-following interior point method (IPM) with the same complexity as small neighborhood path-following IPMs for semidefinite optimization that use the Nesterov–Todd direction. In the case where $\eta$ is chosen to be in the order of $\sqrt{n}$, our result coincides with the results for classical large neighborhood IPMs. Some preliminary numerical results also confirm the efficiency of the algorithm.

  Link(s) to publication:

  http://dx.doi.org/10.1137/080729311

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