Browsing by Author "Zhang, Jian"

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    Dynamic Pricing and Scheduling for the Coordination of a One-of-a-kind Production Supply Chain
    (2013-09-13) Zhang, Jian; Tu, Yiliu
    In this thesis work, we study the dynamic pricing strategy (DPS) for different cases and its influences on supply chain coordination. First, we study a DPS for a one-of-a-kind production (OKP) firm with two classes of orders (due-date guaranteed and due-date unguaranteed). We model the DPS using Bellman equation and compare it with a static pricing strategy (SPS). Second, we study the pricing problem for a third-party-logistics (3PL) provider that provides warehousing and less-than-truckload (LTL) transportation services. We develop a stochastic-nonlinear-programming (SNLP) model which computes the optimal freight rates for different delivery dates incorporating the 3PL provider's current holding cost and available transportation capacity. We develop an adjusted multinomial logit (MNL) function to predict customer choices so that our SNLP model can obtain near optimal freight rate settings. Finally, we study dynamic pricing based on a practical OKP firm which is currently employing a SPS. For the three cases, we show the increase of the price-setting firm's profit, customer and social welfare when DPS is employed through simulation, and consequently show the DPS's influence on the performance of the supply chain. We also develop a scheduling method for a manufacturer whose suppliers offer different delivery times at different prices. We abstract the problem to a one-machine scheduling problem which is featured by: (a) the release date of each job is compressible and stochastic, (b) each job has to be delivered before its due date (deadline) and (c) the manufacturer can expedite the production with costly overtime. The target is to minimize the total cost including the compressing cost and the overtime production cost. We coin a concept of a job's late-release-impact factor (LRIF) and we propose a LRIF based heuristic algorithm. Through the numerical test, we shows that the LRIF based algorithm can obtain a better schedule comparing to the ones that are commonly used in practice. By this thesis work, we are trying to integrate an OKP supply chain, which is critical to reduce the cost of OKP companies.
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    A dynamic pricing strategy for a 3PL provider with heterogeneous customers
    (2015-06-08) Zhang, Jian; Nault, Barrie R.; Tu, Paul Yiliu L.
    We study the pricing problem for a third-party-logistics (3PL) provider that provides ware-housing and transportation services. When customers arrive at the 3PL provider, they specify the delivery dates for their freight, and before the specified delivery dates, their freight is stocked in the 3PL provider’s warehouse. We propose a dynamic pricing strategy (DPS) and develop a stochastic-nonlinear-programming (SNLP) model which computes the optimal freight rates for different delivery dates incorporating the 3PL provider’s current holding cost and available transportation capacity for each route. As customers are heterogeneous in their valuations and price sensitivities for delivery dates, and the distributions of the customers’ delivery date preferences are unknown to the 3PL provider, we modify the standard multinomial logit (MNL) function to predict customer choices. Through a simulation experiment, we show that the proposed MNL function can be a good replacement for the mixed MNL function when the mixed MNL function is not applicable. Through simulation we also compare the proposed DPS with a static pricing strategy. We show that with our DPS both the 3PL provider and its customers are better off, and the 3PL provider has different investment incentives for increasing transportation capacity. Our results can be also applied in similar settings that feature holding costs, limited production capacity and delivery-date-sensitive customers. Keywords: dynamic pricing, multinomial logit, third-party logistics, stochastic programming
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    How Should Information Technology be Regulated?
    (2021-07-15) Vijairaghavan, Vaarun; Nault, Barrie; Dao, Duy; Hidaji, Hooman; Zhang, Jian; Anderson, Mark; Anand, Krishnan
    With Information Technology (IT) playing a more central role in the modern economy, governments the world over are displaying an active interest in regulating IT firms with the goal to increase competition, foster local firms or increase social surplus. My research informs such policymakers in two critical and relevant areas: the effect of regulations on IT investments and societal wellbeing (measured as total surplus), and the net productivity effects of increased IT investments. In my first research stream, I use analytical modelling methodologies to evaluate the impact of specific regulatory mechanisms, thereby creating a theoretical template for how such regulations can be evaluated. In my first chapter in this stream, I analyze how firm investments can be incentivized and coordinated through a platform, and whether the platform as an institutional mechanism requires regulatory intervention. In my second chapter, I analyze Data Portability Regulations which require that platforms allow users to download their data and port it to competing firms. Such laws have been passed by the E.U. and the state of California, and are being considered by the U.S. Congress. I study whether this regulation accomplishes the law’s goal of encouraging competition in the data economy. In a second research stream, I use structural econometric models to measure the effect of IT investments on energy productivity. I execute this by mathematically deriving a structural econometric model to estimate the impact of IT on the output elasticity of energy. Thus, if a policy decision to regulate platforms causes a decrease in firm IT investments, my empirical work measures the impact of such decreases, both in terms of its direct effect on marginal product as well its indirect effect through the change in the productivity of energy.