Decision, Operations & Information Technologieshttp://hdl.handle.net/1903/22302017-07-23T16:40:34Z2017-07-23T16:40:34ZOnline Appendix for “Gradient-Based Myopic Allocation Policy: An Efficient Sampling Procedure in a Low-Confidence Scenario”Peng, YijieChen, Chun-HungFu, MichaelHu, Jian-Qianghttp://hdl.handle.net/1903/195882017-07-04T02:33:56Z2017-01-01T00:00:00ZOnline Appendix for “Gradient-Based Myopic Allocation Policy: An Efficient Sampling Procedure in a Low-Confidence Scenario”
Peng, Yijie; Chen, Chun-Hung; Fu, Michael; Hu, Jian-Qiang
This is the online appendix, which includes theoretical and numerical supplements containing some technical details and three additional numerical examples, which could not fit in the main body due to page limits by the journal for a technical note.
The abstract for the main body is as follows:
In this note, we study a simulation optimization problem of selecting the alternative with the best performance from a finite set, or a so-called ranking and selection problem, in a special low-confidence scenario. The most popular sampling allocation procedures in ranking and selection do not perform well in this scenario, because they all ignore certain induced correlations that significantly affect the probability of correct selection in this scenario. We propose a gradient-based myopic allocation policy (G-MAP) that takes the induced correlations into account, reflecting a trade-off between the induced correlation and the two factors (mean-variance) found in the optimal computing budget allocation formula. Numerical experiments substantiate the efficiency of the new procedure in the low-confidence scenario.
2017-01-01T00:00:00ZOnline Appendix for “Ranking and Selection as Stochastic Control”Peng, YijieChong, Edwin K. P.Chen, Chun-HungFu, Michael C.http://hdl.handle.net/1903/191912017-06-30T03:36:37Z2017-04-01T00:00:00ZOnline Appendix for “Ranking and Selection as Stochastic Control”
Peng, Yijie; Chong, Edwin K. P.; Chen, Chun-Hung; Fu, Michael C.
2017-04-01T00:00:00ZMathematical Programming Models for Influence Maximization on Social NetworksZhang, Ruihttp://hdl.handle.net/1903/187522017-04-28T21:59:56Z2016-01-01T00:00:00ZMathematical Programming Models for Influence Maximization on Social Networks
Zhang, Rui
In this dissertation, we apply mathematical programming techniques (i.e., integer programming and polyhedral combinatorics) to develop exact approaches for influence maximization on social networks. We study four combinatorial optimization problems that deal with maximizing influence at minimum cost over a social network. To our knowl- edge, all previous work to date involving influence maximization problems has focused on heuristics and approximation.
We start with the following viral marketing problem that has attracted a significant amount of interest from the computer science literature. Given a social network, find a target set of customers to seed with a product. Then, a cascade will be caused by these initial adopters and other people start to adopt this product due to the influence they re- ceive from earlier adopters. The idea is to find the minimum cost that results in the entire network adopting the product.
We first study a problem called the Weighted Target Set Selection (WTSS) Prob- lem. In the WTSS problem, the diffusion can take place over as many time periods as
needed and a free product is given out to the individuals in the target set. Restricting the number of time periods that the diffusion takes place over to be one, we obtain a problem called the Positive Influence Dominating Set (PIDS) problem. Next, incorporating partial incentives, we consider a problem called the Least Cost Influence Problem (LCIP). The fourth problem studied is the One Time Period Least Cost Influence Problem (1TPLCIP) which is identical to the LCIP except that we restrict the number of time periods that the diffusion takes place over to be one.
We apply a common research paradigm to each of these four problems. First, we work on special graphs: trees and cycles. Based on the insights we obtain from special graphs, we develop efficient methods for general graphs. On trees, first, we propose a polynomial time algorithm. More importantly, we present a tight and compact extended formulation. We also project the extended formulation onto the space of the natural vari- ables that gives the polytope on trees. Next, building upon the result for trees---we derive the polytope on cycles for the WTSS problem; as well as a polynomial time algorithm on cycles.
This leads to our contribution on general graphs. For the WTSS problem and the LCIP, using the observation that the influence propagation network must be a directed acyclic graph (DAG), the strong formulation for trees can be embedded into a formulation on general graphs. We use this to design and implement a branch-and-cut approach for the WTSS problem and the LCIP. In our computational study, we are able to obtain high quality solutions for random graph instances with up to 10,000 nodes and 20,000 edges (40,000 arcs) within a reasonable amount of time.
2016-01-01T00:00:00ZEssays on Supply Chain FinanceZhu, Weiminghttp://hdl.handle.net/1903/187512017-04-28T21:56:07Z2016-01-01T00:00:00ZEssays on Supply Chain Finance
Zhu, Weiming
I study how a larger party within a supply chain could use its superior knowledge about its partner, who is considered to be financially constrained, to help its partner gain access to cheap finance. In particular, I consider two scenarios: (i) Retailer intermediation in supplier finance and (ii) The Effectiveness of Supplier Buy Back Finance.
In the fist chapter, I study how a large buyer could help small suppliers obtain financing for their operations. Especially in developing economies, traditional financing methods can be very costly or unavailable to such suppliers. In order to reduce channel costs, in recent years large buyers started to implement their own financing methods that intermediate between suppliers and financing institutions. In this paper, I analyze the role and efficiency of buyer intermediation in supplier financing. Building a game-theoretical model, I show that buyer intermediated financing can significantly improve supply chain performance. Using data from a large Chinese online retailer and through structural regression estimation based on the theoretical analysis, I demonstrate that buyer intermediation induces lower interest rates and wholesale prices, increases order quantities, and boosts supplier borrowing. The analysis also shows that the retailer systematically overestimates the consumer demand. Based on counterfactual analysis, I predict that the implementation of buyer intermediated financing for the online retailer in 2013 improved channel profits by 18.3%, yielding more than $68M projected savings.
In the second chapter, I study a novel buy-back financing scheme employed by large manufacturers in some emerging markets. A large manufacturer can secure financing for its budget-constrained downstream partners by assuming a part of the risk for their inventory by committing to buy back some unsold units. Buy back commitment could help a small downstream party secure a bank loan and further induce a higher order quantity through better allocation of risk in the supply chain. However, such a commitment may undermine the supply chain performance as it imposes extra costs on the supplier incurred by the return of large or costly-to-handle items. I first theoretically analyze the buy-back financing contract employed by a leading Chinese automative manufacturer and some variants of this contracting scheme. In order to measure the effectiveness of buy-back financing contracts, I utilize contract and sales data from the company and structurally estimate the theoretical model. Through counterfactual analysis, I study the efficiency of various buy-back financing schemes and compare them to traditional financing methods. I find that buy-back contract agreements can improve channel efficiency significantly compared to simple contracts with no buy-back, whether the downstream retailer can secure financing on its own or not.
2016-01-01T00:00:00Z