Chih-Yang Tsai

Minimum cost capacity installation for multicommodity network flows

Consider a directed graphG = (V,A), and a set of traffic demands to be shipped between pairs of nodes inV. Capacity has to be installed on the edges of this graph (in integer multiples of a base unit) so that traffic can be routed. In this paper we consider the problem of minimum cost installation of capacity on the arcs to ensure that the required demands can be shipped simultaneously between node pairs. We study two different approaches for solving problems of this type. The first one is based on the idea of metric inequalities (see Onaga and Kakusho, On feasibility conditions of multicommodity flows in networks, IEEE Transactions on Circuit Theory, CT-18 (4) (1971) 425–429.), and uses a formulation with only |A| variables. The second uses an aggregated multicommodity flow formulation and has |V||A| variables. We first describe two classes of strong valid inequalities and use them to obtain a complete polyhedral description of the associated polyhedron for the complete graph on three nodes. Next we explain our solution methods for both of the approaches in detail and present computational results. Our computational experience shows that the two formulations are comparable and yield effective algorithms for solving real-life problems.

On supply chain cash flow risks

This study models the supply chain related cash flow risks for a business entity measured by the standard deviations of cash inflows, outflows, and netflows of each period in a planning horizon. The goal is to provide an insightful look on how common practices that intend to improve the Cash Conversion Cycle (CCC), e.g., offering early payment discounts, may contribute to cash flow risks. We show the benefits and recommend the best policy of using Asset-Backed Securities (ABS) to finance accounts receivable as a means to shorten the CCC and lower the cash inflow risk. It is particularly helpful to small vendors having tight cash reserves and high financing costs.

An iterative feature reduction algorithm for probabilistic neural networks

The issue of noise and redundant feature variables in classification problems has been well addressed in statistical stepwise discriminant analysis. However, no feature reduction algorithm designed specifically for Probabilistic Neural Network (PNN) is found in literature. In this study, we develop an ad hoc iterative feature (variable) reduction algorithm for basic PNNs to identify noise and redundant variables. A basic PNN applying the same smoothing factor to all variables does not distinguish variables adding little or no predictive power to the model from others. The proposed iterative approach utilizes a weighted PNN with one smoothing factor for each variable in the feature reduction stage. Once a subset of variables is selected, a basic PNN is developed on the chosen variables for future applications. Computational experiments on five data sets obtained from publicly available sources indicate that the basic PNN with variables chosen by the ad hoc approach outperforms the PNN using variables selected by two benchmark methods, back-propagation neural network and stepwise linear discriminant analysis.

A branch-and-cut approach for minimum cost multi-level network design

Network design models with more than one facility type have many applications in communication and distribution problems. Due to their complexity, previous studies have focused on finding good heuristic solutions. In this study, we develop algorithms that solve the multi-level network design problem to optimality. In our approach, the problem is converted to a Steiner tree problem and is solved by a branch-and-cut approach. Our computational study shows that the approach outperforms a dual ascent approach in the literature (Mirchandani, INFORMS J. Comput. 8 (3) (1996) 202) not only on solution times but also on the quality of the solutions.

On detecting nonlinear patterns in discriminant problems

We propose a two-stage model for detecting nonlinear patterns in discriminant problems and for solving the problem. The model deploys a Linear Programming Based Discriminator (LPBD) in stage one for treating linear patterns and a Probabilistic Neural Network (PNN) in stage two for handling nonlinear patterns. The LPBD in stage one divides the decision space into a clear zone where observations are (almost) linearly separable and a gray zone where nonlinear patterns are more likely to occur. The PNN in stage two analyzes the gray zone and determines whether a significant nonlinear patterns exist in the observations. Our goal is to avoid using a nonlinear model unless the PNN strongly suggests so to maintain good interpretability and avoid overfitting. Our computational study demonstrates the effectiveness of the two-stage model in both classification accuracy and computational efficiency.

Polyhedral Approaches for the Steiner Tree Problem on Graphs

Given an undirected graph G = (V, E) and a node set \(T \subseteq V\), a Steiner tree for T in G is a set of edges \(S \subseteq E\) such that the graph (V(S), S) contains a path between every pair of nodes in T, where V(S) is the set of nodes incident to the edges in S. Given costs (or weights) on edges and nodes, the Steiner tree problem on a graph (STP) is to find a minimum weight Steiner tree. The problem is known to be \(\mathcal{N}\mathcal{P}\)-hard even for planar graphs, bipartite graphs, and grid graphs.

Computational study of the multiechelon production planning problem

In this article we try to identify appropriate solution procedures for different types of multiechelon production planning problems. We conduct an extensive computational study on uncapacitated multiechelon production planning problems with serial and assembly types of bill-of-material structures. Problems are formulated as both single-source fixed charge network problems and as multicommodity flow problems with fixed charges. Solution procedures considered are branch and cut, Lagrangean relaxation (for the network formulation), and branch and bound (for the multicommodity formulation). Three hundred problems with various problem structures are tested. Our conclusions suggest the best approach for each type of problem structure.

The impact of cost structure on supply chain cash flow risk

Managing supply chain cash flow risk has become a crucial task for many cash-stressed businesses. Cash flows usually lag behind operating earnings and exhibit higher volatility than earnings, making them less predictable. Through the three components of the cash conversion cycle – days in inventory, receivables, and payables, this study investigates the impact of cost structure – cost ratios and cost compositions – on cash flow risk (standard deviation) and explains why and how management philosophies, such as Just-In-Time, and new production technologies, such as 3D printing, can lower cash flow risk by shortening days in inventory and shifting cost structure. An analytical model built on a simple demand process examines the basic relationship between cost structure and cash flow risk while a simulation approach studies the same relationship under a more general demand process with additional modelling considerations and a benchmark test. The simulation approach is applied on a publicly traded company’s financial data to demonstrate how supply chain process improvement can reduce the cash flow risk.

Hospital rounding--EHR's impact.

PURPOSE The purpose of this paper is to understand the time spent on various tasks during physician inpatient rounds and to examine the new electronic health records (EHRs) impact on time distribution. DESIGN/METHODOLOGY/APPROACH Trained observers shadowed hospital physicians to record times for various tasks before and after EHR implementation. FINDINGS Electronic records did not improve efficiency. However, task times were redistributed. Physicians spent more time reviewing patient charts using time saved from miscellaneous work. RESEARCH LIMITATIONS/IMPLICATIONS The study focusses solely on work distribution and the changes it underwent. It does not include quality measures either on patient results or physician satisfaction. PRACTICAL IMPLICATIONS As EHR provides rich information and easier access to patient records, it motivates physicians to spend more time reviewing patient charts. Hospital administrators seeking immediate returns on EHR investment, therefore, may be disappointed. ORIGINALITY/VALUE Unlike previous work, this study was conducted in a non-teaching hospital, providing a task-time comparison without any educational and team factor influence. The result serves as a benchmark for many community hospital managers seeking to address the same issue.

The Account Data Model

A new data model is proposed using a traditional accounting approach to model an organizations transactional data and extending this model to include not only financial information but also internal business process, learning and growth, and customer perspectives. It provides an organization with a single data model that supports both transactional and data warehousing needs. The model has a very small footprint and uses replication tables for data mining purposes. The model is generic so that changes in information requirements of the application do not imply the need to change the model itself. Implementations of the model can provide a uniform report generation environment for standard reports as well as OLAP and data mining activities. Implementations can include data used in query optimization strategies for applications such as association rule mining.