>> CURRENT FACULTY RESEARCH PROJECTS

For further information on any of these research projects, please contact us at cscm@rbs.rutgers.edu.

Supply Chain Logistics Optimization

Optimizing the Integrated Production,

Inventory, and Distribution Operations

Professors Lei Lei, Andrzej Ruszczynski, and

Sunju Park

The integrated production, inventory and distribution routing problem is concerned with coordinating the production, inventory and delivery operations to meet a target customer service level while minimizing the operation cost. Optimally solving such an integrated problem is in general not easy due to its combinatorial nature, especially when transporter routing is involved.

In this project, we propose new solution approach and a two-phase solution approach to this problem. Phase I solves a mixed integer programming model which includes all the constraints in the original model except the transporter routings are restricted to direct shipment between facilities and customer demand centers. The resulting optimal solution to the Phase I problem is always feasible to the original model. Phase II solves an associated consolidation problem to handle the potential inefficiency of direct shipment. The delivery consolidation problem is formulated as a capacitated transportation problem with additional constraints and is solved by an efficient heuristic routing algorithm. The main advantage of this proposed approach, over the classical decoupled approach, is its ability to simultaneously optimize the production, inventory and transportation operations (subject to restricted routing/direct shipments) without the needs for aggregating the demand and relaxing the constraints on transportation capacities. We evaluate the performance of this proposed two-phase approach and report its application to a real-life supply network which motivated this study.

Optimal Employee Assignment in Integrated Supply Chain Processes
Professor Ronald D. Armstrong

The efficient operation of a company’s supply chain requires a near optimal assignment of employees to tasks on the supply chain. Every employee has specific skills and available time. Every task requires certain skills and must be completed within a timeframe. Simplifications of the employee to task assignment problem appear in the literature; however, complications make most applications of the literature models impractical. The temporal, logistical and human factor issues must be considered. This proposed research will study the many issues involved in employee to task assignment, develop realistic models and solve associated problems.

Optimal Pricing Policies for a Three-Partner Supply Chain Process with a Price-Sensitive Market Demand
Professors Lei Lei and Qiang Wang

This project analyzes the effect of business partners’ collaboration and pricing policies on company profitability in a supply chain process involving a supplier (manufacturer), a retailer, and a third-party logistics partner. Assuming the market demand to the product can be approximated as a convex decreasing function of the product selling price, we prove that the joint profit in a highly collaborative environment where the partners adapt optimal inventory policies and pricing policies is at least 1/3 time higher than the sum of individual business partner’s maximum annual profit in a low-level collaboration environment. We also develop simple mathematical formulas to define optimal operation policies.

Our results can also be used as a quick estimation on the potential profit improvement by adapting better business policies or as a guideline for between-partner profit sharing budget planning.

Inventory Positioning in Supply Chain Networks
Professors Yao Zhao and Lei Lei

Supply chains often have complicated network structure with multiple layers of suppliers, assembly plants and distribution centers. Supply chains are often managed in a decentralized way in which each facility controls its inventory using a simple policy and only has access to the inputs from adjacent upstream and downstream facilities. The project is on managing inventory in decentralized supply chains, specifically, it is on positioning the right amount of safety stock at the right location so as to minimize system-wide inventory cost while meeting the service requirement of external customers.

Designing and Managing Multi-stage Assembly Systems
Professor Yao Zhao

The manufacturing processes of many industrial products have multi-stages, i.e., components manufacturing, sub-assembly, and final assembly. Joint design of products and multi-stage assembly systems has frequently showed huge success in many industries including electronics and computer. However, an important part of the joint design: the optimal production-inventory control of multi-stage assembly systems is never well understood. Base on a new modeling approach developed recently in Zhao (2003), the project aims at designing efficient algorithms to evaluate and optimize the production-inventory activities in multi-stage assembly systems.

Evaluation and Optimization of Assemble-to-Order Systems
Professor Yao Zhao

An Assemble-To-Order (ATO) system includes several components and several products. Demands occur only for products, but the system keeps inventory only of components. Each product requires a subset of the components, and each component supplies one or more products. Assembly for a product can be done only when all the necessary components are available.

ATO systems are becoming increasingly more important for today's manufacturing firms as companies strive to increase product variety and responsiveness to demand without sacrificing on inventory. This project is on designing efficient algorithms to evaluate and optimize various ATO systems with or without batching ordering.

Optimization under Uncertainty and Risk
Professor Andrzej Ruszczynski

In this project, we develop models of decision-making in the presence of uncertainty and risk, methods for evaluating and comparing risk, and numerical techniques for finding optimal decisions in the presence of risk and uncertainty.

Coordinating the Production and Distribution for a Product with Short Shelf-Life
Professors Ronald Armstrong and Lei Lei

An Extended Multi-Objective Mathematical Programming Model for Audit Samples of Balances for Accounts Receivable
Professors Kenneth D. Lawrence, Ronald Klimberg and Sheila M. Lawrence

This paper will detail the development of a multi-objective mathematical programming model for audit sampling of balances for accounts receivable. The nonlinear nature of the model structure will require the use of a nonlinear solution algorithm, such as the GRG or the genetic algorithm embedded in a Solver spreadsheet modeling system, to obtain appropriate results.

Generalized Variance of Multivariate Omega Functions and Duality
Professor Lee Papayanopoulos

The covariance of probabilistic variables and the geometry of cones in deterministic optimization traditionally belong in distinct domains of study. This paper aims to show a relationship between the generalized variance of multidimensional joint omega functions and the duality of certain linear programs. Omega distributions are ubiquitous, polymorphic, and multifunctional but have been overlooked, partly due to a lack of closed form. However, the covariance/correlation matrix of joint omega functions can be stated. The geometry that links distributional covariance and generalized variance to the volume of dual cones is an exquisitely simple one.

Supply Chain Partnership, Information Sharing, Networking

Geographical Context of Supply Chains:
Calibrating the Effects of Physical Distance on Knowledge-Intensive Linkages
Professor Varghese P. George

Flourishing businesses and commerce usually agglomerate into distinct clusters. New Jersey’s Pharmaceutical Cluster, California’s Silicon Valley, and Massachusetts’s Route 128 are all examples of this phenomenon. The extensive literature on geographical clusters argues that firms stand to gain from the short supply chains, knowledge spillovers and the easy availability of skills in such regional agglomerations. However, given the pace and sophistication of technological change, it is not possible for firms just to depend on local resources. Supply chains necessarily have to extend to ‘extra local’ sources. They bring with them problems of increased coordination, and difficulties in communication and information exchange. In this research I examine and calibrate the best practices to handle proximate versus distant supply linkages.

Information Sharing and Supply Chain Integration
Professors Yao Zhao and Xiaolong Zhang

Information technology is an important enabler of efficient supply chain strategies. Indeed, much of the current interest in supply chain management is motivated by the possibilities introduced by the abundance of data and the savings inherent in sophisticated analysis of these data. For example, the sales of two retail chains (e.g., McDonald and Burger King) operating in the same area are often interacting and correlated. Thus the knowledge of one party’s sale history can substantially improve the other party’s forecast accuracy. Furthermore, sharing this information with the common supplier may also help the supplier, thus the retailers themselves, better plan for future demand. This project quantifies the benefits of sharing the information among supply chain partners and identifies managerial conditions in which the benefits are substantial.

Flexible Regulation of Distributed Coalitions
Professors Xuhui Ao and Naftaly H. Minsky

There is a growing tendency for organizations to form coalitions in order to collaborate--by sharing some of their resources, or by coordinating some of their activities. Such coalitions are increasingly common in various domains, such as business-to-business (B2B) commerce, under names such as "virtual enterprises" or "supply chains;" and in grid computing; and among educational institutions and government agencies. All such coalitions need to be regulated, in order to ensure conformance with the policy that is supposed to govern the coalition as a whole, and in order to protect the interests of member organizations. This need triggered a great deal of recent access-control research for coalitions, exhibiting several different views of the problem, and employing different techniques for its solution. This paper presents another view of this problem.

Social Networking in Multi-Culture Supply Chain Environment
Professor Chao Chen

Supply Chain Purchasing, Auction and E-Commerce

Combinatorial Auction Design
Professor Michael H. Rothkopf

Combinatorial auctions of significant size have been made possible by recent developments in communications and computation. They are now finding use in both government sales and in industrial procurement where they assure fairness and allow suppliers to take account of synergies, such as shipping backhauls, and economies of scale. Shippers dealing with trucking companies and Mars, Inc dealing with its suppliers have found substantial mutual gains in using combinatorial auctions in procurement. We are studying the issues in the design of combinatorial auctions.

Combinatorial auctions have two features that greatly affect their design: computational complexity of winner determination and opportunities for cooperation among competitors. Dealing with these forces tradeoffs between desirable auction properties such as allocative efficiency, revenue maximization (or cost minimization), low transaction costs, fairness, failure freeness, and scalability. Computational complexity can be dealt with algorithmically, by relegating the computational burden to bidders, by maintaining fairness in the face of computational limitations, by limiting biddable combinations, and by limiting the use of combinatorial bids. Combinatorial auction designs include single-round first-price sealed bidding, Vickrey-Clarke-Groves mechanisms, uniform and market-clearing-price auctions, and iterative combinatorial auctions. Combinatorial auction designs must deal with exposure problems, threshold problems, ways to keep the bidding moving at a reasonable pace, avoiding and resolving ties, and controlling complexity.

This is a joint work with Professor Aleksandar Pekec of the Fuqua School at Duke University.

Equilibrium Prices in Markets Modeled with Mixed Integer Programs
Professor Michael H. Rothkopf

Dual variables give useful prices in markets modeled with linear programs. However, many markets have important economies of scale such as fixed or start up costs. These markets can be modeled using mixed integer programs (MIPs). Ever since Gomory and Baumol examined the dual of cutting planes in MIPs in 1960, we have “known” that there are no economically satisfactory prices for markets modeled with MIPs. In 1990 and 1994, Yale professor Herbert Scarf explained and lamented the problem in Operations Research and in the widely circulated Journal of Economic Perspectives.

We have found a simple way to get prices in MIPs that support an economic equilibrium. We are able to do this by pricing the discrete variables as well as the continuous ones. We find the prices in two stages. First we solve the MIP. Then, we add linear constraints that force the optimal solution and drop the integrality constraints. We show that the dual variables on the added constraints in the resulting linear program effectively price the integer variables and that the dual prices support an economic equilibrium. In particular, we exhibit equilibrium-supporting prices for the example problem Professor Scarf used to explain the problem to economists. In addition to their theoretical importance, our results have immediate practical relevance to electricity auctions where generators have start up costs and minimum run level constraints. These prices are currently used in the daily dispatch of the Pennsylvania-New-Jersey-Maryland electrical system and the New York electrical system.

These results should also be useful in running supply chains that are centrally planned to take advantage of synergies but independently operated. The prices they provide give no incentive to the independent operations to deviate from the optimal solution.

This is a joint work with Dr. Richard P. O’Neill of the Federal Energy Regulatory Commission, Professor Paul M. Sotkiewicz of the University of Florida, Professor Benjamin F. Hobbs of Johns Hopkins University, and Professor William R. Stewart, Jr. of the College of William and Mary.

Efficient and Secure Server Support for E-Commerce Applications
Professors Benjamin Melamed and Victoria Ungureanu

The increasing prevalence of e-commerce applications is giving rise to an ever increasing volume of online business transactions in the context of supply chains, purchasing, marketing, product browsing and contracting. The processing of such transactions must be cost effective in order to confer a competitive advantage on business organizations. Cost effectiveness metrics can be classified as customer-oriented and system-oriented. An example of the former is the response time experienced by online customers; an example of the latter is system utilization for cost-effective amortization.

E-commerce transactions are processed on specialized servers. Such servers are organized in various architectures, and their operation is driven by transaction scheduling algorithms. In particular, cluster-based server architectures combine good performance and low cost, and are commonly used for applications that generate heavy loads. The many benefits of cluster-based servers make them a good choice for e-commerce applications as well.

We propose to study scheduling and security for e-commerce server clusters. More specifically, we maintain that additional efficiencies can be attained by more efficient scheduling algorithms that better utilize e-commerce server clusters, while reducing response times for e-commerce transactions. Finally, the choice of proper scheduling algorithms will also confer resistance to denial-of-server attacks on e-commerce server.

Flexible and Scalable Support for E-Commerce Contracts
Professor Victoria Ungureanu

In order to stay competitive in the marketplace, an increasing number of enterprises are striving to achieve efficiency by migrating to a business model where transactions between trading parties are conducted on-line. Trading relations among business parties, including business transactions, are based on contracts. Generally, such contracts enumerate agents authorized to participate in transactions, and spell out the rights and obligations of each party, as well as the terms and conditions applicable to particular trades.

The objective of this proposal is to study and develop scalable control mechanisms for e-commerce applications. The starting point of this project is the thesis that digital signatures can be used not only for establishing subject identity, but also to authenticate valid contracts. We believe that the proposed mechanism should be relatively easy and inexpensive to deploy, since it uses an infrastructure that is already in place, and consequently, is well known and understood. Finally, we propose to develop performance metrics for comparing the efficacy and trade-offs of various mechanisms, and to create analytical and simulation models to assess their efficacies.

A Multi-Criteria DEA Framework for Evaluating E-Commerce Efficiency
Professors Kenneth D. Lawrence, Ronald Klimberg and Sheila M. Lawrence

The Internet is being widely deployed commercially. As the widespread use and dependency on Internet technology increases, so does the need to assess factors associated with e-commerce success. This study proposes a framework for evaluating e-commerce efficiency using data envelopment analysis (DEA) and an extension of DEA called multiple objective DEA (MODEA). The framework includes not only financial and operational measures, but also e-commerce specific measures.