Jeffrey D. Tew

Outbound supply chain network design with mode selection, lead times and capacitated vehicle distribution centers

Most distribution network design models considered to date have focused on minimizing fixed costs of facility location and transportation costs. Measures of customer satisfaction driven by the operational dynamics such as lead times have seldom been considered. We consider the design of an outbound supply chain network considering lead times, location of distribution facilities and choice of transportation mode. We present a Lagrangian heuristic that gives excellent solution quality in reasonable computational time. Scenario analyses are conducted on industrial data using this algorithm to observe how the supply chain behaves under different parameter values. 2004 Elsevier B.V. All rights reserved.

Auction-Based Mechanisms for Electronic Procurement

Auction-based mechanisms are extremely relevant in modern day electronic procurement systems since they enable a promising way of automating negotiations with suppliers and achieve the ideal goals of procurement efficiency and cost minimization. This paper surveys recent research and current art in the area of auction-based mechanisms for e-procurement. The survey delineates different representative scenarios in e-procurement where auctions can be deployed and describes the conceptual and mathematical aspects of different categories of procurement auctions. We discuss three broad categories: 1) single-item auctions: auctions for procuring a single unit or multiple units of a single homogeneous type of item; 2) multi-item auctions: auctions for procuring a single unit or multiple units of multiple items; and 3) multiattribute auctions where the procurement decisions are based not only on costs but also on attributes, such as lead times, maintenance contracts, quality, etc. In our review, we present the mathematical formulations under each of the above categories, bring out the game theoretic and computational issues involved in solving the problems, and summarize the current art. We also present a significant case study of auction based e-procurement at General Motors.

Multiattribute electronic procurement using goal programming

One of the key challenges of current day electronic procurement systems is to enable procurement decisions transcend beyond a single attribute such as cost. Consequently, multiattribute procurement have emerged as an important research direction. In this paper, we develop a multiattribute e-procurement system for procuring large volume of a single item. Our system is motivated by an industrial procurement scenario for procuring raw material. The procurement scenario demands multiattribute bids, volume discount cost functions, inclusion of business constraints, and consideration of multiple criteria in bid evaluation. We develop a generic framework for an e- procurement system that meets the above requirements. The bid evaluation problem is formulated as a mixed linear integer multiple criteria optimization problem and goal programming is used as the solution technique. We present a case study for which we illustrate the proposed approach and a heuristic is proposed to handle the computational complexity arising out of the cost functions used in the bids.

The process-oriented multivariate capability index

Recent literature has proposed multivariate capability indices, but does not suggest a method for measuring quality characteristics in a way that links production irregularities directly to their causes. Our objective is to present a new approach to multivariate capability indices that uses process-oriented basis representation (POBREP) which allows the computing of cause-related index values. The proposed method focuses on independent process-oriented multivariate data by employing regression coefficients as data. These coefficients measure the amount of the characteristic patterns induced by particular problems or incidents that can occur in the system. Two examples from the electronics industry (the chip capacitor process and solder paste process) use simulated data and Monte Carlo integration to demonstrate the new process-oriented capability method. A reduction of estimation error was realized when using process-oriented capability. For the chip capacitor problem, capability error is 24–54% when using ordinary multivariate data. However, when using process-oriented data the error is less than 3%. Capability is difficult to compute from sample data in the solder paste example without the process-oriented approach. Future research should propose a multivariate capability measure for dependent process-oriented data.

Optimal auctions for multi-unit procurement with volume discount bids

In this paper, we design an optimal procurement mechanism for procuring multiple units of a single homogeneous item based on volume discount bids submitted by rational and intelligent suppliers. We develop an elegant auction mechanism, VD-OPT, that minimises the cost to the buyer, satisfying at the same time: Bayesian Incentive Compatibility (BIC); interim individual rationality.

A groves mechanism approach to decentralized design of supply chains

In this paper, a generic optimization problem arising in supply chain design is modeled in a game theoretic framework and solved as a decentralized problem using a mechanism design approach. We show that the entities in a supply chain network can be naturally modeled as selfish, rational, and intelligent agents interested in maximizing certain payoffs. This enables us to define a supply chain design game and we show that the well known Groves mechanisms can be used to solve the underlying design optimization problem. We illustrate our approach with a representative three stage distribution process of a typical automotive supply chain.

Modelling the effect of custom and stock orders on supply-chain performance

We model a manufacturer–manufacturer supply chain producing a custom, termed make-to-order (MTO) product, and a stock, termed make-to-stock (MTS) product. The key parameters considered by the model are correlations in the MTO and MTS demand processes, the relative proportion of the MTO demands, production planning strategy by facility, the length of time in advance of production that the schedule must be frozen for each facility, transportation lead time and delays in the transmission of information. Supply-chain performance is measured by production variability and the amount of safety stock required to satisfy a fixed fill rate. The results of the analysis underscore the criticality of taking an integrated view of demand management, production planning, transportation service selection and information flow across the supply-chain.

Simulation anywhere any time: Web-based simulation implementation for evaluating order-to-delivery systems and processes

GM Enterprise Systems Laboratory (GMESL) has developed a standalone single user simulation program for evaluating and predicting order-to-delivery (OTD) systems and processes. In order for more people to be able to access this simulator, to share the simulation results, and to analyze simulation collaboratively, we have designed, developed and implemented an Internet-based three-tiered client/server framework, which consists of the three tiers: database, execution and user interface. The corresponding components are: database server, execution server, and Web based user interface. The relational database server enables users to interact with the persistent data sets for simulation study and maintains data integrity. The multi-agent based execution server guarantees stable user responsiveness by virtue of the multi-agent flexible architecture, accordingly achieving a high level of processing scalability. Finally the Web-based graphical user interface helps users to easily conduct the simulation study from anywhere at any time, and the visual simulation analysis tool helps users to make decisions effectively.

Dynamic shipment planning in an automobile shipment yard using real-time radio frequency identification (RFID) information

Wireless communication technology has been increasingly adopted in various business environments of supply chains. Radio frequency identification technology provides real-time information of vehicle movements and, associated shipment operations can benefit from it for better, efficient load makeup. A Markov decision process model is proposed for dynamically building the shipment loads using real-time information of the wireless tracking system. Simulation results demonstrate the superior performance of dynamic decision approach compared to conventional shipment practices in terms of reducing vehicle dwell time, improving customer fulfillment, and increasing utilization of the transportation carriers.

An improved response surface methodology algorithm with an application to traffic signal optimization for urban networks

Illustrates the use of the simulation-optimization technique of response surface methodology (RSM) in traffic signal optimization of urban networks. It also quantifies the gains of using the common random number (CRN) variance reduction strategy in such an optimization procedure. An enhanced RSM algorithm which employs conjugate gradient search techniques and successive second-order models is presented instead of the conventional approach. An illustrative example using an urban traffic network exhibits the superiority of using the CRN strategy over direct simulation in performing traffic signal optimization. The relative performance of the two strategies is quantified with computational results using the total network-wide delay as the measure of effectiveness.