This article examines three categories of companies with regard to electronic data interchange (EDI) usage: (1) companies that use no EDI at all, (2) companies that use traditional EDI, and (3) companies that use Web-based EDI. Performance is examined for these three types of companies using the following dimensions: process cost; operational efficiency; customer satisfaction, coordination, cooperation, and commitment between EDI partners; and overall performance. Results show that companies using Web-based EDI experience superior performance in commitment between EDI partners as well as overall performance, while companies using Web-based or traditional EDI experience superior performance in internal operational efficiency as well as overall performance.

Many business decisions can be modeled as multiobjective linear programming (MOLP) problems. MOLP algorithms seek solutions to these problems by interacting with decision makers to arrive at an acceptable solution. However, due in part to the increasing complexity of these algorithms, and in part to the failure of developers to use graphical user interfaces, testing and comparison of competing algorithms has been minimal. We present herein results of research designed to address this circumstance. Using widely available microcomputer tools, we designed and built a Decision Support System (DSS) capable of running MOLP algorithms, and conducted a field test which asked 98 decision makers to solve a business case using the system. Two algorithms were programmed into the DSS, one a new and more mathematically complex algorithm, and one a previously used benchmark. Results demonstrate that the more complex algorithm was preferred as a decision-making aid over the benchmark. Additionally, results show that users found the DSS equally easy to work with for both algorithms, suggesting that the graphical user interface sufficiently masked the complexity of the new algorithm. This result is encouraging for the possibility of the implementation and testing of increasingly sophisticated MOLP algorithms.

Four multi-objective linear programming algorithms are implemented on microcomputer software packages and a large field experiment is conducted using the implemented algorithms. Two new algorithms which incorporate formal models of decision maker behavior are tested along with two established algorithms which include no formal models of decision maker behavior. The new algorithms are shown to outperform the established algorithms.

Multiobjective linear programming algorithms are typically based on value maximization. However, there is a growing body of experimental evidence showing that decision maker behavior is inconsistent with value maximization. Tversky and Simonson provide an alternative model for problems with a discrete set of choices. Their model, called the componential context model, has been shown to capture observed decision maker behavior. In this paper, an interactive multiobjective linear programming algorithm is developed which follows the rationale of Tversky and Simonson The algorithm is illustrated with an example solved using standard linear programming software. Finally, an interactive decision support system based on this algorithm is developed to field test the usefulness of the algorithm. Results show that this algorithm compares favorably with an established algorithm in the field.

This paper presents methods for generating stochastic, nondominated solutions for multiobjective math programming problems. The methods are based on the assumption that the objective function coefficients are random variables with probability distributions which are known or can be approximated. The methods then generate solutions that are nondominated in terms of the expected value of each objective and the probability that each objective meets or exceeds a target value. Heuristics for generating these solutions and choosing the preferred solution are presented and illustrated with examples. The paper also discusses computational issues and issues of nonlinearity.