Prabuddha De

The discrete time-cost tradeoff problem revisited

Abstract In the management of a project, the project duration can often be compressed by accelerating some of its activities at an additional expense. This is the so-called time-cost tradeoff problem which has been studied extensively in the project management literature. However, the discrete version of the problem, encountered frequently in practice and also useful in modeling general time-cost relationships, has received only scant and sporadic attention. Prompted by the present emphasis on time-based competition and recent developments concerning problem complexity and solution, we reexamine this important problem in this paper. We begin by formally describing the problem and discussing the difficulties associated with its solution. We then provide an overview of the past solution approaches, identify their shortcomings, and present a new solution approach. Next, we present network decomposition/reduction as a convenient basis for solving the problem and analyzing its difficulty. Finally, we point to several new directions for future research, where we highlight the need for developing and evaluating effective procedures for solving the general time-cost tradeoff problem. To the best of our knowledge, the popular project management software packages do not include provisions for time-cost tradeoff analyses. Our work, we hope, will provide the groundwork and an incentive for alleviating this deficiency.

Complexity of the Discrete Time-Cost Tradeoff Problem for Project Networks

This note addresses the discrete version of the well-known time-cost tradeoff problem for project networks, which has been studied previously in the standard project management literature as well as in the related literature on Decision-CPM. All the algorithms proposed thus far for the solution of the general problem exhibit exponential worst-case complexity, with the notable exception of the pseudo-polynomial dynamic program due to Hindelang and Muth. We first demonstrate that this algorithm is flawed, and that when we correct it, it no longer remains pseudo-polynomial. Continuing on in the main result of the note, we show that this is not at all surprising, since the problem is strongly NP-hard. Finally, we discuss the complexities of various network structures and validate an old conjecture that certain structures are necessarily more difficult to solve.

On the minimization of completion time variance with a bicriteria extension

We discuss a single-machine scheduling problem where the objective is to minimize the variance of job completion times. To date, the problem has not been solved in polynomial time. This paper presents a dynamic programming algorithm that is pseudopolynomial in complexity. We also propose a fully polynomial approximation scheme and derive a lower bound that is useful in its implementation. Furthermore, we show that the dynamic programming solution is easy to extend to a bicriteria version of the problem in which it is desired to simultaneously minimize the mean completion time.

A distance-based approach to entity reconciliation in heterogeneous databases

In modern organizations, decision makers must often be able to quickly access information from diverse sources in order to make timely decisions. A critical problem facing many such organizations is the inability to easily reconcile the information contained in heterogeneous data sources. To overcome this limitation, an organization must resolve several types of heterogeneity problems that may exist across different sources. We examine one such problem called the entity heterogeneity problem, which arises when the same real-world entity type is represented using different identifiers in different applications. A decision-theoretic model to resolve the problem is proposed. Our model uses a distance measure to express the similarity between two entity instances. We have implemented the model and tested it on real-world data. The results indicate that the model performs quite well in terms of its ability to predict whether two entity instances should be matched or not. The model is shown to be computationally efficient. It also scales well to large relations from the perspective of the accuracy of prediction. Overall, the test results imply that this is certainly a viable approach in practical situations.

On the usability of OO representations

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Locating concentrators for primary and secondary coverage in a computer communications network

A model is developed that assigns primary and secondary (backup) concentrator coverage to each terminal site. The objective is to minimize communications costs as well as costs for setting up and operating the concentrators subject to capacity constraints. A relaxation of the problem is studied, and an effective solution procedure that makes the use of this relaxation is developed. Experimental results over a wide range of problem structures show that this solution procedure is very effective. It is also found to be significantly faster than a state-of-the-art commercial integer programming package. >

Achieving Success in Large Projects: Implications from a Study of ERP Implementations

Executives in charge of large projects must decide how to spend their energies, even though typically they are not trained to manage such projects. We have derived two implications for managers based on prior research: adhere to the fundamentals of project management and unearth the best practices for large-project success. Through a study of more than 70 enterprise-resource-planning (ERP) projects, we have investigated our hypothesis that greater success in implementation is related to greater adoption of the best practices. For most of the participants in our study, our hypothesized model holds. For some, however, careful deviation from this model also proved successful. Additional implications we have derived include recommendations to specify a model of the project outcomes, understand the factors that make a project large and risky, and include a focus on managing large projects in executive education and development.

Applying machine learning to model management in decision support systems

Abstract Model management systems have become increasingly important in handling complicated decision problems in decision support systems (DSS). Aiming at overcoming the weaknesses of currently used model management systems, we present a new framework of model management system which is capable of performing model manipulation more effectively. The new approach incorporates machine learning to acquire model manipulation knowledge, stored in the form of schemata, and to refine these acquired schemata. In addition, we also address two issues that have so far been overlooked in the DSS literature: (1) to refine existing model representations as more experiences are accumulated and (2) to create model selection heuristics adaptive to the DSS environment.

An experimental investigation of turnover intentions among new entrants in it

Although much research has focused on factors driving the turnover behaviors of information technology (IT) professionals once they are in an employment relationship, little is known about their ex ante intentions to stay when embarking on a new employment relationship. The present study investigates the effects of individual and situational factors on the turnover intentions of new entrants into the IT workforce. Arguably, the IT revolution of the past decade, both at its peak as well as its decline, has changed the expectations and values of this population of workers considerably. Adopting an interactionist perspective, we examine the interaction between individual preferences for organizational risk and variety, and the level of entrepreneurial risk inherent in the business model of a given employer together with the amount of variety that IT work with that employer offers. The posited relationships are tested in a policy capturing experiment with graduating information systems majors as subjects. The results provide support for the interactionist perspective, while at the same time questioning its basic premise.

On the minimization of the weighted number of tardy jobs with random processing times and deadline

Abstract We consider a stochastic scheduling problem where a set of jobs with random processing times are sequenced on a single machine in order to minimize the total weighted number of jobs which finish after an exponentially distributed common deadline. A simple sequencing rule is identified which minimizes the expected weighted number of tardy jobs. Sufficient conditions are determined for the existence of a sequence which stochastically minimizes the total weighted number of tardy jobs, and a sequencing rule is derived which stochastically minimizes the total number of tardy jobs. The equivalence of static and dynamic sequencing policies is shown for a large class of processing time distributions.