Muzaffar Shaikh

A structured approximate reasoning-based approach for gathering “lessons learned” information from system development projects

In this paper, a structured approach for collecting “lessons learned” information obtained from system development projects is discussed. To aid in the analysis of lessons learned, the collection framework incorporates techniques associated with approximate reasoning. The primary goal of collecting lessons learned is to improve organizational and project performance. Therefore, this approach concentrates on focusing the collection of lessons learned on this goal. This approach ties the collection of lessons learned to the process model used by the organization. This model is typically based on capability models such as the Systems Engineering Capability Maturity Model (SE-CMM1) and Software CMM (SW-CMM), as well as process standards such as ISO 9000 and the organizations standard engineering practices. This approach utilizes the organizations process framework to provide a structured, lessons learned program which can be used by projects during and at the completion of their execution.

A 'peak shaving' approach to project staff reallocation

Abstract This paper presents an analytical model, which recomputes monthly staffing profile by truncating the original peak allocation and staffing the project before its actual start date. This staff leveling approach, which assumes the same completion date as the original one, has the benefit of avoiding delays resulting from small to no staff allocated at the scheduled start date. The approach uses the Lead Time concept from Inventory Theory to first compute the person-month value needed by the project under study at the scheduled start date. Next, it reduces (‘shaves’) the peak using an analytical equation from the original staffing allocation and adjusts the remaining person-month values by keeping the target date constant. The presented model was actually implemented in an industrial setting, but due date sensitivity, both the firm name and actual data are disguised.

Project schedule recomputation after risk inclusion

In this paper, based on the assumption that the project schedule has already been calculated prior to risk impact, a statistical method is presented to recompute the expected schedule, its standard deviation, and 95% confidence for the true but unknown schedule. The confidence interval can serve to scientifically establish the upper and lower bounds for the schedule (and the cost) for use in, for example, the fee calculation of cost-plus-fixed-fee type projects. Even though the discussion in this paper is limited to project schedule matters, the method can be applied to project cost recomputation as well.

Minimization of drilling costs: a closed-form solution

Abstract This paper discusses a cost minimization model which finds a closed-form solution for the optimal penetration rate in a drill operation. This work is an extension of a previous empirical model developed by the authors in which the penetration rate was varied over the allowable range to determine the minimum drill operation cost. In this paper, we propose a specific tool life function in which the tool life is assumed to diminish in a negative exponential manner with the increase in the drill penetration rate.

Efficient simulation experiments for comparing communication network routing algorithms

In this paper, we propose efficient statistical designs of simulation experiments and statistical methods for systematically comparing communica tion network routing algorithms via simulation. The design of experiments technique establishes the minimum number of observations or simulation runs without losing much information needed to gain insight into the real world system. Statistical models use this efficient set of observations in providing (1) insight into the intrinsic behavior of the algorithm (i.e., sensitivity of the algorithm to changes in factors), (2) a statistical basis for com paring alternate algorithms that perform common functions, and (3) predictor equations that can be used to predict algorithm performance for the user- specified values of influencing factors. The empha sis in the paper is on the use of statistical testing and comparison methods in a simulation setting and not on the nature of algorithms themselves. These methods should pave the way for perform ance analysis of other algorithms as well. The testing environment includes a parameter- driven simulator which has the capability to modularly include alternate routing algorithms one at a time. The model generates a threat scenario which transforms into the generation of appropriate messages. The impact analysis of five influencing factors on three measures of perform ance is proposed.

A proposed testbed for evaluating adaptive routing algorithms

Abstract This paper discusses a newly-designed tool for evaluating routing algorithms in computer-communication networks. It is called the Adaptive Routing Testbed (ART). When implemented, ART will provide an efficient, effective means for evaluating the performance of routing algorithms in point-to-point networks. The method of simulation was chosen for this tool due to its advantages over analytical methods for this application. A major contribution of ART is its flexibility. It will simulate and evaluate the performance of a wide variety of routing algorithms under any valid network topology, switching mechanism, and traffic load. It will also simulate variations in the topology and traffic load while the algorithm is operating. Another significant contribution of ART is that the flexibility is provided without requiring the user to have programming skills. This is accomplished through a fully parameter-driven design. The specifications of the networks, traffic, component life, and routing algorithms are based on assigning values to parameters. The testbed will apply rules of logic when querying the user for parameter values: the next set of vlaues requested will be based on previous responses.