Research Articles (Industrial and Systems Engineering)http://hdl.handle.net/2263/23742019-02-13T10:46:11Z2019-02-13T10:46:11ZDEMO and the story-card method : requirements elicitation for agile software development at scaleDe Vries, Marnehttp://hdl.handle.net/2263/681432019-01-16T01:08:33Z2018-10-01T00:00:00ZDEMO and the story-card method : requirements elicitation for agile software development at scale
De Vries, Marne
Enterprises of today are faced with rapidly changing technologies and customer needs within unpredictable environments that require a new mindset for creating an agile enterprise. Agile practices gained momentum within software development communities due to their speed-of-delivery and incremental value delivery. Yet, for software development projects at scale, theorists believe that stakeholders first need to have a common understanding of the enterprise operational context, sharing a common big picture as part of requirements elicitation. The design and engineering methodology for organizations (DEMO) encapsulates an organization construction diagram (OCD) that is useful for representing the enterprise operational context, i.e. removing unnecessary clutter of technology implementation detail. Theory indicates that abstract OCD concepts are concise and used in a consistent way. Yet, agile methodologies require models that encourage collaboration, are easy to understand and relate to a concrete world, rather than an abstract world. The main contribution of this article is to present a different means of introducing the OCD to software development stakeholders, relating abstract concepts of the OCD back to a concrete world. Using design science research, this study suggests and evaluates a story-card method that incorporates collaborative and easy-to-use technologies, i.e. sticky notes as story cards. Feedback from 21 research participants indicated that the story-card method indeed facilitated translation of a concrete world into more abstract (and concise) concepts of the OCD, also improving the possibility of adopting the OCD at an enterprise as a means to represent a common understanding of the enterprise operational context.
2018-10-01T00:00:00ZBayesian energy measurement and verification analysisCarstens, HermanXia, XiaohuaYadavalli, Venkata S. Sarmahttp://hdl.handle.net/2263/672712018-11-17T01:14:53Z2018-01-01T00:00:00ZBayesian energy measurement and verification analysis
Carstens, Herman; Xia, Xiaohua; Yadavalli, Venkata S. Sarma
Energy Measurement and Verification (M&V) aims to make inferences about the savings
achieved in energy projects, given the data and other information at hand. Traditionally, a frequentist
approach has been used to quantify these savings and their associated uncertainties. We demonstrate
that the Bayesian paradigm is an intuitive, coherent, and powerful alternative framework within
which M&V can be done. Its advantages and limitations are discussed, and two examples from the
industry-standard International Performance Measurement and Verification Protocol (IPMVP) are
solved using the framework. Bayesian analysis is shown to describe the problem more thoroughly
and yield richer information and uncertainty quantification results than the standard methods while
not sacrificing model simplicity. We also show that Bayesian methods can be more robust to outliers.
Bayesian alternatives to standard M&V methods are listed, and examples from literature are cited.
2018-01-01T00:00:00ZA comparative study of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models in distribution system with nondeterministic inputsOkwu, Modestus O.Adetunji, Olufemihttp://hdl.handle.net/2263/672602018-11-15T01:14:41Z2018-01-01T00:00:00ZA comparative study of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models in distribution system with nondeterministic inputs
Okwu, Modestus O.; Adetunji, Olufemi
Most deterministic optimization models use average values of nondeterministic variables as their inputs. It is, therefore,
expected that a model that can accept the distribution of a random variable, while this may involve some more computational
complexity, would likely produce better results than the model using the average value. Artificial neural
network (ANN) is a standard technique for solving complex stochastic problems. In this research, ANN and adaptive
neuro-fuzzy inference system (ANFIS) have been implemented for modeling and optimizing product distribution in a multiechelon
transshipment system. Two inputs parameters, product demand and unit cost of shipment, are considered
nondeterministic in this problem. The solutions of ANFIS and ANN were compared to that of the classical transshipment
model. The optimal total cost of distribution using the classical model within the period of investigation was 6,332,304.00.
In the search for a better solution, an ANN model was trained, tested, and validated. This approach reduced the cost to
4,170,500.00. ANFIS approach reduced the cost to 4,053,661. This implies that 34% of the current operational cost was
saved using the ANN model, while 36% was saved using the ANFIS model. This suggests that the result obtained from the
ANFIS model also seems marginally better than that of the ANN. Also, the ANFIS model is capable of adjusting the values
of input and output variables and parameters to obtain a more robust solution.
2018-01-01T00:00:00ZOn the capacitated step-fixed charge and facility location problem : a row perturbation heuristicOyewole, Gbeminiyi JohnAdetunji, Olufemihttp://hdl.handle.net/2263/668162018-10-10T04:04:38Z2018-09-01T00:00:00ZOn the capacitated step-fixed charge and facility location problem : a row perturbation heuristic
Oyewole, Gbeminiyi John; Adetunji, Olufemi
The Classical Transportation Problem (TP) Tableau which utilizes continuous variable cost has been used to model and solve distribution problems. However, many real distribution problem decisions which require various combination of fixed and variable cost and having several mixed variables of the binary integers and continuous types make this approach limited. This challenge requires new integrated models that are also NP hard for which exact algorithms such as Branch and bound, cutting plane algorithm may be inefficient to use as the problem size increases in practical business cases. We present in this paper, an integrated model of Facility Location (FL) and Step Fixed charge Transportation Problem (SFCTP). This problem is solved using a solution heuristic that utilizes relaxation and linearization approach to recast it to the classical TP as a starting solution. For the improved solution, a low cost and efficient perturbation heuristic that works in a row-wise manner is developed. We also propose a lower bound based on literature as a guide in achieving a solution. Lastly, a numerical example is presented to illustrate the procedures of the solution.
2018-09-01T00:00:00Z