Modeling for Insight

Powell, Stephen G.; Batt, Robert J.

doi:10.1002/9780470387207

Cited by 20 publications

(5 citation statements)

References 21 publications

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“…Considering the fact that optimization depends not only on efficient and robust algorithms but also on good modeling techniques, careful interpretation of results and user-friendly software [18], we followed acknowledged principles of spreadsheet modeling and post-solution analysis [19,35,38,39,41]. This corresponds to a continuous effort for improving the conceptualization and formulation of the model, and using the model as a tool for gaining real insights into the problem.…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

“…In addition to the optimal solution obtained, the decision maker may also gain valuable insights into the initial problem and diverse instances thereof. Generally, spreadsheet modeling offers unique possibilities for extracting useful business information and creating valuable documentation, but requires well-informed programmers who would not miss such opportunities [38]. In this model, the decision-maker is able to see whether the constraints expressed as binding (i.e., hold as equalities) or nonbinding (i.e., not met exactly) inequalities and thus identify which of the potential limitations become actual limitations.…”

Section: Sensitivity Analysismentioning

confidence: 99%

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A Dynamic Multi-Period, Mixed-Integer Linear Programming Model for Cost Minimization of a Three-Echelon, Multi-Site and Multi-Product Construction Supply Chain

Koutsokosta

Katsavounis

2020

Logistics

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Quantifying the benefits of construction supply chain management through prescriptive models is a challenging and fast-growing research area that still lacks standardized optimization models with full integrative potential. In response to the needs and the peculiarities of the construction industry, this paper proposes an innovative model that merges temporal and project-based supply chains into a sustainable network with repetitive flows, large scope contracts, strategic alliances and economies of scale. It is a dynamic mixed-integer linear programming model for cost minimization of a three-echelon supply chain serving multiple sites with multiple products over a time horizon. Its novelty lies in yielding optimal decisions on network design, product quantities to be purchased and transported, shipments and inventory levels in all echelons under any logistics system in a multi-period, multi-product and multi-project environment with discount schemes and strategic preferences. The model is general enough to be implemented by any general contractor acting as a system integrator but also allows customization with logical constraints. All these features constitute an innovative, versatile and flexible managerial decision making tool. Model implementation is based on a spreadsheet optimization software and is followed by post-solution analysis, sensitivity analysis and multiple parameterized optimizations.

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Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Section: Sensitivity Analysismentioning

confidence: 99%

A Dynamic Multi-Period, Mixed-Integer Linear Programming Model for Cost Minimization of a Three-Echelon, Multi-Site and Multi-Product Construction Supply Chain

Koutsokosta

Katsavounis

2020

Logistics

View full text Add to dashboard Cite

show abstract

“…Following steps of [16], in particular, this study chooses the GRG Non-Linear method as an estimation method. According to [17], the existing values in the worksheet cells for each decision variable are taken as an initial solution such that any small change will improve the objective value. In this way, the objective value will increase if the objective is maximization, or decrease if the objective is minimized until it achieves optimal solution.…”

Section: Estimation and Selection A Parameter Estimationmentioning

confidence: 99%

Evaluating the Fitting Performance of AGARCH(1,1), NAGARCH(1,1), and VGARCH(1,1) Models

Nugroho,

Ningtyas,

Parhusip

2023

IJCSAM

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This study compares the performance of the GARCH(1,1), AGARCH(1,1), NAGARCH(1,1), and VGARCH(1,1) models fitted to real data. The observed real data are the USD exchange rate against IDR in the daily period from January 2010 to December 2017. To identify the superiority and evaluate the performance of those models in capturing the heavy-tailed and skewed character in exchange rate distribution, the return error is assumed to be the Normal, Skew Normal (SN), Skew Curved Normal (SCN), and Student-t distributions. The model's parameters are estimated using the GRG Non-Linear method in Excel Solver and the ARWM method in the MCMC scheme implemented in the Scilab program. Estimation results using Excel's Solver have similar values to the estimates obtained using MCMC, concluding that Excel's Solver has a good ability in estimating the model's parameters. Based on AIC values, this study concludes that the NAGARCH(1,1) model under Student-t distribution performs the best.

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“…where q i,d is the visual quality of the listed apartment, qi,d,v is the model's measure of the apartment's descriptive quality in the ordinal form and A i,d,v is the test parameter checking, whether the descriptive quality matches the visual quality of the apartment. The following (4) The optimization procedure has been conducted with the use of an evolutionary algorithm (Powell & Batt, 2008). Figure 4 presents the visualisation of the calibration phase procedure.…”

Section: Models' Calibration Phasementioning

confidence: 99%

Quality information gaps in housing listings: Do words mean the same as pictures?

Hebdzyński

2023

J Hous and the Built Environ

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The incomplete or defective information on the listed housing quality may impair the market clearing processes, may lead to the adverse selection and would have its reflection in the data needed to perform market analyses. To pinpoint the existing information gaps, this paper inspects the quality-related information disclosure strategies of agents who list apartments for sale or rent in online listing platforms. The first part of the empirical study compares the direct declarations of the listed housing quality with the quality indirectly signaled via photos attached to listings and shows that there extists a discrepancy between them. Combined with the high share of observations, for which the full information on quality is not being disclosed it contributes to the existence of informations gaps. The second part of the study answers, whether indirect textual quality signals (descriptions of apartments), processed with the use of Wordscores—a supervised machine learning algorithm are consistent with visual quality signals. It has been documented, that the sales listings’ quality signals have agreed in 63–90% of cases, depending on the model’s variant. For the rental market, the descriptive quality signals have matched the visual ones in 71–83% of cases. Moreover, it has been shown that among rental listings the consistency has been higher for listings posted by landlords, while for sales listings—by brokers. Finally, it may be argued that to fill the existing information gaps one may utilize the often-unused information conveyed via textual quality-signalling channel.

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Modeling for Insight

Cited by 20 publications

References 21 publications

A Dynamic Multi-Period, Mixed-Integer Linear Programming Model for Cost Minimization of a Three-Echelon, Multi-Site and Multi-Product Construction Supply Chain

A Dynamic Multi-Period, Mixed-Integer Linear Programming Model for Cost Minimization of a Three-Echelon, Multi-Site and Multi-Product Construction Supply Chain

Evaluating the Fitting Performance of AGARCH(1,1), NAGARCH(1,1), and VGARCH(1,1) Models

Quality information gaps in housing listings: Do words mean the same as pictures?

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