Shortest Route: A Mobile Application for Route Optimization using Digital Map

Businesses that have embarked on using digital maps have been able to increase employee productivity, communicate visually; reduce cost of logistics, planning, resources by more than half of its initial cost. Many industries that have benefitted from this technology include Online Markets, Delivery companies, Agriculture, Real Estate, Engineering, Media, Energy and Utilities, Insurance, Architecture. Seeing this need especially in Nigeria where cost of logistics is high, resources are wasted in the process and productive time is also wasted leading to fatigue and low outcome; there is therefore the need for route optimization for businesses in Nigeria. TSP (Travelling Salesman Problem) - Nearest Neighbour Algorithm is used to solve the problem of route optimization on Google MAP. This study developed a mobile application in Java, HTML and Google SDKs, to find shortest route between various numbers of locations enumerated on digital maps on a smart device. The application was implemented successfully on the Android Operating System for mobile devices. Anyone can download it from the Google play store, install and freely use

Data-driven satisficing measure and ranking

We propose an computational framework for real-time risk assessment and prioritizing for random outcomes without prior information on probability distributions. The basic model is built based on satisficing measure (SM) which yields a single index for risk comparison. Since SM is a dual representation for a family of risk measures, we consider problems constrained by general convex risk measures and specifically by Conditional value-at-risk. Starting from offline optimization, we apply sample average approximation technique and argue the convergence rate and validation of optimal solutions. In online stochastic optimization case, we develop primal-dual stochastic approximation algorithms respectively for general risk constrained problems, and derive their regret bounds. For both offline and online cases, we illustrate the relationship between risk ranking accuracy with sample size (or iterations).Comment: 26 Pages, 6 Figure

Calibration of Distributionally Robust Empirical Optimization Models

We study the out-of-sample properties of robust empirical optimization problems with smooth $\phi$-divergence penalties and smooth concave objective functions, and develop a theory for data-driven calibration of the non-negative "robustness parameter" $\delta$ that controls the size of the deviations from the nominal model. Building on the intuition that robust optimization reduces the sensitivity of the expected reward to errors in the model by controlling the spread of the reward distribution, we show that the first-order benefit of ``little bit of robustness" (i.e., $\delta$ small, positive) is a significant reduction in the variance of the out-of-sample reward while the corresponding impact on the mean is almost an order of magnitude smaller. One implication is that substantial variance (sensitivity) reduction is possible at little cost if the robustness parameter is properly calibrated. To this end, we introduce the notion of a robust mean-variance frontier to select the robustness parameter and show that it can be approximated using resampling methods like the bootstrap. Our examples show that robust solutions resulting from "open loop" calibration methods (e.g., selecting a $90\%$ confidence level regardless of the data and objective function) can be very conservative out-of-sample, while those corresponding to the robustness parameter that optimizes an estimate of the out-of-sample expected reward (e.g., via the bootstrap) with no regard for the variance are often insufficiently robust.Comment: 51 page

The Impact of a Target on Newsvendor Decisions

Goal achieving is a commonly observed phenomenon in practice, and it plays an important role in decision making. In this paper, we investigate the impact of a target on newsvendor decisions. We take into account the risk and model the effect of a target by maximizing the satisficing measure of a newsvendor’s profit with respect to that target. We study two satisficing measures: (i) conditional value at risk (CVaR) satisficing measure that evaluates the highest confidence level of CVaR achieving the target; (ii) entropic satisficing measure that assesses the smallest risk tolerance level under which the certainty equivalent for exponential utility function achieves the target. For both satisficing measures, we find that the optimal ordering quantity increases with the target level. We determine an optimal order quantity for a target-based newsvendor and characterize its properties with respect to, for example, product’s profit margin

Theoretical and experimental examinations of target-based decision making

Target based decision making occurs when a decision maker wishes to maximize the probability of attaining some output or performance level. The target may be set externally, for example, by an employer as part of an incentive system. Or, the target may simply be based on a decision maker’s individual goals and understanding of implicit expectations. Questions arise concerning the effects of these decisions on the performance of an organization. Further, if these decisions affect organizational value, how well can these decisions be characterized and predicted? This research explores these questions—and others—using both theoretical and experimental means. A value gap model is developed that facilitates simulation based insights to target optimality. The effect of a target is characterized in terms of a value gap that is defined as the difference in value between what was selected based on a target and what the organization would have preferred. A copula based method is used to simulate future decision situations and the expected value gap is calculated as a function of model parameters. Several trends in target optimality are observed that are robust to changes in the probability distributions over future decision alternatives. Specifically, the optimal target (i) decreases as the organization’s risk aversion increases, (ii) increases as the number of available alternatives increase, and (iii) the presence of an efficient frontier of decision alternatives reduces the consequences of setting targets higher than optimal. A behavioral experiment is conducted to compare target-based decision making to decisions in the absence of a target. The results show that while target based decision making can be well predicted based on the properties of the decision alternatives alone. Decisions in the absence of a target, however, cannot be predicted based on the alternatives alone. Information about individualized differences in risk preferences is required to identify trends in the decision making behavior. These results have strong implications for decisions about whether or not to used target based incentives within an organization. The research concludes with an application to engineering systems and a discussion of additional questions raised by the research that point to directions for new research

Aspirational preferences and their representation by risk measures

10.1287/mnsc.1120.1537Management Science58112095-2113MSCI