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SCARA with Path trajectory
No full textThe following Matlab project contains the source code and Matlab examples used for SCARA with Path trajectory. By defining the initial position and final position the robot will follow the path between these two point
Assessment of patient classification in appointment system design
No full textDue to copyright restrictions, the access to the full text of this article is only available via subscription.This paper investigates two approaches to patient classification: using patient classification only for sequencing patient appointments at the time of booking and using patient classification for both sequencing and appointment interval adjustment. In the latter approach, appointment intervals are adjusted to match the consultation time characteristics of different patient classes. Our simulation results indicate that new appointment systems that utilize interval adjustment for patient class are successful in improving doctors' idle time, doctors' overtime and patients' waiting times without any trade-offs. Best performing appointment systems are identified for different clinic environments characterized characterized by walk-ins, no-shows, the percentage of new patients, and the ratio of the mean consultation time of new patients to the mean consultation time of return patients. As a result, practical guidelines are developed for managers who are responsible for designing appointment systems
Private-label use and store loyalty
Full text linkThe authors develop an econometric model of the relationship between a household’s private-label (PL) share and its behavioral store loyalty. The model includes major drivers of these two behaviors and controls for simultaneity and nonlinearity in the relationship between them. The model is estimated with a unique data set that combinescomplete purchase records of a panel of Dutch households with demographic and psychographic data. The authorsestimate the model for two retail chains in the Netherlands the leading service chain with a well-differentiatedhigh-share PL and the leading value chain with a lower-share PL. They find that PL share significantly affects all three measures of behavioral loyalty in the study: share of wallet, share of items purchased, andshare of shopping trips. In addition, behavioral loyalty has a significant effect on PL share. For the service chain, the authors find that both effects are in the form of an inverted U. For the value chain, the effects are positive and nonlinear, but they do not exhibit nonmonotonicity, because PL share has not yet reached high enough levels. The managerial implications of this research are important. Retailers can reap the benefits of a virtuous cycle; greater PL share increases share of wallet, and greater share of wallet increases PL share. However, this virtuous cycle operates only to a point because heavy PL buyers tend to be loyal to price savings and PLs in general, not to the PL of any particular chain.pre-prin
Optimal ambulance location with random delays and travel times
Full text linkWe describe an ambulance location optimization model that minimizes the number of ambulances needed tonprovide a specified service level. The model measures service level as the fraction of calls reached within a given time standard and considers response time to be composed of a random delay (prior to travel to the scene) plus a random travel time. In addition to modeling the uncertainty in the delay and in the travel time, we incorporate uncertainty in the ambulance availability in determining the response time. Models that do not account for the uncertainty in all three of these components may overestimate the possible service level for a given number of ambulances and underestimate the number of ambulances needed to provide a specified service level. By explicitly modeling the randomness in the ambulance availability and in the delays and the travel times, we arrive at a more realistic ambulance location model. Our model is tractable enough to be solved with general-purpose optimization solvers for cities with populations around one Million. We illustrate the use of the model using actual data from Edmonton.Natural Sciences and Engineering Research Council of Canadapre-prin
Optimal solution of the discrete cost multicommodity network design problem
No full textDue to copyright restrictions, the access to the full text of this article is only available via subscription.We investigate a multicommodity network design problem where a discrete set of Technologies with step-increasing cost and capacity functions should be installed on the edges. This problem is a fundamental network design problem having many important applications in contemporary telecommunication networks. We describe an exact constraint generation approach and we show that the conjunctive use of valid inequalities, bipartition inequalities that are generated using max-flow computations, as well as an exact separation algorithm of metric inequalities makes it feasible to solve to optimality instances with up to 50 nodes and 100 edges
Ambulance location for maximum survival
Full text linkThis article proposes new location models for emergency medical service stations. The models are generated by incorporating a survival function into existing covering models. A survival function is a monotonically decreasing function of the response time of an emergency medical service (EMS) vehicle to a patient that returns the probability of survival for the patient. The survival function allows for the calculation of tangible outcome measures—the expected number of survivors in case of cardiac arrests. The survival-maximizing location models are better suited for EMS location than the covering models which do not adequately differentiate between consequences of different response times. We demonstrate empirically the superiority of the survival-maximizing models using data from the Edmonton EMS system.NSERCpre-prin