Optimizing a Law School’s Course Schedule

[Excerpt] “Just like other educational institutions, law schools must schedule courses by taking into consideration student needs, faculty resources, and logistical support such as classroom size and equipment needs. Course scheduling is an administrative function, typically handled by an Assistant Dean or an Associate Dean, who works with the faculty and the registrar to balance these considerations in advance of the registration process. Usually, the entire academic year is scheduled in advance, although the spring semester may be labeled tentative until registration begins for that semester. It’s hard to imagine, but some schools even publish a two-year schedule of upper-division courses so that students can plan their entire law school career in advance. In order to give assistance to those academics involved for the first time in the scheduling process, this article discusses the law school scheduling process and how a scheduling software package has worked to successfully automate what has been seen as one of the most abysmal administrative tasks of an Associate Dean. We first provide a background to course scheduling at a typical law school. We then present a review of the tools for, and literature on, course scheduling, followed by a discussion of how technology can be applied to course scheduling in general, and our outcomes of applying this technology in a law school environment. We close with a brief summary.

Dedicated or Combinable? A Simulation to Determine Optimal Restaurant Table Configuration

Using a computer simulation, one can determine what the optimum table arrangement would be for restaurants of various sizes that accept walk-in customers only and take no reservations. At issue is whether the restaurateur can gain more revenue when its tables are dedicated to seating parties of specific sizes (for example, parties of one and two people would be served at 2-tops, while parties of one to four people would be served at 4-tops) or whether the restaurant should use tables that can be combined as needed according to party size. The simulation predicted that combinable tables would prove most useful in a small restaurant with a small average party size. Combining tables in that situation increased revenue per available seat hour by about 2 percent compared to having only dedicated tables. In a large restaurant or any restaurant with a large average party size, the simulation found that dedicated tables were superior to combinable tables. A loss in productivity occurs when some number of tables are held out of service until adjacent tables become available (so that the tables can be combined to seat a large party). The simulation found that the most efficient approach is for a restaurant’s table-size mix to match its customer party size mix, since doing so increases the restaurant’s effective customer-service capacity. However, that customer mix cannot always be known before a restaurant is constructed, and that mix might change during different dayparts. Moreover, the simulation makes certain assumptions that may need further examination, and it does not take into account such aesthetic factors as customers’ reactions to a particular restaurant layout

Restaurant Capacity Effectiveness: Leaving Money on the Tables

In fall 2005 The Center for Hospitality Research (CHR) at Cornell University released the Restaurant Table Mix Optimizer (or RTMO), which I developed. This tool identifies the best mix of tables for a restaurant, based on a variety of inputs. The tool itself is web-based, with the CHR storing users\u27 data anonymously in a database. As of mid March 2007, a total of 1,543 people had registered to use the RTMO. However, not all of those registrants created a valid table-mix scenario. With unusable scenarios eliminated, the final study analyzed the table mixes of 68 restaurants. While eight of the restaurants had the actual optimum table mix for peak operating times, the other 60 restaurants were leaving some money on the table. That is, most restaurants could improve their table mix. On average, the restaurants in this sample could increase their peak revenue by almost 15 percent by implementing a more effective table mix. Almost one-fifth of the restaurants in this sample could improve revenue by more than 20 percent just by having the appropriate mix of right-size tables

Workforce Scheduling: A Guide for the Hospitality Industry

Creating a workforce schedule that ensures appropriate service levels is a key management function. The many complexities of scheduling can be captured through a process that comprises the following four major steps: (1) forecasting demand, (2) translating the demand forecast into employee requirements, (3) scheduling the employees, and (4) controlling the schedule as the day unfolds. Each of those steps involves its own set of tasks. To create a forecast, a manager must determine what needs to be done to meet the expected demand for a given planning period. While a planning period may be of any duration, a 15-minute period is an effective one to use. In particular, the manager must identify the demand drivers and assess whether they are time variant (that is, variable over short periods) or time invariant (relatively stable over short periods). Another part of the forecasting step is determining the tasks to be done in a given period. Some of the tasks (notably, those involving direct customer service) are uncontrollable, because they must be done on the spot. Other tasks, though, such as side work, are controllable because they can be performed at any time (within reason). Having created a fairly reliable estimate of demand, the manager must next translate that demand into the number of workers needed, using an economics-based labor standard. At this point, the manager is ready to construct a schedule that will do the best job of deploying the staff to achieve the desired economic standards without overstaffing and inflating costs. Scheduling is subject to hard constraints, or factors that must be addressed (such as the number of hours an employee can work in a day), and soft constraints, or factors that are desirable in a schedule but not essential (such as employees’ desires for when they work and what tasks they perform). Having created a schedule that will meet the economic standards within the constraints, a manager must finally monitor and fine tune the schedule as the day goes on. Most critically, the manager must decide early on whether the demand estimate for the day is correct—meaning the staffing levels will be sufficient—or whether the actual demand is different from the estimate. If the demand estimate proves incorrect, the manager must further decide whether to take such long-lived actions as calling in workers to take care of a big day (or send them home if business has died off ) or merely take a short-lived action (such as sending employees on break) to account for momentary fluctuations in actual demand. Computer applications can assist managers in most of the workforce-scheduling tasks, but a manager needs to understand the process if only to judge whether the application in question is providing solutions that are reasonably close to the optimal schedule

Managing Service Operations Based on Customer Preferences

This article presents the results of a study using discrete choice analysis (DCA) in the dine-in pizza industry. DCA offers an effective approach for incorporating customer preferences into operating decisions in service businesses. Our results show how customers tradeoff among several determinant attributes (e.g. price, waiting time, quality) when choosing a dine-in pizza restaurant. The article also offers evidence that managers\u27 perceptions of customer choice patterns are not the same as customers\u27 actual choice patterns for the businesses we examined. Finally, we show how our results can be easily incorporated into a decision support system for structuring service operations according to customer preferences

Social Media Use in the Restaurant Industry: A Work in Progress

A survey of 166 restaurant managers reveals a mixed picture in their use of social media and its impact on operations. Although many restaurants are using social media, the study found that many restaurateurs lack well-defined social media goals, both in terms of the purpose of the restaurants’ social media activities and the target of their social media messages. Although the restaurant operators in this convenience sample were generally supportive of the use of social media, well over half were not certain that social media met one or more of three specific goals, namely, increasing customer loyalty, bringing in new customers, and boosting revenues. The respondents generally rely more heavily on non-financial metrics than on actual financial numbers to measure the return on their social media investment, due to the large degree of uncertainty surrounding how to measure the financial returns of social media on operations. On balance, independent restaurants made more use of social media than did chains. The study’s findings suggest that restaurateurs should reevaluate their social media approaches to ensure that they are strategically designed and executed

Optimizing a Law School\u27s Course Schedule

If you have ever attempted to prepare a law school class schedule—juggling curricular needs, classroom sizes, professorial whims—you will know how hard a task is involved. If you bother the person in charge of the schedule too much, he or she might unleash the powers of the scheduler upon you. Next year you may find yourself teaching “Legal Spelling” on Saturday mornings at 8:00 A.M

Observations of ozone production in a dissipating tropical convective cell during TC4

From 13 July–9 August 2007, 25 ozonesondes were launched from Las Tablas, Panama as part of the Tropical Composition, Cloud, and Climate Coupling (TC4) mission. On 5 August, a strong convective cell formed in the Gulf of Panama. World Wide Lightning Location Network (WWLLN) data indicated 563 flashes (09:00–17:00 UTC) in the Gulf. NO2 data from the Ozone Monitoring Instrument (OMI) show enhancements, suggesting lightning production of NOx. At 15:05 UTC, an ozonesonde ascended into the southern edge of the now dissipating convective cell as it moved west across the Azuero Peninsula. The balloon oscillated from 2.5–5.1 km five times (15:12–17:00 UTC), providing a unique examination of ozone (O3) photochemistry on the edge of a convective cell. Ozone increased at a rate of 1.6–4.6 ppbv/hr between the first and last ascent, resulting cell wide in an increase of (2.1–2.5)×106 moles of O3. This estimate agrees to within a factor of two of our estimates of photochemical lightning O3 production from the WWLLN flashes, from the radar-inferred lightning flash data, and from the OMI NO2 data (1.2, 1.0, and 1.7×106 moles, respectively), though all estimates have large uncertainties. Examination of DC-8 in situ and lidar O3 data gathered around the Gulf that day suggests 70–97% of the O3 change occurred in 2.5–5.1 km layer. A photochemical box model initialized with nearby TC4 aircraft trace gas data suggests these O3 production rates are possible with our present understanding of photochemistry

Optimizing a Personal Wine Cellar

This report takes what we believe to be the first scientific approach to optimizing a personal wine cellar. We identify the key factors related to optimizing a personal cellar: performance metrics, such as drinking the best possible wine; constraints, such as budget and cellar capacity; and decisions, specifically what to buy and when to consume the purchased wines. We describe the Personal Wine Cellar Optimizer, which is a tool designed to identify the optimum cellar management plan. Using scenarios differing in cellar capacity, cellar life, and wine budget, we examine how the constraints affect the optimal cellar management plan. Using an example of a real cellar, we also illustrate how the recommendations can be used to improve the cellar management. This report is cosponsored by The Vance A. Christian Beverage Management Center, Cornell University School of Hotel Administration

Mid-infrared quantum optics in silicon

Applied quantum optics stands to revolutionise many aspects of information technology, provided performance can be maintained when scaled up. Silicon quantum photonics satisfies the scaling requirements of miniaturisation and manufacturability, but at 1.55 $\mu$m it suffers from unacceptable linear and nonlinear loss. Here we show that, by translating silicon quantum photonics to the mid-infrared, a new quantum optics platform is created which can simultaneously maximise manufacturability and miniaturisation, while minimising loss. We demonstrate the necessary platform components: photon-pair generation, single-photon detection, and high-visibility quantum interference, all at wavelengths beyond 2 $\mu$m. Across various regimes, we observe a maximum net coincidence rate of 448 $\pm$ 12 Hz, a coincidence-to-accidental ratio of 25.7 $\pm$ 1.1, and, a net two photon quantum interference visibility of 0.993 $\pm$ 0.017. Mid-infrared silicon quantum photonics will bring new quantum applications within reach.Comment: 8 pages, 4 figures; revised figures, updated discussion in section 3, typos corrected, added referenc