No abstract

Deregulation has opened up many opportunities and challenges in the transportation industry—opportunities to increase profits and challenges to keep from being outflanked by competition. A goal of particular interest to the scheduled airlines is to set prices more adaptively and to change them more rapidly. A difficult problem arises when many passengers with different itineraries compete for a limited number of seats on a single-flight segment. The problem is complicated by the existence of different fare classes, many flight segments, and different demands across time. For any given set of prices, flight-segment capacities, and passenger-carrying demand, there is some number of passengers at each fare class on each flight segment that will optimize revenue. Knowledge of such an optimum can be used not only in pricing analysis but also in setting policies to influence the passenger fare-class mix so that the optimum will be more nearly achieved in actual practice. We describe a method for identifying the optimum fare-class mix and the design of a system for that purpose which we built and implemented for Frontier Airlines. The recognition and formulation of the problem has become even more important as the number of aircraft in the sky has been reduced and the competition for a limited number of seats has become more intense.

By using sites for the restriction nuclease Hpa II, the information for the anticodon stem and loop of an altered Su+2 amber suppressor tRNA (a mutant of tRNAG-n) has been transplanted to a specially prepared tRNATrP gene, which lacks its homologous anticodon stem and loop sequence. The resulting tRNA gene was cloned under lac operator-promoter control. The result is a functional, hybrid, amber-suppressor tRNA that can exhibit a moderately high efficiency in translation. It appears less efficient, however, than Su+7 tRNA, the amber suppressor that results from a direct anticodon mutation in tRNATrP. As judged by its suppressor spectrum, which is almost identical to the spectra of Su+2 and Su+7, the recomposed tRNA inserts glutamine at amber sites. This experiment is the prototype of a series of constructions that examine the role of the nucleotides in the anticodon region.

No abstract

We describe a system for the automatic scheduling of employees in the particular setting in which: the number of employees wanted on duty throughout the week fluctuates; the availabilities of the employees varies and changes from week to week; and a new schedule must be produced each week, by virtue of the changing demand for service. The problem which we address appears in a variety of settings, including: airline reservation offices; telephone offices; supermarkets; fast food restaurants; banks and hotels. Previous approaches to the problem have relied chiefly on formal methods, generally involving one or another variation of linear or integer, mathematical programming. We suggest that except in cases involving very small problems (only a handful of employees) that those approaches have not proven promising, especially where union rules and management requirements impose complex constraints on the problem, and that a heuristic approach has proven to be substantially superior. We set forth the general features of our heuristic approach, which we see as an application of artificial intelligence; we show how, in contrast to other approaches, which design shifts as if employees were always available and try to fit those shifts to employees who are not always available, our system design shifts with deference to the employees' limited availabilities; we suggest that, for a given service level, our system produces schedules with a better “fit”—number of employees actually on duty comparing more favorably with the number wanted; and we state that while, for a given service level, a ‘manual scheduler’ may take up to 8 hours each week to prepare a good schedule, our system, on most micro computers, routinely produces better schedules involving up to 100 employees in about 20 minutes. The scheduling of employees is generally considered to be a managerial function, in the setting of the problem we address. When a craft employee is replaced on an assembly line by a machine which performs the same function, we speak of the replacing mechanism as an industrial robot. We suggest that systems like that which we describe deserve a name, to distinguish them from comparable, computer based systems which do not replace, but rather supplement a manager, and we suggest the name ‘managerial robot’ for such systems. We set forth the characteristics which we feel would justify applying the term ‘managerial robot’ to a computer based system, and suggest that classification is basic to understanding and communication and that just as terms such as decision support systems and expert systems prove useful in our increasingly advanced, technological society, so also the term managerial robot has a place in our scheme of things. Decision support systems do not qualify as managerial robots for the reason that managerial robots don't simply support the decision making process, but rather replace the manager in his performance of a function which, when performed by a human being, is considered a managerial function. Nor do we consider ...