This paper presents a new methodology for determining suboptimum relative location patterns for physical facilities. It presents a computer program governed by an algorithm which determines how relative location patterns should be altered to obtain sequentially the most improved pattern with each change, commands their alteration, evaluates the results of alterations, and identifies the sub-optimum relative location patterns. 3 The computer output yields a block diagramatic layout of the facility areas, and the areas need not be equal. 4 A manufacturing plant layout example is used because the methodology is well illustrated by reference to some kind of example, and this one is commonly known. The methodology itself, however, is general in nature and not restricted to such applications.

Abstract The article focuses on the differences between commercial and institutional accounting. Increasing importance of the role of higher education in the economy have provided a challenge to all members of the accounting profession. Much of the theoretical knowledge relative to commercial accounting practice must be reassessed when accounting for institutions of higher education. There is truly a separate and distinct set of generally accepted accounting principles for colleges and universities. At the same time, it is interesting to conjecture that as more emphasis is placed on the idea of dollar's worth for each dollar spent by colleges and universities, and with such measurement devices as performance budgeting, these non-profit organizations may be moving toward profit and loss applications. Meanwhile, as large business enterprises become more service oriented, they appear to be assuming trusteeship aspects similar to those in institutional accounting. Just as it may be true that institutional accounting can benefit from commercial accounting, the reverse is equally likely.

Abstract This article focuses on the role of certified public accountants (CPA) in management services. Demands on businesses have multiplied over the years as a result of the changing nature of competitive markets, rapid developments in technology of production equipment, availability and increased use of high-speed computers, expansion of knowledge in almost every field of learning, and a constant increase in the number, variety, and complexity of laws affecting business operations. Yet, in spite of all the encouragement given by professional organizations, the cries of business managers for more help and the cold statistics on the volume and lucrative rewards of the consulting business, many CPA have apparently not entered this field nor are they relatively frequently called upon by firms seeking consulting services. Some CPA firms are very active in rendering management services, but the number of firms who are is quite small. Further- more, revenue received by a CPA for management services is a very small portion of the total annual fees of $550 million paid by businesses for consulting work, as well as a small percentage of the total revenues realized by a CPA.

A linear programming problem is said to be stochastic if one or more of the coefficients in the objective function or the system of constraints or resource availabilities is known only by its probability distribution. Various approaches are available in this case, which may be classified into three broad types: ‘chance constrained programming’, ‘two-stage programming under uncertainty’ and ‘stochastic linear programming’. For problems of ‘stochastic linear programming’ a distinction is usually made between two related approaches to stochastic programming, the passive and the active approach respectively. In the passive approach to stochastic linear programming the statistical distribution of the optimum value of the objective function is estimated either exactly or approximately by numerical methods and optimum decision rules are based on the different characteristics of the estimated distribution. In the active approach, a new set of decision variables are introduced which indicate the proportions of different resources to be allocated to the various activities. One effect of introducing this set of new decision variables in the active approach is the truncation of the statistical distribution of optimal value of the objective function of the passive approach. In this aspect the active approach is useful in suggesting criteria for changing from one optimum decision rule to another, as the probability distribution of the objective function is specified less and less incompletely for different choices of the set of new decision variables. This paper investigates some mathematical relations between the active and passive approach and derives some inequalities for the case when the elements of the coefficient matrix of the inequalities are random variables. Since an ’approximate solution’ of a stochastic linear programming problem is defined usually by replacing each random element by its expected value and then solving the resulting non-stochastic program, some conditions have been derived under which the expected value of the objective function for the optimal solution will exceed the optimal value of the objective function for ’the approximate solution’. Based on the properties of the distribution of extreme values and other order statistics, some approximate bounds have also been specified for the passive approach, which can easily be extended to the active approach.

Marshall D. Ketchum, John C. Clendenin, Dorsey Richardson, Growth and Impact of Mutual Funds: Some Analytical and Public Policy Issues: Discussion, The Journal of Finance, Vol. 18, No. 2 (May, 1963), pp. 392-403