Basil Kalymon

• Kalymon, B., 1981, "Methods of Large Project Assessment Given Uncertainty in Future Energy Pricing", Management Science, April 27(4): 377 - 395.
  Abstract: The continuing oil crisis has created opportunities for developing sources of oil that had only been marginally economic previously. Many such projects, however, require large-scale investments that are recoverable only over very long lifetimes of operation. The profitability of such investments depends critically on the long-run pricing of oil in world markets and the interaction of the economic conditions of consuming nations. This paper develops a framework for analyzing the economics of such projects. Under the conventional assumption that appropriate levels of compensation for the required capital are needed before such large-scale projects are undertaken, the analysis focuses on the rate of return required to induce the large-scale capital expenditures. Both systematic and nonsystematic risk exposure of such projects are identified and the critical linkage to world oil markets is defined through a simple market of world oil pricing. The understanding of the nature of project risks is critical to the development of government policy. The risk exposure of investors must be understood both for the development of risk reduction measures and for the establishment of suitable guidelines for capital compensation. The role of governments in energy investment has escalated and the implications for investments by the private sector require explicit recognition and study. It is shown that the traditional theory of risk pricing through the capital asset pricing model generally leads to conclusions that appear to be contradicted by observed investor behavior. Explanation and understanding of the nature of project risks incurred by the large-scale energy project must rely on more complex, disequilibrium models of risk determination. To illustrate the methodology developed, the paper considers investment in the Canadian tar-sands projects in Western Canada. A sensitivity analysis was performed on the rate of return of this project using simulation. The project was described in terms of both a world oil market and a taxation environment. For the range of conditions investigated, the rate of return was found to range between 4 and 20%, with a mean of 14.3% and a standard deviation of 3.3%.
• Kalymon, B., 1975, "An Optimization Algorithm for a Linear Model of a Simulation System", Management Science, January 21(5): 516 - 530.
  Abstract: This paper explores the normative theory of simulation within the context of an optimization algorithm for a linear programming model of the experimental setting. The simulation is viewed as a random generalized mathematical function which provides an uncertain evaluation of any policy within a specified constraint set. A sequential experimental design aimed at identifying the feasible levels of the policy variables which provide the optimal level of expected response is presented and analyzed. The results of numerical tests of the proposed procedure are discussed.
• Kalymon, B., 1972, "A Decomposition Algorithm for Arborescence Inventory Systems", Operations Research, August 20(4): 860 - 874.
  Abstract: This paper develops an algorithm for solving an arborescence-structured production and inventory system. Arborescence structures model multiechelon production systems in which each facility requires input from a unique immediate predecessor. Assuming known demands, and no backlogging, the objective is to schedule production over a finite planning horizon to minimize production and holding costs. The algorithm is applicable to systems in which, at all facilities with followers in the system, the production costs consist of a set-up charge plus linear costs and holding costs are linear. General costs are permitted at the lowest-echelon facilities (those without followers) with special computational efficiency resulting when these costs are concave. The algorithm exploits the known results on the structure of optimal policies in arborescence systems to decompose the problem into single-stage problems at each lowest-echelon facility. This decomposition is achieved by enumerating implicitly the feasible production set-up patterns at facilities with followers. As a result, the computational effort might increase exponentially with the number of facilities with followers, but is increasing only linearly with the number of lowest-echelon facilities in the system.
• Kalymon, B., 1972, "Machine Replacement with Stochastic Costs", Management Science, January 18(5): 288 - 298.
  Abstract: The machine replacement model studied assumes that, at the end of each discrete interval, the state of deterioration of a machine and the current cost of replacement become known. A decision to keep or to replace must then be made, given that future deterioration and costs are determined by a known Markovian process. A finite set of possible states of deterioration is considered, including a 'failed' state at which replacement by a 'new' machine must occur. The operating cost is an increasing function of the level of deterioration, and replacement cost is the difference between new machine cost and salvage value. For both a finite and infinite planning horizon, given current cost, the optimality of a 'control level' policy is demonstrated. Linear programming and policy iteration methods exploiting problem structure for the calculation of optimal policies are derived.
• Kalymon, B., 1971, "Bond Refunding with Stochastic Interest Rates", Management Science, November 18(3): 171 - 183.
  Abstract: The bond refunding problem is formulated as a multiperiod decision process in which future interest rates are determined by a Markovian stochastic process. It is assumed that a single bond is to be outstanding at a given time. Given the future requirements for debt financing, the decision maker must decide whether to keep his current bond or to refund by issuing a new bond at the current market interest rates. Over a finite planning horizon, the structure of policies which minimize expected total discounted costs is studied.
• Kalymon, B., 1971, "Stochastic Prices in a Single-Item Inventory Purchasing Model", Operations Research, October 19(6): 1434 - 1458.
  Abstract: This paper studies a single-item multi-period inventory model in which future prices of the purchased item are assumed to be determined by a Markovian stochastic process instead of being known with certainty. Convex holding and shortage costs and a set-up charge for ordering are assumed. Such a model applies to purchasing a commodity whose price fluctuates widely because of speculative activity and large variations in supply or demand. For both a finite and infinite planning horizon, the paper determines the form and bounds of optimal policies and discusses computational approaches exploiting structure.
• Kalymon, B., 1971, "Estimation Risk in the Portfolio Selection Model", Journal of Financial and Quantitative Analysis, January 6(1): 559 - 582.

For more publications please see our Research Database