Pakorn Adulbhan

Bicriterion linear programming

Abstract Basically leaning on the concept of “best” compromise, the technique seeks the optimal solution by fair relaxations of the objectives commensurate with their degrees of importance until the optimum feasible compromise is reached. The concept is made operational by deriving a linear constraint (referred to as the compromise constraint) to be added into the original set. An offspring of both objective functions, the compromise constraint cuts the original feasible region and forces both objectives to settle on a common point along this added restriction. The resulting singular equivalent of the bicriterion problem optimizes any one of the two objective functions and their equivalent sum (referred to as the supergoal) subject to the new set of constraints. Post optimality analysis is employed to minimize the computational effort usually done by a computer. A very attractive feature of this new practical technique is its ability to search for the optimum in any point in the feasible region, even other than the vertices of the convex set.

A mathematical model for industrial project evaluation

Different approaches to the evaluation of alternative industrial projects range from listing of subjective factors for use as guidelines on the one hand to construction of mathematical models with quantitative objective data on the other. This paper presents the method of developing a comprehensive model with all relevant factors, both subjective and objective. The approach used here is to identify and evaluate all important objective and subjective factors on consideration of the macro-level national objectives as well as the micro-economic investment criteria for each alternative project, and to convert them into consistent and dimensionless indices which are finally combined together to yield the appropriate project measure of utility in each case for comprehensive evaluation. Thus, the model, presented here, can provide a new tool for industrial project evaluation and also for establishing priorities among projects competing for allocation of resources.

An aggregate production planning for a heavy manufacturing industry

Abstract This paper proposes a linear programming formulation of the aggregate production planning problem in the context of a heavy manufacturing industry. A basic model is first developed under the special requirements of the industry, to minimize the total cost of production which is assumed to be piecewise linear. The basic model is then transformed into a linear programming model to seek an optimal solution for a series of planning periods within the planning horizon. The results of the application to a real world situation are also included.

System dynamics modeling for forecasting technological substitution

Abstract It is very common, nowadays, to find that a new product or technology is substituting an older one to satisfy a particular consumer need. But with the advancement of technology, there are many instances where a particular product or technology is replacing an older one while at the same time being replaced by a newer one. This relatively new substitution phenonenon can be called a multilevel substitution process. It seems that until now no effort has been directed to study the multilevel substitution cases. Moreover, any forecast made at a given point in time needs to be updated as varying circumstances influence the elements of the forecast. This paper, therefore, presents a systematic methodology for forecasting multilevel technological substitution incorporating various forms of time dependent parameters in an existing trend extrapolation model. The methodology is based on Forresters “System Dynamics” technique. The developed model makes forecast of the market share as well as the actual size of the market of each of the competing technology or product under various assumptions about the growth of the total joint market based on past trend or future anticipations.

A probabilistic programming model for blending aggregates

Abstract The paper introduces the formulation of a probabilistic programming model to find the optimum mix proportion of aggregates to meet the specific grading requirement in order to minimize the cost which consists of the material cost and the expected penalty cost. The model is probabilistic since the gradation, which is the major parameter, is a random variable. A linear programming model is first formulated. Using the LP solution as initial value, a direct search technique is then employed to solve the problem. The model is expected to be applicable to any problem of aggregates blending. In this paper, however, the mixing aggregates of an asphalt mixing plant is exemplified to test the applicability of the model.

A biobjective model for production planning in a cement factory

Abstract This paper is an illustration of the application of biobjective linear programming to determining the optimal allocation of production materials to the major production facilities. The system under consideration is a cement factory. However, the approach and methodology are sufficiently general that the method can be applied to similar problems in other firms. Two objectives of the factory are distinguished: minimization of manufacturing cost and maximization of capacity utilization. Addressed to the needs of the biobjective case, a technique, which is referred to as ‘compromise constraint’ technique, is introduced.

A Goal Programming Applied to Regional Water Quality Management

Abstract Out of all the optimization techniques used in regional water quality management studies, linear programming is certainly the simplest. In the case of linear programming, however, only one goal can be optimized. When there are multiple goals with the same or different priorities; goal programming is a useful decision making tool. The paper illustrates the application of goal programming to regional water quality management problem where the following two goals are considered: (1) minimize the total costs, (2) maintain the water quality close to the minimum desired level.

Models for determining the optimal in-process inspection specification limits in multistage production systems

Abstract A multistage production system is characterized by items being processed through a sequence of production operations. In general, it may be desirable to have inspection operations performed between some production operations for the purpose of detecting a defective item prior to the final inspection. Furthermore, it may even be more economically desirable to consider tightening the specifications—that is. rejecting a manufacturing unit having defects less than the maximum number to be tolerated. Thus, the hypothesis examined in this paper is that there could exist a method for determination of economically optimal specifications for in-process inspection of a multistage production system. Two production environments considered are: (a) a manufacturer producing to satisfy a fixed production goal, so that any defective item must be replaced by reprocessing a substitute; (b) a manufacturer producing until be exhausts fixed levels of resource availability, so that a rejected item results in a loss in revenue. An economic dynamic programming model is formulated for each of the two production environments. Illustrative examples are presented. Indications are that the concept of specification tightening should be considered for utilization, and that the dynamic programming approach can be used to determine the most economic specification limits for a given inspection program.