β Scribed by Jiafu Tang; Dingwei Wang; Richard Y.K. Fung
No coin nor oath required. For personal study only.
The current models and methods for PLP are usually restricted on some special types and usually the same type of possibilistic distribution. This paper focuses on linear programming problems with general possibilistic resources (GRPLP) and linear programming problems with general possibilistic objective coe cients (GOPLP). By introducing some new concepts of the largest most possible point, the smallest most possible point, the most optimistic point and the most possible decision, a new approach for formulating possibilistic constraints through general possibilistic resources and a satisfactory solution method will be discussed in this paper. A most possible decision method for GRPLP and a dual approach for GOPLP will be proposed for solving complex industrial decision problems.
π SIMILAR VOLUMES
In solving optimal control problems, the conventional dynamic programming method often requires interpolations to determine the optimal control law. As a consequence, interpolation errors often degenerate the accuracy of the conventional dynamic programming method. In view of this problem, this pape