Showing papers on "Goal programming published in 1970"
TL;DR: A general mathematical optimization model for such systems is developed which has broad applications for the planning, system design and evaluation of many transportation systems and three types of solution techniques are discussed.
Abstract: Transportation systems have multi-objective functions and there are multi-factor decision situations. A general mathematical optimization model for such systems is developed which has broad applications for the planning, system design and evaluation of many transportation systems. Three types of solution techniques are discussed. For multi-objective linear programs, a solution is obtained which satisfies the decision maker's preferences and optimization from the decision maker's point of view is considered. A goal programming solution technique is given when goals for the system can be defined. If this is not possible, an overall utility function is defined on the various objective functions and a concept of additive utilities is explored and a parametric programming solution is given.
31 citations
21 citations
12 May 1970
TL;DR: In this paper, the authors extend the OMM Models to include training elements along with related constraints. But they do not consider the training elements in their models and do not address the training requirements of OMM models.
Abstract: : Models for manpower planning previously devised for the U. S. Navy's Office of Civilian Manpower Management have all utilized goal programming constructs with embedded Markoff processes. These models--referred to as 'OCMM Models'--are here extended to include training elements along with related constraints.
13 citations
01 Sep 1970
4 citations
TL;DR: In this paper, a hybrid genetic optimization nonlinear programming algorithm is applied to the design of structural structures, where the genetic optimizer controls the topology of the structure, while a gradient based, non-linear programming method refines the local geometry and crossectional properties.
Abstract: A multiple objective, goal programming formulation is coupled to a hybrid genetic optimization nonlinear programming algorithm and applied to the design of structures. The combination provides a unique design environment which can generate solutions not attainable by a traditional approach to structural optimization. Crossectional, geometric and topological change are considered in the formulation. Design goals include weight, cost and robust character. Both hard and soft constraints are included. The genetic optimizer controls the topology of the structure, while a gradient based, nonlinear programming method refines the local geometry and crossectional properties. A specific example involving a ten bar truss is presented which highlights the benefit of the approach over previously reported results.
3 citations
01 Dec 1970
TL;DR: A model which includes the salient features of decentralization problems and behavior by a two-level goal-programming approach is presented, which seems to have a good potential for the solution of other non-convex problems, like those which arise from chance-constrained programming when zero-order decision rules are used.
Abstract: : The paper presents a model which includes the salient features of decentralization problems and behavior by a two-level goal-programming approach. The fact that it turns out to be a non-convex programming problem is of interest, both for the managerial implications and the mathematical problems involved. The technique used to reach a solution, or some similar methods, seems to have a good potential for the solution of other non-convex problems, like those which arise from chance-constrained programming when zero-order decision rules are used, or other types of dynamic planning which involve decomposition of goals over time similar to the decomposition between units dealt with here. (Author)
1 citations