Two approaches to formal multiple objective decision analysis are described , illustrated and compared. These two approaches, generating techniques and goal programming, are illustrated by the solution of a simple numerical example. This solution combined with some lessons from the field of psychology provide a basis for some judgments on the relative merits of the approaches.

In this paper, a Goal Programming (GP) model is converted into a multi-objective optimization problem (MOO) of minimizing deviations from fixed goals. To solve the resulting MOO problem, a hybrid metaheuristic with two steps is proposed to find the Pareto set’s solutions. First, a Record-to-Record Travel with an adaptive memory is used to find first non-dominated Pareto frontier solutions preem...

Maintaining the health of people during and after a disaster is one of the most important issues in disaster management. Blood products are among the essential items needed to save the human life and the lack of them may lead to significant losses in human health. In this paper a comprehensive mathematical model of blood products supply chain is presented to respond the need for blood products ...

Semantics has become a key topic of research in Genetic Programming (GP). refers to the outputs (behaviour) GP individual when this is run on dataset. The majority works that focus semantic diversity single-objective indicates it highly beneficial evolutionary search. Surprisingly, there minuscule conducted semantics Multi-objective (MOGP). In work we make leap beyond our understanding MOGP and...