Many real-world problems can be formulated as multi-objective optimisation problems, in which many potentially conflicting objectives need to be optimized simultaneously. Multi-objective optimisation algorithms based on Evolutionary Algorithms (EAs) such as Genetic Algorithms (GAs) have been proven to be superior to other traditional algorithms such as goal programming. In the past years, sever...

This paper investigates empirically the implications of delegating monetary policy to a central bank. We argue that the theoretical literature justifying central bank independence is highly stylised and is deficient in three respects: it typically ignores nominal or real rigidities, it assumes there is only a single policy instrument under the control of a single policymaking authority and it t...

In this paper, a quadratic programming (FFQP) problem is considered in which all of the cost coefficients, constraints coefficients, and right hand side of the constraints are characterized by L-R fuzzy numbers. Through this paper, the concept of α- level of fuzzy numbers for the objective function, and the order relations on the fuzzy numbers for the constraints are considered.  To optimize th...

In practical applications of mathematical programming it is frequently observed that the decision maker prefers apparently suboptimal solutions. A natural explanation for this phenomenon is that the applied mathematical model was not sufficiently realistic and did not fully represent all the decision makers criteria and constraints. Since multicriteria optimization approaches are specifically d...

Environmental concerns prompt the world for a transition to renewable energy sources from fossil energy. Reducing dependency on non-renewable of is prerequisite sustainable and less environmental pollution. Biogas energy, which one most important sources, produced by burning organic wastes can be used in many different fields. In this study, two-stage approach undertaken optimize biogas supply ...

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...

It is clear that there exist many practical control problems in which the consideration of multiple objectives is typically required, and these objectives may conflict with each other. For example, in many practical control systems, control error often conflicts with energy consumption. In the past ten years, we have been studying the greenhouse environment control problem and have gained a con...

A multiobjective programming for transportation model with the consideration of both depot to customer and customer to customer relationships is proposed in this research. The objectives are to minimize the total transportation cost which is the baseline objective and to minimize the overall independence value. A Lexicographic Goal Programming (LGP) is applied to the proposed model. A minimizat...

The Multi-objective transportation problem (MOTSP) is a of linear type optimization problem which contains all equality type constraints and the multi variable objectives. Here we have presented an application of Evolutionary Algorithms to the multi-objective transportation problem (MOTSP). We consider the multi-objective transportation problem as linear optimization problem and use a special t...