Multi-objective goal

Target Multi-Objective Goals Most traditional AI systems deal with problems in a piecemeal fashion, one at a time in contrast, the individual agents in an agent-based system must deal with many conflicting goals simultaneously. 

Multi-objective goal programming (MOGoP) Multi-objective geometric programming (MOGeP)... 

For PRE this implies the combination of several disciplines such as polymer chemistry, thermodynamics, characterization, modeling, safety, mechanics, physics, and process technology. PRE problems are often of a multi-scale and multifunctional nature to achieve a multi-objective goal. One particular feature of PRE is that the scope ranges from the micro scale on a molecular level up to the macro scale of complete industrial systems. PRE plays a crucial role in the transfer of information across the boundaries of different scale regions and to provide a comprehensive and coherent basis for the description of these processes [19]. 

Consider the multi-criteria optimization problem defined in Eq. (11). Because of the fact that these objective functions usually conflict with each other in practice, the optimization of one objective implies the sacrifice of other targets it is thus impossible to attain their own optima, Js, s e <5 = [1,..., 5], simultaneously. Therefore, the decision maker (DM) must make some compromise among these goals. In contrast to the optimality used in single objective optimization problems, Pareto optimality characterizes the solutions in a multi-objective optimization problem [13]. 

Tan, K. C., Lee, T. H. and Khor, E. F. (1999). Evolutionary Algorithms with Goal and Priority Information for Multi-Objective Optimization, IEEE Conf. Evolutionary Computation, 1, pp. 106-113. 

In contrast to single-objective optimization, in multi-objective optimization two goals have to be achieved ... 

While the first goal comes along with single-objective optimization, the second goal is characteristic for multi-objective optimization problems. Hence, multi-objective optimization procedures try to spread the pool of non-dominated configurations along the Pareto front. To do so, the diversity of non-dominated configurations has to be incorporated in the solution procedure. 

Ramos, M.A., Boix, M., Montastruc, L. and Domenech, S. (2014) Multi-objective optimization using goal programming for industrial water network design. Industrial and Engineering Chemistry Research, 53 (45), 17722-17735. 

Hu, C. R, C. J. Teng, and S. Y. Li. 2007. A fuzzy goal programming approach to multi objective optimization problem with priorities. European Journal of Operational Research. 176 1319-1333. 

On the other hand, concentration on cost minimization as a single objective will lead to reduced variety to minimal mode (8 varieties) which is unexpected for managers and cannot meet the customer satisfaction and market demand. The results distinguish existent trade-off between objectives clearly and accentuate that product variety goal should be considered as multi-objective model. In fact, the model with optimization objectives under constraints has provided the best results inclusive fewer costs, higher product variety and quality in which aligned with decision maker s preferences. 

Multi-objective optimisation using goal programming techniques. 

The problem is formulated as a multie-objective optimisation model and is solved using Goal Programming technique. The optimal solution includes one oil boiler (B3), one... 

Design and synthesis of chemical processes are multi-objective optimisation problems in nature where several objectives are required to be optimised, maximised or minimised, simultaneously within a specified range of constraints. In this section the proposed integrated framework is introduced. The approach formulates the decision maker preferences into mathematical forms and then integrates these goals as a multi-objective optimiser into an overall stepwise procedure to cover all aspects of optimal design considerations, i.e. economical, environmental, heat integration as well as operability/controllability issues. 

K.C. Tan, T.H. Lee and E.F. Khor, Evolutionary algorithms with goal and priority information for multi-objective optimization , in Proc. 1999 Congr. Evolutionary Computation, vol. 1, Washington DC, July 1999,106-113. 

Deb, K. Non-linear Goal Programming Using Multi-Objective Genetic Algorithms, in Computational Intelligence, Universitat Dortmund (2004)... 

Mehrbod, M., Tu, N., Miao, L., and Wenjing, D. Interactive fuzzy goal programming for a multi-objective closed-loop logistics network. Annals of Operations Research 201, no. 1 (2012) 367-381. 

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