A Generic Decision Tree to Integrate Multi-Criteria Decision-Making (MCDM) with Multi-Objective Optimization (MOO), Including Consideration of Uncertainty

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Poster Number:  V-08 

Main Presenter:    Jannatul Ferdous 

Co-Authors:   Farid Bensebaa     Abbas Milani      Kasun Hewage      Pankaj Bhowmik      Nathan Pelletier                        

Optimization refers to finding the best solution or a set of outperforming solutions for a specific search space shaped by predefined constraints. It includes one or multiple objective functions for defining the goal. Typically, Multi-Objective Optimization (MOO) problems are converted into single-objective optimization problems by aggregating similar objectives or through the weighted sum method. But, in reality, objective functions often conflict with each other so MOO is conducted to generate a set of Pareto-Optimal solutions instead of one unique solution. The Pareto-Optimal solution set represents non-dominated solutions with varying degrees of trade-offs between the objective functions. If we need to obtain the best solution, an additional step for decision-making is required, however, which typically involves the use of different Multi-Criteria Decision-Making (MCDM) methods. MCDM methods refer to problems with no explicit objective functions and rather comprise a decision
matrix with a list of alternatives/options along with pre-defined criteria values. To convert MOO problems to MCDM problems, we need to consider the solutions as alternatives and the objective functions will be the criteria. MCDM methods consist of different weighting (subjective, objective, combination) and ranking methods (i.e., TOPSIS, PROMETHEE II). The integration of Multi-Objective Optimization (MOO) and Multi-Criteria Decision Making (MCDM) has garnered significant attention across various scientific research domains as this hybrid approach enhances the efficacy of final decisions. However, a critical gap exists in terms of providing clear methodological guidance, particularly when dealing with data uncertainties. This systematic review was designed to develop a generic decision tree that serves as a practical roadmap for practitioners who seek to perform MOO and MCDM in an integrated way, including consideration of uncertainty. Instead of identifying the superior MOO or MCDM
methods, the study rather investigates the strategies for integrating these two common methodologies because results do not vary significantly among different MCDM methods. Integration can occur either as a priori, a posteriori, or through a combination of both, each offering distinct advantages and drawbacks. Finally, a real-world case study for the pulse fractionation process in Canada is used as a basis for demonstrating the various pathways presented in the decision tree and their application to identifying the optimized processing pathways for obtaining pulse protein.

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