Preference Ranking on the Basis of Ideal-Average Distance Method for Multi-Criteria Decision-Making

Abstract

Over the past 2 decades, numerous papers have been published on multi-objective optimization (MOO) in chemical engineering. For conflicting objectives, MOO produces a set of non-dominated optimal solutions that are equally good. Only one of these optimal solutions has to be selected for implementation. Ranking of non-dominated solutions and this selection have been discussed, mostly in the area of multi-criteria decision-making or -analysis (MCDM/MCDA). There is limited research in this area for chemical engineering applications. This paper proposes a novel MCDM method, the PROBID (preference ranking on the basis of ideal-average distance) method, and its simpler variant, the sPROBID. The PROBID method comprehensively considers a spectrum of ideal solutions and the average solution to determine the performance score of each optimal solution. The sPROBID method takes into consideration only the first and last quarters of ideal solutions. Comparative sensitivity analysis is performed, and both the methods are seen to be sensitive to changes in the weights of objectives, which is desirable. Then, the ranking consistency of the PROBID and sPROBID is tested and compared with 7 popular/recent MCDM methods, for 12 scenarios on 3 different data sets from the literature; these scenarios include the linear transformation of measurement unit, equivalent objective formulation, and addition/deletion of optimal solutions in the data set. The results of these tests show that the PROBID method is more consistent and robust compared to the other MCDM methods tested. Although sPROBID is less robust than PROBID, it outperforms four of the seven MCDM methods in terms of ranking consistency.

Multi-Objective Building Energy System Optimization Considering Ev Infrastructure

“…However, ultimately only one solution has to be chosen from the set of Pareto-optimal solutions for implementation (Wang et al, 2020) [6]. In the present study, PROBID method (Wang et al, 2021) [7] is utilized to recommend one solution from the Pareto-optimal front because of its proven ranking consistency and robustness.…”

Sensitivity Analysis of Multi-Criteria Decision-Making Methods for Engineering Applications

“…As a subset of operations research, MCDM has been attracting much attention from various fields in recent years, including finance and economics, − artificial intelligence and machine learning, − and chemical engineering. − This increasing research can be seen in Figure a, which shows that the number of published papers per year pertaining to MCDM has increased by 6.6-fold from 284 in the year 2010 to 1887 in the year 2022; the increase is particularly significant since the year 2017. Our research group has been active in MOO study in chemical engineering for more than 20 years and has published two books , and many papers.…”

“…Recent studies by Mufazzal and Muzakkir, Wang et al, Shih, and Dehshiri and Firoozabadi emphasize the sensitivity or limitation of nearly all MCDM methods; that is, the rankings of some alternatives change when linear transformation of objectives (e.g., conversion of net present value from US dollar to Singapore dollar) is introduced, when an objective is reformulated equivalently (e.g., maximization of profit can be formulated as minimization of 1/profit or constant - profit), and/or when some solutions are removed from the set of alternatives (e.g., that could occur in case MOO did not find all Pareto-optimal solutions). The sensitivity due to removal of some alternatives is called rank reversal in some papers in the literature.…”

“…Although rank reversal is discussed in some papers, scope of its study is limited in terms of MCDM methods and number/variety of applications tested. For example, Pamučar and Ćirović considered six MCDM methods for only one application; Mufazzal and Muzakkir considered 8 MCDM methods and 7 case studies; and Wang et al, whose main contribution is a new MCDM method with comparatively lower occurrence of rank reversal, tested 9 MCDM methods on 3 case studies.…”

Sustainable decision making for chemical process systems via dimensionality reduction of many objective problems

“…While this approach does not give a single implementable solution, obtaining and visualizing the full Pareto frontier of objective tradeoffs enables making fully informed sustainable decisions, the responsibility of which ultimately lies with human actors and can be supported by preference ranking algorithms. 14 Multiobjective approaches have been applied quite broadly in chemical process systems research. 15 With respect to sustainability, it is most common to analyze the tradeoffs between a single environmental and a single economic objective, with example applications in plant design, [16][17][18] supply chain management, 19,20 and process operations, 21,22 to name a few.…”