Decision-Making Methods in Product Design Evaluation

Product design evaluation encompasses a variety of structured decision-making methods aimed at selecting, ranking and refining design alternatives according to multiple criteria. Central to this endeavour are multi-criteria decision-making (MCDM) approaches that formalise expert judgments and quantitative measures of performance, cost, sustainability and user satisfaction. Techniques such as the Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Evolutionary Game algorithms enable designers to navigate conflicting objectives, integrate subjective confidence and ensure consistency in weighting criteria. Recent advances emphasise hybrid frameworks that combine objective entropy weights with subjective expert assessments, fuzzy set theories to handle uncertainty, and genetic or game-theoretic optimisation for multi-objective solution search. Practical applications range from conceptual schemes for machinery and consumer electronics to green and emotion-driven product evaluation, demonstrating global significance in shortening development cycles, enhancing consensus among stakeholders and improving the alignment of design outcomes with market and sustainability goals.

Research from Nature Portfolio

A novel two-layer expert weighting framework has been proposed to enhance design concept evaluation by integrating an entropy-based weight model with a multiplicative AHP method in the first layer, followed by a variance-minimisation consensus model in the second layer. This hybrid approach yields robust expert weights that reflect both diversity of expertise and collective agreement. A real-world cruise ship cabin design study demonstrated significant improvements in evaluation accuracy and decision confidence compared with unweighted expert schemes, highlighting the method’s potential for complex, large-scale product systems.

Research from all publishers

In recognition of decision-maker confidence, a Z-number extension of AHP and TOPSIS has been developed to incorporate both preference assessments and their associated certainty. Applied to kitchen waste-container design, this method achieves more practical and transparent scheme rankings by modelling fuzzy linguistic evaluations and confidence levels simultaneously. A TOPSIS-MOGA integrated model addresses the continuity of conceptual and detailed design phases by first selecting top concepts via a hesitant fuzzy TOPSIS with entropy-derived weights and then refining detailed solutions through a multi-objective genetic algorithm. A case study on high-voltage electric power fittings confirms its effectiveness in balancing multiple performance indicators across design stages. Furthermore, a two-stage multi-objective analysis for centrifugal compressor concept selection employs non-dominated solution elimination followed by a weighted TOPSIS decision step, significantly reducing the workload of scheme comparison while providing a clear pathway to optimal design under assembly, manufacturing and cost constraints.

Decision-Making Methods in Product Design Evaluation publication trend

The graph below shows the total number of articles in decision-making methods in product design evaluation across all publications each year (not limited to Nature Index journals).

Technical terms

Analytical Hierarchy Process (AHP): A structured technique for decomposing a decision problem into a hierarchy, assigning pairwise comparison weights to criteria and deriving priority scales for alternatives.

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS): A ranking method that identifies the alternative closest to an ideal solution and furthest from a nadir solution based on weighted distance measures.

Z-numbers: An uncertainty representation combining a fuzzy number for a variable and a second fuzzy set for the confidence in that variable, enabling joint modelling of preference and certainty.

Grey Relational Analysis: An approach for evaluating the similarity or relational degree between alternatives and ideal profiles, particularly useful when information is incomplete or uncertain.

Multi-Objective Genetic Algorithm (MOGA): An evolutionary computation technique that optimises multiple conflicting objectives simultaneously by evolving a population of candidate solutions toward Pareto efficiency.

References

1. An integrated expert weight determination method for design concept evaluation. Scientific Reports (2022).
2. Conceptual Design Evaluation Considering Confidence Based on Z-AHP-TOPSIS Method. Applied Sciences (2021).
3. Multi-Indicators Decision for Product Design Solutions: A TOPSIS-MOGA Integrated Model. Processes (2022).
4. Optimization and selection of the multi-objective conceptual design scheme for considering product assembly, manufacturing and cost. Discover Applied Sciences (2022).