An integrated product conceptual scheme decision approach based on Shapley value method and fuzzy logic for economic-technical objectives trade-off under uncertainty

Jing, Liting; Zhan, Yong; Li, Qizhi; Peng, Xiang; Li, Jiquan; Gao, Fei; Jiang, Shaofei

doi:10.1016/j.cie.2021.107281

Cited by 19 publications

(14 citation statements)

References 51 publications

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“…It is widely known that TOPSIS is a classical multi-objective ranking method [49,54], which can be used to obtain the optimal scheme closest to the ideal solution. However, before determining the ideal utility reference, the weights of the CV and the economy objectives need to be set, followed by the analysis of the decision results.…”

Section: Comparison With Rough-topsis Methodsmentioning

confidence: 99%

“…Three possible non-empty coalitions c exist: {P}, {E}, and {P, E}. To ensure that all players gain utility by cooperating with each other, instead of abandoning the coalition to form single coalitions (such as {P} or {E}), a pessimistic standard [25,49] was adapted to define the characteristic function that calculates the coalitional utility v({c}) of each player, as expressed in Equation ( 16). This approach ensures that coalitional utility is maximized if the players cooperate with each other, regardless of the strategy adopted by the coalition {c}.…”

Section: Decision Of the Optimal Solution Based On The Coalitional Ga...mentioning

confidence: 99%

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Conceptual Solution Decision Based on Rough Sets and Shapley Value for Product-Service System: Customer Value-Economic Objective Trade-Off Perspective

Feng

Jiang

et al. 2021

Applied Sciences

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The product service system (PSS), as a design concept for integrated products and services, needs to be evaluated in the early design stage to maximize the value for stakeholders of the PSS concept, which is a crucial task for enterprises. However, existing methods focus on the ranking and value assessment of PSS evaluation criteria (e.g., quality, sustainability, cost), ignoring the needs conflict between customer value and economic objectives in PSS design, resulting in decision results that are not applicable to industrial enterprises. Furthermore, the influence of weight preference and uncertain information on solution evaluation is seldom considered when calculating the weight of each criterion. To fill this gap, integrating rough sets and the Shapley value decision approach for product-service system design considering customer value-economic objective trade-off is proposed, which mainly includes two parts: firstly, the best worst method (BWM) and the entropy weight method are integrated to obtain the comprehensive weight of evaluation criteria in the customer value and economic objectives; secondly, the Shapley value method in the coalition game is used to solve the optimal expectation allocation of the two objectives, so as to select the solution closest to the allocation, i.e., the optimal solution. In addition, rough set techniques are used to capture and integrate subjective assessment information originating from DMs under uncertainty. Finally, a case study of the electric forklift design is illustrated to verify the proposed decision model. The decision results show that this approach is more reliable through sensitivity and comparison analysis, and provide a valuable recommendation for enterprises to consider product service in forklift design.

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Section: Comparison With Rough-topsis Methodsmentioning

confidence: 99%

Section: Decision Of the Optimal Solution Based On The Coalitional Ga...mentioning

confidence: 99%

Conceptual Solution Decision Based on Rough Sets and Shapley Value for Product-Service System: Customer Value-Economic Objective Trade-Off Perspective

Feng

Jiang

et al. 2021

Applied Sciences

Self Cite

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“…998 In order to rank the advantages and disadvantages of the five alternative conceptual design function options, the relative closeness of each option is calculated according to Equations ( 9) and (10), and the higher the value of the relative closeness means that the option meets the design requirements with higher design potential, and the results are calculated by MATLAB 6.0, and is shown as follows:…”

Section: Conceptual Design and Solution Decisionmentioning

confidence: 99%

Multi-Indicators Decision for Product Design Solutions: A TOPSIS-MOGA Integrated Model

Zhao

Guo

et al. 2022

Processes

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Design decisions occur in all phases of product design and largely affect the merits of the final solution, which will ultimately determine the success or failure of the product in the market. Product design is a continuous process, and a large number of existing studies have proposed decision methods and decision indicators for the characteristics of different stages of design. These methods and indicators can meet the requirements of one of the phases: demand analysis, conceptual design, or detailed design. However, further research can still be conducted on the integration of methods throughout the design phase, using intelligent design methods, and improving the design continuity and efficiency. To address this problem, a TOPSIS-MOGA-based multi-indicators decision model for product design solutions is proposed, including its product design process, decision algorithm, and selection method. First, a TOPSIS-MOGA integrated model for conceptual design and detailed design process is established, the continuity of decision-making methods is achieved by integrating decision indicators. Second, conceptual design solutions are selected through the technique for order of preference by similarity to ideal solution (TOPSIS), based on hesitant fuzzy linguistic term sets and entropy weight method. Finally, detailed design solutions are selected through a multiobjective genetic algorithm (MOGA), based on a polynomial-based response surface model and central combination experimental design method. A case study of the decision-making in the design of high-voltage electric power fittings is presented, the conceptual design phase and the detailed design phase are connected through the indicators, which demonstrates that the proposed approach is helpful in the decision-making of the product design solutions.

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“…16 Multiple MCDM techniques have been presented to evaluate the design concept under various scenarios. 40,41 Likewise, MCDM would be a good choice for the biological inspiration evaluation in BID.…”

Section: Bid and Biological Inspiration Evaluationmentioning

confidence: 99%

Evaluating biological inspiration for biologically inspired design: An integrated DEMATEL‐MAIRCA based on fuzzy rough numbers

Zhu

2021

Int J Intell Syst

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Biological inspiration evaluation has been widely acknowledged as one of the most important phases in biologically inspired design (BID) as it substantially determines the direction of the following-up design activities. However, it is inherently an interdisciplinary assessment, which includes both the engineering domain and the biological systems. Due to the lack of knowledge at the early stage of product design, the risk assessments mainly depend on experts' subjective judgments, which values are vague, imprecise, and even inconsistent. How to objectively evaluate the biological inspiration under such uncertain and interdisciplinary scenarios remains an open issue. To bridge such gaps, this study proposes a fuzzy rough number extended multi-criteria group decision-making (MCGDM) to evaluate the biological inspiration for BID. A fuzzy rough number is introduced to represent the individual decision maker's risk assessment and aggregate respective evaluation values within the decision-making group. A fuzzy rough number extended decision-making trial and evaluation laboratory is presented to determine the criteria weights and a fuzzy rough number extended multi-attribute ideal real comparative analysis is proposed to rank the candidate biological inspirations. Experimental results and comparative analysis validate the superiority of the proposed MCGDM in handling the subjectivity and uncertainty in biological inspiration evaluation.biological inspiration evaluation, biologically inspired design, DEMATEL, fuzzy rough number, MAIRCA | INTRODUCTIONBiologically inspired (i.e., bio-inspired) design (BID) is a promising and rapidly increasing paradigm for design innovation and sustainable design. 1 For millions of years, a huge number of biological functions, behaviors, and structures have been evolved in nature, providing a robust and efficient manner that enables plants and animals to sustain their existence in diverse ways. 2 As a knowledge-driven design methodology, BID aims to systematically mine biological knowledge (e.g., biological functions, behaviors, and structures) to resolve engineering problems, such as eco-design and design creativity-inspiring. 3 It starts with the problem formulation and follows with the problem reframing, biological inspiration identification (i.e., biological solution acquisition, biological solution definition, biological inspiration generation, and biological inspiration evaluation), and the final biological inspiration application. 4 Due to its prominent performance in creativity-inspiring and technical innovation, BID has been widely investigated in various areas and a vast amount of biomimetic applications have emerged in our daily life, such as algorithm development, 5 robotics, 6 material design, 7 optics, 8 and image processing. 9 BID is inherently interdisciplinary. 10 To facilitate the knowledge analogy between the engineering world and biological systems, designers should be equipped with a strong background in the engineering discipline as well as good expertise in the biol...

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An integrated product conceptual scheme decision approach based on Shapley value method and fuzzy logic for economic-technical objectives trade-off under uncertainty

Cited by 19 publications

References 51 publications

Conceptual Solution Decision Based on Rough Sets and Shapley Value for Product-Service System: Customer Value-Economic Objective Trade-Off Perspective

Conceptual Solution Decision Based on Rough Sets and Shapley Value for Product-Service System: Customer Value-Economic Objective Trade-Off Perspective

Multi-Indicators Decision for Product Design Solutions: A TOPSIS-MOGA Integrated Model

Evaluating biological inspiration for biologically inspired design: An integrated DEMATEL‐MAIRCA based on fuzzy rough numbers

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