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Additive manufacturing (AM) has been increasingly used in various applications in recent years. However, it is still challenge when it comes to selecting a suitable AM process. This is because the outcome may vary due to not only different materials and printers but also different parameters and post-processes. This paper aims to develop an efficient method to help users understand trade-offs and make right decisions.

A hybrid method is proposed to help users select appropriate options from a large-scale and discrete option space in an interactive way. First, the design-by-shopping approach is applied to allow users exploring and refining the option space. The analytical hierarchical process method is then used to capture customers’ preferences. After analyzing the results of different normalization methods, a modified Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) approach is proposed to rank solutions and provide suggestions.

The usefulness of proposed method is illustrated in a case study. The results show that it can help customers understand performance distributions and find most suitable options accurately. The ranking of the modified TOPSIS method is more reasonable.

Due to the complexity of AM technologies, the process selection is considered at the parameter level. A new system framework is proposed for decision support. The TOPSIS method is modified to achieve a stable performance.

Prefabricated components sustainable supplier (PCSS) selection is critical to the success of prefabricated projects. However, limited studies have addressed the uncertainty and complexities during the selection process, particularly in multi-criterion group decision-making (MCGDM) circumstances. Hence, the research aims to develop a group decision-making model using a modified fuzzy MCGDM approach for PCSS selection under uncertain situation.

The proposed study develops a framework for sorting decisions in PCSS selection by using the hesitant fuzzy technique for order preference by similarity to ideal solution (HF-TOPSIS) method. The maximum consistency (MC) model is used to calculate the weights of decision makers (DMs) based on the cardinality and sequence of decision data.

The proposed framework has been successfully applied and illustrated in the case example of CB01 contract section in Hong Kong-Zhuhai-Macao Bridge (HZMB) megaproject. The results show various complicated decision-making scenarios can be addressed through the proposed approach. The MC model is able to calculate the weights of DMs based on the cardinality and sequence of decision data.

The research contributes to improving accuracy and reliability decision-making processes for PCSS selection, especially under hesitant and fuzzy situations in prefabricated megaprojects.

The purpose of this study is to rank additive manufacturing (AM) processes for microfabrication using integrated fuzzy analytic hierarchy process (AHP)-technique for order of preference by similarity to ideal solution (TOPSIS).

AM technology selection is formulated as multi-criteria decision-making (MCDM) problem and ranking is obtained using fuzzy AHP-TOPSIS. Five candidate processes considered are laser-induced forward transfer (LIFT), microstereolithography, micro-selective laser sintering (micro-SLS), inkjet, micro 3D printing.

Criteria weights are obtained using fuzzy AHP, and ranking is obtained using fuzzy TOPSIS. The top ranked criteria include material compatibility, geometrical complexity and minimum feature size. The ranking sequence is LIFT > microstereolithography > micro-SLS > inkjet > micro-3D printing.

In the present study, ten criteria and five alternatives are used. In future, additional criteria and alternatives could be considered in line with technological advancements.

The generated ranking enabled the selection of appropriate AM process for microfabrication.

The application of hybrid MCDM approach for ranking AM processes for microfabrication is the contribution of the study.

This paper aims to optimize the air pressure regulation scheme of the aeroengine pressure test bench.

Based on the requirements of pressure regulation process and the operating mechanism of aeroengine pressure test bench, a grey performance evaluation index system is constructed. The combination of principal component analysis and grey theory is employed to assign weights to grey indexes. The grey target evaluation model is introduced to evaluate the performance of historical regulation processes, and the evaluation results are analyzed to derive optimization mechanism for pressure regulating schemes.

A case study based on monitoring data from nearly 300 regulation processes verifies the feasibility of the proposed method. On the one hand, the improved principal component analysis method can achieve rational weighting for grey indexes. On the other hand, the method comparison intuitively shows that the proposed method performs better.

The pressure test bench is a fundamental technical equipment in the aviation industry, serving the development and testing of aircraft engines. Due to the complex system composition, the pressure and flow adjustment of the test bench heavily rely on manual experience, leading to issues such as slow adjustment speed and insufficient accuracy. This paper proposes a performance evaluation method for the regulation process of pressure test bench, which can draw knowledge from historical regulation processes, provide guidance for the pressure regulation of test benches, and ultimately achieve the goal of reducing equipment operating costs.

Availability of accurate quantity of materials, at correct place and at right time is extremely critical for increasing production effectiveness of any manufacturing organization. This can be achieved through employing an appropriate material handling equipment (MHE) capable of performing the desired operation. Therefore, choosing a right MHE from the available options is a key concern for the success, growth and competitiveness of a manufacturing organization. The purpose of this paper is to describe the design and development of an expert system based on quality function deployment (QFD) methodology in Visual Basic 6.0 for selecting the most appropriate industrial truck which is a commonly practiced MHE in any manufacturing organization.

A QFD-based approach is adopted to incorporate customers’ needs into the evaluation criteria on the basis of which industrial truck selection is carried out. The applicability of the developed expert system in solving industrial truck selection problems is demonstrated using two illustrative examples.

While applying this QFD-based model, CPCD 80x manufactured by Heli is recognized as the most suitable forklift truck for transporting unitized loads within a manufacturing unit with some spatial constraints, and for loading/unloading packages/boxes/cartons and place them at the desired locations in a manufacturing unit, ETV 216 manufactured by Jungheinrich evolves out as the most suitable reach truck.

Till date, numerous research articles have been published suggesting the applications of various mathematical models, multi-criteria decision-making methods and knowledge-based systems for solving MHE selection problems, and it is intriguing to note that none of the previously adopted methods has proposed a systematic procedure for selection of the evaluation criteria and interrelated the needs of customers with the technical specifications of MHEs while identifying the best alternative for performing a specified operation. These issues can be addressed through application of this developed QFD-based expert system, which can translate customers’ needs into organizational functions that are implementable in the decision-making/selection procedure.

The primary aim of this study is to provide actionable guidance for augmenting profitability in photovoltaic power plant investments within Iran’s solar energy sector. By emphasizing prudent capital management and strategic investment decisions, our research seeks to assist emerging businesses in attaining sustained success in this domain.

This study presents a comprehensive approach to refined decision-making in Iran’s solar energy sector. Our methodology integrates the best-worst method, ArcGIS software for site selection, and the TOPSIS method for decision-making, aiming to enhance precision and reliability.

Our research has identified ten promising regions suitable for photovoltaic power plant installations in Iran. Leveraging the TOPSIS method, we have made optimal selections among these alternatives. Furthermore, our exhaustive cost analysis, incorporating factors like land prices, system maintenance, revenue estimation, and various financial scenarios, has yielded insights into project cost-effectiveness.

By filling a notable gap in the literature regarding optimal site selection and investment strategies for photovoltaic power plants in Iran, our research contributes to the sustainable development of solar energy infrastructure. Through a thorough literature review and the development of a novel methodology, we offer valuable guidance for businesses and investors seeking success in Iran’s solar energy sector. Our study represents a significant advancement by introducing a novel methodology that integrates the best-worst method, ArcGIS software, and the TOPSIS method for site selection and investment analysis. These findings furnish valuable guidance for businesses seeking success in the solar energy sector, thereby contributing to the sustainable development of renewable energy infrastructure in Iran and beyond.

Due to the increasing demand of customer and competitive market pressure, manufacturing organizations should be modernized in strategies, production operations, processes and its procedures to remain competitive. So, a flexible manufacturing system (FMS) was adopted by the manufacturing system to fight with competitive pressure. The purpose of this paper is to enhance the performance of manufacturing system, with a focus on its factors.

In this research, the ranking of the performance factor of FMSs is done by using multiple attribute decision-making (MADM) methods as multi-objective optimization on the basis of ratio analysis (MOORA) and preference selection index (PSI). Weights of attributes are defined by the AHP method.

Ranking of performance factor is done on the basis of six variables which affect three elements of performance of FMS, i.e. productivity, flexibility and quality. MOORA is applied in three ways such as the ratio-based, reference point and full multiplicative method. In the MOORA method, ranking was done considering weights of attributes and also without it. A PSI method is used to find the best factor among the factors. The results of these methodologies, i.e. MOORA and PSI, are same, i.e. productivity is the primary factor in the manufacturing system. The ranking is validated by the result of different methodology used in this research.

This research has evaluated the important factors and performance variables which can enhance the performance of manufacturing organizations. So, the manufacturing persons can focus on these to enhance its performance.

Combined MADM methods, i.e. MOORA and PSI methodologies, are used in this paper to deal with the ranking of performance factors of the FMS considering qualitative characteristics. These approaches show the conversion of a qualitative attribute to quantitative attributes by using fuzzy logic.

This paper aims to guide about prioritisation and ranking of the solutions and overcoming barriers to facilitate the adoption of Lean Six Sigma (LSS) by using a hybrid framework.

It identifies LSS barriers and solutions to facilitate LSS adoption through literature review and by involving subject experts. The study makes use of fuzzy set theory and proposes a fuzzy analytical hierarchy process (AHP)-modified TOPSIS (technique for order preference by similarity to ideal solution) framework. It uses sensitivity analysis to establish framework robustness.

The key findings of this techno-managerial study are identification and prioritisation of 27 LSS barriers and 22 solutions to overcome adoption challenges, proposition and usage of fuzzy AHP-modified TOPSIS framework, guidance regarding where to focus for facilitating LSS adoption and ensuring robustness using sensitivity analysis, which establishes insignificant deviation in rankings when criteria weights are altered.

Some biasness and subjectivity may exist during pairwise comparisons as human judgements are involved.

Handling a hybrid solution like LSS is never easy. It is expected that the study will help industry professionals to plan their LSS adoption attempts effectively. Guidance regarding LSS barriers will assist in observing necessary precautions to avoid failures. It will open up new research fronts for researchers also.

Literature is full of studies regarding LSS barriers and its rankings. It is very rare to witness a study like ours, which discusses the barriers and links with solutions and its prioritisation. Proposed hybrid framework for a hybrid techno-managerial approach such as LSS is unique and acts as the roadmap for smooth implementation.

Three-parameter interval grey numbers (TPIGNs) have been extensively studied as an extended form of interval numbers. However, most existing TPIGN multi-attribute decision-making methods only consider the similarity of positions, ignore the similarity of developmental directions and focus primarily on static evaluation. To address these limitations, in this study, the authors propose a dynamic technique for order preference by similarity to an ideal solution (TOPSIS) based on modified Jaccard similarity and angle similarity for TPIGNs.

First, the authors extend Jaccard similarity to a TPIGN environment to represent positional similarity. A simple example is provided to illustrate the limitations of the traditional Jaccard similarity. Then, the authors introduce an angle similarity measure to represent developmental directional similarity. Finally, a dynamic TOPSIS model is constructed by incorporating time-series data into conventional two-dimensional static data. Stage weights are obtained by an objective function designed to maximize the amount and minimize the fluctuation of decision information. A quadratic weighting method is adopted to derive the overall evaluation value of alternatives.

To evaluate the effectiveness of the proposed method, this study takes the pre-assessment of ice disaster and the selection of cooperative enterprises as examples. The authors then provide the results of comparative and sensitivity analyses, which demonstrate the effectiveness and flexibility of the proposed method.

The proposed TOPSIS method in a TPIGN environment can take a more holistic and dynamic perspective for decision-making, which helps mitigate the limitations of single-perspective or static evaluations.

Digital technology, which is regarded as a prominent and transformational force in modern society, encompasses a wide variety of technology that utilize digital data to process, store and transfer various types of information. Digital technologies have continually been introduced as cutting-edge information tools in order to achieve effective management of vast information that arises from the prefabrication supply chain. However, without a sufficient performance evaluation, drawbacks of technology investment, such as financial losses and ineffective resource allocation, keep occurring, which hinders the widespread implementation of digital technologies. This study demonstrates a comprehensive evaluation of digital technologies’ effects on the prefabrication supply chain based on multi-criteria decision analysis (MCDA) theory.

Specifically, the targeted digital technologies and project constraints were first identified through a systematic literature review. The effects of the digital technologies were then scored using a questionnaire survey. The TOPSIS model was established to quantitatively rank the effectiveness of selected digital technologies.

Overall, BIM technology shone out in the rankings and is regarded as the most beneficial digital solution by multi-stakeholders to the existing constraints, such as working efficiency. Collaboration patterns between different stakeholders and technology integration trend were also indicated.

Compared with existing outcomes, this study specifically focused on examining the effects of digital technologies on the prefabrication supply chain, the most significant link in the process for prefabricated structures. New findings indicate the overall performance that considered both multi-stakeholders’ preferences and project constraints. The quantitative evaluation presents a comprehensive understanding of digital technologies’ effects, enabling industrial participants to reach well-informed, strategic and profitable investment decisions.