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The study aims at evaluating the most appropriate transport project which is one of the critical concerns of transport infrastructure scheduling. This process will be applied considering a set of criteria and discussed alternatives with sustainable perspectives.

In this paper, a complex proportional assessment (COPRAS) framework is discussed to handle the sustainable transport investment project (STIP) assessment problem within a single-valued neutrosophic set (SVNSs). To form the procedure more useful in handling with uncertain features, a SVNS is applied as a valuable procedure to handle uncertainty. First, a new discrimination measure for SVNSs is introduced and discussed some elegant properties to determine the significance degree or weight values of criteria with the sustainabality perspectives. Second, an integrated approach is introduced based on the discrimination measure and the COPRAS method on SVNSs and named as SVN-COPRAS.

A case study of an STIP evaluation problem is used to confirm the practicality and effectiveness of the SVN-COPRAS framework. Lastly, comparative discussion and sensitivity investigation are illustrated to prove the strength and solidity of the proposed framework.

The SVNSs enrich the essence of linguistic information when a decision expert (DE) vacillates among different linguistic values (LVs) to measure a sustainable transport project alternative problem. The utilization of SVNSs provides a more stable procedure to describe DEs' evaluations. So, an elegant methodology is developed to incorporate the DEs' awareness and experience for electing the desired STIPs. The introduced methodology has higher operability than the single-valued neutrosophic set technique for order preference by similarity to an ideal solution (SVN-TOPSIS) procedure during the larger numbers of attribute(s) or option(s). For the SVN-COPRAS methodology, there is no need to estimate the single-valued neutrosophic ideal solution (SVN-IS) and single-valued neutrosophic anti-ideal solution (SVNA-IS). The outcomes are calculated with handling the realistic data, which elucidates that the introduced model can tackle more intricate and realistic multi-criteria decision-making issues.

Sulphur dioxide, a dangerous atmospheric pollutant, is a major concern with the increasing use of coal as a combustion fuel in thermal power plants. Numerous efforts were made through these years to minimize the emission of sulphur dioxide and one such effort is the desulphurization of the flue gas generated during combustion, commonly termed as flue gas desulphurization (FGD). However, the materials of construction used for FGD systems, usually of type 316L stainless steel were reported to be failed due to the localized corrosion attack by the aggressiveness of the environment, mainly of chloride, fluoride, acidity and temperature encountered during the scrubbing of SO2.

Functionally graded materials are a special class of composites in which material are graded either continuously or layered wise depending upon its applications. With such variations of materials, the properties of structure vary either lengthwise or thickness wise. This paper aims to investigate models for effective estimation of material properties, as it is necessary for industries to identify the properties of composites or functionally graded materials (FGM’s) before manufacturing and also to develop novel material combinations.

Available models were compared for different material combinations and tested with experimental data for properties such as Young’s modulus, density, coefficient of thermal expansion (CTE) and thermal conductivity. Combinations of metal–ceramic and metal–metal were selected such that their ratios cover a wide range of materials.

This study reveals different models will be required depending on the material used and properties to be identified.

The results of the present work will help researchers in the effective modeling of composites or FGM’s for any analysis.

This paper presents a comparison and review of various analytical methods with experimental data graphically to find out the best suitable method. For the first time, the Halpin-Tsai model was extended in the analysis of the CTE which shows good approximations.

The emission of sulphur dioxide, a dangerous atmospheric pollutant, is the major problem with the increasing use of coal as a fuel in various industries. A variety of methods in minimizing the sulphur dioxide emission have been reported, among which the desulphurization of the flue gas generated by combustion of the coal receives much attention and it is termed as flue gas desulphurization (FGD).

Many European businesses are small and medium enterprises (SMEs), contributing significantly to the well-being of local economies and regions. Even so, SMEs face many challenges in fostering innovation and improving performance. Furthermore, the raw material consumption is increasing globally, necessitating the development of strategies that will reduce the number of raw materials extracted and imported while improving the sustainability of small and medium-sized businesses. Consequently, promoting circular economy (CE) strategies, such as industrial symbiosis (IS) partnerships, whereby waste products from other industries serve as a source of raw materials for companies, is critical. Identifying and analysing enablers or drivers that support IS collaborations among SMEs is necessary to achieve this goal. In this regard, the purpose of this study will explore the enablers of IS among SMEs considering sustainability dimensions (environmental, social and economic). As facing a decision-making (DM) problem, the multiple attribute decision-making (MADM) approach was applied in a hesitant fuzzy (HF) environment in this research to answer the research questions. In this regard, in phase 1, IS enablers were identified and extracted using a literature review and experts’ opinions. In phase 2, the hesitant fuzzy Delphi (HFD) method was implemented to screen and finalise the enablers identified. In phase 3, casual relations among final enablers were determined using the hesitant fuzzy ANP (HF-ANP) method. Finally, in phase 4, the relative importance of enablers was calculated using the hesitant fuzzy best–worst method (HF-BWM). Consequently, this study provided potential strategies for IS that can be implemented quickly and used by local authorities to support SMEs in achieving circular waste management.

Due to increasing socio-ecological concerns, manufacturers are paying ample attention to the strategic decision-making for enhancing customer satisfaction considering Industry 4.0 requirements. Customers' preferences are being considered vital into the decisions related to sustainable supplier selection for building competitive gain. Thus, the objective of this study is to develop a real-case-based empirical approach for evaluating performance of suppliers based on customers' feedback, an area not explored much in literature. The novelty of current study lies in the development of an integrated supplier evaluation and selection model involving three phases: (1) identifying sustainable criteria according to Industry 4.0 requirement through customers' feedback, (2) calculating relative scores of criteria using Z-numbers and (3) determining evaluation weights of suppliers using fuzzy-TOPSIS. The contribution of the study lies in effective validation of the model by considering the case of a manufacturing firm, which aids the firm in evaluating performance of suppliers based on customers' socio-ecological expectations while considering reliable information provided by decision-makers (DMs).

The purpose of this paper is to propose a multi-criteria decision-making model based on the case-based reasoning (CBR) method for interval-valued probabilistic linguistic term set (IVPLTS), which can cluster different categories of building suppliers for targeted management.

First, a new score function and distance measure for IVPLTS are proposed. Second, a green building supplier evaluation criterion system is constructed from five aspects: operation management, green management, cooperation potential, service level and product information. Finally, the IVPLTS-CBR model is used to evaluate the green building suppliers and groups them into three preset categories.

The feasibility and validity of the proposed method are verified by comparing with the advanced TOPSIS method and the IVPLTS-based VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. The compared results show that the proposed method is more consistent with the actual situation and has strong theoretical significance and practical value.

This paper presents a new method for clustering construction suppliers. Decision makers can use this method to classify construction suppliers into different categories, so that they can be targeted management. In this way, suppliers can be better guided and motivated to accelerate the green transformation and contribute their share to achieve the strategic goal of carbon neutral and carbon peak as soon as possible.

A new score function and distance measure for IVPLTS are proposed. Besides, a novel IVPLTS-CBR method is applied to rank and cluster building suppliers.

To develop a high‐order compact finite‐difference method for solving flow problems containing shock waves.

A numerical algorithm based on high‐order compact finite‐difference schemes is developed for solving Navier‐Stokes equations in two‐dimensional space. The convective flux terms are discretized by using advection upstream splitting method (AUSM). The developed method is then used to compute some example laminar flow problems. The problems considered have a range of Mach number that corresponds to subsonic incompressible flow to hypersonic compressible flows that contain shock waves and shock/boundary‐layer interaction.

The paper shows that the AUSM flux splitting and high‐order compact finite‐difference methods can be used accurately and robustly in resolving shear layers and capturing shock waves. The highly diffusive nature of conventional flux splitting especially on coarse grids makes them inaccurate for boundary layers even with high‐order discretization.

This paper presents a high‐order numerical method that can accurately and robustly capture shock waves without deteriorating oscillations and resolve boundary layers and shock/boundary layer interaction.

In the process of group decision-making, there may be multilayer subjects. In other words, members of the decision-making group may come from different layers and there is interest game among decision experts. Therefore, it is an extremely important topic to aggregate the information of decision experts who are involved in hierarchical relations and gaming relations so as to effectively address game conflicts and reach game cooperation.

First, a programming model is established to minimize the difference of expert opinions in hierarchical decision-making, and the method to solve the optimal solution is given. Second, the cooperative game model and its properties are discussed by using cooperative game and Shapley value, and the method to determine the weight vector between layers is also proposed.

This model can quickly aggregate information and achieve game equilibrium among decision-makers with hierarchical relationships. It can be widely used in decision evaluation with hierarchy structure and has certain practical value.

In order to solve the problem that experts at different levels may have conflicts of interest in multilayer grey situation group decision-making process, cooperative game and Shapley value theory are introduced into the study, and a multilayer grey situation group decision-making model based on cooperative game is constructed. The validity and practicability of the model are illustrated by an example.

The purpose of this paper is to develop a scale for the measurement of service quality at the manufacturer–doctor interface of the pharmaceutical supply chain and to study the impact of service quality on doctor’s satisfaction and doctor’s prescribing behavior. Doctors from two major states of South India were selected for the study. A doctor perceived service quality scale with three dimensions having eight items was developed through confirmatory factor analysis (CFA) in the pharmaceutical context. Structural equation modeling (SEM) technique was used to show the relationship between service quality, satisfaction and prescribing behavior. The critical factors of service quality were identified, and a model was developed showing the relationship between service quality, doctor’s satisfaction and doctor’s prescribing behavior which has not been explored in any research. This model will be helpful in further development of new concepts and for analyzing the reasons for the failure of doctors in providing quality service. 

A total of 200 doctors from three major cities of South India were selected. A doctor perceived service quality scale with three dimensions having eight items was developed through CFA using Parasuraman Service quality scale (Parasuraman, 1985, 1986, 1988) as the basis in the pharmaceutical context after focus group discussions with company experts, retailers, doctors and academicians. SEM technique was used to examine the impact of service quality on doctor’s satisfaction and prescribing behavior.

There is no universal set of dimensions and items that determine service quality in manufacturing industries, especially at the manufacturer–doctor interface of the pharmaceutical supply chain though service quality plays a very important role in affecting the performance of manufacturing industries. The critical factors affecting the quality of service for a pharmaceutical company at the manufacturer–doctor interface of the supply chain were identified, and its impact on doctor’s satisfaction and their prescribing behavior were studied.

This research contributes to the development of service quality scale for measuring service quality in pharmaceutical manufacturing company, especially with reference to manufacturer–doctor interface of the supply chain which was not thoroughly explored earlier. A model was developed showing the positive relationship between service quality and doctor’s satisfaction and doctor’s prescribing behavior in pharmaceutical supply chain which is a new concept not proved experimentally.

The study is very useful for the pharmaceutical manufacturing companies to identify the service quality factors affecting doctor’s satisfaction and their prescribing behavior thereby leading to development of new measures for improving the performance of the pharmaceutical supply chain. This study can lead to identification of problems involved in pharmaceutical supply chain and also leads to generation of new ideas and development of new concepts for influencing doctor’s satisfaction and doctor’s prescribing behavior which in turn can help in providing better health.

This study actually has a direct impact on the society. If factors affecting doctor’s satisfaction and prescribing behavior are identified automatically, the end consumer, i.e. patient, can be satisfied in a better way, and better medical care can be provided. If doctor’s problems are identified, then better solutions can be provided to patients; this in turn has a lot of positive impact on the pharmaceutical company and society in general.

This research will act as a base for generating ideas relating to how quality service provided by a company will have an impact on doctor’s satisfaction and his prescribing behavior in pharmaceutical supply chain .To the best of the authors’ knowledge, this study is the first of its kind of the conceptual aspects of service quality, satisfaction and loyalty explained in terms of pharmaceutical supply chain as service quality, doctor’s satisfaction and doctor’s prescribing behavior and proved experimentally.