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The purpose of this paper is to propose a simple methodology in solving multi‐response optimisation problems by employing Taguchi methods and a non‐parametric statistical technique.

There is a continuous interest in developing effective and statistically sound multi‐response optimisation methods such that they will provide a firm framework in global product and process improvement. A non‐parametric approach is proposed for the first time in a five‐step methodology that exploits Taguchi's fractional factorial designs and the concept of signal‐to‐noise ratio in data consolidation. The distinct feature of this method is the transformation of each response variable to a single rank variable. The subsequent incorporation of the squared ranks for each of the investigated responses issues a single master‐rank response suitably referred to conveniently as a “Super Rank” (SR) response, thus collapsing all dependent product characteristic information into a single non‐dimensional variable. This SR variable is handled by standard non‐parametric methods such as Wilcoxon's two‐sample, rank sum test or Mann‐Whitney's test eliminating at the same time multi‐distribution effects and small‐sample complications expected for this type of experimentation.

The proposed methodology is tested on already published data pertaining a design problem in the electronic assembly technology field. The case study requires six‐factor simultaneous optimisation of three response variables. A second example is analyzed by the proposed method focusing on the optimisation of a submerged arc‐welding process problem due to a group of five factors. The Mann‐Whitney's test contrasts the effects of factor settings one‐to‐one on the SR response in order to assign statistical significance to the optimal factor settings.

The application of this methodology is tested at the same time in a real three‐response optimisation case study where each response belongs to different optimisation category.

The methodology outlined in this work eliminates the need for sophisticated multi‐response data handling. In addition, small‐sample considerations and multi‐distribution effects that may be inherent do not restrict the applicability of the method presented herein by this type of experimentation.

This investigation provides a new angle to the published methods of multi‐response optimisation by supporting Taguchi's design of experiments methods through a multi‐ranking scheme that leads to non‐parametric factor resolution.

The aim of this paper is to circumvent the multi‐distribution effects and small sample constraints that may arise in unreplicated‐saturated fractional factorial designs during construction blueprint screening.

A simple additive ranking scheme is devised based on converting the responses of interest to rank variables regardless of the nature of each response and the optimization direction that may be issued for each of them. Collapsing all ranked responses to a single rank response, appropriately referred to as “Super‐Ranking”, allows simultaneous optimization for all factor settings considered.

The Super‐Rank response is treated by Wilcoxon's rank sum test or Mann‐Whitney's test, aiming to establish possible factor‐setting differences by exploring their statistical significance. An optimal value for each response is predicted.

It is stressed, by example, that the model may handle simultaneously any number of quality characteristics. A case study based on a real geotechnical engineering project is used to illustrate how this method may be applied for optimizing simultaneously three quality characteristics that belong to each of the three possible cases, i.e. “nominal‐is‐best”, “larger‐is‐better”, and “smaller‐is‐better” respectively. For this reason, a screening set of experiments is performed on a professional CAD/CAE software package making use of an L₈(2⁷) orthogonal array where all seven factor columns are saturated by group excavation controls.

The statistical nature of this method is discussed in comparison with results produced by the desirability method for the case of exhausted degrees of freedom for the error. The case study itself is a unique paradigm from the area of construction operations management.

This paper aims to present a multi-response robust design (RD) optimization approach for U-shaped assembly cells (ACs) with multi-functional walking-workers by using operational design (OD) factors in a simulation setting. The proposed methodology incorporated the design factors related to the operation of ACs into an RD framework. Utilization of OD factors provided a practical design approach for ACs addressing system robustness without modifying the cell structure.

Taguchi’s design philosophy and response surface meta-models have been combined for robust simulation optimization (SO). Multiple performance measures have been considered for the study and concurrently optimized by using a multi-response optimization (MRO) approach. Simulation setting provided flexibility in experimental design selection and facilitated experiments by avoiding cost and time constraints in real-world experiments.

The present approach is illustrated through RD of an AC for performance measures: average throughput time, average WIP inventory and cycle time. Findings are in line with expectations that a significant reduction in performance variability is attainable by trading-off optimality for robustness. Reductions in expected performance (optimality) values are negligible in comparison to reductions in performance variability (robustness).

ACs designed for robustness are more likely to meet design objectives once they are implemented, preventing changes or roll-backs. Successful implementations serve as examples to shop-floor personnel alleviating issues such as operator/supervisor resistance and scepticism, encouraging participation and facilitating teamwork.

ACs include many activities related to cell operation which can be used for performance optimization. The proposed framework is a realistic design approach using OD factors and considering system stochasticity in terms of noise factors for RD optimization through simulation. To the best of the authors’ knowledge, it is the first time a multi-response RD optimization approach for U-shaped manual ACs with multi-functional walking-workers using factors related to AC operation is proposed.

The purpose of this paper is to optimize the environmental growth factors for maximum yield of biomass and protein content of Spirulina platensis var lonor.

Central composite design with four independent variables (namely, Temperature (°C), Light intensity (μmol m⁻² s⁻¹), incubation period (days) and inoculum concentration (per cent)) and two dependent variables (namely, Biomass yield (g/L) and Protein content (per cent)) were used for designing the experiment. The quadratic model was selected for analysis of data using analysis of variance and optimization was performed using response surface methodology (RSM). Central Food Technological Research Institute (CFTRI) medium was used for growing the organisms.

The predicted values of optimization showed that the maximum yield of dried biomass (1.13 g/L) and protein (57.45 per cent) were obtained at temperature of 33.6°C, light intensity of 67.50 μmol m⁻² s⁻¹, incubation period of 16 days and inoculum concentration of 20 per cent. The actual values of biomass (1.23 g/L) and protein (58.46 per cent) yield were almost similar to the predicted responses.

The data obtained with this optimization method help Spirulina researchers/producers to cultivate the S. platensis var lonor strain with maximum biomass and protein content using the CFTRI medium.

Earlier works on optimization of growth conditions for Spirulina biomass, revealed that maximum of two factors were employed at a time using factorial design. But unlike other studies here in this study, optimization of multi factors using at a time using RSM on better biomass and protein yield was employed.

The objective of this work is to examine the factors that influence the decision of Brazilian companies to export.

A questionnaire was used to collect data from a statistically significant sample of 318 Brazilian exporting firms. Three types of study were carried out; an exploratory study that simplified the information through a principal component analysis, a descriptive study of the factors that influence the decision to export by Brazilian companies and a study of variance that allowed comparison of groups of firms.

After an analysis of the literature, 11 factors were proposed that influence the decision to export. Two unique factors resulted from the principal components analysis: “resource optimisation” and “performance and development”. The “performance and development” factor was more valued than the “resource optimisation” factor. From the analysis of variance, significant differences were only found in the variable “firm size”.

This paper contributes to the literature by presenting further knowledge of export factors in developing countries, the management of companies and instruments for decision making in the area of internationalisation.

Esta investigación tiene como objetivo examinar los factores que influyen en la decisión de exportar de las empresas brasileñas.

Se utilizó un cuestionario para recopilar datos de una muestra estadísticamente significativa de 318 empresas exportadoras brasileñas. Se realizan tres tipos de estudio, un estudio exploratorio que permite simplificar la información a través de un análisis de componentes principales, un estudio descriptivo de los factores que influyen en la exportación de las empresas de Brasil, y un estudio de varianza que permite comparar grupos de empresas.

Después de un análisis de la literatura, se propusieron 11 factores que influyen en la decisión de exportar. Del análisis de componentes principales, resultan dos únicos factores: “optimización de recursos” y “rendimiento y desarrollo”. El factor “rendimiento y desarrollo” es más valorado que el factor “optimización de recursos”. Del análisis de varianza, solo se encontraron diferencias significativas en la variable “tamaño de la empresa”.

Este trabajo contribuye a la literatura al presentar un mayor conocimiento de los factores de exportación en los países en desarrollo, además, los resultados permiten mejorar la gestión de empresas, esencialmente en la toma de decisiones en el área de la internacionalización.

The purpose of this paper is to develop a building information modelling (BIM)-based multi-objective optimization (MOO) framework for volumetric analysis of buildings during early design stages. The objective is to optimize volumetric spaces (3D) instead of 2D spaces to enhance space utilization, thermal comfort, constructability and rental value of buildings

The integration of two fundamental concepts – BIM and MOO, forms the basis of proposed framework. In the early design phases of a project, BIM is used to generate precise building volume data. The non-sorting genetic algorithm-II, a MOO algorithm, is then used to optimize extracted volume data from 3D BIM models, considering four objectives: space utilization, thermal comfort, rental value and construction cost. The framework is implemented in context of a school of architecture building project.

The findings of case study demonstrate significant improvements resulting from MOO of building volumes. Space utilization increased by 30%, while thermal comfort improved by 20%, and construction costs were reduced by 10%. Furthermore, rental value of the case study building increased by 33%.

The proposed framework offers practical implications by enabling project teams to generate optimal building floor layouts during early design stages, thereby avoiding late costly changes during construction phase of project.

The integration of BIM and MOO in this study provides a unique approach to optimize building volumes considering multiple factors during early design stages of a project

The construction industry is tasked with creating sustainable, efficient and cost-effective buildings. This study aims to develop a building information modeling (BIM)-based multiobjective optimization (MOO) model integrating the nondominated sorting genetic algorithm III (NSGA-III) to enhance sustainability. The goal is to reduce embodied energy and cost in the design process.

Through a case study research method, this study uses BIM, NSGA-III and real-world data in five phases: literature review, identification of factors, BIM model development, MOO model creation and validation in the architecture, engineering and construction sectors.

The innovative BIM-based MOO model optimizes embodied energy and cost to achieve sustainable construction. A commercial building case study validation showed a reduction of 30% in embodied energy and 21% in cost. This study validates the model’s effectiveness in integrating sustainability goals, enhancing decision-making, collaboration, efficiency and providing superior assessment.

This model delivers a unified approach to sustainable design, cutting carbon footprint and strengthening the industry’s ability to attain sustainable solutions. It holds potential for broader application and future integration of social and economic factors.

The research presents a novel BIM-based MOO model, uniquely focusing on sustainable construction with embodied energy and cost considerations. This holistic and innovative framework extends existing methodologies applicable to various buildings and paves the way for additional research in this area.

The chapter examines a comprehensive review of cross-disciplinary literature in the domain of supply chain forecasting during research period 1991–2017, with the primary aim of exploring the growth of literature from operational to demand centric forecasting and decision making in service supply chain systems. A noted list of 15,000 articles from journals and search results are used from academic databases (viz. Science Direct, Web of Sciences). Out of various content analysis techniques (Seuring & Gold, 2012), latent sementic analysis (LSA) is used as a content analysis tool (Wei, Yang, & Lin, 2008; Kundu et al., 2015). The reason for adoption of LSA over existing bibliometric techniques is to use the combination of text analysis and mining method to formulate latent factors. LSA creates the scientific grounding to understand the trends. Using LSA, Understanding future research trends will assist researchers in the area of service supply chain forecasting. The study will be beneficial for practitioners of the strategic and operational aspects of service supply chain decision making. The chapter incorporates four sections. The first section describes the introduction to service supply chain management and research development in this domain. The second section describes usage of LSA for current study. The third section describes the finding and results. The fourth and final sections conclude the chapter with a brief discussion on research findings, its limitations, and the implications for future research. The outcomes of analysis presented in this chapter also provide opportunities for researchers/professionals to position their future service supply chain research and/or implementation strategies.

In this article, a novel hybrid composite patch consisting of unidirectional carbon fiber and glass fiber is considered for repair of the aircraft structure. The purpose of this paper is to assess the performance of hybrid composite patch repair of cracked structure and propose an optimized solution to a designer for selection of the appropriate level of a parameter to ensure effective repair solution.

Elastic properties of the hybrid composites are estimated by micromechanical modeling. Performance of hybrid composite patch repair is evaluated by numerical analysis of stress intensity factor (SIF), shear stress, and peel stress. Design of experiment is used to determine responses for a different combination of design parameters. The second-order mathematical model is suggested for SIF and peel stress. Adequacy of the model is checked by ANOVA and used as a fitness function. Multiobjective optimization is carried out with a genetic algorithm to arrive at the optimal solution.

The hybrid composite patch has maintained equilibrium between the SIF reduction and rise of the peel stress. The repair efficiency and repair durability can be ensured by selection of an optimum value of volume fraction of glass fiber, applied stress, and adhesive thickness.

The composite patch with varying stiffness is realized by hybridization with different volume fraction of fibers. Analysis and identification of optimum parameter to reduce the SIF and peel stress for hybrid composite patch repair are presented in this article.

The purpose of this paper is to identify the influencing factors which cause casting defects and determination of optimum value of factors to minimize these defects in a melt shop industry, situated in north India. Percentage contribution of these factors is also estimated to develop an empirical expression between process performance and independent input variables.

Optimization technique for melt shop process parameters of a cast iron differential housing cover based on the Taguchi method is proposed. The focus of this paper is on the robustness of the sand casting process and the case study is based upon a leading automobile foundry industry, located in north India. Taguchi's experimental design and regression analysis techniques are used to optimize the control factors, resulting in improvement of the product quality and stability. The various confirmation tests are also carried out in the range of process parameters.

The outcome of this case study is to optimize the process parameters of the melt shop process, which leads to minimizing the casting defects. The process parameters considered are: mild steel, pig iron, cast iron, ferrosilicon, lime stone, ferromanganese, cock and ferrochrome. Best proportions of charge constituents that are contributing to casting defects in melt shop are identified in the first stage. These identified factors are analyzed using “Design of Experiments” approach in the second stage. ANOVA analysis is also performed for robust design of factor values and an appropriate empirical model is formulated.

A lot of effort has been put into developing the appropriate empirical model for the automobile foundry industry but additional work may also be done for gating design of the casting industry.

The paper shows that the process parameters of any casting industry can be optimized and casting defects in the melt shop can be identified in the first stage.

The research findings could be applied to various manufacturing industries, especially the casting industries.