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The purpose of this paper is to present a tri-optimization approach to optimize design solutions regarding the building shape and envelope properties considering their implications on thermal comfort, visual comfort and building energy consumption (EN). The optimization approach has been applied to obtain the optimal design solutions in five typical cities across all climatic regions of China.

The method comprises a tri-optimization process with nine main steps to optimize the three objectives (thermal comfort, visual comfort and building EN). The design variables considered are four types of building shape (pyramid, rectangular, cylindrical and dome shape) and different envelope properties (insulation thickness [INS] of external walls/roof, window type [WT] and window-to-envelop surface area ratio [WESR]). The optimization is performed by using the Taguchi and constraint limit method.

The results show that the optimal design solutions for all climatic regions favor cylindrical shape and triple-layer low-E glazing window. The highest insulation level of 150 mm is preferred in three climatic regions, and the INS of 90 mm is preferred in the other two climate regions. In total, 10% WESR is preferred in all climatic regions, except the mild region. When the constraint limit of lighting intensity requirement by Leadership in Energy and Environmental Design (LEED) is applied, the rectangular shape building is the optimal solution for those with 10% WESR.

The method proposed in the paper is innovative in that it optimizes three different objectives simultaneously in building design with better accuracy and calculation speed.

Building designers can easily follow the proposed design guide in their practice which effectively bridges the gap between theory and practice. The optimal design solutions can provide a more comfortable living environment and yet less EN, which can help achieve the sustainability requirement of green buildings.

The solutions presented in the paper can serve as a useful guide for practical building designers which creates economic and commercial impact. In addition, the theory and practical examples of the study can be used by building regulators to improve the energy-efficient building design standard in China.

The research is the first attempt that adopts tri-optimization approach to generate the optimal solutions for building shape and envelope design. The tri-optimization approach can be used by building designers to generate satisfactory design solutions from the architectural viewpoint and meanwhile to find combinations of the building shape and envelope properties that lead to design solutions with optimal building performance.

An economic statistical design approach takes statistical properties into account while designing control charts economically. It improves both statistical design and economic design. In this paper, we present a statistically constrained economic model for the optimal design of S control chart for controlling process variability. In the model, the process quality can be affected by an assignable cause resulting in a shift of the variance of the distribution of output when it is operating according to its capability. The parameters are obtained by minimizing a total cost function proposed by Lorenzen and Vance, which is embellished with Taguchi loss function, subject to additional statistical constraints on average run length or average time‐to‐signal (ATS). Sensitivity analysis of the minimum cost will be performed to depict the effect of the choice of ATS bounds.

The purpose of this paper is to present some fundamental and critical differences between the two methods of experimental design (i.e. Taguchi and classical design of experiments (DOE)). It also aims to present an application of Taguchi method of experimental design for the development of an optical fiber sensor in a cost effective and timely manner.

The first part of the paper shows the differences between classical DOE and Taguchi methods from a practitioner's perspective. The second part of the paper illustrates a simple framework which provides guidance in the selection of a suitable DOE strategy. The last part is focused on a simple case study demonstrating the power of Taguchi methods of experimental design.

One of the key questions from many quality and production related personnel in organisations are “when to use Taguchi and Classical DOE?”. The purpose of this paper is to make an attempt to address the above question from a practitioner's perspective.

The case study is based on Taguchi method of experimental design. It would be great to see the results of the study if classical DOE is performed to this study.

The paper will be an excellent resource for both research and industrial fraternities who are involved in DOE projects.

Case study and frame work.

This paper aims to study the hub transportation system in supply chain networks which would contribute to reducing costs and environmental pollution, as well as to economic development and social responsibility. As not all customers tend to buy green products, several customer groups should be considered in terms of need type.

In this paper, a multi-objective hub location problem is developed for designing a sustainable supply chain network based on customer segmentation. It deals with the aspects of economic (cost reduction), environment (minimizing greenhouse gas emissions by the transport sector) and social responsibility (creating employment and community development). The epsilon-constraint method and augmented epsilon-constraint (AEC) method are used to solve the small-sized instances of this multi-objective problem. Due to the non-deterministic polynomial-time hardness of this problem, two non-dominated sorting genetic algorithm-II (NSGA-II) and multi-objective grey wolf optimizer (MOGWO) metaheuristic algorithms are also applied to tackle the large-sized instances of this problem.

As expected, the AEC method is able to provide better Pareto solutions according to the goals of the decision-makers. The Taguchi method was used for setting the parameters of the two metaheuristic algorithms. Considering the meaningful difference, the MOGWO algorithm outperforms the NSGA-II algorithm according to the rate of achievement to two objectives simultaneously and the spread of non-dominance solutions indexes. Regarding the other indexes, there was no meaningful difference between the performance of the two algorithms.

The model of this research provides a comprehensive solution for supply chain companies that want to achieve a rational balance between the three aspects of sustainability.

The importance of considering customer diversity on the one hand and saving on hub transportation costs, on the other hand, triggered us to propose a hub location model for designing a sustainable supply chain network based on customer segmentation.

This paper seeks to provide a tool for decision makers to make more informed decisions regarding their outsourcing decisions and selection of the appropriate supplier.

The method uses the Taguchi loss function for the inclusion of intangibles in the evaluation and selection of suppliers. Intangibles are defined as factors that have an impact on the selection of an appropriate supplier but are not easily quantified to be included in the financial evaluation. These intangibles are classified as the benefits and risks of using a supplier to perform the outsourcing function. A decision maker has certain expectations regarding these intangibles and a loss occurs when a supplier's performance does not meet the decision maker's expectations. The Taguchi loss function has been selected as a means of measuring the loss. The decision maker defines the target value and the specification limits for each benefit and risk category. The weighted loss scores are calculated where the weights are the importance ratings assigned to benefit/risk categories by the decision maker. Based on this analysis each supplier will receive a weighted loss score for all the pertinent benefit categories and one weighted loss score for all the risk categories. To achieve a single measure, the aforementioned weighted loss scores are combined to determine a single aggregate loss score for each supplier, which is then used to rank them. The supplier who receives the highest ranking (minimum loss score) will be selected to perform the outsourcing function.

The procedure proposed here can help companies to identify the best supplier to perform an outsourcing function.

The paper presents a phased decision model that begins with economic evaluation and then uses Taguchi functions to measure the impact of intangibles.

Experimental design (ED) is a powerful technique which involves the process of planning and designing an experiment so that appropriate data can be collected and then analysed by statistical methods, resulting in objective and valid conclusions. It is an alternative to the traditional inefficient and unreliable one‐factor‐at‐a‐time approach to experimentation, where an experimenter generally varies one factor or process parameter at a time keeping all other factors at a constant level. This paper presents a step‐by‐step approach to the optimisation of a production process (of retaining a metal ring in a plastic body by a hot forming method) through the utilisation of Taguchi methods of experimental design. The experiment enabled the behaviour of the system to be understood by the engineering team in a short period of time and resulted in significantly improved performance (with the opportunity to design further experiments for possible greater improvements).

This study aims to develop a mathematical programming model for preemptive multi-mode resource-constrained project scheduling problems in construction with the objective of levelling resources considering renewable and non-renewable resources.

The proposed model was solved by the exact method and the genetic algorithm integrated with the solution modification procedure coded with MATLAB software. The Taguchi method was applied for setting the parameters of the genetic algorithm. Different numerical examples were used to show the validation of the proposed model and the capability of the genetic algorithm in solving large-sized problems. In addition, the sensitivity analysis of two parameters, including resource factor and order strength, was conducted to investigate their impact on computational time.

The results showed that preemptive activities obtained better results than non-preemptive activities. In addition, the validity of the genetic algorithm was evaluated by comparing its solutions to the ones of the exact methods. Although the exact method could not find the optimal solution for large-scale problems, the genetic algorithm obtained close to optimal solutions within a short computational time. Moreover, the findings demonstrated that the genetic algorithm was capable of achieving optimal solutions for small-sized problems. The proposed model assists construction project practitioners with developing a realistic project schedule to better estimate the project completion time and minimize fluctuations in resource usage during the entire project horizon.

There has been no study considering the interruption of multi-mode activities with fluctuations in resource usage over an entire project horizon. In this regard, fluctuations in resource consumption are an important issue that needs the attention of project planners.

Compliant mechanism has been receiving a great interest in precision engineering. However, analytical methods involving their behavior analysis is still a challenge because there are unclear kinematic behaviors. Especially, design optimization for compliant mechanisms becomes an important task when the problem is more and more complex. Therefore, the purpose of this study is to design a new hybrid computational method. The hybridized method is an integration of statistics, numerical method, computational intelligence and optimization.

A tensural bistable compliant mechanism is used to clarify the efficiency of the developed method. A pseudo model of the mechanism is designed and simulations are planned to retrieve the data sets. Main contributions of design variables are analyzed by analysis of variance to initialize several new populations. Next, objective functions are transformed into the desirability, which are inputs of the fuzzy inference system (FIS). The FIS modeling is aimed to initialize a single-combined objective function (SCOF). Subsequently, adaptive neuro-fuzzy inference system is developed to modeling a relation of the main geometrical parameters and the SCOF. Finally, the SCOF is maximized by lightning attachment procedure optimization algorithm to yield a global optimality.

The results prove that the present method is better than a combination of fuzzy logic and Taguchi. The present method is also superior to other algorithms by conducting non-parameter tests. The proposed computational method is a usefully systematic method that can be applied to compliant mechanisms with complex structures and multiple-constrained optimization problems.

The novelty of this work is to make a new approach by combining statistical techniques, numerical method, computational intelligence and metaheuristic algorithm. The feasibility of the method is capable of solving a multi-objective optimization problem for compliant mechanisms with nonlinear complexity.

This paper aims to present a new model for solving the integrated production planning and scheduling. Usually, the two decision levels are treated sequentially because of their complexity. Scheduling depends on the lot sizes calculated at the tactical level and ignoring scheduling constraints generates unrealistic and inconsistent decisions. Therefore, integrating more detail scheduling constraint in production planning is important for managing efficiently operations. Therefore, an integrated model was developed, and two evolutionary optimization approaches were suggested for solving it, namely, genetic algorithm (GA) and the hybridization of simulated annealing (SA) with GA HSAGA. The proposed algorithms have some parameters that must be adjusted using Taguchi method. Therefore, to evaluate the proposed algorithm, the authors compared the results given by GA and the hybridization. The SA-based local search is embedded into a GA search mechanism to move the GA away from being closed within local optima. The analysis shows that the combination of simulated annealing with GA gives better solutions and minimizes the total production costs.

The paper opted for an approached resolution method particularly GA and simulated annealing. The study represents a comparison between the results found using GA and the hybridization of simulated annealing and GA. A total of 45 instances were studied to evaluate job-shop problems of different sizes.

The results illustrate that for 36 instances of 45, the hybridization of simulated annealing and GA HSAGA has provided best production costs. The efficiency demonstrated by HSAGA approach is related to the combination between the exploration ability of GA and the capacity to escape local optimum of simulated annealing.

This study provides a new resolution approach to the integration of planning and scheduling while considering a new operational constrain. The model suggested aims to control the available capacity of the resources and guaranties that the resources to be consumed do not exceed the real availability to avoid the blocking that results from the unavailability of resources. Furthermore, to solve the MILP model, a GA is proposed and then it is combined to simulated annealing.

This paper aims to optimize the process parameters of the fused deposition modelling (FDM) process using the Grey-based Taguchi method and the results to be verified based on a technique for order preference by similarity to ideal solution (TOPSIS) and analytical hierarchy process (AHP) calculation.

The optimization of process parameters is gaining a potential role to develop robust products. In this context, this paper presents the parametric optimization of the FDM process using Grey-based Taguchi, TOPSIS and AHP method. The effect of slice height (SH), part fill style (PFS) and build orientation (BO) are investigated with the response parameters machining time, surface roughness and hardness (HD). Multiple objective optimizations were performed with weights of w1 = 60%, w2 = 20% and w3 = 20%. The significance of the process parameters over response parameters is identified through analysis of variance (ANOVA). Comparisons are made in terms of rank order with respect to grey relation grade (GRG), relative closeness and AHP index values. Response table, percentage contributions of process parameters for both GRG and TOPSIS evaluation are done.

The optimum factor levels are identified using GRG via the Grey Taguchi method and TOPSIS via relative closeness values. The optimized factor levels are SH (0.013 in), PFS (solid) and BO (45°) using GRG and SH (0.013 in), PFS (sparse-low density) and BO (45°) using TOPSIS relative closeness value. SH has higher significance in both Grey relational analysis and TOPSIS which were analysed using ANOVA.

In this research, the multiple objective optimizations were done on an automotive component using GRG, TOPSIS and AHP which showed a 27% similarity in their ranking order among the experiments. In the future, other advanced optimization techniques will be applied to further improve the similarity in ranking order.

The study presents the case of an automotive component, which illustrates practical relevance.

In several research studies, optimization was done on the standard test specimens but not on a real-time component. Here, the multiple objective optimizations were applied to a case automotive component using Grey-based Taguchi and verified with TOPSIS. Hence, an effort has been taken to find optimum process parameters on FDM, for achieving smooth, hardened automotive components with enhanced printing time. The component can be explored as a replacement for the existing product.