Nowadays, process optimization has been an interest in engineering design for improving the performance and reducing cost. In practice, in addition to uncertainty or noise parameters, a comprehensive optimization model must be able to attend other circumstances which might be imposed in problems under real operational conditions such as dynamic objectives, multi-responses, various probabilistic distribution, discrete and continuous data, physical constraints to design parameters, performance cost, computational complexity and multi-process environment. The main goal of this paper is to give a general overview on topics with brief systematic review and concise discussions about the recent development on comprehensive robust design optimization methods under hybrid aforesaid circumstances. Both optimization methods of mathematical programming based on Taguchi approach and robust optimization based on scenario sets are briefly described. Metamodels hybrid robust design is discussed as an appropriate methodology to decrease computational complexity in problems under uncertainty. In this context, the authors’ policy is to choose important topics for giving a systematic picture to those who wish to be more familiar with recent studies about robust design optimization hybrid metamodels, also by attending real circumstances in practice. In particular, production and project management are considered as two important methodologies that could be improved by applications of advanced robust design combining with metamodel methods.

This paper presents a new approach for design concept selection by using an integrated Fuzzy Analytical Hierarchy Process (FAHP) and an Interval-valued intuitionistic fuzzy modified TOP-SIS (IVIF-modified TOPSIS) model. The integrated model which uses the improved score func-tion and a weighted normalized Euclidean distance method for the calculation of the separation measures of alternatives from the positive and negative intuitionistic ideal solutions provides a new approach for the computation of intuitionistic fuzzy ideal solutions. The results of the two approaches are integrated using a reflection defuzzification integration formula. To ensure the feasibility and the rationality of the integrated model, the method is successfully applied for eval-uating and selecting some design related problems including a real-life case study for the selec-tion of the best concept design for a new printed-circuit-board (PCB) and for a hypothetical ex-ample. The model which provides a novel alternative, has been compared with similar computa-tional methods in the literature.

Lean product development is a major innovative business strategy that employs sets of practices to achieve an efficient, innovative and a sustainable product development. Despite the many benefits and high hopes in the lean strategy, many companies are still struggling, and unable to either achieve or sustain substantial positive results with their lean implementation efforts. However, as the first step towards addressing this issue, this paper seeks to propose a systematic model that considers the administrative and implementation limitations of lean thinking practices in the product development process. The model which is based on the integration of fuzzy Shannon’s entropy and Modified Technique for Order Preference by Similarity to the Ideal Solution (M-TOPSIS) model for the lean product development practices implementation with respective to different criteria including management and leadership, financial capabilities, skills and expertise and organization culture, provides a guide or roadmap for product development managers on the lean implementation route.

In this paper, a subjective and objective fuzzy-based Analytical Hierarchy Process (AHP) model is proposed. The model which is based on a newly defined evaluation matrix replaces the fuzzy comparison matrix (FCM) in the traditional fuzzy AHP model, which has been found ineffective and time-consuming when criteria/alternatives are increased. The main advantage of the new model is that it is straightforward and completely eliminates the repetitive adjustment of data that is common with the FCM in traditional AHP model. The model reduces the complete dependen-cy on human judgment in prioritization assessment since the weights values are solved automati-cally using the evaluation matrix and the modified priority weight formula in the proposed mod-el. By virtue of a numerical case study, the model is successfully applied in the determination of the implementation priorities of lean practices for a product development environment and com-pared with similar computational methods in the literature.

Although mediating variables are of importance in the HPWS-Performance nexus, identification of the fitting intervening variable(s) that mediates between the nexuses is a crucial issue, and the number of the mediating variable(s) which would unravel the so called “black box” remains un-resolved and the most burning theoretical and empirical challenge in the Strategic HRM research field. Likewise, moderating variables do have impacts on the HPWS-Performance nexus since the context within which firm operates and the strategic orientations of firms have bearing on the application of HR practices and its effect on the firm’s performance. Given this, this work pro-posed a model which is theorized based on the far-reaching survey of the extant literature. The model theorizes the mediating role of employee creativity and potential moderating role of man-agement philosophy in the HPWS-performance nexus via extensive theoretical and logical argu-ment and exposition. Also, as established by the extant research, HPWS measurement in the context of SME should be employee-oriented, therefore, this work measured HPWS with eight employee-oriented HR architectures, and performance is measured with financial and non-financial performance, for being a widespread adopted measurement in many fields of study.

Cell load variation is considered a significant shortcoming in scheduling of cellular manufacturing systems. In this article, a new method is proposed for scheduling dynamic cellular manufacturing systems in the presence of bottleneck and parallel machines. The aim of this method is to control cell load variation during the process of determining the best trading off values between in-house manufacturing and outsourcing. A genetic algorithm (GA) is developed because of the high potential of trapping in the local optima, and results are compared with the results of LINGO® 12.0 software. The Taguchi method (an L_9 orthogonal optimization) is used to estimate parameters of GA in order to solve experiments derived from literature. An in-depth analysis is conducted on the results in consideration of various factors, and control charts are used on machine-load variation. Our findings indicate that the dynamic condition of product demands affects the routing of product parts and may induce machine-load variations that yield to cell-load diversity. An increase in product uncertainty level causes the loading level of each cell to vary, which in turn results in the development of “complex dummy sub-cells”. The effect of the complex sub-cells is measured using another mathematical index. The results showed that the proposed GA can provide solutions with limited cell-load variations.

Job selection and scheduling are among the most important decisions for production planning in today’s manufacturing systems. However, the studies that take into account both problems together are scarce. Given that such problems are strongly NP-hard, this paper presents an approach based on two heuristic algorithms for simultaneous job selection and scheduling. The objective is to select a subset of jobs and schedule them in such a way that the total net profit is maximized. The cost components considered include jobs & apos; processing costs and weighted earliness/tardiness penalties. Two heuristic algorithms; namely scatter search (SS) and simulated annealing (SA), were employed to solve the problem for single machine environments. The algorithms were applied to several examples of different sizes with sequence-dependent setup times. Computational results were compared in terms of quality of solutions and convergence speed. Both algorithms were found to be efficient in solving the problem. While SS could provide solutions with slightly higher quality for large size problems, SA could achieve solutions in a more reasonable computational time.

The role of leaders toward the development of entrepreneurship has been fully acknowledged. However, Leaders’ characteristics such as strategic improvisation and entrepreneurial self-efficacy were mainly examined in the private sector. Hence, it is imperative to extend empirical studies to public sector organizations. The present study, therefore, proposed and validated a model linking leaders’ strategic improvisation and entrepreneurial self-efficacy to corporate entrepreneurship in Nigerian higher education institutions (HEIs). Using a structured questionnaire, 220 responses were generated from large HEIs in Kano State, Nigeria. The data of the study was analysed using SmartPLS 3.0 to ascertain both measurement and structural model validity of the model. The results showed that both leaders’ strategic improvisation and entrepreneurial self-efficacy were significantly related to corporate entrepreneurship in HEIs. Implication and limitation of the study; and suggestions for future study are also provided.

Improving employee and organizational performance has been the main concern of many organizations for decades and several factors have also been studied as predictors of employee performance in organizations. However, studies that comprehensively measured all the dimensions of employee performance that enhance organizational effectiveness are limited. This paper explored the influence of HRM practices on the employee performance in the Nigerian public sector. Using cross sectional method on 265 participants from local government areas, the findings revealed that HRM practices such as job rotation, job autonomy and career planning had a significant and positive influence on all the three dimensions of employee performance (task, contextual and adaptive performance).

In this paper, we propose an exponential-related function (ER) and develop an intuitionistic fuzzy TOPSIS model based on the function (IF-TOPSISEF) to solve multi-attribute decision making (MADM) problems in which the performance ratings are expressed in intuitionistic fuzzy sets (IFSs). The main advantage of this new approach is that the exponential-related function is able to represent the aggregated effect of the positive and negative evaluations in the performance ratings of alternatives based on the intuitionistic fuzzy set (IFS) data. It also serves as a mean for the computations of the separation measures of each alternative from the intuitionistic fuzzy positive and negative ideal solutions to determine the relative closeness coefficients. To demonstrate the effectiveness of the proposed method, the proposed IF-TOPSISEF is applied for the evaluation of the concept designs of a part in an HDD machine (The drill pipe slider), and for some hypothetical examples.