Siti Nurmaya Musa

An integrated model for green supplier selection under fuzzy environment: application of data envelopment analysis and genetic programming approach

Supplier evaluation plays a critical role in a successful supply chain management. Hence, the evaluation and selection of the right suppliers have become a central decision of manufacturing business activities around the world. Consequently, numerous individual and integrated methods have been presented to evaluate and select suppliers. The current literature shows that hybrid artificial intelligence (AI)-based models have received much attention for supplier evaluation. Integrated data envelopment analysis–artificial neural network (DEA–ANN) is one of the combined methods that have recently garnered great attention from academics and practitioners. However, DEA–ANN model has some drawbacks, which make some limitation in the evaluation process. In this study, we aim at improving the previous DEA–AI models by integrating the Kourosh and Arash method as a robust model of DEA with a new AI approach namely genetic programming (GP) to overcome the shortcomings of previous DEA–AI models in supplier selection. Indeed, in this paper, GP provides a robust nonlinear mathematical equation for the suppliers’ efficiency using the determined criteria. To validate the model, adaptive neuro-fuzzy inference system as a powerful tool was used to compare the result with GP-based model. In addition, parametric analysis and unseen data set were used to validate the precision of the model.

A decision support model for sustainable supplier selection in sustainable supply chain management

Selecting the most suitable sustainability criteria using questionnaire.Applying various statistical tests to validate the developed criteria.Developing an integrated FPP-FTOPSIS model for sustainable supplier selection.Calculating weights using FPP and ranking suppliers using FTOPSIS.Explaining the model using the real case study based on the developed criteria. This study is aimed at developing the most important and applicable criteria and their corresponding sub-criteria for sustainable supplier selection through a questionnaire-based survey. In addition, a hybrid model is proposed to identify the most sustainable supplier with respect to the determined attributes using an Iranian textile manufacturing company as case study. The first contribution of the research is developing a comprehensive list of sustainability criteria and sub-criteria and incorporating them into a questionnaire and distributing the questionnaire to academics and practitioners for establishing the importance and applicability of these criteria and sub-criteria. In order to demonstrate the robustness of the data obtained from the questionnaire, different established statistical tests (Cronbachs alpha and Mann-Withney U-Test) were applied. The results show that economic aspect is still the most essential aspect, followed by environmental aspect and finally social aspect. The second contribution is the development of a new hybrid model by integrating fuzzy preference programing, as one of the newest and most accurate fuzzy modification of Analytical Hierarchy Process, with Fuzzy Technique for Order of Preference by Similarity to Ideal Solution. Fuzzy Preference Programming overcomes the shortcomings of the previous methods for obtaining the weight and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution prioritizes the suppliers and finds the best one under uncertainty. Generally, the developed list provides a basis that is helpful in improving suppliers performance in terms of sustainability which leads to improvement in sustainable supply chain management performance. In addition, the developed hybrid model can deal with inconsistency, uncertainty and calculation complexity. Generally, the framework (including the first and second objectives) can be applied by managers to evaluate and determine their appropriate suppliers in the presence of uncertainty.

Fuzzy-based sustainability evaluation method for manufacturing SMEs using balanced scorecard framework

Sustainability has become a necessity, partly due to the threats created by traditional manufacturing practices, and due to regulations imposed by stakeholders. Performance evaluation is an important component of sustainability initiatives in manufacturing organizations. This study proposes a sustainability evaluation method for manufacturing SMEs using integrated fuzzy analytical hierarchal process (FAHP) and fuzzy inference system (FIS) approach. The performance indicators are identified from literature considering the characteristics of SMEs. Balanced scorecard framework is used to categorize the indicators among its four aspects. The linguistic variables are used to collect the opinions of decision makers about the performance ratings and importance of the aspects and corresponding indicators. The FAHP method is applied to determine the relative weights of measures and indicators. The performance ratings of the organization with respect to indicators and relative weights of indicators are combined to obtain the weighted performance ratings. The weighted performance ratings are considered as inputs to FIS. The hierarchal FIS is applied to derive the overall sustainability performance. Using a case study of manufacturing SME, the sustainability score of the organization was elicited in accordance with this procedure. Consequently, a sensitivity analysis of the proposed method reveals the most important basic indicators affecting overall sustainability, identifying areas which decision makers should place special attention. This method can also assist managers of larger enterprises to assess the effectiveness of their sustainability strategies, especially when dealing with suppliers from the SMEs.

GEP-based method to formulate adhesion strength and hardness of Nb PVD coated on Ti–6Al–7Nb aimed at developing mixed oxide nanotubular arrays

PVD process as a thin film coating method is highly applicable for both metallic and ceramic materials, which is faced with the necessity of choosing the correct parameters to achieve optimal results. In the present study, a GEP-based model for the first time was proposed as a safe and accurate method to predict the adhesion strength and hardness of the Nb PVD coated aimed at growing the mixed oxide nanotubular arrays on Ti67. Here, the training and testing analysis were executed for both adhesion strength and hardness. The optimum parameter combination for the scratch adhesion strength and micro hardness was determined by the maximum mean S/N ratio, which was 350W, 20 sccm, and a DC bias of 90V. Results showed that the values calculated in the training and testing in GEP model were very close to the actual experiments designed by Taguchi. The as-sputtered Nb coating with highest adhesion strength and microhardness was electrochemically anodized at 20V for 4h. From the FESEM images and EDS results of the annealed sample, a thick layer of bone-like apatite was formed on the sample surface after soaking in SBF for 10 days, which can be connected to the development of a highly ordered nanotube arrays. This novel approach provides an outline for the future design of nanostructured coatings for a wide range of applications.

Multi-Item Multiperiodic Inventory Control Problem with Variable Demand and Discounts: A Particle Swarm Optimization Algorithm

A multi-item multiperiod inventory control model is developed for known-deterministic variable demands under limited available budget. Assuming the order quantity is more than the shortage quantity in each period, the shortage in combination of backorder and lost sale is considered. The orders are placed in batch sizes and the decision variables are assumed integer. Moreover, all unit discounts for a number of products and incremental quantity discount for some other items are considered. While the objectives are to minimize both the total inventory cost and the required storage space, the model is formulated into a fuzzy multicriteria decision making (FMCDM) framework and is shown to be a mixed integer nonlinear programming type. In order to solve the model, a multiobjective particle swarm optimization (MOPSO) approach is applied. A set of compromise solution including optimum and near optimum ones via MOPSO has been derived for some numerical illustration, where the results are compared with those obtained using a weighting approach. To assess the efficiency of the proposed MOPSO, the model is solved using multi-objective genetic algorithm (MOGA) as well. A large number of numerical examples are generated at the end, where graphical and statistical approaches show more efficiency of MOPSO compared with MOGA.

A hybrid model for supplier selection: integration of AHP and multi expression programming (MEP)

Abstract Supplier evaluation and selection is a complicated process which deals with conflicting attributes such as quality, cost. To mitigate the computational complexity, intelligent-based techniques have gained much popularity. But the main shortcoming of the existing models in this regard is to be a black box system. In this paper, we aim to combine analytical hierarchy process with multi-expression programming to both introduce a new evolutionary approach in the field of supplier evaluation and selection and cope with the earlier problem. To show the validity of the model, statistical test was carried out. The finding showed that the proposed model is accurate and acceptable for using in the evaluation process.

Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization

Flexible manufacturing system (FMS) enhances the firm’s flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs). An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GA-PSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs’ battery charge. Assessment of the numerical examples’ scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software.

Optimization of economic lot scheduling problem with backordering and shelf-life considerations using calibrated metaheuristic algorithms

Optimization of the economic lot scheduling problem with shelf life and backordering.It is allowed to produce each item more than once in every cycle.Allowing multiple setups leads to improving solutions.Four metaheuristic methods GA, SA, PSO, and ABC were used to solve the problem.Metaheuristic methods outperformed other reported procedures in the literature. This paper addresses the optimization of economic lot scheduling problem, where multiple items are produced on a single machine in a cyclical pattern. It is assumed that each item can be produced more than once in every cycle, each product has a shelf-life restriction, and backordering is permitted. The aim is to determine the optimal production rate, production frequency, cycle time, as well as a feasible manufacturing schedule for the family of items, and to minimize the long-run average costs. Efficient search procedures are presented to obtain the optimum solutions by employing four well-known metaheuristic algorithms, namely genetic algorithm (GA), particle swarm optimization (PSO), simulated annealing (SA), and artificial bee colony (ABC). Furthermore, to make the algorithms more effective, Taguchi method is employed to tune various parameters of the proposed algorithms. The computational performance and statistical optimization results show the effectiveness and superiority of the metaheuristic algorithms over other reported methods in the literature.

A Review on Cybersickness and Usability in Virtual Environments

With the emerge of new technologies many systems are presented to a wider range of users at reasonable costs. Virtual Reality (VR) technology has also entered many new economical areas such as tourism, business, online games, and also cultural heritage. The new advancement in VR and its availability to the end user in many forms necessitates considering the health issues because cybersickness is one of the drawbacks of Virtual Environments (VE). In addition, usability of the VE and the provided VR technology and system is of paramount importance in the market to attract the user. However, usability measurement of the VE also has become a difficult issue due to the vast range of products and users. A review on the cybersickness and usability issues in VE is prepared and presented in this paper.

A Predictive Integrated Genetic-Based Model for Supplier Evaluation and Selection

Supplier evaluation and selection is a complicated multiple criteria decision-making process which affects supply chain management (SCM) directly. Recent studies emphasize that artificial intelligence approaches obtain better performance than conventional methods in evaluating the suppliers’ performance and determining the best suppliers. Hence, this study proposes a new robust genetic-based intelligent approach, namely gene expression programming (GEP), to improve the supplier selection process for a supply chain and to cope with the drawback of the other intelligent approaches in this area. The applicability of this method was exhibited by a case study in the textile manufacturing industry. To show the performance of the mathematical-genetic model, comparisons with four intelligent techniques, namely multi-layer perceptron (MLP) neural network, radial basis function (RBF) neural network, adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM), were performed. The results derived from the intelligent approaches were compared by using a collected dataset from a textile factory. The obtained results demonstrated that first the GEP-based model provides a mathematical model for the suppliers’ performance based on the determined criteria, and the developed GEP model is more accurate than the four other intelligent models in terms of accuracy in performance estimation. In addition, to verify the validity of the developed model, different statistical tests were used and the results showed that the GEP model is statistically powerful.