Advanced Materials Research Vol. 845

Abstract: Demand prediction is one of most sophisticated steps in planning and investments. Although many studies are conducted to find the appropriate forecasting models, dynamic nature of forecasted parameters and their effecting factors are apparent evidences for continuous researches. ARIMA, Artificial Neural Network (ANN), and ARIMA-ANN hybrid model are well-known forecasting models. Many researchers concluded that the Hybrid model is the predominant forecasting model in comparison with ARIMA and ANN individual models. Most of these researches are based on non-stationary or seasonal timeseries, whereas in this article, hybrid models forecast ability by stationary time series is studied. Some following demand time steps from a paint manufacturing company are forecasted by all previously mentioned models and ARIMA-ANN hybrid model fails to present the best forecasts.

Abstract: In recent years the topic of sustainable supply chain has received dramatic attention and become an important research area. These days there are some new challenges which companies should consider them such as climate changes, the negative effect of downturn, face the growing attention of people to ecology. In order to evaluate the sustainability performance measurement system becomes an important tool to assess the sustainability on environmental, social and economy aspect of supply chain management. For making the business case for sustainability issues in supply chain, it is critical to call for effective performance measurement system and metrics development.

Abstract: Third-party reverse logistics providers (3PRLPs) selection has become an important logistics function which can help companies to maintain their competitive edge. Traditionally, companies solely considered economic aspects for selecting their 3PRLPs. However, due to increased pressure from different types of stakeholders regarding environmental and social issues in recent years, companies have been obliged to incorporate these issues in their logistics and supply chains functions. Although many studies have been conducted in the field of 3PRLPs selection, much less attention has been devoted to incorporating all three aspects of sustainability (social, environmental, and economic) in this field. In this research, a hybrid fuzzy multi-criteria decision-making (FMCDM) approach is proposed for selecting 3PRLPs while all three dimensions of sustainability were taken into account. Fuzzy analytic hierarchy process (FAHP) was used in order to weight the selected sustainability criteria and subcriteria. Then, fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) was applied for determining the ranking of suppliers. The applicability of the proposed approach was tested in an electronic manufacturing company.

Abstract: Capacitated minimax facility location-allocation (LA) is a type of facility problem which is of prime importance for emergency situation, since it directly affects the service response time to customers. Capacitated minimax LA problem is concerned with locating some new facilities and allocating their capacity to customers when the maximum travelled distance from customers to facilities is minimized. This study involves a fixed line barrier in a region with some border crossings along it which divides the area into two subregions. Although several studies have recently been done on this problem in which the customer locations are known with certainty, much less attention has been devoted to developing a comprehensive mathematical model for the probabilistic extension of customer locations when there are some restrictions in the region. In order to fill this gap, a Mixed Integer Nonlinear Programming (MINLP) model is proposed for facility LA when customer locations are randomly distributed according to a bivariate normal probability distribution. Finally, the BARON solver in the GAMS software is used to solve the model, and a numerical example is provided to demonstrate its efficiency.

Abstract: This study aims at improving the multi-floor layout of a card and packet production company based on the Systematic Layout Planning (SLP) method. A detailed study of the facility layout such as its operational processes, flow of materials and activity relationships has been done. Long distance, cross-traffic, and cost have been identified as the major problems of the current multi-floor layout. Three alternative layouts were suggested by SLP and the best alternative was selected and compared with the current layout. The results revealed that the new alternative layout could considerably improve the companys layout problems.

Abstract: Activated sludge process is the most efficient technique used for municipal wastewater treatment plants. However, a pH value outside the limit of 6-9 could inhibit the activities of microorganisms responsible for treating the wastewater, and low pH value may cause damage to the treatment system. Therefore, prediction of pH value is essential for smooth and trouble-free operation of the process. This paper presents an adaptive neuro-fuzzy inference system (ANFIS) model for effluent pH quality prediction in the process. For comparison, artificial neural network is used. The model validation is achieved through use of full-scale data from the domestic wastewater treatment plant in Kuala Lumpur, Malaysia. Simulation results indicate that the ANFIS model predictions were highly accurate having the root mean square error (RMSE) of 0.18250, mean absolute percentage deviation (MAPD) of 9.482% and the correlation coefficient (R) of 0.72706. The proposed model is efficient and valuable tool for the activated sludge wastewater treatment process.

Abstract: Large disturbances and highly nonlinear nature of the wastewater treatment system makes its control very difficult and challenging. The control of the system using conventional techniques becomes hard and often impossible. This paper presents a comparison of an adaptive neuro-fuzzy inference system (ANFIS) and neural network (NN) inverse control applied to the system. The performances of the controllers were evaluated based on the rise time; percent overshot and the mean error. Simulation results revealed that the ANFIS controller performance was slightly better compared to the neural network controller. The proposed ANFIS controller is effective and useful to the process.

Abstract: Institutions of Higher Learning in Malaysia had been viewed as the source for new innovation. Hence, few universities in Malaysia had been upgraded to the status of Research University to reinforce the nations New Economic Model via research, development and commercialization of new innovation. As such, Technology Driven innovation model that traditionally used by academic in Institutions of Higher Learning is now insufficient to support the innovation work within the scope and definition of Research University, this is due to it is lack of focus on commercialization element. Therefore, this paper aims to propose an innovation model devotes to Research Universities in Malaysia. In line with this, the paper reviews the important elements of innovation, followed by comparison of elements in corporate and academic innovation models. Finding from this paper suggested that building-up innovation competency in Research University requires an innovation model that integrates all innovation elements from holistic business points of view. The paper proposed an integrated innovation model based on Total Innovation Management paradigm. The proposed model has descriptive value in terms of studying, classifying and defining the important elements and relationships that govern innovation process and management aspects in Research universities within Malaysia.

Abstract: The monitoring of dissolved combustible gases in power transformer oil could enable early detection of disastrous fault. The conventional dissolved gases in oil monitoring activities have these characteristic: 1) periodically sampling and 2) manual interpretation of combustible gases. However, periodical sampling increases number of undetected fault due to long sampling interval and manual interpretation of dissolved gas is often too complex for system operator to digest, resulting in reduced reliability of the power system and lack of situational awareness. To enhance the condition based monitoring activities for power transformer; TNB Research is embarking on online monitoring and knowledge-based system research project to address both issues related to periodical sampling method. This paper outlines the conceptual framework of the research project which was recently approved. It includes (1) the system architecture of the online monitoring system, (2) brief explanation of the mechanism of photo-acoustic spectroscopy, (3) the engineering system situational awareness framework which integrates different levels of automation and (4) blocks of knowledge sources theory used in modeling the engineering system.

Abstract: Job shop scheduling problems are immensely complicated problems in machine scheduling area, and they are classified as NP-hard problems. Finding optimal solutions for job shop scheduling problems with exact methods incur high cost, therefore, looking for approximate solutions with meta-heuristics are favored instead. In this paper, a hybrid framework which is based on a combination of genetic algorithm and simulated annealing is proposed in order to minimize maximum completion time i.e. makespan. In the proposed algorithm, precedence preserving order-based crossover is applied which is able to generate feasible offspring. Two types of mutation operators namely swapping and insertion mutation are used in order to maintain diversity of population and to perform intensive search. Furthermore, a new approach is applied for arranging operations on machines, which improved solution quality and decreased computational time. The proposed hybrid genetic algorithm is tested with a set of benchmarking problems, and simulation results revealed efficiency of the proposed hybrid genetic algorithm compared to conventional genetic based algorithm.