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This paper reveals the high demand of fish products in many countries, which subsequently highlighted the high demand of grouper fish species for human consumption. This high demand leads to the insufficient supply of wild ocean grouper fish in the market, thus justifying the need for farmed or cultured grouper fish. Basically, in grouper fish farming, large amounts of trash fish are needed as the feed for grouper fish, which is the carnivorous type of fish. However, since the cost of trash fish is too high, searching for alternative ingredients for the feed through modelling of feed formulation is an option for reducing or minimizing the farming cost. This led to the search for methods in giving the best combination of feedstuff ingredients with appropriate nutrients in formulating the feed. One prospective method is the Evolutionary Algorithm (EA) that has been applied in solving similar problems of diet formulation for several types of animals including livestock, poultry and shrimp. Hence, in this paper, an improved EA method known as the SR-SD-EA is proposed highlighting three important EA operators, which are initialization, selection and mutation. A semi random initialization operator is introduced to filter some important constraints thus increase the chances of obtaining feasible formulations or solutions. Subsequently, the novel selection operator embeds the concept of standard deviation in the SR-SD-EA as part of the function in minimizing the total cost of the formulated grouper fish feed. Eventually, the enhanced boundary-based mutation is also introduced in the algorithm to ensure the crucial constraint of the ingredients' total weight must be met. The overall structure of the SR-SD-EA is presented as a framework, where the three methodological contributions are embedded. The preliminary findings of SR-SD-EA show that the obtained cost computed based on the Best-So-Far feed formulation as the solution is comparable, while all the crucial constraints are fulfilled. [ABSTRACT FROM AUTHOR]

Because port operations have rapidly been extending, air pollution resulted from ports has been increased and become a persistent concern for environmentalist and policy makers. The objective of this paper is to measure the environmental efficiency of ports in Korea. The main characteristic of the environmental efficiency assessment problem is that undesirable output of carbon dioxide (CO2) emissions and exogenous fixed input of the terminal area of ports should concurrently be considered. By analysing the impacts of the exogenous fixed input and undesirable outputs on decision making units (DMUs) performance, a super efficiency slacks-based measure in data envelopment analysis (SE-SBM-DEA) approach is proposed. The proposed approach consists of two models: slacks-based measure (SBM) model and super efficiency SBM (SE-SBM) model. The models effectively discriminate between efficient and inefficient ports, and rank their efficiencies. To restrict any decreases or increases in the fixed input levels, the slacks of fixed inputs are removed from the target functions and their relevant constraints of the proposed approach. In addition, the undesirable outputs are formulated according to the weak disposability assumption, so that they can only be reduced with the reduction of certain desirable outputs. Hence, its slack should also be removed from the SBM and SE-SBM models. As a result, the scalar measures of the models only deal with the discretionary inputs and desirable outputs of a DMU being evaluated. We examine the applicability of the proposed approach, using real data, for 19 ports in Korea. [ABSTRACT FROM AUTHOR]

This paper presents an intelligent tabu search (TS) approach for solving a complex real-world nurse rostering problem (NRP). Previous study has suggested that improvement on neighborhoods and smart intensification of a TS could produce faster and fitted solution. In order to enhance the TS, this paper introduces an improvement to the neighborhoods and explores on the neighborhoods exploitations of TS to solve the NRP. The methodology consists of two phases: initialization and neighborhood. The semi-random initialization is employed for finding a good initial solution during the initialization phase which avoids the violation of hard constraints, while the neighborhood phase is established for further improving the solution quality with a special representation and innovative neighborhood generations within TS algorithm. The aim is to move sample points towards a high-quality solution while avoiding local optima by utilising a calculated force value. It is observed that the enhancement strategy could improve the solution quality of the constructed roster. It is concluded that the TS with enhancements approach is able to assign effective and efficient shift duties for the NRP especially when related with real-world working regulations and nurses preferences.

Multilayer Perceptron Network (MLP) has a better prediction performance compared to other networks since the structure of the MLP is suitable for training processes in solving prediction problems. However, to the best of our knowledge, there is no rule of thumb in determining the number of hidden nodes within the MLP structure. Researchers normally test with various numbers of hidden nodes to obtain the lowest square error value for optimal prediction results since none of the approaches has yet to be claimed as the best practice. Thus, the aim of this study is to determine the best MLP network by varying the number of hidden nodes of developed networks to predict cycle time for producing a new audio product on a production line. The networks were trained and validated through 100 sets of production lots from a selected audio manufacturer. As a result, the 3-2-1 MLP network was the best network based on the lowest square error value compared to the 3-1-1 and 3-3-1 networks. The 3-2-1 predicted the best cycle time of 5 seconds to produce a new audio product. Hence, the prediction result could facilitate production planners in managing assembly processes on the production line. [ABSTRACT FROM AUTHOR]

CSR UUMWiFi is a CSR project under Universiti Utara Malaysia (UUM) that provides unlimited free internet connection for the Changlun community launched in 2015, the service has accumulated a huge number of users with diverse background and interest. This paper aims to uncover interesting service users' behavior by mining the usage data. To achieve that, the access log for 3 months with 24,000 online users were downloaded from the Wi- Fi network server, pre-process and analyzed. The finding reveals that there were many loyal users who have been using this service on a daily basissince 2015 and the community spent 20-60 minutes per session. Besides that, the social media and leisure based application such YouTube, Facebook, Instagram, chatting applications, and miscellaneous web applications were among the top applications accessed by the Changlun community which contributes to huge data usage. It is also found that there were few users have used the CSR UUMWiFifor academicor business purposes. The identified patterns benefits the management team in providing a better quality service for community in future and setting up new policies for the service. [ABSTRACT FROM AUTHOR]