In MCDM problems, the decision maker is often ready to adopt the closest solution to the reference values in a choice or ranking problem. The reference values represent the desired results as established subjectively by the decision maker or determined through various scientific tools. In a criterion, the reference value could be the maximum value, the minimum value, or a specific value or range. Also, the acceptances degrees of ranges outside the reference may differ from each other in a criterion. Furthermore, measurements in a criterion may have been obtained with any of the nominal, ordinal, interval, and ratio scales. For the decision problems, including qualitative criteria, the solution cannot be achieved without scaling of criteria with the existing MCDM methods. The purpose of this study is to propose the Nearest Solution to References (REF) Method, a novel reference-based MCDM method, for the solution of decision problems having mixed data structure where references can be determined for criteria.

The objective of this research is to determine the location for the dock and office of naval bases in Padang city of Indonesia. Following this objective, the Regional Government of Padang Mentawai Islands District provides 3 (three) alternative places namely in Semabuk Bay, Siuban Bay, and Semebai Bay for the location of dock and office of Naval Base. For the selection of Mentawai base, the method uses BORDA and PROMETHEE, since the methods can consider alternative evaluation based on factors that are both qualitative and quantitative. Based on the research of BORDA method calculation on 16 naval base selection criteria, it is found that criterion of Sailing Flow maintains the highest weight value that is equal to 10.9% and the lowest criterion weighted value belongs to criterion Political Condition for about 2%. For the results of ranking against the alternative using the Promethee method, the study obtains Semebai Bay as the best location to serve for the location of the base of the Mentawai Naval Base.

The challenge of improving construction safety performance is observed in many countries. Safety is considered by practitioners and researchers as an important topic in construction industry sites. Despite the findings of implementing safety programs, it is revealed that accidents and injuries are not perfectly reduced in construction projects. In the literature, authors tried to establish several frameworks and proposed methods to reach this objective by identifying the key factors affecting safety performance. The aim of this study is to present critical factors used in the implementation of safety programs and to explore their relationships using Interpretive Structural Modeling (ISM). Then, via ISM technique, the overall structure among factors was revealed. By using the Fuzzy MICMAC analysis, the factors were classified into four groups based on their driving power and dependence power. The results showed that “Safety Training” and “Management Commitment” have the most important impact on safety programs, but also it is very important to study the interactions among factors at different stages. This analysis offers key resources for practitioners and decision makers by analyzing the relationships between factors and its driving or dependence strength. These results shed lights on the effective development of measures to facilitate the implementation of safety programs in the construction sector.

The development of the shipbuilding industry is expected to meet the needs of the Indonesian Navy and the commercial vessels, and to support Indonesia's marine policy. The purpose of this study is to see the Shipbuilding industry Competitiveness, the influence of Technology Transfer to the Shipbuilding industry Competitiveness, and the influence of the industrial clusters on the Shipbuilding industry Competitiveness, as well as to analyze the institutional model of the Shipbuilding industry Competitiveness. This study uses the descriptive analysis, the Structural Equation Modeling (SEM) for the model causality testing, and the Interpretative Structural Modeling (ISM) for the institutional model of the competitiveness of the Shipbuilding Industry. This study uses the primary data, namely a survey of defense industry players, the national industry, the defense equipment users, the government institutions, the research institutes, and the universities that are determined purposively. ISM data are obtained from questionnaires and Forum Group Discussion (FGD) with 13 speakers representing academia, industry, and government. The results of the analysis of SEM state that the indicators on the industrial clusters, the competitiveness, and the technology transfer have a significant and real contribution to these variables. This research also shows that the industrial clusters and the technology transfer have a direct and significant effect on the competitiveness and the industrial clusters directly and significantly affect the technology transfer. However, the industrial clusters also have an indirect effect on competitiveness through the technology transfer to the shipbuilding industry. The results of the analysis of ISM conclude that the stakeholders involved have the greatest driving force, namely the Ministry of Defense and Ministry of State-Owned Enterprises, while the important factor affected by the stakeholders in strengthening the competitiveness of the shipbuilding industry is the Indonesian Navy Headquarters.

Due to the important environmental effects on human life and the conflict between the mining process and environmental factors, the enactment of protectionist environmental laws in the mineral industry has received extensive attention especially in countries with high mining potential. In this regard, using an extended fuzzy TOPSIS method, this study develops a novel conceptual framework to identify the key protection laws in Iran by considering the mineral successful laws and regulations in mineral-developed countries such as Australia, Chile, India, Turkey, Canada, South Africa, and China. This helps to apply the experiences of the abovementioned countries in resolving similar conflicts in Iran’s mineral industry. The key protection laws are selected based on their national and international environmental treaties, mines’ environmental protection laws, conflict solution methods, social responsibilities in mining activity, and laws for the abandoned mines. The proposed framework demonstrates that Iran’s mineral laws require revision and more transparency to avoid ambiguity and conflict with mineral property laws and governmental rights.

As a country surrounded by the ocean, Indonesia is categorized as a country that has marine potential. The fishermen communities’ economy depends on ocean. However, the fishermen communities live below the poverty line and their average income is less than regional minimum wage. In conjunction with the issue, this study seeks to investigate the factors affecting the income of fishermen communities and right decision to fishermen in covering with welfare insurance in Cirebon, Indonesia. The quantitative study is designed using cross-sectional approach. The data collected by applying random sampling with open-ended questions and interview. A total of 100 fishermen’s have participated in this study. The study used some factors in measuring the fishermen community income, namely coastal environment condition, fish catching technology and location, operational capital, climate (season) condition, fishermen’s age, fishermen’s education, and fishing experience. The data are analyzed using the multinomial logistic regression model by assisting the statistical software, i.e., SPSS-23. The results show that coastal environment condition, fish catching technology and location, operational capital, climate (season) condition, fishermen’s age, fishermen’s education, and fishing experience have significant effects on fishermen income. Interestingly, the factor of coastal environment condition and climate (season) condition have significant negative effects on fishermen income. In conclusion, this study identified that two important factors reduced the welfare level of fishermen (via income). Also, in line with that things, the right decision which can provide to support and assist the fishermen community was by providing the welfare insurance. It is purposely to give them the protection from various risks faced by fishermen.

In a decision-making environment related to risk, there are four basic circumstances, namely certainty, risk, uncertainty and conflict. The dynamics of the strategic environment in Southeast Asia cannot be separated from the movement of the development of the Islamic State (IS). The terror threat in Southeast Asia is currently divided into different generations of terror, namely the threat of the Al-Qaeda terror network and the threat of the ISIS terror network. This study aims to analyze and identify the risk value of the development of the Islamic State network in Southeast Asia using the Borda and TOPSIS methods. The Borda method is used to give weight to the criteria related to risk analysis. The TOPSIS method is used to provide a criteria-based risk score. This research is limited to the Southeast Asia region with 4 (four) major countries, namely Indonesia, Malaysia, Thailand, and the Philippines. This research is expected to contribute to control the development of Islamic state networks in the Southeast Asian region. Based on the results of the overall risk analysis, it was found that the Philippines has the highest risk factor value for Islamic State (IS) with a value of 0.550 at level 4 in the High category. Indonesia maintains a risk factor value of 0.307. Thailand has a risk factor value of 0.427. Indonesia and Thailand are at level 3 with the Medium category. Meanwhile, Malaysia has a risk factor value of 0.203 at level 2 in the Low category.

Bidding strategy is an important part of a negotiation strategy in automated multi-issue negotiations. In order to present good offers, which help maximize the agent’s utility, we need to search the outcome space and find appropriate bids. Bid search can become challenging in large outcome spaces with more than ten thousands of possible bids. The traditional search methods such as exhaustive or binary search are not efficient enough to find the right bids in a large space. This is mostly due to the high number of issues, high number of possible values for each issue, and increased time complexity of usual search methods. In this paper, we investigate the potential of using meta-heuristic methods for optimizing bid search in large outcome spaces. We apply some of the most popular meta-heuristic algorithms for bid search in bidding strategy of baseline negotiating agents and evaluate their impacts on negotiation performance in different negotiation domains. The evaluation results obtained through comprehensive experiments show how meta-heuristic algorithms can help improve bid search capability and consequently negotiation performance of the agents on different performance criteria. In addition, we show which search algorithm is most suitable for improving any particular performance criterion.

Patient Bed Assignment (PBA) consists of assigning patients to hospital beds according to specific requirements such as patient diagnosis, equipment requirements, age and gender policies, among others. We worked in conjunction with the Hospital Universitario San Ignacio (HUSI) with the goal of designing an application to support decision-making during the bed assignment process. We introduced a mathematical model for the PBA. We used Analytic Hierarchy Process (AHP) to determine the weights attributed to each part of the objective function. Due to the long execution time required, we used a Greedy Algorithm and Tabu Search (TS) to optimize the match between the patient’s requirements and the characteristics of the assigned bed. To test the algorithms, we created 15 test instances of various sizes. The results showed that the gap between the value of the objective function resulting from using the Greedy/TS in comparison with the optimal solution is on average 6.2%. Also, the TS takes 84% less time than the MILP for medium and large instances. We collected data from real life instances and compared the actual method with the designed metaheuristic. On average, the value of the objective function resulting from using the proposed Greedy/Tabu algorithm is 8.6% higher.

Machine vision systems are among the novel tools proven to be useful in different applications, among which monitoring and controlling manufacturing processes is one of the most important ones. However, due to the complexity resulted from high-dimensional image data and their inherent correlations, the acquisition of traditional statistical process control tools seems inapplicable. To overcome the shortcomings of the traditional methods in this regard, a statistical model is proposed in this paper which utilizes the concepts of both the PCA-based T2 control chart and the classification methods to develop a tool capable of controlling an image-based process. By defining the warning zones, collected data taken from an image-based process are classified into more than the two classes related to in-control and out-of-control processes. This helps practitioners to define rules to make it easier to realize when the process is getting out of control. Through simulation, the accuracy performance and the speed of four different types of classifiers including linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), kth nearest neighbors (KNN), and support vector machine (SVM) are assessed in different scenarios, based on which the functionality of the proposed approach is evaluated in in-control and out-of-control conditions.