EVALUATION OF THE POTENTIAL HAZARDS OF SHIP-GENERATED WASTE IN MARITIME INDUSTRY USING A TYPE-2 FUZZY ANALYTICAL HIERARCHY PROCESS

Ships create a large amount of pollutions to marine and coastal environment where its pollutants have negative effects to human and maritime habitat. Depending on the nature of ship-generated waste, rules and regulations determine discharging procedures. This paper focuses on evaluating of potential hazards of ship-generated wastes on the environment whether it may be discharged into the sea or disposed of on port facilities. Thus, marine and costal environmental effects can be discussed analytically to improve human and ecological health. To achieve this purpose, analytic hierarchy process (AHP) extended with interval type-2 fuzzy sets (IT2FSs) is used. While AHP method is used for prioritizing the potential ship- generated waste, the IT2FSs deal with uncertainty and vagueness in the process of obtaining expert decision. Beside assessment of the environmental impacts, the paper contributes to enhance coastal and marine environmental awareness with respect to the ship-generated waste.

Prioritization of Contracting Methods for Water and Wastewater Projects Using the Fuzzy Analytic Hierarchy Process Method

This study uses the fuzzy analytical hierarchy process (FAHP) method to prioritize contracting methods to determine the most suitable contract option for water and wastewater projects (WWP). Content analysis, a two-round Delphi survey technique, and a series of validation and reliability tests helped establish the 18 key criteria for FAHP analysis. Consequently, data collected from experts through a pairwise comparison questionnaire form the basis for the inputs for the FAHP analysis. Consequently, the final weightings were derived for each of the key criteria and available contracting methods. The results indicate that the bilateral, cooperative, and trilateral contracting methods are the most suitable for WWP in Iran, with the highest weighting. The study provides useful guidance for the top management of project firms in selecting the optimal contracting method for their projects and offers significant contributions from theoretical and practical perspectives.

PRIORITIZATION OF FOREST FIRE HAZARD RISK SIMULATION USING HYBRID GREY RELATIVITY ANALYSIS (HGRA) AND FUZZY ANALYTICAL HIERARCHY PROCESS (FAHP) COUPLED WITH MULTICRITERIA DECISION ANALYSIS (MCDA) TECHNIQUES – A COMPARATIVE STUDY ANALYSIS

Forests are important dynamic systems which are widely attracted by wild fires worldwide. Due to the complexity and non-linearity of the causative forest fire problems, employing sophisticated hybrid evolutionary algorithms is a logical task to achieve a reliable approximation of this environmental threats. This estimate will provide the outline of priority areas for preventing activities and allocation of fire fighters’ stations, seeking to minimize possible damages caused by fires. This study aims at prioritizing the forest fire risk of Wassa West district of Ghana. The study considered static causative factors such as Land use and land cover (which include forest, built-ups and settlement areas), slope, aspect, linear features (water bodies and roads) and dynamic causative factors such as wind speed, precipitation, and temperature were used. The methods employed include a Hybrid Grey Relativity Analysis (HGRA) and Fuzzy Analytical Hierarchy Process (FAHP) techniques. The fuzzy sets integrated with AHP in a decision-making algorithm using geographic information system (GIS) was used to model the fire risk in the study area. FAHP and HGRA methods were used for estimating the importance (weights) of the effective factors in forest fire modelling. Based on their modelling methods, the expert ideas were used to express the relative importance and priority of the major criteria and sub-criteria in forest fire risk in the study area. The expert ideas were analyzed based on FAHP and HGRA. The major criteria models and fire risk model were presented based on these FAHP and HGRA weights. On the other hand, the spatial data of the sub criteria were provided and assembled in GIS environment to obtain the sub-criteria maps. Each sub-criterion map was converted to raster format and it was reclassified based on risks of its classes to fire occurrence. The maps of each major criterion were obtained by weighted overlay of its sub criteria maps considering to major criterion model in GIS environment. Finally, the map of fire risk was obtained by weighted overlay of major criteria maps considering to fire risk model in GIS. The results showed that the FAHP model showed superiority than HGRA in prioritizing forest fire risk of the study area in terms of statistical analysis with a standard deviation of 0.09277 m as compared to 0.1122 m respectively. The obtained fire risk map can be used as a decision support system for predicting of the future trends in the study area. The optimized structures of the proposed models could serve as a good alternative to traditional forest predictive models, and this can be a promisingly testament used for future planning and decision making in the proposed areas.

An analysis of supply chain collaboration index by using FAHP and SCCI: a case study of organic fertiliser company X, Indonesia

Supply chain collaboration is one of the most crucial variables of driving business success in organic fertiliser Companies, especially to maintain the continual flow from upstream to downstream. Therefore, understanding the level of collaboration factors is vital in sustaining the partnership as well as reducing any conflicts among stakeholders. This study aims to measure the depth of collaboration among Company X and its suppliers. The assessment of the supply chain collaboration index will perform Fuzzy Analytical Hierarchy Process (FAHP) to weight the collaboration behaviour factors and Supply Chain Collaboration Index (SCCI) to measure the depth level of collaboration. The collaboration behaviour factors examined in this study are including joint effort, collaboration values, sharing activities, adaptation, trust, power, stability, commitment, continuous improvement and coordination. Based on the computation process using SCCI, the collaboration index among company X and its supplier is 76.72 on a scale of 1-100. It implies that the collaboration is at a moderate level. Consequently, the company needs to recognise the low score factors and develop a strategy for improvement. Some aspects that deserve further attention are sharing activities, power, and stability. By enhancing the performance of these factors, the supply chain collaboration index can also be increased.