42 research outputs found

    The Improvement for Optimization of Head Stack Assembly (HSA) Assembling Process by Using the Virtual Reality 3D Simulation Model

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    Currently, a case study in the Head Stack Assembly (HSA) assembling process has an average production rate of 198 units an hour, which inadequates in the future of customer satisfaction. Therefore, this research aims to determine an approach for increasing the production rate by using the Arena program in order to build the simulation model and using the Theory of Constraints (TOC) to improve the assembly process. Moreover, the researchers develops the 3D virtual reality model by using the Arena 3DPlayer program, which assists to support for decision working efficiently and applies the OptQuest for Arena for determine the optimal amounts of the shuttles and flow fixtures

    The Packaging Design Approach and Evaluation Process by Integrating ISO/TR 14062 in a Decision Support Methodology

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    The environmental, economic and social conscious are particularly important in the sustainable design product. ISO/TR 14062 suggests the conceptual guideline in environmental management into product and packaging design and development stage. However, the evaluation of the sustainable design selection based upon life cycle thinking is unavailable in detail design process. The objectives of this research are to develop the sustainable  packaging methodology at the conceptual design phase, and to enhance the new guidelines to quantify  an efficient sustainable packaging evaluation process by integrating ISO/TR 14062 in a decision support  methodology. It is intended to integrate between life cycle thinking and major stakeholders for functional quality, cost, and environmental aspects in the early design phases. The methodology has been tested with a very large enterprise in the section of hard disk drive internal factory packaging and it was found that the approach of  a new packaging design can assist the designer to develop the sustainable packaging whilst achieving desirable functions, increasing environmental conscious and cost effectiveness. In addition, the evaluation process can assess decision scenarios on the new design according to the investment comparison

    The Effects of Diesel-waste Plastic Oil Blends on Engine Performance Characteristics

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    The objective of this research is to present results of the performance (torque, power, thermal efficiency and specific fuel consumption) in a heavy-duty diesel engine when fueled with diesel-waste plastic pyrolysis oil (WPO) blends in full load condition. The tested engine is installed on an engine test bench and is attached with several sensors. The full factorial experimental design is performed to investigate both main and interaction effects. It is shown that fuel blends, engine speed and interaction of both factors significantly affect all engine performance parameters. The functional relationships between parameters are developed by second-order quadratic models. The result shows that the mathematical models are able to predict the performance characteristic with mean absolute percentage error (MAPE) in the range of 1.614 to 2.987%. The increase of mixing ratio to WPO 75% greatly decreases engine output torque and power approximately by 23.79%. Consequently, thermal efficiency can be reduced by 5.97% while specific fuel consumption can be increased by 31.22%. The results of error analyses, the graphical presentations, the discussions and conclusions are also presented

    Design of a Decision Support System on Selection of Multimodal Transportation with Environmental Consideration between Thailand and Vietnam

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    The objective of this research is to design and develop a decision support system (DSS) to select multimodal transportation route between Thailand and Vietnam under the conditions in term of budget, time, transport risk, and the environmental impact. The developed DSS model uses Analytic Hierarchy Process (AHP) as a tool to bring consistency weight whose decision criteria (both quantitative and qualitative) are expressed in subjective measures according to the point of view of users. Next, weighting derived from the results of AHP is taken as a weight of objective function in goal programming model. In this research, the Zero-One Goal Programming model is used to generate an optimal multimodal transportation routing based upon the criteria in term of budget, time, transport risk, and importantly, the environmental setting which is important to a number of countries. The case study of this research is a transported service, originating from Bangkok in Thailand to a destination at Da Nang port in Vietnam. There are, for example, the user can set the budget at 5,000 USD for 8-day period of transportation, with route risk scale and the environmental impact scale. The results found that the optimal route is sea transport departed from Bangkok to Da Nang Port, and truck service is deliver goods to customers. Transportation cost is equal to 1,080 USD for 8-day period of transportation, route risk scale is equal to 2, an environmental impact scale is equal to 3 and standard deviation is equal to 15.99. The results show that the DSS can guide to choose the lowest cost route in accordance with overall criteria, and minimise the environmental impact effectively. The results analysis, recommendations and limitations are also presented

    āļ­āļļāļ•āļŠāļēāļŦāļāļĢāļĢāļĄāļĒāļēāļ™āļĒāļ™āļ•āđŒāđ„āļŸāļŸāđ‰āļēāđāļĨāļ°āļœāļĨāļāļĢāļ°āļ—āļšāļ•āđˆāļ­āļ­āļļāļ•āļŠāļēāļŦāļāļĢāļĢāļĄāđ„āļ—āļĒElectric Vehicle: EV Industry and Impacts to Thai Industry

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    āļāļ§āđˆāļēāļŠāļīāļšāļ›āļĩāļĄāļēāđāļĨāđ‰āļ§āļ—āļĩāđˆāļ­āļļāļ•āļŠāļēāļŦāļāļĢāļĢāļĄ 4.0 (Industry 4.0) āđ„āļ”āđ‰āļĄāļĩāļāļēāļĢāļ™āļģāđ€āļŠāļ™āļ­āđāļ™āļ§āļ„āļīāļ”āđƒāļ™āļ›āļĢāļ°āđ€āļ—āļĻāđ€āļĒāļ­āļĢāļĄāļ™āļĩ āļˆāļēāļāļ™āļąāđ‰āļ™āļ­āļļāļ•āļŠāļēāļŦāļāļĢāļĢāļĄāļ•āđˆāļēāļ‡āđ† āđƒāļ™āļ›āļĢāļ°āđ€āļ—āļĻāđ„āļ—āļĒāđ„āļ”āđ‰āđ€āļĢāļīāđˆāļĄāļ›āļĢāļąāļšāļ•āļąāļ§āđ€āļ‚āđ‰āļēāļŠāļđāđˆāļāļēāļĢāđ€āļ›āđ‡āļ™āļ­āļļāļ•āļŠāļēāļŦāļāļĢāļĢāļĄ 4.0 āļ”āđ‰āļ§āļĒāļāļēāļĢāļ™āļģāļŦāļļāđˆāļ™āļĒāļ™āļ•āđŒ (Robot) āđ€āļ‚āđ‰āļēāļĄāļēāđƒāļŠāđ‰āđƒāļ™āļāļĢāļ°āļšāļ§āļ™āļāļēāļĢāļœāļĨāļīāļ• āđ‚āļ”āļĒāđƒāļ™āļĒāļļāļ„āđāļĢāļāļˆāļ°āđƒāļŠāđ‰āđāļšāļšāđ€āļ”āļĩāđˆāļĒāļ§ (Stand Alone) āļ•āđˆāļ­āļĄāļēāļĄāļĩāļāļēāļĢāđ€āļŠāļ·āđˆāļ­āļĄāļ•āđˆāļ­ (Connected) āđ€āļ‚āđ‰āļēāļ”āđ‰āļ§āļĒāļāļąāļ™āđ€āļ›āđ‡āļ™āļ„āļĨāļąāļŠāđ€āļ•āļ­āļĢāđŒ (Cluster) āđ€āļžāļ·āđˆāļ­āđƒāļŦāđ‰āļŠāļēāļĄāļēāļĢāļ–āļ—āļģāļ‡āļēāļ™āļĢāđˆāļ§āļĄāļāļąāļ™ (Synchronized) āļāļąāļšāļŠāļļāļ”āđ€āļ‹āļ™āđ€āļ‹āļ­āļĢāđŒ (Sensors) āđƒāļ™āļāļĢāļ°āļšāļ§āļ™āļāļēāļĢāļœāļĨāļīāļ• āļ­āļąāļ™āļˆāļ°āļ—āļģāđƒāļŦāđ‰āļ„āļ§āļēāļĄāļŠāļēāļĄāļēāļĢāļ–āđƒāļ™āļāļēāļĢāļœāļĨāļīāļ• (Productivity) āļŠāļđāļ‡āļ‚āļķāđ‰āļ™āđƒāļ™āļŠāđˆāļ§āļ‡āđ€āļ§āļĨāļēāđ€āļ”āļĩāļĒāļ§āļāļąāļ™āļ™āļąāđ‰āļ™āļ­āļļāļ•āļŠāļēāļŦāļāļĢāļĢāļĄāļĒāļēāļ™āļĒāļ™āļ•āđŒāđ„āļŸāļŸāđ‰āļē (Electric Vehicle; EV Industry) āđƒāļ™āļ›āļĢāļ°āđ€āļ—āļĻāđ„āļ—āļĒāđ„āļ”āđ‰āļĢāļąāļšāļāļēāļĢāļ•āļ­āļšāļĢāļąāļšāļ”āļĩāļĄāļēāļāļ‚āļķāđ‰āļ™āļˆāļēāļāļœāļđāđ‰āđƒāļŠāđ‰ āļ”āļąāļ‡āļˆāļ°āđ€āļŦāđ‡āļ™āđ„āļ”āđ‰āļˆāļēāļāļĒāļ­āļ”āļ‚āļēāļĒāļ—āļĩāđˆāđ€āļžāļīāđˆāļĄāļŠāļđāļ‡āļ‚āļķāđ‰āļ™āļˆāļēāļāļœāļđāđ‰āļœāļĨāļīāļ•āļĒāļēāļ™āļĒāļ™āļ•āđŒāļ•āđˆāļēāļ‡āđ† āđ‚āļ”āļĒāđƒāļ™āļ›āļĢāļ°āđ€āļ—āļĻāđ„āļ—āļĒāļĒāļēāļ™āļĒāļ™āļ•āđŒāļ›āļĢāļ°āđ€āļ āļ—āđƒāļŠāđ‰āļ‡āļēāļ™āļĢāđˆāļ§āļĄāļāļąāļ™āļĢāļ°āļŦāļ§āđˆāļēāļ‡āļĢāļ°āļšāļšāđ€āļ„āļĢāļ·āđˆāļ­āļ‡āļĒāļ™āļ•āđŒāđ€āļŠāļ·āđ‰āļ­āđ€āļžāļĨāļīāļ‡āļŠāļąāļ™āļ”āļēāļ› (Combustion Engine) āđāļĨāļ°āļĢāļ°āļšāļšāđ„āļŸāļŸāđ‰āļēāļ‚āļąāļšāđ€āļ„āļĨāļ·āđˆāļ­āļ™āļ”āđ‰āļ§āļĒāđāļšāļ•āđ€āļ•āļ­āļĢāļĩāđˆ (Battery Driving) āļ‹āļķāđˆāļ‡āļĄāļąāļāđ€āļĢāļĩāļĒāļāđ‚āļ”āļĒāļĢāļ§āļĄāļ§āđˆāļēāļĒāļēāļ™āļĒāļ™āļ•āđŒāđ„āļŪāļšāļĢāļīāļ” (Hybrid Electric Vehicles; HEVs) āđāļĨāļ°āļĒāļēāļ™āļĒāļ™āļ•āđŒāļ›āļĨāļąāđŠāļāļ­āļīāļ™āđ„āļŪāļšāļĢāļīāļ” (Plugin Hybrid Electric Vehicles; PHEVs) āđ€āļ›āđ‡āļ™āļ—āļĩāđˆāļ™āļīāļĒāļĄāđƒāļŠāđ‰āđƒāļ™āļ›āļĢāļ°āđ€āļ—āļĻāđ„āļ—āļĒāļ­āļļāļ•āļŠāļēāļŦāļāļĢāļĢāļĄāļĒāļēāļ™āļĒāļ™āļ•āđŒāđ„āļŸāļŸāđ‰āļēāđƒāļ™āļ›āļĢāļ°āđ€āļ—āļĻāđ„āļ—āļĒāļāļģāļĨāļąāļ‡āđ€āļĢāļīāđˆāļĄāļ•āđ‰āļ™āđ€āļ›āļĨāļĩāđˆāļĒāļ™āđ€āļ‚āđ‰āļēāļŠāļđāđˆāļĒāļļāļ„āļāļēāļĢāđƒāļŠāđ‰āļĒāļēāļ™āļĒāļ™āļ•āđŒāļ‚āļąāļšāđ€āļ„āļĨāļ·āđˆāļ­āļ™āļ”āđ‰āļ§āļĒāļžāļĨāļąāļ‡āļ‡āļēāļ™āđ„āļŸāļŸāđ‰āļēāđ€āļžāļĩāļĒāļ‡āļ­āļĒāđˆāļēāļ‡āđ€āļ”āļĩāļĒāļ§ āļ‹āļķāđˆāļ‡āļĄāļąāļāđ€āļĢāļĩāļĒāļāļ§āđˆāļē āļĒāļēāļ™āļĒāļ™āļ•āđŒāđ„āļŸāļŸāđ‰āļē (Electric Vehicles; EVs) āđ‚āļ”āļĒāđƒāļ™āļ›āļĩ 2020 āļĢāļąāļāļšāļēāļĨāđ„āļ—āļĒāđ„āļ”āđ‰āļ§āļēāļ‡āđāļœāļ™āđ‚āļĢāļ”āđāļĄāļž (Roadmap) āđƒāļŦāđ‰āļĄāļĩāļāļēāļĢāļœāļĨāļīāļ•āļĢāļ–āļĒāļ™āļ•āđŒ EVs āļˆāļģāļ™āļ§āļ™ 250,000 āļ„āļąāļ™ āļĢāļ–āđ€āļĄāļĨāđŒ EV āļˆāļģāļ™āļ§āļ™ 3,000 āļ„āļąāļ™ āđāļĨāļ°āļĢāļ–āļĄāļ­āđ€āļ•āļ­āļĢāđŒāđ„āļ‹āļ•āđŒ (Electric Motorcycle) āļˆāļģāļ™āļ§āļ™ 53,000 āļ„āļąāļ™ āļ āļēāļĒāđƒāļ™āļ›āļĩ 202

    āļāļēāļĢāļŦāļēāļ™āđ‰āļģāļŦāļ™āļąāļāļ„āļ§āļēāļĄāļŠāļģāļ„āļąāļāļ‚āļ­āļ‡āļžāļēāļĢāļēāļĄāļīāđ€āļ•āļ­āļĢāđŒāđ€āļ„āļĢāļ·āđˆāļ­āļ‡āļĒāļ™āļ•āđŒāļ”āļĩāđ€āļ‹āļĨ āļ”āđ‰āļ§āļĒāļāļēāļĢāļ•āļąāļ”āļŠāļīāļ™āđƒāļˆāđāļšāļšāļāļĨāļļāđˆāļĄ Weighting of Diesel Engine’s Parameters by Using Group Decision Making

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    āļšāļ—āļ„āļąāļ”āļĒāđˆāļ­Â āļāļēāļĢāļ™āļģāļœāļĨāļāļēāļĢāļ§āļīāļˆāļąāļĒāļ”āđ‰āļēāļ™āļžāļĨāļąāļ‡āļ‡āļēāļ™āļ—āļ”āđāļ—āļ™āđƒāļ™āđ€āļ„āļĢāļ·āđˆāļ­āļ‡āļĒāļ™āļ•āđŒāļ”āļĩāđ€āļ‹āļĨāđ„āļ›āđƒāļŠāđ‰āļ‡āļēāļ™āļˆāļĢāļīāļ‡āļ•āđ‰āļ­āļ‡āļ„āļģāļ™āļķāļ‡āļ—āļąāđ‰āļ‡āļ”āđ‰āļēāļ™āļŠāļĄāļĢāļĢāļ–āļ™āļ°āđ€āļ„āļĢāļ·āđˆāļ­āļ‡āļĒāļ™āļ•āđŒāđāļĨāļ°āļĄāļĨāļžāļīāļĐāđ„āļ­āđ€āļŠāļĩāļĒāļ—āļĩāđˆāđ€āļāļīāļ”āļ‚āļķāđ‰āļ™ āļ­āļĒāđˆāļēāļ‡āđ„āļĢāļāđ‡āļ•āļēāļĄ āļ™āļąāļāļ§āļīāļˆāļąāļĒāđ„āļĄāđˆāļĄāļĩāļ‚āđ‰āļ­āļĄāļđāļĨāļ™āđ‰āļģāļŦāļ™āļąāļāļ„āļ§āļēāļĄāļŠāļģāļ„āļąāļāļ‚āļ­āļ‡āļžāļēāļĢāļēāļĄāļīāđ€āļ•āļ­āļĢāđŒāļ‚āļ­āļ‡āđ€āļ„āļĢāļ·āđˆāļ­āļ‡āļĒāļ™āļ•āđŒāļ”āļĩāđ€āļ‹āļĨāļˆāļķāļ‡āļ—āļģāđƒāļŦāđ‰āļāļēāļĢāļ•āļąāļ”āļŠāļīāļ™āđƒāļˆāļ āļēāļĒāđƒāļ•āđ‰āđ€āļāļ“āļ‘āđŒāļžāļŦāļļāļ„āļđāļ“ (multi-criteria decision making) āđ„āļĄāđˆāļĄāļĩāļ›āļĢāļ°āļŠāļīāļ—āļ˜āļīāļ āļēāļž āļ‡āļēāļ™āļ§āļīāļˆāļąāļĒāļ™āļĩāđ‰āļˆāļķāļ‡āļĄāļĩāļ§āļąāļ•āļ–āļļāļ›āļĢāļ°āļŠāļ‡āļ„āđŒāđ€āļžāļ·āđˆāļ­āļĻāļķāļāļĐāļēāļ§āļīāļ˜āļĩāļŦāļēāļ™āđ‰āļģāļŦāļ™āļąāļāļ„āļ§āļēāļĄāļŠāļģāļ„āļąāļāļ‚āļ­āļ‡āļžāļēāļĢāļēāļĄāļīāđ€āļ•āļ­āļĢāđŒāđ€āļ„āļĢāļ·āđˆāļ­āļ‡āļĒāļ™āļ•āđŒāļ”āļĩāđ€āļ‹āļĨāļ—āļĩāđˆāļŠāļģāļ„āļąāļāļˆāļģāļ™āļ§āļ™ 8 āļ„āđˆāļē āđ‚āļ”āļĒāļāļēāļĢāļœāļŠāļĄāļœāļŠāļēāļ™āļ§āļīāļ˜āļĩāļāļēāļĢāļ•āļąāļ”āļŠāļīāļ™āđƒāļˆāļ”āđ‰āļ§āļĒāļāļĢāļ°āļšāļ§āļ™āļāļēāļĢāļĨāļģāļ”āļąāļšāļ‚āļąāđ‰āļ™āđ€āļŠāļīāļ‡āļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒ (AHP) āđ€āļ‚āđ‰āļēāļāļąāļšāļ§āļīāļ˜āļĩāđ€āļ”āļĨāļŸāļēāļĒ (Delphi) āđ€āļžāļ·āđˆāļ­āļ—āļģāļāļēāļĢāļ•āļąāļ”āļŠāļīāļ™āđƒāļˆāđāļšāļšāļāļĨāļļāđˆāļĄ (group decision making) āļ‹āļķāđˆāļ‡āļœāļĨāļāļēāļĢāļĻāļķāļāļĐāļēāļžāļšāļ§āđˆāļē AHP āļŠāļēāļĄāļēāļĢāļ–āđ€āļ›āļĨāļĩāđˆāļĒāļ™āļ„āļ§āļēāļĄāđ€āļŦāđ‡āļ™āļ‚āļ­āļ‡āļœāļđāđ‰āđ€āļŠāļĩāđˆāļĒāļ§āļŠāļēāļ āļ‹āļķāđˆāļ‡āđ€āļ›āđ‡āļ™āļ‚āđ‰āļ­āļĄāļđāļĨāđ€āļŠāļīāļ‡āļ„āļļāļ“āļ āļēāļžāđƒāļŦāđ‰āđ€āļ›āđ‡āļ™āļ‚āđ‰āļ­āļĄāļđāļĨāđ€āļŠāļīāļ‡āļ›āļĢāļīāļĄāļēāļ“āđ„āļ”āđ‰āļ­āļĒāđˆāļēāļ‡āļĄāļĩāļ›āļĢāļ°āļŠāļīāļ—āļ˜āļīāļ āļēāļž āļ­āļĩāļāļ—āļąāđ‰āļ‡ AHP āļĄāļĩāļāļēāļĢāļ•āļĢāļ§āļˆāļŠāļ­āļšāļ„āđˆāļēāļ­āļąāļ•āļĢāļēāļŠāđˆāļ§āļ™āļ„āļ§āļēāļĄāļŠāļ­āļ”āļ„āļĨāđ‰āļ­āļ‡āļāļąāļ™āļ‚āļ­āļ‡āļ‚āđ‰āļ­āļĄāļđāļĨ (consistency ratio) āļŠāļēāļĄāļēāļĢāļ–āļĒāļ·āļ™āļĒāļąāļ™āļ„āļļāļ“āļ āļēāļžāļ‚āļ­āļ‡āļ‚āđ‰āļ­āļĄāļđāļĨāļāļēāļĢāļĻāļķāļāļĐāļēāđ„āļ”āđ‰āđ€āļ›āđ‡āļ™āļ­āļĒāđˆāļēāļ‡āļ”āļĩ āđƒāļ™āļŠāđˆāļ§āļ‡āļ•āđ‰āļ™āļ‚āļ­āļ‡āļ‡āļēāļ™āļ§āļīāļˆāļąāļĒ āļāļēāļĢāļ›āļĢāļ°āđ€āļĄāļīāļ™āļ™āđ‰āļģāļŦāļ™āļąāļāļ„āļ§āļēāļĄāļŠāļģāļ„āļąāļāļžāļšāļ§āđˆāļēāļœāļđāđ‰āđ€āļŠāļĩāđˆāļĒāļ§āļŠāļēāļāļ—āļąāđ‰āļ‡ 8 āļ„āļ™ āļĄāļĩāļ„āļ§āļēāļĄāđ€āļŦāđ‡āļ™āļ—āļĩāđˆāđāļ•āļāļ•āđˆāļēāļ‡āļāļąāļ™ āļ‡āļēāļ™āļ§āļīāļˆāļąāļĒāļ™āļĩāđ‰āļˆāļķāļ‡āđƒāļŠāđ‰āđ€āļ—āļ„āļ™āļīāļ„āđ€āļ”āļĨāļŸāļēāļĒāđ€āļžāļ·āđˆāļ­āļšāļĢāļīāļŦāļēāļĢāļˆāļąāļ”āļāļēāļĢāļ„āļ§āļēāļĄāđ€āļŦāđ‡āļ™āļ—āļĩāđˆāđāļ•āļāļ•āđˆāļēāļ‡āļāļąāļ™āļ āļēāļĒāđƒāļ™āļāļĨāļļāđˆāļĄāļœāļđāđ‰āđ€āļŠāļĩāđˆāļĒāļ§āļŠāļēāļ āļ‹āļķāđˆāļ‡āļžāļšāļ§āđˆāļēāļŦāļĨāļąāļ‡āļˆāļēāļāļāļēāļĢāļ›āļĢāļ°āđ€āļĄāļīāļ™āļ™āđ‰āļģāļŦāļ™āļąāļāļ„āļ§āļēāļĄāļŠāļģāļ„āļąāļāļˆāļģāļ™āļ§āļ™ 3 āļ„āļĢāļąāđ‰āļ‡ āļ‚āđ‰āļ­āļĄāļđāļĨāļĄāļĩāđāļ™āļ§āđ‚āļ™āđ‰āļĄāļŠāļąāļ”āđ€āļˆāļ™āļ§āđˆāļēāļœāļđāđ‰āđ€āļŠāļĩāđˆāļĒāļ§āļŠāļēāļāļ—āļļāļāļ„āļ™āđ€āļāļīāļ”āļ„āļ§āļēāļĄāđ€āļŦāđ‡āļ™āļžāđ‰āļ­āļ‡āļ•āđ‰āļ­āļ‡āļāļąāļ™ āļœāļĨāļāļēāļĢāļ§āļīāļˆāļąāļĒāļŠāļĢāļļāļ›āđ„āļ”āđ‰āļ§āđˆāļēāļ›āļĢāļ°āļŠāļīāļ—āļ˜āļīāļ āļēāļž, āđāļĢāļ‡āļšāļīāļ”, āđ€āļ‚āļĄāđˆāļēāļ„āļ§āļąāļ™āļ”āļģ āđāļĨāļ°āļ­āļ­āļāđ„āļ‹āļ”āđŒāļ‚āļ­āļ‡āđ„āļ™āđ‚āļ•āļĢāđ€āļˆāļ™āļĄāļĩāļ„āļ§āļēāļĄāļŠāļģāļ„āļąāļāļāļąāļšāđ€āļ„āļĢāļ·āđˆāļ­āļ‡āļĒāļ™āļ•āđŒāļ”āļĩāđ€āļ‹āļĨāļĄāļēāļāļ–āļķāļ‡ 23.67, 20.93, 21.72 āđāļĨāļ° 10.38% āļ•āļēāļĄāļĨāļģāļ”āļąāļš āđƒāļ™āļ‚āļ“āļ°āļ—āļĩāđˆāļ„āļēāļĢāđŒāļšāļ­āļ™āđ„āļ”āļ­āļ­āļāđ„āļ‹āļ”āđŒ, āđ„āļŪāđ‚āļ”āļĢāļ„āļēāļĢāđŒāļšāļ­āļ™, āļāļģāļĨāļąāļ‡ āđāļĨāļ°āļ„āļēāļĢāđŒāļšāļ­āļ™āļĄāļ­āļ™āļ­āļāđ„āļ‹āļ”āđŒāļĄāļĩāļ„āļ§āļēāļĄāļŠāļģāļ„āļąāļāļ™āđ‰āļ­āļĒāļĨāļ‡āļ•āļēāļĄāļĨāļģāļ”āļąāļš āļœāļĨāļāļēāļĢāļ›āļĢāļ°āđ€āļĄāļīāļ™āđ‚āļ”āļĒāļœāļđāđ‰āđ€āļŠāļĩāđˆāļĒāļ§āļŠāļēāļ āļāļēāļĢāļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒāļœāļĨ āđāļĨāļ°āļāļēāļĢāļ„āļģāļ™āļ§āļ“āļ™āđ‰āļģāļŦāļ™āļąāļāļ„āļ§āļēāļĄāļŠāļģāļ„āļąāļāļ‚āļ­āļ‡āļžāļēāļĢāļēāļĄāļīāđ€āļ•āļ­āļĢāđŒāđ„āļ”āđ‰āļ–āļđāļāļ™āļģāđ€āļŠāļ™āļ­āļ­āļĒāļđāđˆāļ āļēāļĒāđƒāļ™āļšāļ—āļ„āļ§āļēāļĄAbstractThe applications of alternative fuel in diesel engine must consider both engine output performance and exhaust gas emissions. However, researchers do not have any data of the weight of each parameter. This leads to the low efficiency in multi-criteria decision making. Therefore, this research aims to investigate the weight of eight important diesel engine’s parameters by using integrated Analytical Hierarchy Process (AHP) and Delphi technique for group decision making. The investigation shows that AHP can convert specialist’s perceptions, which is qualitative data, to quantitative data efficiently. Moreover, consistency ratios are verified and confirm the quality of research data. Evaluation results from eight specialists reveal incongruities in the beginning of the research. Hence, Delphi technique has performed in this study, which aims to manage these disagreements. After 3 rounds of specialist’s evaluations, the data shows very apparent trend of the group consensus. The results show that engine efficiency, torque, Particulate Matter (PM) and Oxides of Nitrogen (NOx) receive a high significance with the weights of 23.67, 20.93, 21.72 and 10.38%, respectively. While Carbon dioxide (CO2), Total Hydrocarbon (THC), power and Carbon monoxide (CO) show less importance, respectively. Specialist’s evaluations, discussions and weight calculations are also presented.
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