Nowadays, people are challenges to do everything practically, efficiently and effectively. Utilization of technology into an effort to realize it is supported by a variety of equipment. Equipment capable of supporting human performance in order to produce satisfactory performance. Products that are closest and often used by humans and become a lifestyle generally become the focus of the development of this research. This study aims to design a product that is able to support human performance is the development of warmer lamp products which in the development process used House of Quality method to create a quality product and in accordance with the needs of consumers in general. From the results of the design in getting a warmer lamp product that has a speensi that is felt able to meet the needs of consumers selected based on the assessment matrix of six concepts that have been proposed.
Abstract. This paper presents the design and analysis of computer mouse using Design for Disassembly methodology. Basically, the existing computer mouse model consist a number of unnecessary part that cause the assembly and disassembly time in production. The objective of this project is to design a new computer mouse based on Design for Disassembly (DFD) methodology. The main methodology of this paper was proposed from sketch generation, concept selection, and concept scoring. Based on the design screening, design concept B was selected for further analysis. New design of computer mouse is proposed using fastening system. Furthermore, three materials of ABS, Polycarbonate, and PE high density were prepared to determine the environmental impact category. Sustainable analysis was conducted using software SolidWorks. As a result, PE High Density gives the lowers amount in the environmental category with great maximum stress value.
This research deals with a problem that minimizes makespan in a non-identical parallel machine system with sequence and machine dependent setup times and machine dependent processing times. We first present a new mixed integer programming formulation for the problem, and using this formulation, one can easily find optimal solutions for small problems. However, since the problem is NP-hard and the size of a real problem is large, we propose four heuristic algorithms including genetic algorithm based heuristics to solve the practical big-size problems in a reasonable computational time. To assess the performance of the algorithms, we conduct a computational experiment, from which we found the heuristic algorithms show different performances as the problem characteristics are changed and the simple heuristics show better performances than genetic algorithm based heuristics for the case when the numbers of jobs and/or machines are large. †
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