Considering temporal convenience in routing of municipal solid waste collection and recycling

• Main Authors: Jing-kai Lin, 林靖凱
• Other Authors: Hung-Yueh Lin
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/97445011064879377971

碩士 === 朝陽科技大學 === 環境工程與管理系碩士班 === 95 === Collections and transports of the municipal solid wastes (MSW) are important works for local environmental authorities. The cost of collection and transport is often more than half of the total budget of MSW collection and treatment services. Nowadays the public are more willing to participate in recycling works as the environment protection consensus gradually grows. In Taiwan, the major recycle efforts are from curbside collection, it means the public used to contribute the recycled materials to the MSW collection vehicles where they dump wastes to. However, the concerns of former MSW routing were mainly in cost and equity issues, factors about time convenience of the public are not included in these routing models. Due to the inappropriate collection time schedule, some citizens could not dump their waste during the collection tours, and not to say to participate in the curbside recycling works. Thereby, the proposed model with time convenience was explored in this study. The mathematical model with different definitions of time convenience was compared, in respects of cost, time and other MSW collection factors. In the process of analysis, a large scale scenario was found to cost much more time than expected to find the optimal solution. The heuristic method, which is the genetic algorithms in our work, was thus applied to reduce the solving time. Based on the results of scenarios, the routing plans generated by the proposed model averagely saved 30% of the total collection time than those generated by traditional models. In addition, the results of the proposed model provided both conveniences of spatial and temporal. Although the results of genetic algorithms are not as good as those generated by the proposed model, they still achieve an average 70 % of the optimal solutions and can be solved under expected time. The results also demonstrate the satisfaction and flexibility of the proposed model and genetic algorithms to be applied in the MSW collection and curbside recycling problems.