This research develops a model for determining the location of municipal solid waste facilities, such as transfer stations, compost centers, and landfills that are to be established. The model formulated as a mixed-integer optimization problem. Since the problem of locating waste management facilities can also be referred as locating undesirable facilities that faced many different criteria, multi-objective used in the model. There are three different objectives in this study. The first is to minimize the total cost including setup cost, processing cost, and transporting cost. The second is to minimize the pollution effect around the facilities. The last objective is to minimize the social impact to residents around facilities and around route for transporting waste. First, we build a multi-objective model to formulate the problem. Then we use the normalized weighted sum method and epsilon-constrain method to generate the Pareto solution. We formulated and used this model to solve the data in Surabaya, East Java, Indonesia. We also provide a computational result for a variety of parameter and discuss it in the case study.

This research develops a model for determining the location of municipal solid waste facilities, such as transfer stations, compost centers, and landfills that are to be established. The model formulated as a mixed-integer optimization problem. Since the problem of locating waste management facilities can also be referred as locating undesirable facilities that faced many different criteria, multi-objective used in the model. There are three different objectives in this study. The first is to minimize the total cost including setup cost, processing cost, and transporting cost. The second is to minimize the pollution effect around the facilities. The last objective is to minimize the social impact to residents around facilities and around route for transporting waste. First, we build a multi-objective model to formulate the problem. Then we use the normalized weighted sum method and epsilon-constrain method to generate the Pareto solution. We formulated and used this model to solve the data in Surabaya, East Java, Indonesia. We also provide a computational result for a variety of parameter and discuss it in the case study.

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