Optimizing Indonesian municipal solid waste collection scenarios: integration of multi-objective search simulation and social cost–benefit analysis

Waste collection in developing countries, including Indonesia, lacked attention it deserves. Major cities allocate up to 80% of their management budgets to this and smaller regions sacrifice 30% of their regional budget. Thus, an example consequence is South Tangerang collects just 367.5 tons of waste per day out of a total of 1070 tons, with performance hindered by limited optimization options and objectives. We demonstrated practical methods by minimizing three contradictory objectives—travel distance, road density, and number of intersections—primarily extracted road segment characteristic from Google Maps API such speeds and geography. We employed a multi-objective agent-based search simulation called the Ripple Spreading Algorithm and established a night-shift scenario inspired by the history of Fukuoka City. By extending the work schedule from 21:00 to 06:00 and alleviating peak hours, a significant reduction achieved 46% from total expenditure. The proposed idea increases productivity by 200%. We shed light on a new perspective considering social benefits, such as travel time saving, accidents, and emissions, which predicted in positive cash flows over the next 10 years of operation. Our findings provide solutions, reducing costs from IDR 400,000 or 1.08 USD per ton per waste, to 0.77 USD per ton.