Optimization of schedules for early garbage collection and disposal in the megapolis
DOI:
https://doi.org/10.15587/1729-4061.2022.251082Keywords:
organization of transportation, household waste, schedule of operations, frequency of processes, mixed graphsAbstract
It is shown that due to the growth of waste generated by the metropolis, the processes of their removal and disposal must be more accurately accounted and controlled. If it is impossible to introduce “smart” control systems, it is proposed to search for reserves to increase the efficiency of the processes in their structure. A structural model of operations has been developed that can reduce time costs. The use of incomplete information on the accumulation and removal of garbage leads to unplanned mileage of trucks. In order to avoid unforeseen costs, it is proposed to use early garbage collection, which reduces the frequency of emptying containers. This leads to an increase in the number of truck arrivals to load, but eliminates unforeseen mileage due to inconsistencies in the loading forecast. It is shown that to effectively organize the work of garbage trucks on the transport network of the city, an active, shortest schedule of operations is required, which must be made for several periods. To develop an optimal cyclic schedule of garbage trucks, a method based on the ordering of mixed graphs is proposed. The mixed graph shows the set of garbage collection operations and the time relationships between their execution times. In order to develop an optimal schedule from such a graph, cycles must be removed from the graph. To do this, the “divide and conquer” method was used. The proposed algorithm for graph ordering is used to study the current garbage collection system. As a result of research, higher productivity of garbage trucks and timely removal of organic waste were achieved. The reduction of the weekly working time of 6 garbage trucks with the use of the 70 % container filling level reached 42 hours.
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Copyright (c) 2022 Indira Saukenova, Myroslav Oliskevych, Igor Taran, Aliya Toktamyssova, Dana Aliakbarkyzy, Roman Pelo
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