Högskolan i Skövde

This paper presents a decision support system (DSS) for making the waste collection process more sustainable. Currently, waste collection schedules and routes are created manually in most waste management organizations. Thisis both very time consuming and likely to result in poorsolutions, as the task is extremely difficult due to the large number of bins combined with the many parametersto be considered simultaneously. With a sophisticated DSS, it becomes possible to addressthe complexities of optimal waste collection and improve sustainability—not least from the environmental perspective. The DSS proposed here is designed to be used on the operational level in the waste management organization and supports daily operations and activities. System evaluation indicatesthat it can reduce truck operating time by approximately 25%, corresponding to a saving of approximately 21,300 kg of carbon dioxide and 187 kg of nitrogen oxides per year and truck.

This paper presents a real-world case study of optimizing waste collection in Sweden. The problem, involving approximately 17,000 garbage bins served by three bin lorries, is approached as a travelling salesman problem and solved using simulation-based optimization and an evolutionary algorithm. To improve the performance of the evolutionary algorithm, it is enhanced with a repair function that adjusts its genome values so that shorter routes are found more quickly. The algorithm is tested using two crossover operators, i.e., the order crossover and heuristic crossover, combined with different mutation rates. The results indicate that the order crossover is superior to the heuristics crossover, but that the driving force of the search process is the mutation operator combined with the repair function.