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Nowadays, the collection of separated solid waste for recycling is still an expensive process, specially when performed in large-scale. One main problem resides in fleet-management, since the currently applied strategies usually have low efficiency. The waste collection process can be modelled as a vehicle routing problem, in particular as a Team Orienteering Problem (TOP). In the TOP, a vehicle fleet is assigned to visit a set of customers, while executing optimized routes that maximize total profit and minimize resources needed. The objective of this work is to optimize the waste collection process while addressing the specific issues around fleet-management. This should be achieved by developing a software tool that implements a genetic algorithm to solve the TOP. We were able to accomplish the proposed task, as our computational tests have produced some challenging results in comparison to previous work around this subject of study. Specifically, our results attained 60% of the best known scores in a selection of 24 TOP benchmark instances, with an average error of 18.7 in the remaining instances. The usage of a genetic algorithm to solve the TOP proved to be an efficient method by outputting good results in an acceptable time.

The weighted stress function method is proposed here as a new way of identifying the best solution from a set of nondominated solutions according to the decision maker's preferences, expressed in terms of weights. The method was tested using several benchmark problems from the literature, and the results obtained were compared with those of other methods, namely, the reference point evolutionary multiobjective optimization (EMO), the weighted Tchebycheff metric, and a goal programming method. The weighted stress function method can be seen to exhibit a more direct correspondence between the weights set by the decision maker and the final solutions obtained than the other methods tested.KEYWORDS evolutionary algorithm, multiobjective optimization, preference-based search

This work presents a decision support system for route planning of vehicles performing waste collection for recycling. We propose a prototype system that includes three modules: route optimization, waste generation prediction, and multiple-criteria decision analysis (MCDA). In this work we focus on the application of MCDA in route optimization. The structure and functioning of the DSS is also presented. We modelled the waste collection procedure as a routing problem, more specifically as a team orienteering problem with capacity constraints and time windows. To solve the route optimization problem we developed a cellular genetic algorithm. For the MCDA module, we employed three methods: SMART, ValueFn and Analytic Hierarchy Process (AHP). The decision support system was tested with real-world data from a waste management company that collects recyclables, and the capabilities of the system are discussed.