Comparative Study between the Heuristic Algorithm and the Optimization Technique for Vehicle Routing and Scheduling in a Solid Waste Collection System

Effective operation of a solid waste management program is a challenge to decision-makers who are responsible for meeting the goals of public health and environmental quality in an urban region. In particular, a regional vehicle routing and scheduling program for solid waste collection usually requires evaluating many assignment alternatives in which the vehicles and labor are required to be dispatched optimally with respect to various temporal and spatial constraints. Both heuristic algorithms and optimization techniques were used as effective tools for providing valuable collection programs. However, heavy computational demands frequently make the computational time increase rapidly within a few computational steps when using the optimization models for solving large-scale solid waste collection problems. On the other hand, although the heuristic algorithms only allow decision-makers or planners to analyze solid waste collection alternatives based on a larger service area, they provide a set of near-optimal solutions that may not be economic from a cost-saving perspective. This paper explores a comparative study between the use of a revised heuristic algorithm - minimum spanning tree - and an optimization technique - integer programming model - for investigating the efficiency of corresponding vehicle routing and scheduling program in a solid waste collection system. A case study in the city of Kaohsiung in Taiwan is presented as a typical example for the purpose of illustration. Research findings clearly show that although the revised heuristic algorithm may provide effective planning program for a larger service area than does the optimization model, recent advances in information technology may fast alter the limitation of the optimization model due to higher computational efficiency.