Optimizing paper collection routes: A nonlinear programming approach in a recycling company

Abstract

Efficiency in the collection of recyclable waste is a critical component for strengthening circular economy models in emerging markets. In this context, Traperos de Emaús faces increased operating costs and unnecessary extensions to its paper collection routes, resulting from empirical routes and limited logistical planning. This study develops a nonlinear programming model to optimize these routes, integrating vehicle capacity constraints, operating times, and sustainability criteria, with the aim of evaluating its impact on reducing distances and costs. The proposed methodology employs the Evolutionary algorithm from the Solver plugin, selected for its ability to solve complex global search problems and its suitability for non-convex objective functions. Comparative scenarios between the current operation and the optimized sequence were modeled, allowing for the precise quantification of logistical improvements. The results show significant reductions in transportation costs: 36.90% for unit TM1 and 12.95% for TM2, in addition to a streamlining of routes that reduces overlaps, redundancies, and unproductive time. Taken together, the findings demonstrate that optimization through nonlinear programming is an effective tool for enhancing collection efficiency, consolidating data-driven logistics decisions, and strengthening operational sustainability in recycling organizations.