Optimizing Palm Oil Biomass Collection: Genetic Algorithm Approaches in Solving Location-Routing Problem

Abstract

The transition from traditional fossil fuels to renewable energy sources, such as biofuels derived from palm oil biomass, represents a promising avenue in sustainable energy development. However, managing biomass supply chain (BSC) can be challenging, with biomass collection being particularly complex. Hence, this research delves into the intricacy of a location-routing problem (LRP) within the context of palm oil biomass collection. The study aims to enhance the efficiency of palm oil biomass collection by identifying optimal locations for collection facilities and devising vehicle routing strategies at minimum costs. To achieve these objectives, the research employs genetic algorithm (GA) approaches, incorporating innovative strategies, namely automated mutation operator selection (AMOS) and elite child population (ECP). Three GA variations are proposed to inspect the impacts of strategies in GA solution methods. These approaches are evaluated through computational experiments to measure optimisation quality in solving the LRP within the palm oil BSC. The findings underscore the effectiveness of the proposed methods in addressing the LRP. The proposed solution methods could establish a framework for decision-making processes within the biomass energy industry, particularly concerning facility siting and vehicle routing.