Two-Echelon Collaborative Location Routing Problem With Intuitionistic Fuzzy Multi-Demands for Sorted-Waste Collection and Transportation

This paper explores a novel model for sorted-waste transportation, defined as the two-echelon collaborative location routing problem with intuitionistic fuzzy multi-demands. It considers a reverse logistics network organized in two capacitated echelons: the first echelon aims to build routes for trucks visiting every node for different strains of pick-up requests under intuitionistic fuzzy multi-demands, and the objective of the second echelon is to ascertain the number and position of cooperation clusters from waste transfer stations to treatment facilities. Due to the complexity of the proposed model, the instances that can be solved to optimality are usually small to medium-sized and face computational difficulties in solving larger instances. To overcome this problem, we fashion a distributed heuristic based on fuzzy bi-means and adaptive large neighborhood segmented search to address this novel model. A refined Shapley model for profit allocation and the best coalition combination is constructed for each participant. Extensive computational findings and a practical case study are conducted to show the efficiency of the proposed model and approach. Several relevant managerial insights are also derived to aid decision-making in waste sorting management.