Green location routing problem with flexible multi-compartment for source-separated waste: A Q-learning and multi-strategy-based hyper-heuristic algorithm

• Published in: Engineering applications of artificial intelligence 2023-05, Vol.121, p.105954, Article 105954
• Main Authors: Shang, Chunjian, Ma, Liang, Liu, Yong
• Format: Article
• Language: English
• Subjects: Flexible Multi-compartments; Green location routing problem; Hyper-heuristic algorithm; Q-learning; Source-separated waste collection and transportation
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2023.105954

In this paper, we extend a novel model for source-separated waste collection and transportation, the green location routing problem with multi-compartment (GLRPFMC), for which we design a Q-learning and multi-strategy-based hyper-heuristic algorithm (QLMSHH). The remarkable merits of this paper can be highlighted as the following threefold: (1) The GLRPFMC is novel in that it constructs a variant of the location routing problem with carbon emissions and flexible multi-compartment sizes that occurs in a source-separated waste transportation context. (2) For the methodological contribution, the QLMSHH is presented to design a hyper-heuristic model by intelligently selecting appropriate high-level heuristic components during different stages of the optimization process. (3) The proposed method incorporates the design of solution representations, evolution-acceptance pairs for high-level heuristic construction, the repairing solution scheme, and the local search strategy. Finally, sufficient experiments are conducted on the benchmark, new instances, and simulation data of GLRPFMC and draw some managerial insights. The satisfactory results highlight the efficiency and universality of the proposed model and method.