Bin packing problem (BPP) is one of the fastest-growing research issues within the field of combinatorial optimization. Over the last years, several studies carried out various BPP variants, mathematical models, and proposed methods to the BPPs. The classical BPP consists of packing a set of rectangular items in a minimum number of rectangular bins while respecting some constraints. Throughout the years, an improved typology was introduced by Wäscher et al. (2007), providing an excellent instrument for the organization and categorization criteria that defined the problem categories different from those of Dyckhoff (1990). Several early literature reviews have been conducted on various aspects of related packing problem variants. The contribution of this paper is to provide a comprehensive and refined taxonomy intended for BPPs. In addition to that, it is an up-to-date review based on a chronological taxonomy of the literature and depicts further research horizons. This systematic review allowed us to identify other characteristics and constraints, based on Wäscher's original ideas, mainly distinguished according to real cases studies. The detailed analysis provides a valuable framework for understanding the research gaps for future studies that should be acknowledged while proposing and solving new extensions.

This paper focuses on the Traveling Repairman Problem (TRP), an extension of the Traveling Salesman Problem (TSP), which has gained significant attention in various practical fields such as humanitarian logistics, machine scheduling, computer networking, and service-based distribution. Previous research on the TRP has primarily focused on exact and approximate algorithms, with limited attention given to heuristics. This paper introduces a new hybrid method called Guided Quantum Particle Swarm Optimization (GQPSO) to effectively solve the TRP. This GQPSO approach combines the Quantum Particle Swarm Optimization (QPSO) metaheuristic with the Guided Local Search (GLS) algorithm. We introduced several enhancements to improve the efficiency of QPSO when addressing the TRP: (ⅰ) The initialization phase of the QPSO algorithm is modified. (ⅱ) A guided local search method is integrated within QPSO and used as an intensification strategy for each position generated throughout the search process. (ⅲ) Several acceleration strategies are implemented to expedite the process of the local search procedure. Experimental results obtained on a collection of benchmark problems from the literature illustrate the performance of the proposed hybrid approach to the TRP. When compared to the existing best heuristic methods, our GQPSO approach provides optimal solutions for all small- and medium-sized instances and achieves best-known solutions for most of the large-sized instances. It demonstrates a high competitiveness against state-of-the-art algorithms, occasionally outperforming them, particularly on larger instances, and surpasses other approaches in both solution quality and computational efficiency. These experimental results provide additional validation of the effectiveness and robustness of GQPSO as a powerful alternative for solving the TRP.