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Waste sorting is an imperative and significant issue in China, of which sorted-waste collection and transportation are indispensable parts. Despite its vital yet practical significance, few studies research mathematical models or algorithms of waste collection and transportation from the perspective of waste sorting. To address this issue, we extend a novel transportation model for the waste management system, namely, capacitated location routing problem with queuing time (CLRPQT) and design a cross-entropy and simulated-annealing based hyper-heuristic algorithm (CE-SAHH) for it. The main idea of this paper is three-fold: (1) As a particular property of this problem, source nodes cannot but need to be served by more than one vehicle that causes queuing time between a heterogeneous fleet of vehicles, which is novel in terms of the proposed model; (2) For the methodological contribution, a character encoding scheme, new decoding procedure, and local search strategy are designed embedded in the proposed method; (3) An integration of simulated annealing strategy and the cross-entropy-based hyper-heuristic algorithm is developed to overcome the combinatorial optimization problem with a more complex solution of this study. Finally, the results and analysis of three numeric experiments on benchmark datasets, new instances of CLRPQT, and simulation data in Shanghai, China, verify the effectiveness and universality of the proposed model and method. • A novel model with interactional routes for sorted-waste transportation is proposed. • A cross-entropy-based hyper-heuristic algorithm is proposed and performs well. • The novel model and method based on sorted waste enrich the waste management system. • Real-life data is used to verify the method and some managerial insights are drawn. [ABSTRACT FROM AUTHOR]

• A hybrid autonomous search algorithm called Entrotaxis-Jump is proposed. • Seeking an unknown emission source in large-scale area with road network constraint. • The cognitive search algorithm is combined with the intermittent strategy. • A real chemical cluster in China is selected as the typical research area. In a sudden hazardous material leakage accident, the rapid and accurate localization of the leakage source can effectively reduce casualties and property losses. Utilizing the sensible robot to seek an unknown emission source has become a promising field, while most researches in this field fail to consider some intractable but practical factors, such as the large spatial scale of some search domains and the road network (that can obstruct robot's maneuver). This paper proposes an efficient search algorithm, named as Entrotaxis-Jump, to seek an unknown emission source and obtain other source terms (e.g., source strength) in a large-scale (>0.1 km2) practical scene with road network constraints, such as a chemical cluster. The hybrid algorithm incorporates the Entrotaxis algorithm (a kind of cognitive search algorithm) with the intermittent search strategy, so it can utilize the triggering jump motion to alleviate the negative factors for search (road network constraints, expansive search domain, and turbulence effect). We select a chemical cluster in Shanghai, China as the typical research area and conduct a series of simulations in it to compare the performance of the Entrotaxis with the Entrotaxis-Jump under various release strength Q and wind speed V. The performance is reflected by the success rate (SR) and mean search time (MST), and we propose a skill score S to consider the two indexes synthetically. The results denote that the Entrotaxis-Jump outperforms Entrotaxis in all of our simulated scenarios, especially when the wind speed V > 2, in which case the SR of Entrotaxis drops sharply while the SR of the Entrotaxis-Jump witnesses a little decline but remains over 90%. The Entrotaxis-Jump algorithm proposed in this paper considers more practical factors, compared to previous researches, and is suitable and robust to utilize in real scenarios. [ABSTRACT FROM AUTHOR]