2020
DOI: 10.1155/2020/9291434
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Evolutionary Hyperheuristics for Location-Routing Problem with Simultaneous Pickup and Delivery

Abstract: This paper presents an evolution-based hyperheuristic (EHH) for addressing the capacitated location-routing problem (CLRP) and one of its more practicable variants, namely, CLRP with simultaneous pickup and delivery (CLRPSPD), which are significant and NP-hard model in the complex logistics system. The proposed approaches manage a pool of low-level heuristics (LLH), implementing a set of simple, cheap, and knowledge-poor operators such as “shift” and “swap” to guide the search. Quantum (QS), ant (AS), and part… Show more

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Cited by 2 publications
(3 citation statements)
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References 84 publications
(148 reference statements)
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“…The results show the advantage of the hyperheuristic over the low-level heuristics on its own and also over an adaptive multimethod search from the literature. Zhao et al [ 18 ] use an evolutionary hyperheuristic for location-routing problem with simultaneous pickup delivery where they use various metaheuristic techniques to guide the search and help the hyperheuristic to select the best low-level heuristic. Their proposal got better results than the best fine-tuned bespoke state-of-the-art approaches in the literature.…”
Section: State-of-the-artmentioning
confidence: 99%
“…The results show the advantage of the hyperheuristic over the low-level heuristics on its own and also over an adaptive multimethod search from the literature. Zhao et al [ 18 ] use an evolutionary hyperheuristic for location-routing problem with simultaneous pickup delivery where they use various metaheuristic techniques to guide the search and help the hyperheuristic to select the best low-level heuristic. Their proposal got better results than the best fine-tuned bespoke state-of-the-art approaches in the literature.…”
Section: State-of-the-artmentioning
confidence: 99%
“…We refer to the evolutionary hyperheuristic algorithm proposed in article [31] and improve hyperheuristic algorithm based on the DQN-based hyperheuristic algorithm proposed in article [32]. Improvements are made at the population generation stage based on the vehicle capacity in the historical population: the selected vehicle capacity sequence (disordered) is evaluated based on the fitness value; the obtained sequence, the historical optimal solution, and the reward and punishment evaluation are stored in the rewards and punishments table, respectively, which serves as a guide for future population generation.…”
Section: Hyperheuristic Algorithm Designmentioning
confidence: 99%
“…rough the method described above, a stochastic demand based on the demand of the original customer point was generated, and 1000 historical samples were generated for each customer point (the demand of the customer point with a certain demand being always unchanged), and the results were generated. Table 4 lists the cost of the route distance for each point that has been robustly optimized after the first route planning before the route adjustment using equation (31). e Instance column gives the serial number of the problem.…”
Section: Cvrp Modementioning
confidence: 99%