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 particle-inspired (PS) high-level learning strategies (HLH) are developed as evolutionary selection strategies (ESs) to improve the performance of the hyperheuristic framework. Meanwhile, random permutation (RP), tabu search (TS), and fitness rate rank-based multiarmed bandit (FRR-MAB) are also introduced as baselines for comparisons. We evaluated pairings of nine different selection strategies and four acceptance mechanisms and monitored the performance of the first four outstanding pairs in 36 pairs by solving three sets of benchmark instances from the literature. Experimental results show that the proposed approaches outperform most fine-tuned bespoke state-of-the-art approaches in the literature, and PS-AM and AS-AM perform better when compared to the rest of the pairs in terms of obtaining a good trade-off of solution quality and computing time.

Enhancing Hyperheuristics for the Knapsack Problem through Fuzzy Logic

“…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.…”

Data‐Driven Robust Optimization for Solving the Heterogeneous Vehicle Routing Problem with Customer Demand Uncertainty

“…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.…”

“…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.…”