Journal of Global Optimization
Journal of Global Optimization
A genetic algorithm for joint replenishment based on the exact inventory cost
Computers and Operations Research
Similarity classifier using similarities based on modified probabilistic equivalence relations
Knowledge-Based Systems
Engineering Applications of Artificial Intelligence
Differential Evolution for learning the classification method PROAFTN
Knowledge-Based Systems
Chaotic differential evolution methods for dynamic economic dispatch with valve-point effects
Engineering Applications of Artificial Intelligence
Brief paper: An improved differential evolution algorithm for the task assignment problem
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
Multi-objective hybrid evolutionary algorithms for radial basis function neural network design
Knowledge-Based Systems
IEEE Transactions on Evolutionary Computation
Computational complexity of uncapacitated multi-echelon production planning problems
Operations Research Letters
Model and algorithm of fuzzy joint replenishment problem under credibility measure on fuzzy goal
Knowledge-Based Systems
Differential evolution with local information for neuro-fuzzy systems optimisation
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
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As an important managerial problem, the practical joint replenishment and delivery (JRD) model under stochastic demand can be regarded as the combination of a joint replenishment problem and traveling salesman problem, either one is an NP-hard problem. However, due to the JRD's difficult mathematical properties, high quality solutions for the problem have eluded researchers. This paper firstly proposes an effective and efficient hybrid differential evolution algorithm (HDE) based on the differential evolution algorithm (DE) and genetic algorithm (GA) that can solve this NP-hard problem in a robust and precise way. After determining the appropriate parameters of the HDE by parameters tuning test, the effectiveness and efficiency of the HDE are verified by benchmark functions and numerical examples. We compare the HDE with the available best approach and find that the HDE can always obtain the slightly lower total costs under some situations. Compared with another popular evolutionary algorithm, results of numerical examples also show HDE is faster than GA and the convergence rate of HDE is higher than GA. HDE is a strong candidate for the JRD under stochastic demand.