Monte-Carlo approximation algorithms for enumeration problems
Journal of Algorithms
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Introduction to Algorithms
Genetic Algorithm and Graph Partitioning
IEEE Transactions on Computers
An evolutionary algorithm to solve the joint replenishment problem using direct grouping
Computers and Industrial Engineering
Monte-Carlo algorithms for enumeration and reliability problems
SFCS '83 Proceedings of the 24th Annual Symposium on Foundations of Computer Science
Computational complexity of uncapacitated multi-echelon production planning problems
Operations Research Letters
Computers and Operations Research
Expert Systems with Applications: An International Journal
Meta-heuristic algorithms for solving a fuzzy single-period problem
Mathematical and Computer Modelling: An International Journal
Expert Systems: The Journal of Knowledge Engineering
Model and algorithm of fuzzy joint replenishment problem under credibility measure on fuzzy goal
Knowledge-Based Systems
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Given the order cycles of items in joint replenishment, no closed-form formula or efficient method is known to compute the exact inventory cost. Previous studies avoid the difficulty by restricting the replenishment policy to the cases where the order cycle of each item is a multiple of the cycle of the most frequently ordered item. This simplifies the computation but may entail sub-optimality of a solution. To cope with this, we devise an unbiased estimator of the exact cost which is computable in time polynomial of the problem input size and 1/@e, where @e is a pre-specified relative error of estimation. We then develop a genetic algorithm based on this new cost evaluation, report the experimental results in comparison to the ''RAND'' [Kaspi M, Rosenblatt MJ. An improvement of Silver's algorithm for the joint replenishment problem. IIE Transactions 1983; 15: 264-9] which has been known as a state-of-the-art method for joint replenishment, and discuss their implications.