Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Modeling rolling batch planning as vehicle routing problem with time windows
Computers and Operations Research
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A procedure for the optimization of the dynamic response of a Vendor managed inventory system
Computers and Industrial Engineering - Supply chain management
Computers and Industrial Engineering - Supply chain management
A Multi-Echelon Inventory System with Information Exchange
Management Science
The Value of Information Sharing in a Two-Level Supply Chain
Management Science
Dynamic Programming Approximations for a Stochastic Inventory Routing Problem
Transportation Science
Minimizing the Total Cost in an Integrated Vendor--Managed Inventory System
Journal of Heuristics
Supply chain integration in vendor-managed inventory
Decision Support Systems
Note on supply chain integration in vendor-managed inventory
Decision Support Systems
Situation reactive approach to Vendor Managed Inventory problem
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
In this research, an economic order quantity (EOQ) model is first developed for a two-level supply chain system consisting of several products, one supplier and one-retailer, in which shortages are backordered, the supplier's warehouse has limited capacity and there is an upper bound on the number of orders. In this system, the supplier utilizes the retailer's information in decision making on the replenishments and supplies orders to the retailer according to the well known (R,Q) policy. Since the model of the problem is of a non-linear integer-programming type, a genetic algorithm is then proposed to find the order quantities and the maximum backorder levels such that the total inventory cost of the supply chain is minimized. At the end, a numerical example is given to demonstrate the applicability of the proposed methodology and to evaluate and compare its performances to the ones of a penalty policy approach that is taken to evaluate the fitness function of the genetic algorithm.