Integrated Inventory-Transportation Problem in Vendor-Managed Inventory System

Abstract

The paper presents a two-echelon inventory-transportation problem in Vendor Managed Inventory (VMI) system. We consider a distribution system composed with single supplier, single distribution center and multiple retailers. Single kind of products are required to deliver from the manufacturer through distribution center to the retailers within soft time window. The objective of the problem is to minimize total logistics cost in the distribution network, including inventory cost, distribution cost, and time penalty cost. The upper echelon model focuses on minimizing inventory cost while the lower echelon model on vehicle routing problem. A mixed algorithm is designed to solve the problem with simulated annealing and ant colony with local search. The solution of upper and lower echelon model are substituted into each other based on the mixed algorithm step by step to get the optimization solutions. Computational experiments are executed to compare the performance of independent and integrated inventory-transportation optimization from the dimension of to verify the effectiveness of the model and the algorithms. INDEX TERMS Integrated inventory-transportation, vendor-managed inventory, stimulated annealing, ant colony, local search.

Bir süpermarket zincirinde iki seviyeli tedarikçi yönetimli stok problemi için etkili bir sezgisel yöntem

“…Teng vd. [56], TYS çerçevesinde stok kontrolü ve nakliye çizelgeleme problemlerinin birleşiminden oluşan probleme iki aşamalı bir çözüm üretmişlerdir. Problemin amacı, stok maliyeti, dağıtım maliyeti ve zaman cezası maliyeti dâhil olmak üzere dağıtım ağındaki toplam lojistik maliyetini minimize etmektir.…”

Research on inventory path optimization of VMI large logistics enterprises based on ant colony algorithm

“…It is one of the most successful ant colony algorithms, which is widely used to solve combinatorial optimization problems. The key point of ant colony system algorithm is to update the local information and global information of the algorithm respectively, and use the combination of the two methods to solve the model [9]. Local pheromone updating refers to the pheromone concentration on the path adjusted by the local pheromone when Ma Nu is looking for the ideal path; the global pheromone updating means that all ants will adjust the pheromone concentration on the optimal path found in the search process after all ants have completed the search.…”