A genetic algorithm-Taguchi based approach to inventory routing problem of a single perishable product with transshipment

2017 ◽  
Vol 104 ◽  
pp. 124-133 ◽  
Author(s):  
A. Azadeh ◽  
S. Elahi ◽  
M. Hosseinabadi Farahani ◽  
B. Nasirian
Keyword(s):  
Genetic Algorithm ◽  
Inventory Routing ◽  
Routing Problem ◽  
Inventory Routing Problem ◽  
Perishable Product

Stochastic inventory-routing problem with lateral transshipment for perishable product

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Parviz Fattahi ◽  
Mehdi Tanhatalab
Keyword(s):  
Supply Chain ◽  
Valid Inequalities ◽  
Upper And Lower Bounds ◽  
Inventory Routing ◽  
Routing Problem ◽  
Content Type ◽  
Inventory Routing Problem ◽  
Perishable Product ◽  
New Variant ◽  
Lateral Transshipment

Purpose This study aims to design a supply chain network in an uncertain environment while exists two options for distribution of the perishable product and production lot-sizing is concerned. Design/methodology/approach Owing to the complexity of the mathematical model, a solution approach based on a Lagrangian relaxation (LR) heuristic is developed which provides good-quality upper and lower bounds. Findings The model output is discussed through various examples. The introduction of some enhancements and using some heuristics results in better outputs in the solution procedure. Practical implications This paper covers the modeling of some real-world problems in which demand is uncertain and managers face making some concurrent decisions related to supply chain management, transportation and logistics and inventory control issues. Furthermore, considering the perishability of product in modeling makes the problem more practically significant as these days there are many supply chains handling dairy and other fresh products. Originality/value Considering uncertainty, production, transshipment and perishable product in the inventory-routing problem makes a new variant that has not yet been studied. The proposed novel solution is based on the LR approach that is enhanced by some heuristics and some valid inequalities that make it different from the current version of the LR used by other studies.

scholarly journals A Pseudo-Parallel Genetic Algorithm Integrating Simulated Annealing for Stochastic Location-Inventory-Routing Problem with Consideration of Returns in E-Commerce

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Bailing Liu ◽  
Hui Chen ◽  
Yanhui Li ◽  
Xiang Liu
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Computing Time ◽  
Single Type ◽  
Inventory Routing ◽  
Parallel Genetic Algorithm ◽  
Routing Problem ◽  
Inventory Routing Problem ◽  
Stochastic Location ◽  
Pseudo Parallel Genetic Algorithm

Facility location, inventory control, and vehicle routes scheduling are three key issues to be settled in the design of logistics system for e-commerce. Due to the online shopping features of e-commerce, customer returns are becoming much more than traditional commerce. This paper studies a three-phase supply chain distribution system consisting of one supplier, a set of retailers, and a single type of product with continuous review (Q, r) inventory policy. We formulate a stochastic location-inventory-routing problem (LIRP) model with no quality defects returns. To solve the NP-hand problem, a pseudo-parallel genetic algorithm integrating simulated annealing (PPGASA) is proposed. The computational results show that PPGASA outperforms GA on optimal solution, computing time, and computing stability.

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