A Hybrid Genetic Algorithm for the Inventory Routing Problem

2017 ◽  
Author(s):  
Amri Sakhri Mohamed Salim ◽  
Tlili Mounira ◽  
Korbaa Ouajdi
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Inventory Routing ◽  
Routing Problem ◽  
Inventory Routing Problem

scholarly journals Green Inventory Routing Problem using Hybrid Genetic Algorithm

2021 ◽  
Vol 6 (4) ◽  
pp. 10-19
Author(s):  
Huda Zuhrah Ab Halim ◽  
Nureffa Natasha Mohd Azliana ◽  
Nuridawati Baharom ◽  
Nur Fatihah Fauzi ◽  
Nurizatul Syarfinas Ahmad Bakhtiar ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Genetic Algorithm ◽  
Supply Chain ◽  
Fuel Consumption ◽  
Hybrid Genetic Algorithm ◽  
Inventory Routing ◽  
Routing Problem ◽  
Total Cost ◽  
Inventory Routing Problem ◽  
Consumption Cost

Carbon dioxide (CO2) is known as one of the largest sources of global warming. One of the ways to curb CO2 emissions is by considering the environmental aspect in the supply chain management. This paper analyses the influence of carbon emissions on the Inventory Routing Problem (IRP). The IRP network consists of a depot, an assembly plant and multiple suppliers. The deterministic demands vary and are determined by the assembly plant. Fixed transportation cost, fuel consumption cost and inventory holding cost are used to evaluate the system’s total cost in which fuel consumption cost is determined by fuel consumption rate, distance, and fuel price. Backordering and split pick-up are not allowed. The main purpose of this study is to analyze the distribution network especially the overall costs of the supply chain by considering the CO2 emissions as well. The problem is known as Green Inventory Routing Problem (GIRP). The mixed-integer linear programming of this problem is adopted from Cheng et al. wherein this study a different Hybrid Genetic Algorithm is proposed at mutation operator. As predicted, GIRP has a higher total cost as it considered fuel consumption cost together with the transportation and inventory costs. The results showed the algorithm led to different sequences of routings considering the carbon dioxide emission in the objective function.

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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