scholarly journals A Hybrid Genetic-Simulated Annealing Algorithm for the Location-Inventory-Routing Problem Considering Returns under E-Supply Chain Environment

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Yanhui Li ◽  
Hao Guo ◽  
Lin Wang ◽  
Jing Fu
Keyword(s):  
Supply Chain ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Inventory Routing ◽  
Logistics System ◽  
Routing Problem ◽  
Problem Model ◽  
Inventory Routing Problem ◽  
Genetic Simulated Annealing Algorithm ◽  
Annealing Algorithm

Facility location, inventory control, and vehicle routes scheduling are critical and highly related problems in the design of logistics system for e-business. Meanwhile, the return ratio in Internet sales was significantly higher than in the traditional business. Many of returned merchandise have no quality defects, which can reenter sales channels just after a simple repackaging process. Focusing on the existing problem in e-commerce logistics system, we formulate a location-inventory-routing problem model with no quality defects returns. To solve this NP-hard problem, an effective hybrid genetic simulated annealing algorithm (HGSAA) is proposed. Results of numerical examples show that HGSAA outperforms GA on computing time, optimal solution, and computing stability. The proposed model is very useful to help managers make the right decisions under e-supply chain environment.

Hybrid Genetic Simulated Annealing Algorithm with its Application in Vehicle Routing Problem with Time Windows

2010 ◽  
Vol 148-149 ◽  
pp. 395-398
Author(s):  
Qiang Zhang ◽  
Qing Guo Lin ◽  
Qin He Zhang ◽  
Ji Chen Fang ◽  
Zhan Gen Wang ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Simulated Annealing Algorithm ◽  
Time Windows ◽  
Routing Problem ◽  
Genetic Simulated Annealing Algorithm ◽  
Annealing Algorithm ◽  
Set Up ◽  
Delayed Time

Under the situations of distribution center and customer demand, a mathematical model of Vehicle Routing Problem with Time Windows(VRPTW) is set up, where the main factors of less total distance of vehicles driving and less delayed time of vehicles are considered. For the "premature" convergence in Genetic Algorithms, Simulated Annealing Algorithm is introduced, and GSA is designed to optimize and analyse the VRPTW examples. It is shown that the performance of GSA is better than Genetic Annealing(GA).

The Research on Mutil-Objective Location Routing Problem Based on Genetic Simulated Annealing Algorithm

2014 ◽  
Vol 543-547 ◽  
pp. 2842-2845 ◽  
Author(s):  
Gai Li Du ◽  
Nian Xue
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Fitness Function ◽  
Solution Space ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Genetic Simulated Annealing Algorithm ◽  
Location Routing ◽  
Annealing Algorithm

This paper analysis the basic principles of the genetic algorithm (GA) and simulated annealing algorithm (SA) thoroughly. According to the characteristics of mutil-objective location routing problem, the paper designs the hybrid genetic algorithm in various components, and simulate achieved the GSAA (Genetic Simulated Annealing Algorithm).Which architecture makes it possible to search the solution space easily and effectively without overpass computation. It avoids effectively the defects of premature convergence in traditional genetic algorithm, and enhances the algorithms global convergence. Also it improves the algorithms convergence rate to some extent by using the accelerating fitness function. Still, after comparing with GA and SA, the results show that the proposed Genetic Simulated Annealing Algorithm has better search ability. And the emulation experiments show that this method is valid and practicable.

Logistics Distribution Routing Optimization Algorithm

2014 ◽  
Vol 513-517 ◽  
pp. 1740-1743 ◽  
Author(s):  
Zhang Chun Hua ◽  
Hua Xin ◽  
Zhang Wei
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Search Algorithm ◽  
Memory Function ◽  
Memory Device ◽  
Routing Problem ◽  
Logistics Distribution ◽  
Genetic Simulated Annealing Algorithm ◽  
Annealing Algorithm

Logistics distribution involves preparing goods in the distribution center or logistics node for most reasonable delivery according to the requirements of customers. Genetic algorithm is a random global search algorithm based on the principle of natural evolution. It can be a good solution to optimize the distribution routes. This paper combines genetic algorithm and the simulated annealing algorithm, to which memory device is added, in order to avoid best result losing in the crossover operator of the genetic algorithm. The experimental results show that a memory function with this genetic simulated annealing algorithm in solving the logistics distribution routing problem, can not only get a higher qualified solution, but can also significantly reduce the evolutionary generation that algorithm requires, and obtain solution to the problem in less time.

scholarly journals Algoritma Simulated Annealing untuk Menentukan Rute Kendaraan Heterogen (Studi Kasus)

2020 ◽  
Vol 7 (5) ◽  
pp. 933
Author(s):  
Andriansyah Andriansyah ◽  
Rizky Novatama ◽  
Prima Denny Sentia
Keyword(s):  
Supply Chain ◽  
Simulated Annealing ◽  
Vehicle Routing ◽  
Heuristic Algorithm ◽  
Vehicle Routing Problem ◽  
Simulated Annealing Algorithm ◽  
Routing Problem ◽  
Annealing Algorithm ◽  
Banda Aceh ◽  
Insertion Heuristic

<p>Permasalahan transportasi dalam supply chain management sangat penting untuk dikaji karena dapat menimbulkan biaya logistik yang sangat besar. Salah satu cara untuk mengurangi biaya transportasi adalah dengan penentuan rute kendaraan atau dikenal dengan istilah vehicle routing problem. Objek yang menjadi kajian merupakan perusahaan yang bergerak pada bidang distribusi produk untuk area kota Banda Aceh dan sekitarnya. Dalam proses distribusi, perusahaan ini menggunakan dua jenis kendaraan dengan kapasitas dan biaya operasional yang berbeda sehingga permasalahan menjadi heterogeneous fleet vehicle routing problem. Penentuan rute kendaraan dalam penelitian ini dilakukan dengan tiga metode, yaitu metode analitik, algoritma insertion heuristic sebagai metode heuristik, dan algoritma simulated annealing sebagai metode metaheuristik. Berdasarkan hasil yang diperoleh dari data ujicoba, algoritma simulated annealing merupakan algoritma yang paling baik dalam menyelesaikan permasalahan. Secara rata-rata, algoritma simulated annealing dapat menghasilkan kualitas solusi yang sama dengan metode analitik, namun dengan waktu komputasi yang lebih singkat. Selain itu, algoritma simulated annealing menghasilkan kualitas solusi yang lebih baik dibandingkan algoritma insertion heuristic yang dikembangkan dalam penelitian dan dapat meningkatkkan kualitas solusi sebesar 20,18% dari penelitian sebelumnya dengan waktu komputasi 19,27 detik.</p><p> </p><p><em><strong>Abstract</strong></em></p><p class="Judul2"><em>Transportation problems </em><em>in supply chain </em><em>are very important </em><em>to be discussed </em><em>because </em><em>they </em><em>can </em><em>raises</em><em> enormous logistic cost. </em><em>Route determination of the vehicles known as vehicle routing problem is the one of ways to reduce transportation cost</em><em>. </em><em>The object discussed in this study is the distribution company</em><em> </em><em>for Banda Aceh city and its surroundings</em><em>.</em><em> The company uses two types of vehicle to distribute the product for customers.</em><em> </em><em>The differences each vehicle are vehicle capacity and operational cost. To cover these differences, the problem becomes heterogenous fleet vehicle routing problem. The study uses three methods to solve the problem. Analitycal method, insertion heuristic algorithm as heuristic method and simulated annealing algorithm as metaheuristic method are the methods used. According to the results, simulated anneling algorithm produces the better solutions than two others. On average, solutions produced by simulated annealing algorithm from dataset have same quality with analitycal method, but with faster computation. Furthermore, </em><em>simulated anneling </em><em>algorithm </em><em>produces better quality of solutions than insertion heuristic algorithm both from this stu</em><em>dy and previous study. The solution improves 20,18% with computation time 19,27 seconds.</em></p><p class="Judul2"> </p><p><em><strong><br /></strong></em></p><p class="Abstrak" align="center"> </p>

scholarly journals Hybrid Harmony Search-Simulated Annealing Algorithm for Location-Inventory-Routing Problem in Supply Chain Network Design with Defect and Non-Defect Items

2020 ◽  
Vol 10 (18) ◽  
pp. 6625
Author(s):  
Farahanim Misni ◽  
Lai Soon Lee ◽  
Hsin-Vonn Seow
Keyword(s):  
Supply Chain ◽  
Simulated Annealing ◽  
Network Design ◽  
Harmony Search ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Inventory Routing ◽  
Routing Problem ◽  
Inventory Routing Problem ◽  
The Cost

This paper considers a location-inventory-routing problem (LIRP) that integrates the strategic, tactical, and operational decision planning in supply chain network design. Both defect and non-defect items of returned products are considered in the cost of reverse logistics based on the economic production quantity model. The objective of the LIRP is to minimize the total cost of location-allocation of established depots, the cost of inventory, including production setup and holding cost, as well as the cost of travelled distance by the vehicles between the open depots and assigned customers. A Hybrid Harmony Search-Simulated Annealing (HS-SA) algorithm is proposed in this paper. This algorithm incorporates the dynamic values of harmony memory considering rate and pitch adjustment rate with the local optimization techniques to hybridize with the idea of probabilistic acceptance rule from simulated annealing, to avoid the local extreme points. Computational experiments on popular benchmark data sets show that the proposed hybrid HS-SA algorithm outperforms a standard HS and an improved HS for all cases.

scholarly journals Mixed Replenishment Policy for ATO Supply Chain Based on Hybrid Genetic Simulated Annealing Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Hui Huang ◽  
Yan Jin ◽  
Bo Huang ◽  
Han-Guang Qiu
Keyword(s):  
Supply Chain ◽  
Simulated Annealing ◽  
Optimization Problem ◽  
Simulated Annealing Algorithm ◽  
Optimal Solution ◽  
Genetic Simulated Annealing Algorithm ◽  
Replenishment Policy ◽  
Annealing Algorithm ◽  
Searching Ability

Timely components replenishment is the key to ATO (assemble-to-order) supply chain operating successfully. We developed a production and replenishment model of ATO supply chain, where the ATO manufacturer adopts both JIT and (Q,r) replenishment mode simultaneously to replenish components. The ATO manufacturer’s mixed replenishment policy and component suppliers’ production policies are studied. Furthermore, combining the rapid global searching ability of genetic algorithm and the local searching ability of simulated annealing algorithm, a hybrid genetic simulated annealing algorithm (HGSAA) is proposed to search for the optimal solution of the model. An experiment is given to illustrate the rapid convergence of the HGSAA and the good quality of optimal mixed replenishment policy obtained by the HGSAA. Finally, by comparing the HGSAA with GA, it is proved that the HGSAA is a more effective and reliable algorithm than GA for solving the optimization problem of mixed replenishment policy for ATO supply chain.

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