scholarly journals Research on LRP Integration of E-Commerce Logistics under the Background of Integration of Collection and Distribution

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jiali Li ◽  
Zhijie Zhao ◽  
Tao Cheng
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Search Algorithm ◽  
Neighborhood Search ◽  
Location Routing Problem ◽  
Data Set ◽  
Routing Problem ◽  
Network Location ◽  
Location Routing ◽  
Logistics Enterprises

The distribution network composed of location and route is an important part of e-commerce logistics. With the continuous improvement of e-commerce requirements for logistics level, the practice of planning logistics network only from the perspective of the network location or the vehicle route can no longer meet the actual demand. In addition to the comprehensive consideration of the location-routing problem, the reverse logistics caused by customers’ returning goods should be taken into account. In this paper, the destruction and reorganization strategy of adaptive large-scale neighborhood search algorithm was introduced into the traditional genetic algorithm, so as to conduct research on the logistics location-routing problem under the background of integration of collection and distribution. Finally, the effectiveness of the optimized genetic algorithm was verified by Matlab tools and the existing bench-marking data set of the location-routing problem, which provided reference for the planning and decision-making of logistics enterprises.

scholarly journals Combining Heuristics with Simulation and Fuzzy Logic to Solve a Flexible-Size Location Routing Problem under Uncertainty

Algorithms ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 45
Author(s):  
Rafael D. Tordecilla ◽  
Pedro J. Copado-Méndez ◽  
Javier Panadero ◽  
Carlos L. Quintero-Araujo ◽  
Jairo R. Montoya-Torres ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Local Search ◽  
Vehicle Routing Problem ◽  
Large Scale ◽  
Iterated Local Search ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Routing ◽  
Fuzzy Pattern ◽  
Reasonable Computing

The location routing problem integrates both a facility location and a vehicle routing problem. Each of these problems are NP-hard in nature, which justifies the use of heuristic-based algorithms when dealing with large-scale instances that need to be solved in reasonable computing times. This paper discusses a realistic variant of the problem that considers facilities of different sizes and two types of uncertainty conditions. In particular, we assume that some customers’ demands are stochastic, while others follow a fuzzy pattern. An iterated local search metaheuristic is integrated with simulation and fuzzy logic to solve the aforementioned problem, and a series of computational experiments are run to illustrate the potential of the proposed algorithm.

scholarly journals ANALISA PERBANDINGAN METODE SIMULATED ANNEALING DAN LARGE NEIGHBORHOOD SEARCH UNTUK MEMECAHKAN MASALAH LOKASI DAN RUTE KENDARAAN DUA ESELON

2020 ◽  
Vol 4 (1) ◽  
pp. 35-46
Author(s):  
Winarno (Universitas Singaperbangsa Karawang) ◽  
A. A. N. Perwira Redi (Universitas Pertamina)
Keyword(s):  
Simulated Annealing ◽  
Computational Time ◽  
Neighborhood Search ◽  
Large Neighborhood Search ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Large Neighborhood ◽  
Location Routing ◽  
Node Removal ◽  
Better Than

AbstractTwo-echelon location routing problem (2E-LRP) is a problem that considers distribution problem in a two-level / echelon transport system. The first echelon considers trips from a main depot to a set of selected satellite. The second echelon considers routes to serve customers from the selected satellite. This study proposes two metaheuristics algorithms to solve 2E-LRP: Simulated Annealing (SA) and Large Neighborhood Search (LNS) heuristics. The neighborhood / operator moves of both algorithms are modified specifically to solve 2E-LRP. The proposed SA uses swap, insert, and reverse operators. Meanwhile the proposed LNS uses four destructive operator (random route removal, worst removal, route removal, related node removal, not related node removal) and two constructive operator (greedy insertion and modived greedy insertion). Previously known dataset is used to test the performance of the both algorithms. Numerical experiment results show that SA performs better than LNS. The objective function value for SA and LNS are 176.125 and 181.478, respectively. Besides, the average computational time of SA and LNS are 119.02s and 352.17s, respectively.AbstrakPermasalahan penentuan lokasi fasilitas sekaligus rute kendaraan dengan mempertimbangkan sistem transportasi dua eselon juga dikenal dengan two-echelon location routing problem (2E-LRP) atau masalah lokasi dan rute kendaraan dua eselon (MLRKDE). Pada eselon pertama keputusan yang perlu diambil adalah penentuan lokasi fasilitas (diistilahkan satelit) dan rute kendaraan dari depo ke lokasi satelit terpilih. Pada eselon kedua dilakukan penentuan rute kendaraan dari satelit ke masing-masing pelanggan mempertimbangan jumlah permintaan dan kapasitas kendaraan. Dalam penelitian ini dikembangkan dua algoritma metaheuristik yaitu Simulated Annealing (SA) dan Large Neighborhood Search (LNS). Operator yang digunakan kedua algoritma tersebut didesain khusus untuk permasalahan MLRKDE. Algoritma SA menggunakan operator swap, insert, dan reverse. Algoritma LNS menggunakan operator perusakan (random route removal, worst removal, route removal, related node removal, dan not related node removal) dan perbaikan (greedy insertion dan modified greedy insertion). Benchmark data dari penelitian sebelumnya digunakan untuk menguji performa kedua algoritma tersebut. Hasil eksperimen menunjukkan bahwa performa algoritma SA lebih baik daripada LNS. Rata-rata nilai fungsi objektif dari SA dan LNS adalah 176.125 dan 181.478. Waktu rata-rata komputasi algoritma SA and LNS pada permasalahan ini adalah 119.02 dan 352.17 detik.

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