Variable Neighborhood Search Coupled with ILP-Based Very Large Neighborhood Searches for the (Periodic) Location-Routing Problem

2010 ◽  
pp. 174-189 ◽  
Author(s):  
Sandro Pirkwieser ◽  
Günther R. Raidl
Keyword(s):  
Variable Neighborhood Search ◽  
Neighborhood Search ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Large Neighborhood ◽  
Location Routing

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.

An Augmented Variable Neighborhood Search to Solve the Capacitated Location-Routing Problem

2021 ◽  
Vol 6 (4) ◽  
pp. 67-76
Author(s):  
Alireza Mohamadi-Shad ◽  
Hamed Niakan ◽  
Hasan Manzour
Keyword(s):  
Variable Neighborhood Search ◽  
Computational Time ◽  
Neighborhood Search ◽  
Future Research ◽  
Setup Cost ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Acceptance Criterion ◽  
Local Searches ◽  
Location Routing

In this paper we proposed a new variable neighborhood search (VNS) for solving the location- routing problem with considering capacitated depots and vehicles. A set of capacitated vehicles, a set of depots with restricted capacities, and associated opening costs, and a set of customers with deterministic demands are given. The problem aims to determine the depots to be opened, fleet assignment to each depot, and the routes to be performed to satisfy the demand of the customers. The objective is to minimize the total costs of the open depots, the setup cost associated with the used vehicles, and transportation cost. We proposed a new VNS which is augmented with a probabilistic acceptance criterion as well as a set of efficient local searches. The computational results implemented on four well-known data sets demonstrate that the proposed algorithm is competitive with other well- known algorithms while reaching many best-known solutions and updating six best new results with reasonable computational time. Conclusions and future research avenues close the paper.

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