GRASP with ALNS for solving the location routing problem of infectious waste collection in the Northeast of Thailand

This research proposes a heuristic to solve the problem of the location selection of incinerators and the vehicle routing of infectious waste collection for hospitals in the Northeast of Thailand. The developed heuristic is called the Greedy Randomized Adaptive Large Neighborhood Search Procedure (GRALNSP)and applies the principles of the Greedy Randomized Adaptive Search Procedure (GRASP) and Adaptive Large Neighborhood Search (ALNS) in the local search. The results from GRALNSP are compared with those from the exact method processed by the A Mathematical Programming Language (AMPL) program. For small-sized problems, experiments showed that both methods provided no different results with the global optimal solution, but GRALNSP required less computational time. When the problems were larger-scale and more complicated, AMPL could not find the optimal solution within the limited period of computational time while GRALNSP provided better results with much less computational time. In solving the case study with GRALNSP, the result shows that the suitable locations for opening infectious waste incinerators are the locations of Pathum Ratwongsa district, Amnat Charoen province and Nam Phong district, Khonkaen province. An incinerator with a burning capacity of 600 kilogram/hour is used at both locations. The monthly total distances for infectious waste collection are 24,055.24 and 38,401.88 kilometers, respectively, and the lowest total cost is 6,268,970.40 baht per month.

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

JURNAL MANAJEMEN INDUSTRI DAN LOGISTIK ◽

10.30988/jmil.v4i1.311 ◽

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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An adaptive large neighborhood search procedure applied to the dynamic patient admission scheduling problem

Artificial Intelligence in Medicine ◽

10.1016/j.artmed.2016.10.002 ◽

2016 ◽

Vol 74 ◽

pp. 21-31 ◽

Cited By ~ 18

Author(s):

Richard Martin Lusby ◽

Martin Schwierz ◽

Troels Martin Range ◽

Jesper Larsen

Keyword(s):

Patient Admission ◽

Neighborhood Search ◽

Scheduling Problem ◽

Search Procedure ◽

Large Neighborhood Search ◽

Adaptive Large Neighborhood Search ◽

Large Neighborhood

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Adaptive Large Neighborhood Search to Solve Multi-Level Scheduling and Assignment Problems in Broiler Farms

Journal of Open Innovation Technology Market and Complexity ◽

10.3390/joitmc5030037 ◽

2019 ◽

Vol 5 (3) ◽

pp. 37 ◽

Cited By ~ 1

Author(s):

Praseeratasang ◽

Pitakaso ◽

Sethanan ◽

Kosacka-Olejnik ◽

kaewman ◽

...

Keyword(s):

Mathematical Model ◽

Production Scheduling ◽

Optimal Solution ◽

Neighborhood Search ◽

Large Neighborhood Search ◽

Adaptive Large Neighborhood Search ◽

Large Neighborhood ◽

Algorithm Efficiency ◽

Time Period ◽

Broiler Farm

This research aimed to present a solution to the problem of production scheduling and assignment in broiler farms, which thus enabled the farms to achieve maximum profit. In the operation of farms, there are many factors that affect profits, such as the number of broilers being consistent with the demand of production plants, including profits from the sales and transportation costs. Therefore, we formulated a mathematical model and tested it while using three problem groups through the Lingo v.11 program. The results indicated that this mathematical model could find a suitable solution. However, finding the best solution had time constraints, which resulted in various other problems that prevented a search for an optimal solution due to time consumption exceeding 72 h. We developed an algorithm using the Adaptive Large Neighborhood Search (ALNS) method in order to find another possible solution using a shorter time period, which consisted of ALNS1, ALNS2, and ALNS3. These algorithms are based on a combination of the method of destruction solutions and methods accepting different solutions. We aimed to effectively solve the problems and ensure that they are appropriate for the case study, a broiler farm in Buriram. When comparing the algorithm efficiency with the Lingo v.11 program, it was found that the ALNS1 algorithm was the most suitable for finding the optimal solution in the shortest time, which resulted in a 5.74% increase in operating profits.

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An Adaptive Large Neighborhood Search for the Larger-Scale Instances of Green Vehicle Routing Problem with Time Windows

Complexity ◽

10.1155/2020/8210630 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Zixuan Yu ◽

Ping Zhang ◽

Yang Yu ◽

Wei Sun ◽

Min Huang

Keyword(s):

Vehicle Routing ◽

Carbon Emissions ◽

Vehicle Routing Problem ◽

Computational Time ◽

Neighborhood Search ◽

Large Neighborhood Search ◽

Adaptive Large Neighborhood Search ◽

Routing Problem ◽

Large Neighborhood ◽

Insertion Method

Due to huge amount of greenhouse gases emission (such as CO2), freight has been adversely affecting the global environment in facilitating the global economy. Therefore, green vehicle routing problem (GVRP), aiming to minimize the total carbon emissions in the transportation, has become a hot issue. In this paper, an adaptive large neighborhood search (ALNS) algorithm is proposed to solve large-scale instances of GVRP. The core of ALNS algorithm is destroy operators and repair operators. In the destroy operators, a new removal heuristic applying to the characteristics of GVRP is proposed. The heuristic can quickly remove customers who bring a large amount of carbon emissions with pertinence, and these customers may be arranged more properly in future repair operators. In the repair operators, a fast insertion method is developed. In the fast insertion method, the feasibility of a new route is judged by checking the constraints of partial customers after the inserted customer, instead of checking the constraints of all customers. Thus, the computational time of the ALNS algorithm is greatly saved. Computational experiments were performed on Solomon benchmark with 100 customers and Homberger benchmark instances with up to 1000 customers. Given the same computational time, the proposed ALNS improves the average accuracy by 8.49% compared with the classic ALNS. In the optimal situation, the improvement can achieve 33.61%.

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