The vehicle routing problem in the dairy sector: a case study

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Marta Rinaldi ◽  
Eleonora Bottani ◽  
Federico Solari ◽  
Roberto Montanari
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Nearest Neighbor ◽  
Current System ◽  
Real Life ◽  
Combinatorial Problem ◽  
Food Sector ◽  
Routing Problem ◽  
Practical Tool

Abstract The vehicle routing problem is one of the most studied NP-hard combinatorial problem. In the food sector, the complexity of the issue grows because of the presence of strict constraints. Taking into account the variability and the restrictions typical of the dairy sector, the aim of this paper is to provide a practical tool for solving the milk collection problem in real scenarios. A heuristic approach has been proposed to determine a feasible solution for a real-life problem, including capacity and time constraints. Two different applications of the Nearest Neighbor algorithm have been modelled and compared with the current system. Different tests have been implemented for evaluating the suitability of the outcomes. Results show that the greedy approach allows for involving less vehicles and reducing the travel time. Moreover, the tool has been proved to be flexible, able to solve routing problems with stochastic times and high supply variability.

scholarly journals Mixed Integer Linear Programming Models to Solve a Real-Life Vehicle Routing Problem with Pickup and Delivery

2021 ◽  
Vol 11 (20) ◽  
pp. 9551
Author(s):  
Ali Louati ◽  
Rahma Lahyani ◽  
Abdulaziz Aldaej ◽  
Racem Mellouli ◽  
Muneer Nusir
Keyword(s):  
Linear Programming ◽  
Vehicle Routing ◽  
Integer Linear Programming ◽  
Vehicle Routing Problem ◽  
Mixed Integer Linear Programming ◽  
Real Life ◽  
Mixed Integer ◽  
Pickup And Delivery ◽  
Routing Problem

This paper presents multiple readings to solve a vehicle routing problem with pickup and delivery (VRPPD) based on a real-life case study. Compared to theoretical problems, real-life ones are more difficult to address due to their richness and complexity. To handle multiple points of view in modeling our problem, we developed three different Mixed Integer Linear Programming (MILP) models, where each model covers particular constraints. The suggested models are designed for a mega poultry company in Tunisia, called CHAHIA. Our mission was to develop a prototype for CHAHIA that helps decision-makers find the best path for simultaneously delivering the company’s products and collecting the empty boxes. Based on data provided by CHAHIA, we conducted computational experiments, which have shown interesting and promising results.

scholarly journals OPTIMALISASI JUMLAH KENDARAAN DAN RUTE DISTRIBUSI LOGISTIK PEMILIHAN DI KABUPATEN KEDIRI PADA MASA PANDEMI

2021 ◽  
Vol 3 (1) ◽  
pp. 1-25
Author(s):  
Eka Wisnu Wardhana ◽  
Oki Anita Candra Dewi
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Nearest Neighbor ◽  
Visual Basic ◽  
Microsoft Excel ◽  
Google Maps ◽  
Routing Problem ◽  
Visual Basic Application

Artikel ini membahas tentang permasalahan distribusi logistik pemilihan kepala daerah (Pemilihan) yang berkaitan dengan penentuan jumlah kendaraan yang tepat dan pemilihan rute terdekat dengan mempertimbangkan jarak yang dapat dilalui oleh kendaraan roda 4 atau lebih. Penentuan jumlah kendaraan dan rute yang tepat ini sangat penting bagi Komisi Pemilihan Umum (KPU) karena aspek logistik dan distribusi sangat berpengaruh dalam memastikan suara masyarakat tersampaikan dengan baik. Selain itu aspek ini memiliki keterkaitan satu dengan lainnya baik bersifat strategis maupun teknis. Selama pandemi, distribusi logistik Pemilihan tidak hanya berkaitan dengan kebutuhan alat pemungutan suara namun juga alat pelindung diri (APD) selama pelaksanaan pemungutan. Oleh karena itu kegiatan logistik menjadi lebih banyak dengan tetap mempertimbangkan protokol kesehatan sehingga diperlukan sebuah aplikasi yang dapat menentukan jumlah kendaraan dan rute yang tepat sebagai dasar untuk mempercepat pengambilan keputusan. Penelitian ini fokus pada penentuan jumlah kendaraan dan rute distribusi logistik yang optimal pada Pemilihan tahun 2020 di masa pandemic COVID-19 di KPU Kabupaten Kediri menggunakan pendekatan vehicle routing problem dengan memperhatikan jarak dari google maps serta menggunakan algoritma nearest neighbor dalam menentukan jarak terdekat antar titik. Aplikasi yang dikembangkan adalah penentuan rute menggunakan Visual Basic Application (VBA) pada Microsoft Excel. Penelitian ini menghasilkan jumlah kendaraan sebanyak 12 kendaraan untuk pengiriman logistik Pemilihan maupun APD dengan batas maksimal total perjalanan setiap truk sepanjang 100 km. Selain itu terdapat 6 kecamatan yang dikunjungi dua kali karena total kebutuhan melebihi kapasitas kendaraan.

scholarly journals Methodology to Solve the Combination of the Generalized Assignment Problem and the Vehicle Routing Problem: A Case Study in Drug and Medical Instrument Sales and Service

2018 ◽  
Vol 9 (1) ◽  
pp. 3 ◽  
Author(s):  
Malichan Thongkham ◽  
Sasitorn Kaewman
Keyword(s):  
Local Search ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Routing Problem ◽  
Search Techniques ◽  
De Algorithm ◽  
Local Searches ◽  
Generalized Assignment ◽  
Linear Probability

This article presents algorithms for solving a special case of the vehicle routing problem (VRP). We define our proposed problem of a special VRP case as a combination of two hard problems: the generalized assignment and the vehicle routing problem. The different evolution (DE) algorithm is used to solve the problem. The recombination process of the original DE is modified by adding two more sets of vectors—best vector and random vector—and using two other sets—target vector and trial vector. The linear probability formula is proposed to potentially use one out of the four sets of vectors. This is called the modified DE (MDE) algorithm. Two local searches are integrated into the MDE algorithm: exchange and insert. These procedures create a DE and MDE that use (1) no local search techniques, (2) two local search techniques, (3) only the exchange procedure, and (4) only the insert procedure. This generates four DE algorithms and four MDE algorithms. The proposed methods are tested with 15 tested instances and one case study. The current procedure is compared with all proposed heuristics. The computational result shows that, in the case study, the best DE algorithm (DE-4) has a 1.6% better solution than that of the current practice, whereas the MDE algorithm is 8.2% better. The MDE algorithm that uses the same local search as the DE algorithms generates a maximum 5.814% better solution than that of the DE algorithms.

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