Optimizing vehicle routing via Stackelberg game framework and distributionally robust equilibrium optimization method

Author(s):  
Fanghao Yin, Yi Zhao
2012 ◽  
Vol 468-471 ◽  
pp. 2047-2051 ◽  
Author(s):  
Ai Ling Chen

Vehicle routing optimization problem is one of the major research topics in logistics distribution field. Suitable vehicle routing selection is vital to reduce the logistics cost. The paper presents a hybrid optimization method to solve the vehicle routing problem with time windows. In the hybrid optimization method, discrete particle swarm optimization algorithm is used to assign the customers on routes and simulated annealing (SA) algorithm to avoid becoming trapped in local optimum. The simulation results have shown that the proposed method is feasible and effective for the vehicle routing problem with time windows.


2012 ◽  
Vol 13 (2) ◽  
pp. 151 ◽  
Author(s):  
Thomy Eko Saputro ◽  
Aprilia Prihatina

Thomy Eko Saputro DAN Aprilia PrihatinaJurusan Teknik Industri, Fakultas Teknik, Universitas Muhammadiyah MalangLaman: [email protected] satu hal yang berpengaruh dalam meningkatkan pelayanan konsumen adalah bagaimana mengirimkan produkyang tepat waktu kepada seluruh konsumen. Oleh karena itu pelaku bisnis perlu menerapkan suatu strategi yang tepat agardapat mengefisienkan dan mengefektifkan proses distribusinya. PR 567 sebagai distributor rokok perwakilan Purwodadiberupaya agar pendistribusian berjalan dengan baik karena mengingat proses distribusi dengan jumlah agen yang cukupbanyak seringkali mempersulit distributor untuk menentukan jadwal dan rute yang tepat. Permasalahan pendistribusianini termasuk dalam PVRP (Periodic Vehicle Routing Problem). Penyelesaian dilakukan menggunakan metode clusterfirst-second route dengan penugasan agen ke hari kunjungan menggunakan metode optimasi. Solusi akhir nantinyaakan memberikan jadwal dan rute kendaraan dengan total biaya tranportasi yang paling minimum. Pada penelitian iniwilayah distribusi dibagi menjadi 2 cluster dan dari penyelesaian model PVRP diperoleh frekuensi kunjungan yang tepatuntuk cluster 1 adalah sebanyak sekali dalam seminggu. Sedangkan untuk cluter 2 dikunjungi sebanyak 3 kali dalamseminggu. Hasil penentuan jadwal dan rute dari penelitian inimemberikan total biaya transportasi sebesar Rp725.805per minggu. Dengan kata lain terjadi penghematan sebesar Rp320.189/minggu atau menghemat sebesar 44% per minggudari biaya awal yang harus dikeluarkan.Kata kunci: Vehicle routing, periodic, nearest neighbour, optimasiABSTRACTOne of the main issue in improving the customer service is how to deliver the product on time to customers. Therefore,the stakeholders need to apply an appropriate strategy in order to make distribustion process become more efficient andeffective. Because it is hard to determine appropriate schedule and route when dealing with a lot agents, PR 567 as arepresentative distributor of cigarette in Purwodadi attempts to make its distribution process better. This was done by usingPVRP (Periodic Vehicle Routing Problem) model with cluster first-second route approach and optimization method forassigning vehicle. The result of this research were frequency, schedule, and route with the most minimum transportationcost. In this research, the distribution area was defined into two cluster. The best delivery frequency for cluster one was onceweek, while cluster two was three times a week. The transportation cost was Rp725805/week. In the other hand, the savingcost was Rp320189/week or 44%/week from the initial cost.Key words: Vehicle routing, periodic, nearest neighbour, optimization


2019 ◽  
Vol 9 (4) ◽  
pp. 624 ◽  
Author(s):  
Tao Rui ◽  
Guoli Li ◽  
Qunjing Wang ◽  
Cungang Hu ◽  
Weixiang Shen ◽  
...  

This paper proposes a hierarchical optimization method for the energy scheduling of multiple microgrids (MMGs) in the distribution network of power grids. An energy market operator (EMO) is constructed to regulate energy storage systems (ESSs) and load demands in MMGs. The optimization process is divided into two stages. In the first stage, each MG optimizes the scheduling of its own ESS within a rolling horizon control framework based on a long-term forecast of the local photovoltaic (PV) output, the local load demand and the price sent by the EMO. In the second stage, the EMO establishes an internal price incentive mechanism to maximize its own profits based on the load demand of each MG. The optimization problems in these two stages are solved using mixed integer programming (MIP) and Stackelberg game theory, respectively. Simulation results verified the effectiveness of the proposed method in terms of the promotion of energy trading and improvement of economic benefits of MMGs.


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