scholarly journals A Two-Stage Simulated Annealing Algorithm for the Many-to-Many Milk-Run Routing Problem with Pipeline Inventory Cost

2015 ◽  
Vol 2015 ◽  
pp. 1-22 ◽  
Author(s):  
Yu Lin ◽  
Zheyong Bian ◽  
Shujing Sun ◽  
Tianyi Xu
Keyword(s):  
Simulated Annealing ◽  
Heuristic Algorithm ◽  
Simulated Annealing Algorithm ◽  
Greedy Heuristic ◽  
Two Stage ◽  
Satisfactory Solution ◽  
Routing Problem ◽  
Annealing Algorithm ◽  
The Many ◽  
Milk Run

In recent years, logistics systems with multiple suppliers and plants in neighboring regions have been flourishing worldwide. However, high logistics costs remain a problem for such systems due to lack of information sharing and cooperation. This paper proposes an extended mathematical model that minimizes transportation and pipeline inventory costs via the many-to-many Milk-run routing mode. Because the problem is NP hard, a two-stage heuristic algorithm is developed by comprehensively considering its characteristics. More specifically, an initial satisfactory solution is generated in the first stage through a greedy heuristic algorithm to minimize the total number of vehicle service nodes and the best insertion heuristic algorithm to determine each vehicle’s route. Then, a simulated annealing algorithm (SA) with limited search scope is used to improve the initial satisfactory solution. Thirty numerical examples are employed to test the proposed algorithms. The experiment results demonstrate the effectiveness of this algorithm. Further, the superiority of the many-to-many transportation mode over other modes is demonstrated via two case studies.

scholarly journals Algoritma Simulated Annealing untuk Menentukan Rute Kendaraan Heterogen (Studi Kasus)

2020 ◽  
Vol 7 (5) ◽  
pp. 933
Author(s):  
Andriansyah Andriansyah ◽  
Rizky Novatama ◽  
Prima Denny Sentia
Keyword(s):  
Supply Chain ◽  
Simulated Annealing ◽  
Vehicle Routing ◽  
Heuristic Algorithm ◽  
Vehicle Routing Problem ◽  
Simulated Annealing Algorithm ◽  
Routing Problem ◽  
Annealing Algorithm ◽  
Banda Aceh ◽  
Insertion Heuristic

<p>Permasalahan transportasi dalam supply chain management sangat penting untuk dikaji karena dapat menimbulkan biaya logistik yang sangat besar. Salah satu cara untuk mengurangi biaya transportasi adalah dengan penentuan rute kendaraan atau dikenal dengan istilah vehicle routing problem. Objek yang menjadi kajian merupakan perusahaan yang bergerak pada bidang distribusi produk untuk area kota Banda Aceh dan sekitarnya. Dalam proses distribusi, perusahaan ini menggunakan dua jenis kendaraan dengan kapasitas dan biaya operasional yang berbeda sehingga permasalahan menjadi heterogeneous fleet vehicle routing problem. Penentuan rute kendaraan dalam penelitian ini dilakukan dengan tiga metode, yaitu metode analitik, algoritma insertion heuristic sebagai metode heuristik, dan algoritma simulated annealing sebagai metode metaheuristik. Berdasarkan hasil yang diperoleh dari data ujicoba, algoritma simulated annealing merupakan algoritma yang paling baik dalam menyelesaikan permasalahan. Secara rata-rata, algoritma simulated annealing dapat menghasilkan kualitas solusi yang sama dengan metode analitik, namun dengan waktu komputasi yang lebih singkat. Selain itu, algoritma simulated annealing menghasilkan kualitas solusi yang lebih baik dibandingkan algoritma insertion heuristic yang dikembangkan dalam penelitian dan dapat meningkatkkan kualitas solusi sebesar 20,18% dari penelitian sebelumnya dengan waktu komputasi 19,27 detik.</p><p> </p><p><em><strong>Abstract</strong></em></p><p class="Judul2"><em>Transportation problems </em><em>in supply chain </em><em>are very important </em><em>to be discussed </em><em>because </em><em>they </em><em>can </em><em>raises</em><em> enormous logistic cost. </em><em>Route determination of the vehicles known as vehicle routing problem is the one of ways to reduce transportation cost</em><em>. </em><em>The object discussed in this study is the distribution company</em><em> </em><em>for Banda Aceh city and its surroundings</em><em>.</em><em> The company uses two types of vehicle to distribute the product for customers.</em><em> </em><em>The differences each vehicle are vehicle capacity and operational cost. To cover these differences, the problem becomes heterogenous fleet vehicle routing problem. The study uses three methods to solve the problem. Analitycal method, insertion heuristic algorithm as heuristic method and simulated annealing algorithm as metaheuristic method are the methods used. According to the results, simulated anneling algorithm produces the better solutions than two others. On average, solutions produced by simulated annealing algorithm from dataset have same quality with analitycal method, but with faster computation. Furthermore, </em><em>simulated anneling </em><em>algorithm </em><em>produces better quality of solutions than insertion heuristic algorithm both from this stu</em><em>dy and previous study. The solution improves 20,18% with computation time 19,27 seconds.</em></p><p class="Judul2"> </p><p><em><strong><br /></strong></em></p><p class="Abstrak" align="center"> </p>

scholarly journals Sequential Simulated Annealing for the Vehicle Routing Problem with Time Windows

2009 ◽  
Vol 3 (2) ◽  
pp. 87-100 ◽  
Author(s):  
Marcin Woch ◽  
Piotr Łebkowski
Keyword(s):  
Simulated Annealing ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Time Windows ◽  
High Quality ◽  
Routing Problem ◽  
Annealing Algorithm ◽  
Very High

This article presents a new simulated annealing algorithm that provides very high quality solutions to the vehicle routing problem. The aim of described algorithm is to solve the vehicle routing problem with time windows. The tests were carried out with use of some well known instances of the problem defined by M. Solomon. The empirical evidence indicates that simulated annealing can be successfully applied to bi-criterion optimization problems.

scholarly journals Computing a Complex Network Hierarchical Structure for Financial Market Networks on the Basis of the Hybrid Heuristic Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jiannan Yu ◽  
Jinlou Zhao
Keyword(s):  
Risk Management ◽  
Simulated Annealing ◽  
Hierarchical Structure ◽  
Heuristic Algorithm ◽  
Simulated Annealing Algorithm ◽  
Empirical Studies ◽  
Hybrid Heuristic ◽  
Interbank Markets ◽  
Hybrid Heuristic Algorithm ◽  
Annealing Algorithm

The recent empirical studies showed that money center networks in interbank markets are more robust and stable. Therefore, the research on layered financial networks is a key part of the systemic risk management. Various methods have been proposed in prior studies to find optimal partitioning of interbank networks into core and periphery subsets. However, these methods that have been adopted with approximation methods, in general, do not guarantee optimal bipartition. In this paper, a genetic simulated annealing algorithm is presented to detect a hierarchical structure in interbank networks as a hybrid heuristic algorithm, while its effects are also analyzed. The optimization of the error score for the core-periphery model is mathematically developed firstly as an improved expression of the optimization function, which incorporates the genetic algorithm into a simulated annealing algorithm to guarantee the optimal bipartition and to jump from a local optimization. The results of this algorithm are finally verified by empirical analysis of interbank networks; and, through the immunity strategy under the risk diffusion model, the significance of core-periphery structure to risk management is verified.

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