scholarly journals Optimization of Green Fresh Food Logistics with Heterogeneous Fleet Vehicle Route Problem by Improved Genetic Algorithm

2020 ◽  
Vol 12 (5) ◽  
pp. 1946 ◽  
Author(s):  
Danlian Li ◽  
Qian Cao ◽  
Min Zuo ◽  
Fei Xu
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Improved Genetic Algorithm ◽  
Heterogeneous Fleet ◽  
Vehicle Route Problem ◽  
Route Problem ◽  
Fresh Food ◽  
Mutation Operation ◽  
Logistics Enterprises ◽  
Distribution Cost

In order to reduce the distribution cost of fresh food logistics and achieve the goal of green distribution at the same time, the Green Fresh Food Logistics with Heterogeneous Fleet Vehicle Route Problem (GFLHF-VRP) model is established. Based on the particularity of the model, an improved genetic algorithm called Genetic Algorithm with Adaptive Simulated Annealing Mutation (GAASAM) is proposed in which the mutation operation is upgraded to a simulated annealing mutation operation and its parameters are adjusted by the adaptive operation. The experimental results show that the proposed GAASAM can effectively solve the vehicle routing problem of the proposed model, achieve better performance than the genetic algorithm, and avoid falling into a local optimal trap. The distribution routes obtained by GAASAM are with lower total distribution cost, and achieve the goal of green distribution in which energy, fuel consumption and carbon emissions are reduced at the same time. On the other hand, the proposed GFLHF-VRP and GAASAM can provide a reliable distribution route plan for fresh food logistics enterprises with multiple types of distribution vehicles in real life, which can further reduce the distribution cost and achieve a greener and more environment-friendly distribution solution. The results of this study also provide a managerial method for fresh food logistics enterprises to effectively arrange the distribution work with more social responsibility.

scholarly journals Optimization of Earthwork Allocation Path as Vehicle Route Problem Based on Genetic Algorithm

2020 ◽  
Vol 165 ◽  
pp. 04057
Author(s):  
Naifu Deng ◽  
Xuyang Li ◽  
Yanmin Su
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
Construction Projects ◽  
Vehicle Route Problem ◽  
Vehicle Route ◽  
Route Problem ◽  
Modified Genetic Algorithm ◽  
Global Optimal ◽  
The Cost ◽  
Engineering Projects

In civil engineering, earthwork, prior to the construction of most engineering projects, is a lengthy and time-consuming work involving iterative processes. The cost of many AEC (Architecture, Engineering and Construction) projects is highly dependent on the efficiency of earthworks (e.g. road, embankment, railway and slope engineering). Therefore, designing proper earthwork planning is of importance. This paper simplifies the earthwork allocation problem to Vehicle Route Problem (VRP) which is commonly discussed in the field of transportation and logistics. An optimization model for the earthwork allocation path based on the modified Genetic Algorithm with a self-adaptive mechanism is developed to work out the global optimal hauling path for earthwork. The research results also instruct the initial topographic shaping of the Winter Olympic Skiing Courses Project. Furthermore, this optimization model is highly compatible with other evolutionary algorithms due to its flexibility, therefore, further improvement in this model is feasible and practical.

scholarly journals Optimization and Design of Hammerheads and Fenders on Scrap Metal Shredders Based on Improved Genetic Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
He Tian ◽  
Guoqiang Wang ◽  
Kangkang Sun ◽  
Zeren Chen ◽  
Chuliang Yan ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Dynamic Balance ◽  
Optimal Solution ◽  
Scrap Metal ◽  
Improved Genetic Algorithm ◽  
Multiple Traveling Salesman Problem ◽  
Local Optimal Solution ◽  
Annealing Algorithm

Dynamic unbalance force is an important factor affecting the service life of scrap metal shredders (SMSs) as the product of mass error. Due to the complexity of hammerheads arrangement, it is difficult to take all the parts of the hammerhead into account in the traditional methods. A novel optimization algorithm combining genetic algorithm and simulated annealing algorithm is proposed to improve the dynamic balance of scrap metal shredders. The optimization of hammerheads and fenders on SMS in this paper is considered as a multiple traveling salesman problem (MTSP), which is a kind of NP-hard problem. To solve this problem, an improved genetic algorithm (IGA) combined with the global optimization characteristics of genetic algorithm (GA) and the local optimal solution of simulated annealing algorithm (SA) is proposed in this paper, which adopts SA in the process of selecting subpopulations. The optimization results show that the resultant force of the shredder central shaft by using IGA is less than the traditional metaheuristic algorithm, which greatly improves the dynamic balance of the SMS. Validated via ADAMS simulation, the results are in good agreement with the theoretical optimization analysis.

Improved Genetic Algorithm on Minimum Spanning Tree of Length Constraint Problem

2015 ◽  
Vol 713-715 ◽  
pp. 1737-1740
Author(s):  
Ying Ying Duan ◽  
Kang Zhou ◽  
Wen Bo Dong ◽  
Kai Shao
Keyword(s):  
Genetic Algorithm ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Fitness Function ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
Length Constraint ◽  
C Programming ◽  
Mutation Operation ◽  
Constraint Problem

The first minimum spanning tree of length constraint problem (MSTLCP) is put forward, which can not be solved by traditional algorithms. In order to solve MSTLCP, improved genetic algorithm is put forward based on the idea of global and feasible searching. In the improved genetic algorithm, chromosome is generated to use binary-encoding, and more reasonable fitness function of improved genetic algorithm is designed according to the characteristics of spanning tree and its cotree; in order to ensure the feasibility of chromosome, more succinct check function is introduced to three kinds of genetic operations of improved genetic algorithm (generation of initial population, parental crossover operation and mutation operation); three kinds of methods are used to expand searching scope of algorithm and to ensure optimality of solution, which are as follows: the strategy of preserving superior individuals is adopted, mutation operation is improved in order to enhance the randomness of the operation, crossover rate and mutation rate are further optimized. The validity and correctness of improved genetic algorithm solving MSTLCP are explained by a simulate experiment where improved genetic algorithm is implemented using C programming language. And experimental results are analyzed: selection of population size and iteration times determines the efficiency and precision of the simulate experiment.

Research on Parallel Test Task Scheduling Based on Improved Genetic Algorithm and Petri Net

2014 ◽  
Vol 543-547 ◽  
pp. 1119-1122
Author(s):  
Pei Pei Chen ◽  
Bao Mei Qiu ◽  
Hao Ba
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Task Scheduling ◽  
Petri Net ◽  
Simulated Annealing Algorithm ◽  
Optimal Path ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Test Task ◽  
Parallel Test

Parallel test task scheduling is always complex and difficult to optimize. Aiming at this problem, an improved Genetic Simulated Annealing Algorithm based on Petri net is posed to. At first, a Petri net model is established for the system, then the transition sequence is used as task scheduling sequence set path. Genetic Algorithm is introduced in order to get the optimal path. In the process of search, the sequence will be able to stimulate changes as chromosomes, selection, crossover and mutation. In order to prevent premature convergence of the algorithm appears, into the phenomenon of local optimal solution, the individual needs simulated annealing operation, and finally, we can get the shortest time to complete the test task scheduling sequence.

The Research on the Optimization of Transportation Routing for Fresh Food by Improved Genetic Algorithm

2012 ◽  
Vol 178-181 ◽  
pp. 1790-1796 ◽  
Author(s):  
Ying Wu ◽  
Zi Bo Meng ◽  
Min Peng
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Customer Satisfaction ◽  
Time Window ◽  
Time Windows ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Transport Services ◽  
Fresh Food ◽  
Transportation Routing

In this paper, we research the problem of transportation routing for fresh food. We analyzed the limit of soft and hard time windows in transportation and formed the time window with fuzzy appointment based on customer satisfaction. The optimization of transportation routes mathematical model was structured. The improved genetic algorithm has been applied to matlab progam. This progam has found the optimal solution in the model. We used a case to prove the feasibility of the model and the algorithm. It has twelve customers and one DC need to transport services. The mathematical model is to simulate the transport of fresh food within realistic.The transportation routing is designed to improve customer satisfaction and reduce transportation costs.

scholarly journals Pengembangan Varian Model Vehicle Route Problem (VRP) Untuk Penentuan Rute Angkutan Laut Penumpang Studi Kasus Pt. Pelni (Persero)

2019 ◽  
Vol 26 (3) ◽  
pp. 125
Author(s):  
Imbang Danandjojo ◽  
B Kombaitan ◽  
Idwan Santoso ◽  
Ibnu Syabri
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Route Problem ◽  
Vehicle Route ◽  
Route Problem ◽  
Nearest Neighborhood ◽  
Genetic Algorithm Model

Penelitian ini bertujuan mengembangkan varian modelVRP untuk menyusun rute angkutan umum penumpang. Sebagai bahan pertimbangan adalah karakteristik rutenya tertutup yang berawal dan berakhir pada terminal atau pangkalan yang sama, karakteristik pelanggannya deterministik dengan volume permintaan layanan tetap dan dalam kurun waktu saturound trip tertentu, serta karakteristik kendaraan yang dioperasikan memiliki variasi kapasitas dan biaya operasi. Rute disusun untuk memperoleh efisiensi biaya operasional yang optimal, setiap jalur yang ada dalam jaringan pelayanan dilewati tepat satu kali dengan alasan pemberian frekuensi layanan yang sama untuk setiaplink pergerakan penumpang. Penyusunan rute dilakukan melalui dua tahap, yaitu tahap inisialisasi dengan pendekatan metoda Nearest Addition atau Nearest Neighborhood Heuristic dan tahap perbaikan dengan pendekatan metoda Genetic Algorithm. Model ini belum mempertimbangkan adanya pola rute dengan naik-turun penumpang yang dinamis, serta kecepatan dan waktu tempuh kendaraan yang bersifat stokastik, permintaan pergerakan penumpang setiap jalur yang bersifat stokastik, ataupun jumlah pelabuhan dalam jaringan pelayanan yang bersifat stokastik. Sehingga membuka peluang penelitian lebih lanjut. Hasil penelitian menunjukkan bahwa dengan penyusunan ulang rute pelayanan kapal-kapal milik PELNI, total biaya operasional seluruh kapal dapat ditekan jauh lebih efisien hingga mencapai 64,38% dari total biaya aktual. Sedangkan dari sisi total jarak tempuh, dapat ditekan lebih efektif hingga mencapai 59,64% dari total jarak tempuh aktual. Kata kunci: VRP, angkutan laut penumpang, efisiensi biaya, efektivitas jaringan

Improved Genetic Algorithm Based on the Simulated Annealing and Its Application

2016 ◽  
Vol 13 (10) ◽  
pp. 6495-6500
Author(s):  
He-Xuan Hu
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Local Search ◽  
Optimization Problems ◽  
Global Search ◽  
Letter Recognition ◽  
Improved Genetic Algorithm ◽  
Combinatorial Optimization Problems ◽  
Low Efficiency ◽  
Search Capability

Genetic Algorithm (GA) is an adaptive algorithm of global search optimization formed through the simulation of biological heredity and evolution in the natural environment. By the random selection, the algorithm requires no special needs for the search space and derivations, which is featured with simple operation, rapid convergence, and other advantages. Therefore, it is especially applicable for complex and non-linear problems that are difficult to be solved by the conventional search methods. However, this algorithm is strong in global search capability but insufficient in the local search capability. Simulated annealing (SA) is an algorithm possessed with the stronger local search ability and widely used in combinatorial optimization problems. Due to the inadequate local search capability of GA and deficient global search capability of SA, they were combined in the paper to complement their mutual advantages and take use of the global search capability of GA and local search capability of SA. The poor local search ability of GA and its premature convergence as well as the bad global search capability of SA and its low efficiency were overcome, and the SA-based mixed GA was constructed. Then, standard data sets of wine and letter-recognition in the UCI database were applied for the verification of the algorithm. It was indicated that the convergence rate was improved to some extent by the mixed algorithm proposed in this paper. Finally, the improved genetic algorithm was applied to the actual projects, which indicated the feasibility of the algorithm in engineering.

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