scholarly journals H-PSO Routing Optimization Model for Zoomlion Ghana Limited

2021 ◽  
pp. 91-101
Author(s):  
Justice Kangah ◽  
Emmanuel Ayitey ◽  
Frank B. K. Twenefour
Keyword(s):  
Hybrid Algorithm ◽  
Optimal Solution ◽  
Optimal Route ◽  
Matlab Simulation ◽  
Optimal Result ◽  
Routing Problem ◽  
Routing Optimization ◽  
Mutation Operators ◽  
Continuous Problems ◽  
Crossover And Mutation

This research combines Particle Swarm Optimization (PSO) with Crossover and Mutation Operators of Genetic Algorithm (GA) to produce a hybrid optimization algorithm to solve a routing problem identified at Zoomlion Ghana Limited, Sekondi Takoradi branch. PSO is known to converge prematurely and can be trapped into a local minimum especially with complex problems. On the other hand, GA is a robust and works well with discrete and continuous problems. The Crossover and Mutation operations of GA makes the iterations converges faster and are reliable. The hybrid algorithm therefore merges these operators into PSO to produce a more reliable optimal solution. The hybrid algorithm was then used to solve the routing problem identified at Zoomlion Ghana Limited, Sekondi Takoradi branch. A total of 160 public waste bin centers scattered in the metropolis and the distance between them were considered. The main aim was to determine the best combination of the set of routes connecting all the bin centers in the municipality that will produce the shortest optimal route for the study. MATLAB simulation was run of the list of distances to determine the optimal route. After 10,000 iterations, PSO produced an optimal result of 81.6 km, GA produced an optimal result of 88.9 km and the proposed hybrid model produced an optimal result of 79.9 km

Scheduling Flexible Production Lines with No Intermediate Buffers by Hybrid Algorithms

2012 ◽  
Vol 557-559 ◽  
pp. 2229-2233
Author(s):  
Bing Gang Wang
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Initial Population ◽  
Flexible Production ◽  
Scheduling Problems ◽  
Production Lines ◽  
Solution Quality ◽  
Mutation Operators ◽  
Crossover And Mutation ◽  
Better Than

This paper is concerned about the scheduling problems in flexible production lines with no intermediate buffers. The optimization objective is to minimizing the makespan. The mathematical models are presented. Since the problem is NP-hard, a hybrid algorithm, based on genetic algorithm and tabu search, is put forward for solving the models. In this algorithm, the method of generating the initial population is proposed and the crossover and mutation operators, tabu list, and aspiration rule are newly designed. The performance of the hybrid algorithm is compared with that of the traditional genetic algorithm. The computational results show that satisfactory solutions can be obtained by the hybrid algorithm and it performs better than the genetic algorithm in terms of solution quality.

Chaotic Clonal Genetic Algorithm for Routing Optimization

2014 ◽  
Vol 1046 ◽  
pp. 371-374
Author(s):  
Bing Fan ◽  
Ying Zeng ◽  
Liang Rui Tang
Keyword(s):  
Genetic Algorithm ◽  
Load Balance ◽  
Convergence Speed ◽  
Differentiated Service ◽  
Service Networks ◽  
Routing Optimization ◽  
Mutation Operators ◽  
Selection Step ◽  
Crossover And Mutation ◽  
Local Extreme

Clonal operator which can reserve the elites is introduced in the selection step of traditional genetic algorithm (GA) to accelerate the local convergence speed. Chaotic search which is randomness and ergodicity is applied in crossover and mutation operators to avoid the algorithm stopping at a local extreme value. The above hybrid GA is called chaotic clonal GA (CCGA) which can overcome the instability of optimizing processes and results in traditional GA by the certainty of chaotic trajectory. The CCGA is applied to solve the problem of load balance routing in differentiated service networks. The routing optimization model is created and the optimizing objective is load balance and small path length. The simulation results show that CCGA has fast convergence speed and high stability. It can meet the requirements of important business routings.

Routing Optimization with Generalized Consistency Requirements

2021 ◽  
Author(s):  
Kai Wang ◽  
Lu Zhen ◽  
Jun Xia ◽  
Roberto Baldacci ◽  
Shuaian Wang
Keyword(s):  
Vehicle Routing Problem ◽  
Optimal Solution ◽  
Time Consistency ◽  
Set Partitioning ◽  
The Other ◽  
Routing Problem ◽  
Routing Optimization ◽  
High Level ◽  
Linear Relaxations ◽  
Reduced Problem

The consistent vehicle routing problem (ConVRP) aims to design synchronized routes on multiple days to serve a group of customers while minimizing the total travel cost. It stipulates that customers should be visited at roughly the same time (time consistency) by several familiar drivers (driver consistency). This paper generalizes the ConVRP for any level of driver consistency and additionally addresses route consistency, which means that each driver can traverse at most a certain proportion of different arcs of routes on planning days, which guarantees route familiarity. To solve this problem, we develop two set partitioning-based formulations, one based on routes and the other based on schedules. We investigate valid lower bounds on the linear relaxations of both of the formulations that are used to derive a subset of columns (routes and schedules); within the subset are columns of an optimal solution for each formulation. We then solve the reduced problem of either one of the formulations to achieve an optimal solution. Numerical results show that our exact method can effectively solve most of the medium-sized ConVRP instances in the literature and can also solve some newly generated instances involving up to 50 customers. Our exact solutions explore some managerial findings with respect to the adoption of consistency measures in practice. First, maintaining reasonably high levels of consistency requirements does not necessarily always lead to a substantial increase in cost. Second, a high level of time consistency can potentially be guaranteed by adopting a high level of driver consistency. Third, maintaining high levels of time consistency and driver consistency may lead to lower levels of route consistency.

scholarly journals MULTI-OBJECTIVE DISTRIBUTION ROUTING OPTIMIZATION WITH TIME WINDOW BASED ON IMPROVED GENETIC ALGORITHM

2018 ◽  
Vol 48 (3) ◽  
pp. 151-156
Author(s):  
S. WU ◽  
C. CHEN
Keyword(s):  
Genetic Algorithm ◽  
Time Window ◽  
Time Windows ◽  
Optimal Solution ◽  
Transportation Cost ◽  
Population Diversity ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Logistics Distribution ◽  
Routing Optimization

In order to solve the shortcomings of the traditional genetic algorithm in solving the problem of logistics distribution path, a modified genetic algorithm is proposed to solve the Vehicle Routing Problem with Time Windows (VRPTW) under the condition of vehicle load and time window. In the crossover process, the best genes can be preserved to reduce the inferior individuals resulting from the crossover, thus improving the convergence speed of the algorithm. A mutation operation is designed to ensure the population diversity of the algorithm, reduce the generation of infeasible solutions, and improve the global search ability of the algorithm. The algorithm is implemented on Matlab 2016a. The example shows that the improved genetic algorithm reduces the transportation cost by about 10% compared with the traditional genetic algorithm and can jump out of the local convergence and obtain the optimal solution, thus providing a more reasonable vehicle route.

scholarly journals A hybrid FJA-ALNS algorithm for solving the multi-compartment vehicle routing problem with a heterogeneous fleet of vehicles for the fuel delivery problem

2021 ◽  
Vol 10 (4) ◽  
pp. 497-510 ◽  
Author(s):  
Wasana Chowmali ◽  
Seekharin Sukto
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Hybrid Algorithm ◽  
Optimal Solution ◽  
Computational Time ◽  
Second Phase ◽  
Routing Problem ◽  
Delivery Problem ◽  
Heterogeneous Fleet ◽  
Fuel Delivery

This paper proposes a new hybrid algorithm to solve the multi-compartment vehicle routing problem (MCVRP) with a heterogeneous fleet of vehicles for the fuel delivery problem of a previous study of twenty petrol stations in northeastern Thailand. The proposed heuristic is called the Fisher and Jaikumar Algorithm with Adaptive Large Neighborhood Search (FJA-ALNS algorithm). The objective of this case is to minimize the total distance, while using a minimum number of multi-compartment vehicles. In the first phase, we used the FJA to solve the MCVRP for the fuel delivery problem. The results from solving the FJA were utilized to be the initial solutions in the second phase. In the second phase, a hybrid algorithm, namely the FJA-ALNS algorithm, has been developed to improve the initial solutions of the individual FJA. The results from the FJA-ALNS algorithm are compared with the exact method (LINGO software), individual FJA and individual ALNS. For small-sized problems (N=5), the results of the proposed FJA-ALNS and all methods provided no different results from the global optimal solution, but the proposed FJA-ALNS algorithm required less computational time. For larger-sized problems, LINGO software could not find the optimal solution within the limited period of computational time, while the FJA-ALNS algorithm provided better results with much less computational time. In solving the four numerical examples using the FJA-ALNS algorithm, the result shows that the proposed FJA-ALNS algorithm is effective for solving the MCVRP in this case. Undoubtedly, future work can apply the proposed FJA-ALNS algorithm to other practical cases and other variants of the VRP in real-world situations.

Finding Community of Brain Networks Based on Neighbor Index and DPSO with Dynamic Crossover

2020 ◽  
Vol 15 (4) ◽  
pp. 287-299
Author(s):  
Jie Zhang ◽  
Junhong Feng ◽  
Fang-Xiang Wu
Keyword(s):  
Brain Function ◽  
Brain Networks ◽  
Discrete Particle Swarm Optimization ◽  
Mri Brain ◽  
Mutation Operators ◽  
Neural Unit ◽  
Crossover And Mutation ◽  
The Brain ◽  
Index Table ◽  
Dynamic Crossover

Background: : The brain networks can provide us an effective way to analyze brain function and brain disease detection. In brain networks, there exist some import neural unit modules, which contain meaningful biological insights. Objective:: Therefore, we need to find the optimal neural unit modules effectively and efficiently. Method:: In this study, we propose a novel algorithm to find community modules of brain networks by combining Neighbor Index and Discrete Particle Swarm Optimization (DPSO) with dynamic crossover, abbreviated as NIDPSO. The differences between this study and the existing ones lie in that NIDPSO is proposed first to find community modules of brain networks, and dose not need to predefine and preestimate the number of communities in advance. Results: : We generate a neighbor index table to alleviate and eliminate ineffective searches and design a novel coding by which we can determine the community without computing the distances amongst vertices in brain networks. Furthermore, dynamic crossover and mutation operators are designed to modify NIDPSO so as to alleviate the drawback of premature convergence in DPSO. Conclusion: The numerical results performing on several resting-state functional MRI brain networks demonstrate that NIDPSO outperforms or is comparable with other competing methods in terms of modularity, coverage and conductance metrics.

An efficient hybrid algorithm for integrated order batching, sequencing and routing problem

2015 ◽  
Vol 159 ◽  
pp. 158-167 ◽  
Author(s):  
Tzu-Li Chen ◽  
Chen-Yang Cheng ◽  
Yin-Yann Chen ◽  
Li-Kai Chan
Keyword(s):  
Hybrid Algorithm ◽  
Routing Problem ◽  
Order Batching

Enterprise distribution routing optimization with soft time windows based on genetic algorithm

2021 ◽  
pp. 1-11
Author(s):  
Kaixian Gao ◽  
Guohua Yang ◽  
Xiaobo Sun
Keyword(s):  
Genetic Algorithm ◽  
Customer Satisfaction ◽  
Optimization Problems ◽  
Rapid Development ◽  
Time Windows ◽  
Theory And Practice ◽  
Routing Problem ◽  
Logistics Industry ◽  
Routing Optimization ◽  
A Company

With the rapid development of the logistics industry, the demand of customer become higher and higher. The timeliness of distribution becomes one of the important factors that directly affect the profit and customer satisfaction of the enterprise. If the distribution route is planned rationally, the cost can be greatly reduced and the customer satisfaction can be improved. Aiming at the routing problem of A company’s vehicle distribution link, we establish mathematical models based on theory and practice. According to the characteristics of the model, genetic algorithm is selected as the algorithm of path optimization. At the same time, we simulate the actual situation of a company, and use genetic algorithm to plan the calculus. By contrast, the genetic algorithm suitable for solving complex optimization problems, the practicability of genetic algorithm in this design is highlighted. It solves the problem of unreasonable transportation of A company, so as to get faster efficiency and lower cost.

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