An Improved Simulated Annealing Algorithm and its Application in the Logistics Distribution Center Location Problem

2013 ◽  
Vol 389 ◽  
pp. 990-994 ◽  
Author(s):  
Yue Guang Li
Keyword(s):  
Simulated Annealing ◽  
Location Problem ◽  
Simulated Annealing Algorithm ◽  
Time Window ◽  
Influence Factors ◽  
Distribution Center ◽  
Logistics Distribution ◽  
Distribution Logistics ◽  
Annealing Algorithm ◽  
Selection Operator

In this paper, according to the characteristics and influence factors of the distribution logistics and distribution center problem, a mathematical model of the distribution center of the LRTWP (Location and Routing with Time Window Problem) was established. An improved simulated annealing algorithm was used to solve the model, the parameter and selection operator in the algorithm is setted reasonably. Simulations and results indicate that the improved simulated annealing algorithm has better feasibility and validity for solving the LRTWP.

An Improved Bat Algorithm and its Application in the Logistics Distribution Center Location Problem

2013 ◽  
Vol 404 ◽  
pp. 738-743
Author(s):  
Yue Guang Li
Keyword(s):  
Mathematical Model ◽  
Location Problem ◽  
Time Window ◽  
Influence Factors ◽  
Bat Algorithm ◽  
Distribution Center ◽  
Center Problem ◽  
Logistics Distribution ◽  
Distribution Logistics ◽  
Selection Operator

In this paper, according to the characteristics and influence factors of the distribution logistics and distribution center problem, a mathematical model of the distribution center of the LRTWP (Location and Routing with Time Window Problem) was established. An improved bat algorithm was used to solve the model, the parameter and selection operator in the algorithm is set reasonably. Simulations and results indicate that the improved bat algorithm has better feasibility and validity for solving the LRTWP.

scholarly journals A simulated annealing algorithm for vehicle scheduling problem

2017 ◽  
Vol 1 (Q4) ◽  
pp. 70-77
Author(s):  
Ha Thi Mai Phan
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Time Window ◽  
Fresh Concrete ◽  
Waiting Times ◽  
Optimal Solution ◽  
Production Scale ◽  
Construction Companies ◽  
Annealing Algorithm ◽  
Concrete Tank

As the construction activity has been growing, the companies that supply fresh concrete expand their production scale to meet their customers’ needs. The more customers, the longer queue tank trucks have to wait to pick up the fresh concrete. The customers are construction companies that have different construction works at the same time while the transportation time is only at night. They have to schedule efficiently the fleet of fresh concrete tank trucks during the night (turning the tank trucks a few turns) with constraints on the time window for the transfer of fresh concrete from the concrete company to the construction site as well as constraints on the waiting time for loading fresh concrete in the company. The scheduling for the fleet of construction company’s tank trucks will be modeled to minimize total transportation costs (fixed, variable) with estimated waiting times and tank truck’s turns several times during the night. The model of logistics problem is NP hard; Therefore, two algorithms are proposed to find the nearly optimal solution: heuristics and simulated annealing algorithm. The results will be compared and analyzed.

scholarly journals Combined Simulated Annealing Algorithm for the Discrete Facility Location Problem

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Jin Qin ◽  
Ling-lin Ni ◽  
Feng Shi
Keyword(s):  
Simulated Annealing ◽  
Facility Location ◽  
Location Problem ◽  
Simulated Annealing Algorithm ◽  
Facility Location Problem ◽  
Location Decision ◽  
Previous Algorithm ◽  
Annealing Algorithm ◽  
Discrete Facility Location ◽  
Better Than

The combined simulated annealing (CSA) algorithm was developed for the discrete facility location problem (DFLP) in the paper. The method is a two-layer algorithm, in which the external subalgorithm optimizes the decision of the facility location decision while the internal subalgorithm optimizes the decision of the allocation of customer's demand under the determined location decision. The performance of the CSA is tested by 30 instances with different sizes. The computational results show that CSA works much better than the previous algorithm on DFLP and offers a new reasonable alternative solution method to it.

Logistics Distribution Routing Optimization Algorithm

2014 ◽  
Vol 513-517 ◽  
pp. 1740-1743 ◽  
Author(s):  
Zhang Chun Hua ◽  
Hua Xin ◽  
Zhang Wei
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Search Algorithm ◽  
Memory Function ◽  
Memory Device ◽  
Routing Problem ◽  
Logistics Distribution ◽  
Genetic Simulated Annealing Algorithm ◽  
Annealing Algorithm

Logistics distribution involves preparing goods in the distribution center or logistics node for most reasonable delivery according to the requirements of customers. Genetic algorithm is a random global search algorithm based on the principle of natural evolution. It can be a good solution to optimize the distribution routes. This paper combines genetic algorithm and the simulated annealing algorithm, to which memory device is added, in order to avoid best result losing in the crossover operator of the genetic algorithm. The experimental results show that a memory function with this genetic simulated annealing algorithm in solving the logistics distribution routing problem, can not only get a higher qualified solution, but can also significantly reduce the evolutionary generation that algorithm requires, and obtain solution to the problem in less time.

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