A Solution Method Combining Simulated Annealing and Graph-based Heuristics for Operational Planning and Scheduling Benchmark Problems in an Automatic Picking System

2019 ◽  
Vol 139 (12) ◽  
pp. 1481-1487
Author(s):  
Ryo Kanaya ◽  
Seiichi Koakutsu ◽  
Takashi Okamoto ◽  
Tomoyoshi Shimobaba ◽  
Tomoyoshi Ito
Keyword(s):  
Simulated Annealing ◽  
Solution Method ◽  
Operational Planning ◽  
Benchmark Problems ◽  
Planning And Scheduling ◽  
Automatic Picking ◽  
Picking System

MEMBRANE ALGORITHM WITH BROWNIAN SUBALGORITHM AND GENETIC SUBALGORITHM

2007 ◽  
Vol 18 (06) ◽  
pp. 1353-1360 ◽  
Author(s):  
TAISHIN Y. NISHIDA
Keyword(s):  
Simulated Annealing ◽  
Approximate Solutions ◽  
Search Space ◽  
Traveling Salesman ◽  
Benchmark Problems ◽  
Wide Range ◽  
Membrane Algorithm ◽  
Thermal Movement ◽  
The Traveling Salesman Problem ◽  
Cooling Schedule

Membrane algorithms with subalgorithms inspired by simulated annealing are treated in this paper. Simulated annealing is inherently a kind of local search but it modifies a solution to a worse one with a probability determined by "temperature". The temperature of simulated annealing is changed according to "cooling schedule". On the other hand, the subalgorithm introduced here has constant temperature which is determined by the region where the subalgorithm is. It is called Brownian subalgorithm since the subalgorithm incorporates "thermal movement" of a solution in the search space but does not simulate "annealing". Computer simulations show that a membrane algorithm which has three regions and has a Brownian subalgorithm in each region can obtain very good approximate solutions for several benchmark problems of the traveling salesman problem. However, the algorithm, occasionally, gets quite bad solutions (twice as large as the optimum) for a problem. A membrane algorithm which has both Brownian and genetic subalgorithms never gets such a bad solution (only 8% worse than the optimum) for all problems examined, although, in average, it is not as good as the algorithm with Brownian only. The result indicates that membrane algorithm with subalgorithms under different approximate mechanisms may be robust under a wide range of problems.

A simulated annealing solution method for robust school bus routing

2017 ◽  
Vol 28 (3) ◽  
pp. 307 ◽  
Author(s):  
Saeid Samadi Dana ◽  
Mohammad Mahdi Paydar ◽  
Javid Jouzdani
Keyword(s):  
Simulated Annealing ◽  
Solution Method ◽  
School Bus ◽  
Bus Routing

COMPARISON OF SYSTEMS BASED ON EVOLUTIONARY SEARCH AND SIMULATED ANNEALING TO SOLVE THE VRPTW PROBLEM

2004 ◽  
Vol 04 (01) ◽  
pp. 27-39 ◽  
Author(s):  
OLATZ ARBELAITZ ◽  
CLEMENTE RODRIGUEZ
Keyword(s):  
Simulated Annealing ◽  
Vehicle Routing ◽  
Distribution System ◽  
Hybrid Methods ◽  
Time Windows ◽  
Benchmark Problems ◽  
Evolutionary Search ◽  
Number Of Solutions ◽  
Preliminary Results ◽  
Oil Distribution

This paper presents the design and analysis of several systems to solve Vehicle Routing Problems with Time Windows (VRPTW) limiting the search to a small number of solutions explored. All of them combine a metaheuristic technique with a route building heuristic. Simulated Annealing, different evolutionary approaches and hybrid methods have been tried. Preliminary results for each of the strategies are presented in the paper, where the combination created by some iterations of the best evolutionary approach and some iterations of SA stands out. A more exhaustive analysis of the three methods behaving better is also presented confirming the previous results. The different strategies have been implemented and tested on a series of the well-known Solomon's benchmark problems of size up to 100 customers. One of the described systems combined with a local optimization part that tries to optimize parts of a solution is being used as part of a real oil distribution system, obtaining very satisfactory results for the company.

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