Optimization Problem - by Navaneesh
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The document discusses a route optimization challenge for cleaning services, analyzing various time and resource constraints for a defined set of locations. It proposes a genetic algorithm as a heuristic approach to efficiently determine optimal routes while ensuring compliance with business constraints. The results indicate a significant reduction in travel time, saving approximately 17 hours and 45 minutes, with potential applications in different delivery sectors.
Optimization Problem - by Navaneesh
- 1. Route Optimization Guide: Bhamati INSOFE – CPEE – Batch 14 G R Navaneesh Kumar
- 2. Problem Statement Spring-Cleaning Residential and Commercial Cleaning Time – Resource – Money
- 3. What I have 135 Min 45 Min 35 Min 25 Min 35 Min 32 Min 55 Min 135 Min
- 4. How I will tackle the problem Optimize the Time Travel Time Execution Time Save Time - Save Resource - Gain Profit
- 5. Data – Available JobExecutionTime.csv Stop ID Execution Time (Minutes) TravelTime.csv Stop ID 2 Stop ID 1 Travel Time (Minutes)
- 6. Observations 1. 68 Locations Excluding Depot 2. Max execution Time :420Min 3. 75% Execution time <108 Min Data Analysis-I
- 7. Observations 1. 62 x 61 Travel Time – 3782 obs 2. Max Travel Time :135 Min 3. 75% Execution time <40 Min 4. Mostly Single City Works Data Analysis-II
- 8. Observations 1. 69 x 68 Travel Time – 4692 obs 2. Max Travel Time :135 Min 3. 50% Travel time <50 Min 4. Mostly Single City Works Permutation of Data Data Analysis-III
- 9. Critical Business Conditions Max Route Time < 720 min Route Time = Travel Time + Execution Time Max places vehicle can travel is 7 Excluding Depot it starts and stops Locations it visits should be unique No Location repeats in the travel
- 10. D:X1:D - 68 D:X1:X2:D - 4556 D:X1:X2:X3:D - 3,00,696 D:X1:X2:X3:X4:D - 1,95,45,240 . . . . D:X1:X2…….X67:X68:D - 2.480035542 E+96 248003554243683059960099041 8569171581047399201355367672 3717107380182214457121832960 00000000000000 Challenge : Looks Linear Complex Solution Space No Use of Linear Programming Space and Time complexity issues Business should not stop for our model
- 11. Genetic Algorithm: Heuristic Method Proved in Business, Science and Engineering Best for searching for new solutions Large population Selection should be done Survival of the fittest Evolutionary Biology
- 12. Basics of GA Encoding principles (Gene, Chromosome) Gene - Stop ID – Ex: Depot, 12350484, 12350757 Chromosome - Route - Ex: Depot : 12350484:Depot Initialization procedure (Creation) Creation - Initial Population Ex: Depot : 12350484:Depot Depot : 12350484: 12350757 :Depot Depot : 12350484: 12350757 : 12350740 :Depot
- 13. Selection of parents (Reproduction) Here we use Business Constraints : Ex: Stop ID (Gene) should not exceed 7 Each Stop ID should be visited only once Total Time (Chromosome) = Execution Time + Travel Time < 720 Min
- 14. Genetic operators (Mutation, Crossover) Crossover - Consider Two Chromosomes (Same Length) Route 1: Depot : 12350444 :12350222 : 12350555 : 12350666 :Depot Route 2: Depot : 12350888: 12350333 : 12350999 : 12350777 :Depot 12350444 :12350222 12350555 : 12350666 12350888: 12350333 12350999 : 12350777 Route 1: Depot : 12350999 :12350777 : 12350555 : 12350666 :Depot Route 2: Depot : 12350888: 12350333 : 12350444 :12350222 :Depot
- 15. Mutation Route 1: Depot : 12350666 :12350222 : 12350555 : 12350444 :Depot Depot 12350444 12350222 12350555 12350666 Depot Route 1: Depot : 12350444 :12350222 : 12350555 : 12350666 :Depot Combine
- 16. Evaluation function (Fitness) Termination condition Below 75% of Data after Fitness Evaluation will be removed Fitness = Fitness + Total Time Total Time =Travel Time + Execution Time
- 17. End Start Fitness assessment BestChromosome s to be selected Selection Crossover Initial Population Creation New Population Creation Terminate Condition Yes No
- 18. ADVANTAGES OF GENETIC ALGORITHMS A fastest search technique GAs will produce "close" to optimal results in a "reasonable" amount of time Suitable for parallel processing Fairly simple to develop Makes no assumptions about the problem space
- 19. Initial Population Total Time ,Unique Ids
- 22. What I Achieved Initial Time : 9113 Current Time :8048 Saved Time :1065 Nearly :17Hrs 45Min A Nearly Two Working Days
- 23. Future Scope • Courier Services • Food and Groceries Delivery App • Container Shipping Industry • And More
- 24. Q and A
- 25. Hasta la vista -- Navaneesh Gangala