Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Purpose – The purpose of this paper is to explore a real world vehicle routing problem (VRP) that has multi-depot subcontractors with a heterogeneous fleet of vehicles that are available to pickup/deliver jobs with varying time windows and locations. Both the overall job completion time and number of drivers utilized are analyzed for the automated job allocations and manual job assignments from transportation field experts. Design/methodology/approach – A nested genetic algorithm (GA) is used to automate the job allocation process and minimize the overall time to deliver all jobs, while utilizing the fest number of drivers – as a secondary objective. Findings – Three different real world data sets re used to compare the results of the GA vs transportation field experts’ manual assignments. The job assignments from the GA improved the overall job completion time in 100 percent (30/30) of the cases and maintained the same or fer drivers as BS Logistics (BSL) in 47 percent (14/30) of the cases. Origina...
['Carin Lightner-Laws', 'Vikas Agrawal', 'Constance A. Lightner', 'Neal Wagner']
I am writing a research paper on a idea An evolutionary algorithm approach for the constrained multi-depot vehicle routing problem
[{"from": "human", "value": "I am writing a research paper on a idea An evolutionary algorithm approach for the constrained multi-depot vehicle routing problem"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Purpose \u2013 The purpose of this paper is to explore a real world vehicle routing problem (VRP) that has multi-depot subcontractors with a heterogeneous fleet of vehicles that are available to pickup/deliver jobs with varying time windows and locations. Both the overall job completion time and number of drivers utilized are analyzed for the automated job allocations and manual job assignments from transportation field experts. Design/methodology/approach \u2013 A nested genetic algorithm (GA) is used to automate the job allocation process and minimize the overall time to deliver all jobs, while utilizing the fest number of drivers \u2013 as a secondary objective. Findings \u2013 Three different real world data sets re used to compare the results of the GA vs transportation field experts\u2019 manual assignments. The job assignments from the GA improved the overall job completion time in 100 percent (30/30) of the cases and maintained the same or fer drivers as BS Logistics (BSL) in 47 percent (14/30) of the cases. Origina..."}]