(This article was first published on Enterprise Software Doesn't Have to Suck, and kindly contributed to R-bloggers)
Are there any R packages to solve Vehicle Routing Problem (VRP)?
I looked around but could not find any... Any leads?
VRP is a classic combinatorial optimization challenge and has been an active area of research for operations research gurus for 30+ years. Although a lot of research and progress has been made in academia, the enterprises are far behind in using such technology solutions. The primary reason seems to be the lack of business friendly tools (read Excel).
Basically, I need to solve a VRP-TW (time window) problem. I've a list of JOBS* that need to be serviced by a group of TECHNICIANS**. The schedule created to service these jobs should minimize the total distance traveled by technicians, with the following conditions:
- Customer SLA (time window promised to customer) should be metBasically, I need to solve a VRP-TW (time window) problem. I've a list of JOBS* that need to be serviced by a group of TECHNICIANS**. The schedule created to service these jobs should minimize the total distance traveled by technicians, with the following conditions:
- The technician's skill should match what the job requires
* JOBS: Job ID, Job address, Job duration, Job priority, Earliest start time, Latest finish time, Date, Skill
** RESOURCES: Resource ID, Base location (address), Shift start time, Shift end time, Skill set
MY RESEARCH SO FAR
- So far, I've found C, C++ and some Java implementations on COmputational INfrastructure forOperations Research website.
- I found some modeling tools that looked interesting. E.g. AIMMS: http://www.aimms.com. Download the trial version and checkout the examples like Gate assignment, Distribution center, Transport, Employee Scheduling, Travelling Salesman Problem. Here's a AIMMS case study.
- Some web applications like logVRP offer a great user interface but may not scale to 300+ jobs and 100+ technicians.
More to follow...
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Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
