metaheuristics | art meta-heuristic algorithms using python | Machine Learning library

I have a working Vehicle Routing Problem solution implemented using Google's OR-Tools in Python. I have a time matrix of 16 locations, time windows for each location, and penalties for dropping each location. All values are in units of seconds. I am intentionally solving this with just one vehicle (essentially solving a Traveling Salesperson Problem). I am allowing the vehicle to wait at any location for as long as is needed. I have set the drop penalty extremely high for some locations because I do not want them to ever be dropped.

Each location that is represented in the Time Matrix will have a time window, which indicates the time since the beginning of the day (28800 is the equivalent of 8:00am, and 64800 is the equivalent of 6:00pm, etc.) I set the upper bound maximum value as 64800 because there I want the vehicle to be done by 6:00pm.

I have designated the the first location in the matrix as the start location.
Now, I want the second location in the matrix to be the end location.

I have referenced the OR-Tools documentation for this, but have not found success. I have also found a similar question posted on Stack Overflow, but did not find any particularly helpful responses.

Below is my source code - it runs successfully, but does use the second location in the matrix as the end location in the solution it creates.

Source

I have a working Vehicle Routing Problem (with Time Windows) solution implemented using Google's OR Tools python library. I have a time matrix of 15 locations and time windows for each location. I also have the duration of a visit to each location baked into the cost of each visit. All values are in units of seconds. I am intentionally solving this with just one vehicle (essentially solving a Traveling Salesperson Problem).

I am not trying to add the ability to drop locations from a solution if they are preventing a valid solution from being created. Right now, if I have the duration for each visit set to 3600 seconds, it won't be possible to visit all 15 locations. However, if I instead set the duration for each visit to 900 seconds, then a solution will be possible for all 15 locations. I want to add a disjunction to allow for a solution to be created with these large durations in place, and instead of failing, simply drop a location from the solution.

I have some locations that I do not want to be dropped from the solution, so I have given them absurdly large penalties to ensure that they won't be dropped, while others I assign a penalty of zero. But now, all the other locations are being dropped because their penalty is zero - I assume that this is because the penalty is less than the cost of transit, but I am not completely sure if this is indeed the reason. How should I allow locations to be dropped from the solution, but prevent other locations from being droppable?

Right now the only code I have added is:

I have a working Vehicle Routing Problem solution implemented using Google's OR Tools python library. I have a time matrix of 9 locations, and time windows for each location. All values are in units of seconds.

(For example, the first time window is from 28800 to 28800. 28800 seconds is equivalent to 8:00am. I want this location, the depot, to be visited at exactly 8:00am)

I am intentionally solving this with just one vehicle (essentially solving a Traveling Salesperson Problem). I believe that I have added my dimension correctly, but I could certainly have made a mistake with that - my intention is to allow the vehicle to wait at any location for as long as it would like, as along as it allows it to solve the vehicle routing problem. I set the upper bound maximum value as 86400 because there are 86400 seconds in a day, and I figure that would be a sufficiently high number given this data.

Source