Please, think about the problem yourself at first!
I think you could use this problem to exercise your heuristics and optimization skills and thus I'm sharing a solution that is both easy to think and implement so that somebody less experienced might learn new strategies in optimization problems.


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Yes, it's a Vehicle Routing Problem, as even confirmed by the author on this forum.

Firstly, you can compute —with Dynamic Programming— optimal distance visiting some subset of nodes starting at node 0 and ending at some other node. Let dp[S][last] be the state of such DP, which computes the smallest path cost that visits each vertex of S once and starts at 0 and ends at last. This is a classic TSP DP problem that can be computed in O(N^2 2^n). Note, that you have to use a type shorter than 32-bit to store the DP. The cost of a trip is then dp[S][last] + D(last, 0).

Generate a starting solution that performs a trip for each lorry, M trips in total.

So far, nothing a beginner cannot do!

Perform heuristic search to improve it.
I used a Large Neighbourhood Search heuristic.
Remove some number of lorries from trips from the CURRENT_SOLUTION and try to insert them one by one again. That is, greedily calculate the cost of insertion, using DP, of each lorry in each trip —or creating a new trip— and choose the lorry and a trip with smallest possible value.
Decide if the obtained repaired solution is the new CURRENT_SOLUTION. I implemented this decision using Simulated Annealing, but you can simply do it if the repaired solution has a better cost.
If the CURRENT_SOLUTION is better than the BEST_SOLUTION, well, you know what to do!
I started from the small number of removed lorries and gradually increased it if after some iterations there weren't an update to the BEST_SOLUTION. This is a gradual increase of Neighbourhood.


In fact, my solution is terribly inefficient, and I could use better data representation and save time making some calculations, improved heuristic by adding more complex rules, and perhaps get rid of the DP step in favor of simple random greedy insertions. But, I just decided to keep things simple.

any one help??  I need help...

first  use dynamic programming to find the minimum value of distance from 0 and visit 1~m once then to 0, we can get a chain 0--i1-i2--...-im--0

then we consider to cut this chain, use dynameic programming dp[i] to record distance.we cut first i node of the chain..
dp[m] is the result..

I'll try this idea...

This is a variation of "Vehicle routing problem" (VRP). You can search for some good ideas in the Internet.

This problem may open new type of contest for
difficult ptactice-oriented problems by number of passed tests from big their amount in given TLE