feat: Implement replenishment benchmark - #96
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Thank you for your huge (you were probably right and we should have cut it in multiple smaller PRs) contribution, good start!
I've reviewed most of the src code (I just have not yet checked the maths of the maximizer and of the anticipative solver). I'll review what is missing along with plot utilities and tests in a second wave.
There are some bugs scattered around (see comments) to correct and some things to cleanup (add a bit more doctrings and explanation of what core methods/types do).
Additionally, it would be nice to have a documentation page describing the maths (see other benchmark pages) of this problem (it's quite difficult to understand the details and design choices by only reading the code).
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Second round of review:
- reviewed the new changes
- reviewed the anticipative and maximizer
- still need to review tests and plots
| scenario::Scenario | ||
| "initial stock" | ||
| stock_ini::Vector{Int} | ||
| end |
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If the stock is mutated in place the struct is still non-mutable since we do not overwrite its value (only its content)
| @constraint( | ||
| m, | ||
| [i in 1:N, t in 2:(T + 1)], | ||
| v[t, i] >= |
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question: I'm not sure to understand this constraint. When z[i, t] == 0, this means 0 >= v[t, i] >= this. What am I missing?
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The physical stock is defined as the number of items that arrived in the store ( > delivery_delay time steps) and are not yet sold.
The number of items that have arrived in t in the store is :
arrived_t = stock_ini[i] + sum(y[τ, i] for τ in 1:(t - delivery_delay)
The number of items sold in t is :
sold = sum(α[i, τ, k] for τ in 1:(t - 1) for k in 1:nb_customers[τ])
Therefore, the physical stock is defined as :
max(0, arrived - sold)
To linearize this, we have :
v >= arrived - soldv <= arrived - sold + M * (1 - z)v <= M * z
Therefore :
- if
z=0: necessaryarrived-sold = 0or constraint 1. is violated and we have0 <= v <= 0 - if
z = 1:arrived - sold >= 0andv = arrived - sold
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issue: the quotas matrix needs to be sliced to start at the current time step, in the same way it is done in the anticipative solver
| virtual_stock_cost = prices ./ (max_steps * 10) | ||
| physical_stock_cost = prices ./ (max_steps * 5) | ||
| over_stock_bound_cost = maximum(prices) | ||
| max_quotas = Matrix{Float64}(undef, max_steps, N) |
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issue: max_quotas is defined as Matrix{Int} in the struct definition, but initialized as Matrix{Float64} here
add replenishment benchmarl update docstrings
…parametric solver : definition of z 3. add build environment from sample for dynamic benchmarks 4. add mean_anticipative_replenishment policy
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This Pull Request adds the dynamic replenishment benchmark to the list of available benchmarks.