Add the LRGV water portfolio planning benchmark problem

[gialmisi]

Background

The Lower Rio Grande Valley (LRGV) problem is a many-objective water-supply portfolio planning benchmark: a city chooses a mix of water rights, options, and leases to trade off cost, reliability, surplus, and related risk measures, with Monte-Carlo simulation of hydrology/demand. DESDEO has no implementation.

Heads-up (scoping): this is an advanced problem. The objectives are computed from a stochastic simulation (Monte-Carlo over inflow/demand scenarios), so it is better implemented as a simulator-based problem than as closed-form expressions. Consider building it on desdeo/problem/testproblems/simulator_problem.py. Partial PRs (e.g. a deterministic/simplified version first) are welcome.

What to do

• Locate a usable LRGV formulation/dataset from the reference (objective definitions, decision variables, scenario data).
• Implement the evaluation (Monte-Carlo over scenarios) as a simulator and wrap it as a Problem via the simulator interface.
• Register a constructor (e.g. lrgv_problem(...)) in __init__.py; add a test (small scenario count) asserting reproducible values for a fixed seed.

Reference

Kasprzyk, J. R., Reed, P. M., Kirsch, B. R., & Characklis, G. W. (2009). Managing population and drought risks using many-objective water portfolio planning under uncertainty. Water Resources Research, 45(12).

Done when

• An LRGV instance loads as a DESDEO Problem, evaluates a portfolio to objective values reproducibly, and has a passing test.