**MIPLearn** is an extensible framework for solving discrete optimization problems using a combination of Mixed-Integer Linear Programming (MIP) and Machine Learning (ML). MIPLearn uses ML methods to automatically identify patterns in previously solved instances of the problem, then uses these patterns to accelerate the performance of conventional state-of-the-art MIP solvers such as CPLEX, Gurobi or XPRESS.
**MIPLearn** is an extensible framework for solving discrete optimization problems using a combination of Mixed-Integer Programming (MIP) and Machine Learning (ML). MIPLearn uses ML methods to automatically identify patterns in previously solved instances of the problem, then uses these patterns to accelerate the performance of conventional state-of-the-art MIP solvers such as CPLEX, Gurobi or XPRESS.
Unlike pure ML methods, MIPLearn is not only able to find high-quality solutions to discrete optimization problems, but it can also prove the optimality and feasibility of these solutions. Unlike conventional MIP solvers, MIPLearn can take full advantage of very specific observations that happen to be true in a particular family of instances (such as the observation that a particular constraint is typically redundant, or that a particular variable typically assumes a certain value). For certain classes of problems, this approach may provide significant performance benefits.
"Explored 1 nodes (5 simplex iterations) in 0.02 seconds (0.00 work units)\n",
"Explored 1 nodes (5 simplex iterations) in 0.01 seconds (0.00 work units)\n",
"Thread count was 32 (of 32 available processors)\n",
"\n",
"Solution count 1: -219.14 \n",
@ -984,7 +984,7 @@
"Optimal solution found (tolerance 1.00e-04)\n",
"Best objective -2.191400000000e+02, best bound -2.191400000000e+02, gap 0.0000%\n",
"\n",
"User-callback calls 304, time in user-callback 0.01 sec\n"
"User-callback calls 303, time in user-callback 0.00 sec\n"
]
}
],
@ -1168,7 +1168,7 @@
"Optimal solution found (tolerance 1.00e-04)\n",
"Best objective 2.921000000000e+03, best bound 2.921000000000e+03, gap 0.0000%\n",
"\n",
"User-callback calls 112, time in user-callback 0.00 sec\n"
"User-callback calls 111, time in user-callback 0.00 sec\n"
]
}
],
@ -1221,7 +1221,6 @@
"id": "7048d771",
"metadata": {},
"source": [
"\n",
"<div class=\"alert alert-info\">\n",
"Note\n",
"\n",
@ -1230,7 +1229,7 @@
"\n",
"### Formulation\n",
"\n",
"Let $T$ be the number of time steps, $G$ be the number of generation units, and let $D_t$ be the power demand (in MW) at time $t$. For each generating unit $g$, let $P^\\max_g$ and $P^\\min_g$ be the maximum and minimum amount of power the unit is able to produce when switched on; let $L_g$ and $l_g$ be the minimum up- and down-time for unit $g$; let $C^\\text{fixed}$ be the cost to keep unit $g$ on for one time step, regardless of its power output level; let $C^\\text{start}$ be the cost to switch unit $g$ on; and let $C^\\text{var}$ be the cost for generator $g$ to produce 1 MW of power. In this formulation, we assume linear production costs. For each generator $g$ and time $t$, let $x_{gt}$ be a binary variable which equals one if unit $g$ is on at time $t$, let $w_{gt}$ be a binary variable which equals one if unit $g$ switches from being off at time $t-1$ to being on at time $t$, and let $p_{gt}$ be a continuous variable which indicates the amount of power generated. The formulation is given by:"
"Let $T$ be the number of time steps, $G$ be the number of generation units, and let $D_t$ be the power demand (in MW) at time $t$. For each generating unit $g$, let $P^\\max_g$ and $P^\\min_g$ be the maximum and minimum amount of power the unit is able to produce when switched on; let $L_g$ and $l_g$ be the minimum up- and down-time for unit $g$; let $C^\\text{fixed}$ be the cost to keep unit $g$ on for one time step, regardless of its power output level; let $C^\\text{start}$ be the cost to switch unit $g$ on; let $C^\\text{prod-lin}$ be the linear cost coefficient for generator $g$ to produce 1 MW of power; and let $C^\\text{prod-quad}$ be the quadratic cost coefficient for unit $g$. For each generator $g$ and time $t$, let $x_{gt}$ be a binary variable which equals one if unit $g$ is on at time $t$, let $w_{gt}$ be a binary variable which equals one if unit $g$ switches from being off at time $t-1$ to being on at time $t$, and let $p_{gt}$ be a continuous variable which indicates the amount of power generated. The formulation is given by:"
]
},
{
@ -1238,14 +1237,14 @@
"id": "bec5ee1c",
"metadata": {},
"source": [
"\n",
"$$\n",
"\\begin{align*}\n",
"\\text{minimize} \\;\\;\\;\n",
" & \\sum_{t=1}^T \\sum_{g=1}^G \\left(\n",
" x_{gt} C^\\text{fixed}_g\n",
" + w_{gt} C^\\text{start}_g\n",
" + p_{gt} C^\\text{var}_g\n",
" + p_{gt} C^\\text{prod-lin}_g\n",
" + p_{gt}^2 C^\\text{prod-quad}_g\n",
" \\right)\n",
" \\\\\n",
"\\text{such that} \\;\\;\\;\n",
@ -1287,12 +1286,11 @@
"id": "01bed9fc",
"metadata": {},
"source": [
"\n",
"### Random instance generator\n",
"\n",
"The class `UnitCommitmentGenerator` can be used to generate random instances of this problem.\n",
"\n",
"First, the user-provided probability distributions `n_units` and `n_periods` are sampled to determine the number of generating units and the number of time steps, respectively. Then, for each unit, the probabilities `max_power` and `min_power` are sampled to determine the unit's maximum and minimum power output. To make it easier to generate valid ranges, `min_power` is not specified as the absolute power level in MW, but rather as a multiplier of `max_power`; for example, if `max_power` samples to 100 and `min_power` samples to 0.5, then the unit's power range is set to `[50,100]`. Then, the distributions `cost_startup`, `cost_prod` and `cost_fixed` are sampled to determine the unit's startup, variable and fixed costs, while the distributions `min_uptime` and `min_downtime` are sampled to determine its minimum up/down-time.\n",
"First, the user-provided probability distributions `n_units` and `n_periods` are sampled to determine the number of generating units and the number of time steps, respectively. Then, for each unit, the probabilities `max_power` and `min_power` are sampled to determine the unit's maximum and minimum power output. To make it easier to generate valid ranges, `min_power` is not specified as the absolute power level in MW, but rather as a multiplier of `max_power`; for example, if `max_power` samples to 100 and `min_power` samples to 0.5, then the unit's power range is set to `[50,100]`. Then, the distributions `cost_startup`, `cost_prod`, `cost_prod_quad` and `cost_fixed` are sampled to determine the unit's startup, linear variable, quadratic variable, and fixed costs, while the distributions `min_uptime` and `min_downtime` are sampled to determine its minimum up/down-time.\n",
"\n",
"After parameters for the units have been generated, the class then generates a periodic demand curve, with a peak every 12 time steps, in the range $(0.4C, 0.8C)$, where $C$ is the sum of all units' maximum power output. Finally, all costs and demand values are perturbed by random scaling factors independently sampled from the distributions `cost_jitter` and `demand_jitter`, respectively.\n",
**MIPLearn** is an extensible framework for solving discrete optimization problems using a combination of Mixed-Integer Linear Programming (MIP) and Machine Learning (ML). MIPLearn uses ML methods to automatically identify patterns in previously solved instances of the problem, then uses these patterns to accelerate the performance of conventional state-of-the-art MIP solvers such as CPLEX, Gurobi or XPRESS.
**MIPLearn** is an extensible framework for solving discrete optimization problems using a combination of Mixed-Integer Programming (MIP) and Machine Learning (ML). MIPLearn uses ML methods to automatically identify patterns in previously solved instances of the problem, then uses these patterns to accelerate the performance of conventional state-of-the-art MIP solvers such as CPLEX, Gurobi or XPRESS.
Unlike pure ML methods, MIPLearn is not only able to find high-quality solutions to discrete optimization problems, but it can also prove the optimality and feasibility of these solutions. Unlike conventional MIP solvers, MIPLearn can take full advantage of very specific observations that happen to be true in a particular family of instances (such as the observation that a particular constraint is typically redundant, or that a particular variable typically assumes a certain value). For certain classes of problems, this approach may provide significant performance benefits.
Alinson S. Xavier