Fix #502: Add ResourceConstrainedScheduling model

docs/paper/reductions.typ

@@ -78,6 +78,7 @@
   "SubsetSum": [Subset Sum],
   "MinimumFeedbackArcSet": [Minimum Feedback Arc Set],
   "MinimumFeedbackVertexSet": [Minimum Feedback Vertex Set],
+  "ResourceConstrainedScheduling": [Resource Constrained Scheduling],
   "ShortestCommonSupersequence": [Shortest Common Supersequence],
   "MinimumSumMulticenter": [Minimum Sum Multicenter],
   "SubgraphIsomorphism": [Subgraph Isomorphism],
@@ -1196,6 +1197,14 @@ Biclique Cover is equivalent to factoring the biadjacency matrix $M$ of the bipa
   *Example.* Let $A = {3, 7, 1, 8, 2, 4}$ ($n = 6$) and target $B = 11$. Selecting $A' = {3, 8}$ gives sum $3 + 8 = 11 = B$. Another solution: $A' = {7, 4}$ with sum $7 + 4 = 11 = B$.
 ]
 
+#problem-def("ResourceConstrainedScheduling")[
+  Given a set $T$ of $n$ unit-length tasks, $m$ identical processors, $r$ resources with bounds $B_i$ ($1 <= i <= r$), resource requirements $R_i (t)$ for each task $t$ and resource $i$ ($0 <= R_i (t) <= B_i$), and an overall deadline $D in ZZ^+$, determine whether there exists an $m$-processor schedule $sigma : T -> {0, dots, D-1}$ such that for every time slot $u$, at most $m$ tasks are scheduled at $u$ and $sum_(t : sigma(t) = u) R_i (t) <= B_i$ for each resource $i$.
+][
+  RESOURCE CONSTRAINED SCHEDULING is problem SS10 in Garey & Johnson's compendium @garey1979. It is NP-complete in the strong sense, even for $r = 1$ resource and $m = 3$ processors, by reduction from 3-PARTITION @garey1975. For $m = 2$ processors with arbitrary $r$, the problem is solvable in polynomial time via bipartite matching. The general case subsumes bin-packing-style constraints across multiple resource dimensions.
+
+  *Example.* Let $n = 6$ tasks, $m = 3$ processors, $r = 1$ resource with $B_1 = 20$, and deadline $D = 2$. Resource requirements: $R_1(t_1) = 6$, $R_1(t_2) = 7$, $R_1(t_3) = 7$, $R_1(t_4) = 6$, $R_1(t_5) = 8$, $R_1(t_6) = 6$. Schedule: slot 0 $arrow.l {t_1, t_2, t_3}$ (3 tasks, resource $= 20$), slot 1 $arrow.l {t_4, t_5, t_6}$ (3 tasks, resource $= 20$). Both constraints satisfied; answer: YES.
+]
+
 #problem-def("ShortestCommonSupersequence")[
   Given a finite alphabet $Sigma$, a set $R = {r_1, dots, r_m}$ of strings over $Sigma^*$, and a positive integer $K$, determine whether there exists a string $w in Sigma^*$ with $|w| lt.eq K$ such that every string $r_i in R$ is a _subsequence_ of $w$: there exist indices $1 lt.eq j_1 < j_2 < dots < j_(|r_i|) lt.eq |w|$ with $w[j_k] = r_i [k]$ for all $k$.
 ][

docs/src/reductions/problem_schemas.json

@@ -515,6 +515,32 @@
       }
     ]
   },
+  {
+    "name": "ResourceConstrainedScheduling",
+    "description": "Schedule unit-length tasks on m processors with resource constraints and a deadline",
+    "fields": [
+      {
+        "name": "num_processors",
+        "type_name": "usize",
+        "description": "Number of identical processors m"
+      },
+      {
+        "name": "resource_bounds",
+        "type_name": "Vec<u64>",
+        "description": "Resource bound B_i for each resource i"
+      },
+      {
+        "name": "resource_requirements",
+        "type_name": "Vec<Vec<u64>>",
+        "description": "R_i(t) for each task t and resource i (n x r matrix)"
+      },
+      {
+        "name": "deadline",
+        "type_name": "u64",
+        "description": "Overall deadline D"
+      }
+    ]
+  },
   {
     "name": "RuralPostman",
     "description": "Find a circuit covering required edges with total length at most B (Rural Postman Problem)",

docs/src/reductions/reduction_graph.json

@@ -433,6 +433,13 @@
       "doc_path": "models/algebraic/struct.QUBO.html",
       "complexity": "2^num_vars"
     },
+    {
+      "name": "ResourceConstrainedScheduling",
+      "variant": {},
+      "category": "misc",
+      "doc_path": "models/misc/struct.ResourceConstrainedScheduling.html",
+      "complexity": "deadline ^ num_tasks"
+    },
     {
       "name": "RuralPostman",
       "variant": {
@@ -535,7 +542,7 @@
     },
     {
       "source": 4,
-      "target": 52,
+      "target": 53,
       "overhead": [
         {
           "field": "num_spins",
@@ -699,7 +706,7 @@
     },
     {
       "source": 20,
-      "target": 54,
+      "target": 55,
       "overhead": [
         {
           "field": "num_elements",
@@ -710,7 +717,7 @@
     },
     {
       "source": 21,
-      "target": 49,
+      "target": 50,
       "overhead": [
         {
           "field": "num_clauses",
@@ -755,7 +762,7 @@
     },
     {
       "source": 24,
-      "target": 52,
+      "target": 53,
       "overhead": [
         {
           "field": "num_spins",
@@ -1201,7 +1208,7 @@
     },
     {
       "source": 47,
-      "target": 51,
+      "target": 52,
       "overhead": [
         {
           "field": "num_spins",
@@ -1211,7 +1218,7 @@
       "doc_path": "rules/spinglass_qubo/index.html"
     },
     {
-      "source": 49,
+      "source": 50,
       "target": 4,
       "overhead": [
         {
@@ -1226,7 +1233,7 @@
       "doc_path": "rules/sat_circuitsat/index.html"
     },
     {
-      "source": 49,
+      "source": 50,
       "target": 15,
       "overhead": [
         {
@@ -1241,7 +1248,7 @@
       "doc_path": "rules/sat_coloring/index.html"
     },
     {
-      "source": 49,
+      "source": 50,
       "target": 20,
       "overhead": [
         {
@@ -1256,7 +1263,7 @@
       "doc_path": "rules/sat_ksat/index.html"
     },
     {
-      "source": 49,
+      "source": 50,
       "target": 29,
       "overhead": [
         {
@@ -1271,7 +1278,7 @@
       "doc_path": "rules/sat_maximumindependentset/index.html"
     },
     {
-      "source": 49,
+      "source": 50,
       "target": 38,
       "overhead": [
         {
@@ -1286,7 +1293,7 @@
       "doc_path": "rules/sat_minimumdominatingset/index.html"
     },
     {
-      "source": 51,
+      "source": 52,
       "target": 47,
       "overhead": [
         {
@@ -1297,7 +1304,7 @@
       "doc_path": "rules/spinglass_qubo/index.html"
     },
     {
-      "source": 52,
+      "source": 53,
       "target": 24,
       "overhead": [
         {
@@ -1312,8 +1319,8 @@
       "doc_path": "rules/spinglass_maxcut/index.html"
     },
     {
-      "source": 52,
-      "target": 51,
+      "source": 53,
+      "target": 52,
       "overhead": [
         {
           "field": "num_spins",
@@ -1327,7 +1334,7 @@
       "doc_path": "rules/spinglass_casts/index.html"
     },
     {
-      "source": 55,
+      "source": 56,
       "target": 11,
       "overhead": [
         {
@@ -1342,7 +1349,7 @@
       "doc_path": "rules/travelingsalesman_ilp/index.html"
     },
     {
-      "source": 55,
+      "source": 56,
       "target": 47,
       "overhead": [
         {

problemreductions-cli/src/cli.rs

@@ -239,6 +239,7 @@ Flags by problem type:
   LCS                             --strings
   FAS                             --arcs [--weights] [--num-vertices]
   FVS                             --arcs [--weights] [--num-vertices]
+  ResourceConstrainedScheduling   --num-processors, --resource-bounds, --resource-requirements, --deadline
   FlowShopScheduling              --task-lengths, --deadline [--num-processors]
   SCS                             --strings, --bound [--alphabet-size]
   ILP, CircuitSAT                 (via reduction only)
@@ -382,15 +383,21 @@ pub struct CreateArgs {
     /// Directed arcs for directed graph problems (e.g., 0>1,1>2,2>0)
     #[arg(long)]
     pub arcs: Option<String>,
-    /// Task lengths for FlowShopScheduling (semicolon-separated rows: "3,4,2;2,3,5;4,1,3")
+    /// Number of processors for ResourceConstrainedScheduling or FlowShopScheduling
     #[arg(long)]
-    pub task_lengths: Option<String>,
-    /// Deadline for FlowShopScheduling
+    pub num_processors: Option<usize>,
+    /// Resource bounds for ResourceConstrainedScheduling (comma-separated, e.g., "20,15")
+    #[arg(long)]
+    pub resource_bounds: Option<String>,
+    /// Resource requirements for ResourceConstrainedScheduling (semicolon-separated rows, each row comma-separated, e.g., "6,3;7,4;5,2")
+    #[arg(long)]
+    pub resource_requirements: Option<String>,
+    /// Deadline for ResourceConstrainedScheduling or FlowShopScheduling
     #[arg(long)]
     pub deadline: Option<u64>,
-    /// Number of processors/machines for FlowShopScheduling
+    /// Task lengths for FlowShopScheduling (semicolon-separated rows: "3,4,2;2,3,5;4,1,3")
     #[arg(long)]
-    pub num_processors: Option<usize>,
+    pub task_lengths: Option<String>,
     /// Alphabet size for SCS (optional; inferred from max symbol + 1 if omitted)
     #[arg(long)]
     pub alphabet_size: Option<usize>,

problemreductions-cli/src/commands/create.rs

@@ -9,7 +9,7 @@ use problemreductions::models::algebraic::{ClosestVectorProblem, BMF};
 use problemreductions::models::graph::{GraphPartitioning, HamiltonianPath};
 use problemreductions::models::misc::{
     BinPacking, FlowShopScheduling, LongestCommonSubsequence, PaintShop,
-    ShortestCommonSupersequence, SubsetSum,
+    ResourceConstrainedScheduling, ShortestCommonSupersequence, SubsetSum,
 };
 use problemreductions::prelude::*;
 use problemreductions::registry::collect_schemas;
@@ -57,9 +57,11 @@ fn all_data_flags_empty(args: &CreateArgs) -> bool {
         && args.pattern.is_none()
         && args.strings.is_none()
         && args.arcs.is_none()
+        && args.num_processors.is_none()
+        && args.resource_bounds.is_none()
+        && args.resource_requirements.is_none()
         && args.task_lengths.is_none()
         && args.deadline.is_none()
-        && args.num_processors.is_none()
         && args.alphabet_size.is_none()
 }
 
@@ -744,6 +746,39 @@ pub fn create(args: &CreateArgs, out: &OutputConfig) -> Result<()> {
             )
         }
 
+        // ResourceConstrainedScheduling
+        "ResourceConstrainedScheduling" => {
+            let usage = "Usage: pred create ResourceConstrainedScheduling --num-processors 3 --resource-bounds \"20\" --resource-requirements \"6;7;7;6;8;6\" --deadline 2";
+            let num_processors = args
+                .num_processors
+                .ok_or_else(|| anyhow::anyhow!("ResourceConstrainedScheduling requires --num-processors\n\n{usage}"))?;
+            let bounds_str = args.resource_bounds.as_deref().ok_or_else(|| {
+                anyhow::anyhow!("ResourceConstrainedScheduling requires --resource-bounds\n\n{usage}")
+            })?;
+            let reqs_str = args.resource_requirements.as_deref().ok_or_else(|| {
+                anyhow::anyhow!("ResourceConstrainedScheduling requires --resource-requirements\n\n{usage}")
+            })?;
+            let deadline = args
+                .deadline
+                .ok_or_else(|| anyhow::anyhow!("ResourceConstrainedScheduling requires --deadline\n\n{usage}"))?;
+
+            let resource_bounds: Vec<u64> = util::parse_comma_list(bounds_str)?;
+            let resource_requirements: Vec<Vec<u64>> = reqs_str
+                .split(';')
+                .map(|row| util::parse_comma_list(row.trim()))
+                .collect::<Result<Vec<_>>>()?;
+
+            (
+                ser(ResourceConstrainedScheduling::new(
+                    num_processors,
+                    resource_bounds,
+                    resource_requirements,
+                    deadline,
+                ))?,
+                resolved_variant.clone(),
+            )
+        }
+
         // OptimalLinearArrangement — graph + bound
         "OptimalLinearArrangement" => {
             let (graph, _) = parse_graph(args).map_err(|e| {

src/lib.rs

@@ -56,7 +56,7 @@ pub mod prelude {
     };
     pub use crate::models::misc::{
         BinPacking, Factoring, FlowShopScheduling, Knapsack, LongestCommonSubsequence, PaintShop,
-        ShortestCommonSupersequence, SubsetSum,
+        ResourceConstrainedScheduling, ShortestCommonSupersequence, SubsetSum,
     };
     pub use crate::models::set::{MaximumSetPacking, MinimumSetCovering};
 

src/models/misc/mod.rs

@@ -7,6 +7,7 @@
 //! - [`Knapsack`]: 0-1 Knapsack (maximize value subject to weight capacity)
 //! - [`LongestCommonSubsequence`]: Longest Common Subsequence
 //! - [`PaintShop`]: Minimize color switches in paint shop scheduling
+//! - [`ResourceConstrainedScheduling`]: Schedule unit-length tasks on processors with resource constraints
 //! - [`ShortestCommonSupersequence`]: Find a common supersequence of bounded length
 //! - [`SubsetSum`]: Find a subset summing to exactly a target value
 
@@ -16,6 +17,7 @@ mod flow_shop_scheduling;
 mod knapsack;
 mod longest_common_subsequence;
 pub(crate) mod paintshop;
+pub(crate) mod resource_constrained_scheduling;
 pub(crate) mod shortest_common_supersequence;
 mod subset_sum;
 
@@ -25,6 +27,7 @@ pub use flow_shop_scheduling::FlowShopScheduling;
 pub use knapsack::Knapsack;
 pub use longest_common_subsequence::LongestCommonSubsequence;
 pub use paintshop::PaintShop;
+pub use resource_constrained_scheduling::ResourceConstrainedScheduling;
 pub use shortest_common_supersequence::ShortestCommonSupersequence;
 pub use subset_sum::SubsetSum;
 
@@ -34,5 +37,6 @@ pub(crate) fn canonical_model_example_specs() -> Vec<crate::example_db::specs::M
     specs.extend(factoring::canonical_model_example_specs());
     specs.extend(paintshop::canonical_model_example_specs());
     specs.extend(shortest_common_supersequence::canonical_model_example_specs());
+    specs.extend(resource_constrained_scheduling::canonical_model_example_specs());
     specs
 }