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Clear old FrontierOR files 4501-5400 of 5583

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  1. sadykov2013/gurobi_feasi_result/large_feasi_result_1.json +0 -6
  2. sadykov2013/gurobi_feasi_result/large_feasi_result_2.json +0 -6
  3. sadykov2013/gurobi_feasi_result/large_feasi_result_3.json +0 -6
  4. sadykov2013/gurobi_feasi_result/large_feasi_result_4.json +0 -6
  5. sadykov2013/gurobi_feasi_result/large_feasi_result_5.json +0 -6
  6. sadykov2013/gurobi_feasi_result/tiny_feasi_result.json +0 -6
  7. sadykov2013/gurobi_solution/large_solution_1.json +0 -359
  8. sadykov2013/gurobi_solution/large_solution_2.json +0 -344
  9. sadykov2013/gurobi_solution/large_solution_3.json +0 -354
  10. sadykov2013/gurobi_solution/large_solution_4.json +0 -344
  11. sadykov2013/gurobi_solution/large_solution_5.json +0 -374
  12. sadykov2013/gurobi_solution/tiny_solution.json +0 -190
  13. sadykov2013/gurobi_solution_log/large_log_1.jsonl +0 -4
  14. sadykov2013/gurobi_solution_log/large_log_2.jsonl +0 -2
  15. sadykov2013/gurobi_solution_log/large_log_3.jsonl +0 -4
  16. sadykov2013/gurobi_solution_log/large_log_4.jsonl +0 -4
  17. sadykov2013/gurobi_solution_log/large_log_5.jsonl +0 -10
  18. sadykov2013/gurobi_solution_log/tiny_log.jsonl +0 -2
  19. sadykov2013/instance/large_instance_1.json +0 -0
  20. sadykov2013/instance/large_instance_2.json +0 -2857
  21. sadykov2013/instance/large_instance_3.json +0 -7649
  22. sadykov2013/instance/large_instance_4.json +0 -0
  23. sadykov2013/instance/large_instance_5.json +0 -0
  24. sadykov2013/instance/tiny_instance.json +0 -2253
  25. sadykov2013/instance_schema.json +0 -11
  26. sadykov2013/mathematical_formulation.md +0 -40
  27. sadykov2013/problem_description.txt +0 -5
  28. sadykov2013/solution_logger.py +0 -61
  29. sadykov2013/solution_schema.json +0 -9
  30. salemi2022/feasibility_check.py +0 -441
  31. salemi2022/gurobi_code.py +0 -306
  32. salemi2022/gurobi_feasi_result/large_feasi_result_1.json +0 -6
  33. salemi2022/gurobi_feasi_result/large_feasi_result_2.json +0 -6
  34. salemi2022/gurobi_feasi_result/large_feasi_result_3.json +0 -6
  35. salemi2022/gurobi_feasi_result/large_feasi_result_4.json +0 -6
  36. salemi2022/gurobi_feasi_result/large_feasi_result_5.json +0 -6
  37. salemi2022/gurobi_feasi_result/tiny_feasi_result.json +0 -6
  38. salemi2022/gurobi_solution/large_solution_1.json +0 -0
  39. salemi2022/gurobi_solution/large_solution_2.json +0 -0
  40. salemi2022/gurobi_solution/large_solution_3.json +0 -0
  41. salemi2022/gurobi_solution/large_solution_4.json +0 -0
  42. salemi2022/gurobi_solution/large_solution_5.json +0 -0
  43. salemi2022/gurobi_solution/tiny_solution.json +0 -0
  44. salemi2022/gurobi_solution_log/large_log_1.jsonl +0 -12
  45. salemi2022/gurobi_solution_log/large_log_2.jsonl +0 -12
  46. salemi2022/gurobi_solution_log/large_log_3.jsonl +0 -12
  47. salemi2022/gurobi_solution_log/large_log_4.jsonl +0 -10
  48. salemi2022/gurobi_solution_log/large_log_5.jsonl +0 -17
  49. salemi2022/gurobi_solution_log/tiny_log.jsonl +0 -4
  50. salemi2022/instance/large_instance_1.json +0 -1
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2066
- 41,
2067
- 56
2068
- ],
2069
- [
2070
- 42,
2071
- 44
2072
- ],
2073
- [
2074
- 42,
2075
- 45
2076
- ],
2077
- [
2078
- 42,
2079
- 48
2080
- ],
2081
- [
2082
- 42,
2083
- 51
2084
- ],
2085
- [
2086
- 42,
2087
- 53
2088
- ],
2089
- [
2090
- 42,
2091
- 55
2092
- ],
2093
- [
2094
- 42,
2095
- 56
2096
- ],
2097
- [
2098
- 42,
2099
- 57
2100
- ],
2101
- [
2102
- 44,
2103
- 45
2104
- ],
2105
- [
2106
- 44,
2107
- 48
2108
- ],
2109
- [
2110
- 44,
2111
- 51
2112
- ],
2113
- [
2114
- 44,
2115
- 53
2116
- ],
2117
- [
2118
- 44,
2119
- 55
2120
- ],
2121
- [
2122
- 44,
2123
- 56
2124
- ],
2125
- [
2126
- 44,
2127
- 57
2128
- ],
2129
- [
2130
- 45,
2131
- 47
2132
- ],
2133
- [
2134
- 45,
2135
- 48
2136
- ],
2137
- [
2138
- 45,
2139
- 51
2140
- ],
2141
- [
2142
- 45,
2143
- 53
2144
- ],
2145
- [
2146
- 45,
2147
- 54
2148
- ],
2149
- [
2150
- 45,
2151
- 55
2152
- ],
2153
- [
2154
- 45,
2155
- 56
2156
- ],
2157
- [
2158
- 45,
2159
- 57
2160
- ],
2161
- [
2162
- 47,
2163
- 48
2164
- ],
2165
- [
2166
- 47,
2167
- 51
2168
- ],
2169
- [
2170
- 47,
2171
- 53
2172
- ],
2173
- [
2174
- 47,
2175
- 56
2176
- ],
2177
- [
2178
- 48,
2179
- 51
2180
- ],
2181
- [
2182
- 48,
2183
- 53
2184
- ],
2185
- [
2186
- 48,
2187
- 54
2188
- ],
2189
- [
2190
- 48,
2191
- 55
2192
- ],
2193
- [
2194
- 48,
2195
- 56
2196
- ],
2197
- [
2198
- 48,
2199
- 57
2200
- ],
2201
- [
2202
- 51,
2203
- 53
2204
- ],
2205
- [
2206
- 51,
2207
- 54
2208
- ],
2209
- [
2210
- 51,
2211
- 55
2212
- ],
2213
- [
2214
- 51,
2215
- 56
2216
- ],
2217
- [
2218
- 51,
2219
- 57
2220
- ],
2221
- [
2222
- 53,
2223
- 54
2224
- ],
2225
- [
2226
- 53,
2227
- 55
2228
- ],
2229
- [
2230
- 53,
2231
- 56
2232
- ],
2233
- [
2234
- 53,
2235
- 57
2236
- ],
2237
- [
2238
- 54,
2239
- 56
2240
- ],
2241
- [
2242
- 55,
2243
- 56
2244
- ],
2245
- [
2246
- 56,
2247
- 57
2248
- ]
2249
- ],
2250
- "num_conflict_edges": 499,
2251
- "max_possible_edges": 1770,
2252
- "actual_density": 0.281921
2253
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
sadykov2013/instance_schema.json DELETED
@@ -1,11 +0,0 @@
1
- {
2
- "num_items": "<int> Total number of items to be packed into bins.",
3
- "bin_capacity": "<int> Maximum total weight that each bin can hold.",
4
- "items": [
5
- {
6
- "id": "<int> Unique identifier for the item.",
7
- "weight": "<int> Weight of the item."
8
- }
9
- ],
10
- "conflict_edges": "<list[list[int]], shape [num_conflict_edges][2]> Pairs of items that must not be placed in the same bin."
11
- }
 
 
 
 
 
 
 
 
 
 
 
 
sadykov2013/mathematical_formulation.md DELETED
@@ -1,40 +0,0 @@
1
- # Original Formulation: Bin Packing with Conflicts
2
-
3
- Sadykov & Vanderbeck. “Bin Packing with Conflicts: a Generic Branch-and-Price Algorithm.” Compact formulation (1a)–(1f).
4
-
5
- ## Sets, Indices and Parameters
6
-
7
- - $V = \{1,\dots,n\}$: set of items.
8
-
9
- - $G = (V,E)$: conflict graph where $(i,j)\in E$ indicates items $i$ and $j$ cannot share a bin.
10
-
11
- - $K\le n$: upper bound on the number of identical bins.
12
-
13
- - $W\in\mathbb{Z}_+$: capacity of each bin.
14
-
15
- - $w_i \in \mathbb{Z}_+$: weight of item $i$, with $w_i\le W$.
16
-
17
- ## Decision Variables
18
-
19
- $$\begin{align*}
20
- x_{ik} &\in \{0,1\}, \quad i=1,\dots,n,\ k=1,\dots,K \quad
21
- \text{($=1$ iff item $i$ is placed in bin $k$)} \\
22
- y_k &\in \{0,1\}, \quad k=1,\dots,K \quad
23
- \text{($=1$ iff bin $k$ is used).}
24
- \end{align*}$$
25
-
26
- ## Compact Formulation (1)
27
-
28
- $$\begin{align}
29
- \min\ & \sum_{k=1}^{K} y_k \tag{1a} \\
30
- \text{s.t.}\ & \sum_{k=1}^{K} x_{ik} \ge 1,
31
- && i = 1,\dots,n, \tag{1b} \\
32
- & \sum_{i=1}^{n} w_i\, x_{ik} \le W\, y_k,
33
- && k = 1,\dots,K, \tag{1c} \\
34
- & x_{ik} + x_{jk} \le y_k,
35
- && (i,j)\in E,\ k = 1,\dots,K, \tag{1d} \\
36
- & y_k \in \{0,1\},
37
- && k = 1,\dots,K, \tag{1e} \\
38
- & x_{ik} \in \{0,1\},
39
- && i = 1,\dots,n,\ k = 1,\dots,K. \tag{1f}
40
- \end{align}$$
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
sadykov2013/problem_description.txt DELETED
@@ -1,5 +0,0 @@
1
- # Problem Description
2
-
3
- A collection of items must be packed into identical bins. Each item has a known non-negative integer weight, and every bin has the same integer capacity. In addition to the items and bins, a conflict graph is given: certain pairs of items are declared to be in conflict, meaning they must never be placed in the same bin. The input specifies the number of items, the capacity shared by all bins, the weight of each item (which does not exceed the bin capacity), and the list of conflicting item pairs.
4
-
5
- The task is to assign every item to at least one bin so that two rules are respected simultaneously. First, the total weight of all items assigned to any single bin must not exceed that bin's capacity. Second, no two items that form a conflicting pair may be assigned to the same bin. A bin is considered used if at least one item is assigned to it, and the number of available bins may be assumed large enough that a feasible packing always exists. The goal is to minimize the total number of bins used.
 
 
 
 
 
 
sadykov2013/solution_logger.py DELETED
@@ -1,61 +0,0 @@
1
- """
2
- Convergence logger for optimization algorithms.
3
-
4
- Records incumbent solutions with timestamps to a JSONL file.
5
- This module is provided to LLM-generated programs — they only need to call
6
- `log(objective_value)` whenever a better feasible solution is found.
7
-
8
- Usage in generated code:
9
- from solution_logger import SolutionLogger
10
- logger = SolutionLogger(log_path, sense="minimize") # or "maximize"
11
- # ... inside algorithm loop:
12
- logger.log(objective_value)
13
- """
14
-
15
- import json
16
- import time
17
-
18
-
19
- class SolutionLogger:
20
- def __init__(self, log_path, sense="minimize"):
21
- """
22
- Args:
23
- log_path: Path to the JSONL output file.
24
- sense: "minimize" or "maximize".
25
- """
26
- self.log_path = log_path
27
- self.sense = sense
28
- self.start_time = time.time()
29
- self.best_obj = None
30
- self.min_interval = 0.1 # seconds, avoid excessive writes
31
-
32
- self._last_log_time = 0.0
33
- # Clear the file
34
- with open(self.log_path, "w") as f:
35
- pass
36
-
37
- def log(self, objective_value):
38
- """Record a new incumbent if it improves on the best known."""
39
- if objective_value is None:
40
- return
41
-
42
- # Check if this is an improvement
43
- if self.best_obj is not None:
44
- if self.sense == "minimize" and objective_value >= self.best_obj:
45
- return
46
- if self.sense == "maximize" and objective_value <= self.best_obj:
47
- return
48
-
49
- elapsed = time.time() - self.start_time
50
-
51
- # Throttle writes
52
- if self.best_obj is not None and elapsed - self._last_log_time < self.min_interval:
53
- self.best_obj = objective_value
54
- return
55
-
56
- self.best_obj = objective_value
57
- self._last_log_time = elapsed
58
-
59
- with open(self.log_path, "a") as f:
60
- f.write(json.dumps({"time": round(elapsed, 3),
61
- "objective_value": objective_value}) + "\n")
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
sadykov2013/solution_schema.json DELETED
@@ -1,9 +0,0 @@
1
- {
2
- "objective_value": "<int> Total number of bins used to pack all items.",
3
- "bins": {
4
- "<bin_id>": {
5
- "items": "<list[int]> Identifiers of the items packed into this bin.",
6
- "total_weight": "<int> Sum of the weights of the items packed into this bin."
7
- }
8
- }
9
- }
 
 
 
 
 
 
 
 
 
 
salemi2022/feasibility_check.py DELETED
@@ -1,441 +0,0 @@
1
- """
2
- Feasibility checker for the two-stage stochastic Unit Commitment Problem (UCP)
3
- from Salemi and Davarnia (2022).
4
-
5
- Constraints (numbered top-to-bottom from the formulation):
6
- 1 (1b) Start-up cost linearisation
7
- 2 (1c) Logical relation y - ybar = x_j - x_{j-1}
8
- 3 (1d) Minimum up-time
9
- 4 (1e) Minimum down-time
10
- 5 (1f) Ramp-up
11
- 6 (1g) Ramp-down
12
- 7 (1h) Generation capacity bounds m*x <= p <= pbar <= M*x
13
- 8 (1i) Demand satisfaction
14
- 9 (1j) Spinning reserve
15
- 10 (1k) Binary variable domains
16
- 11 (1l) Non-negativity of q, p, pbar
17
- 12 (Tier C obj recompute) Objective consistency:
18
- reported objective_value must equal
19
- sum_{i,j}(c_f^i x_j^i + q_j^i) + (1/S) sum_{i,j,s} c_g^i p_j^{i,s}
20
- """
21
-
22
- import argparse
23
- import json
24
- import math
25
-
26
- # Bumped from 1e-5 to 1e-3 to absorb Gurobi numerical noise on large
27
- # instances. 1e-5 was rejecting valid OPT solutions; 1e-3 keeps catch
28
- # for genuine infeasibilities.
29
- TOL = 1e-3
30
- EPS = 1e-3
31
-
32
-
33
- def load_json(path):
34
- with open(path, "r") as f:
35
- return json.load(f)
36
-
37
-
38
- def normalizer(rhs):
39
- return max(abs(rhs), EPS)
40
-
41
-
42
- def record(violations_list, magnitudes_list, constraint_idx, msg, lhs, rhs, viol_amount):
43
- norm = normalizer(rhs)
44
- violations_list.append((constraint_idx, msg))
45
- magnitudes_list.append({
46
- "constraint": constraint_idx,
47
- "lhs": lhs,
48
- "rhs": rhs,
49
- "raw_excess": viol_amount,
50
- "normalizer": norm,
51
- "ratio": viol_amount / norm,
52
- })
53
-
54
-
55
- def check_feasibility(instance, solution):
56
- """Return (feasible, violated_constraints, violations, violation_magnitudes)."""
57
-
58
- violations = [] # list of (constraint_idx, message)
59
- magnitudes = [] # list of dicts
60
-
61
- # --- Unpack instance ---
62
- n_gen = instance["n_generators"]
63
- n_scen = instance["n_scenarios"]
64
- T = instance["T"]
65
- gens = instance["generators"]
66
- demand = instance["demand_scenarios"] # [s][j]
67
- reserve = instance["reserve_scenarios"] # [s][j]
68
-
69
- # --- Unpack solution ---
70
- x = solution.get("x")
71
- y = solution.get("y")
72
- y_bar = solution.get("y_bar")
73
- q = solution.get("q")
74
- p = solution.get("p") # p[i][s][j] (gurobi format)
75
- p_bar = solution.get("p_bar") # p_bar[i][s][j]
76
-
77
- # If the solution is empty / null (e.g. INFEASIBLE solver status), nothing to check
78
- if x is None:
79
- return True, [], [], [] # No solution to check; vacuously feasible
80
-
81
- # Derive y, y_bar from x if not provided (using constraint 1c definition)
82
- if y is None:
83
- y = []
84
- for i in range(n_gen):
85
- yi = []
86
- for j in range(T):
87
- x_prev = x[i][j - 1] if j > 0 else 0
88
- diff = x[i][j] - x_prev
89
- yi.append(max(diff, 0))
90
- y.append(yi)
91
- if y_bar is None:
92
- y_bar = []
93
- for i in range(n_gen):
94
- ybi = []
95
- for j in range(T):
96
- x_prev = x[i][j - 1] if j > 0 else 0
97
- diff = x[i][j] - x_prev
98
- ybi.append(max(-diff, 0))
99
- y_bar.append(ybi)
100
-
101
- # Derive q from x if not provided (set to minimum feasible value for checking)
102
- if q is None:
103
- q = []
104
- for i in range(n_gen):
105
- K = gens[i]["startup_costs_K"]
106
- qi = []
107
- for j in range(T):
108
- max_q = 0.0
109
- # Paper (1b): k in {1,...,j-1} for 1-indexed j; with 0-indexed
110
- # j the constraint set is k in {1,...,j} and is empty at j=0.
111
- max_k = min(j, len(K))
112
- for k in range(1, max_k + 1):
113
- past_sum = sum(x[i][j - h] for h in range(1, k + 1) if j - h >= 0)
114
- val = K[k - 1] * (x[i][j] - past_sum)
115
- if val > max_q:
116
- max_q = val
117
- qi.append(max(max_q, 0.0))
118
- q.append(qi)
119
-
120
- # =====================================================================
121
- # Constraint 1 (1b): Start-up cost linearisation
122
- # q_j^i >= K_k^i * (x_j^i - sum_{h=1}^{k} x_{j-h}^i)
123
- # for k in {1,...,j-1} (paper, 1-indexed). With 0-indexed j (j' = j-1)
124
- # that becomes k in {1,...,j'} and is empty at j'=0 (paper's first
125
- # period). gurobi_code.py:88 uses the same convention.
126
- # =====================================================================
127
- for i in range(n_gen):
128
- K = gens[i]["startup_costs_K"]
129
- for j in range(T):
130
- max_k = min(j, len(K))
131
- for k in range(1, max_k + 1):
132
- past_sum = sum(x[i][j - h] for h in range(1, k + 1) if j - h >= 0)
133
- rhs_val = K[k - 1] * (x[i][j] - past_sum)
134
- lhs_val = q[i][j]
135
- # Constraint: lhs >= rhs => violation if rhs - lhs > tol
136
- viol = rhs_val - lhs_val
137
- if viol > TOL:
138
- record(violations, magnitudes, 1,
139
- f"Startup cost violated: gen {i+1}, period {j+1}, k={k}: "
140
- f"q={lhs_val:.4f} < K*(...) = {rhs_val:.4f}",
141
- lhs_val, rhs_val, viol)
142
-
143
- # =====================================================================
144
- # Constraint 2 (1c): Logical relation
145
- # y_j^i - ybar_j^i = x_j^i - x_{j-1}^i
146
- # =====================================================================
147
- for i in range(n_gen):
148
- for j in range(T):
149
- x_prev = x[i][j - 1] if j > 0 else 0
150
- lhs_val = y[i][j] - y_bar[i][j]
151
- rhs_val = x[i][j] - x_prev
152
- viol = abs(lhs_val - rhs_val)
153
- if viol > TOL:
154
- record(violations, magnitudes, 2,
155
- f"Logical relation violated: gen {i+1}, period {j+1}: "
156
- f"y-ybar={lhs_val} != x_j-x_{{j-1}}={rhs_val}",
157
- lhs_val, rhs_val, viol)
158
-
159
- # =====================================================================
160
- # Constraint 3 (1d): Minimum up-time
161
- # sum_{j'=j-L+1}^{j} y_{j'}^i <= x_j^i
162
- # for j in {L^i,...,T} (1-indexed) => j in {L-1,...,T-1} (0-indexed)
163
- # =====================================================================
164
- for i in range(n_gen):
165
- L = gens[i]["L"]
166
- for j in range(L - 1, T):
167
- lhs_val = sum(y[i][jp] for jp in range(j - L + 1, j + 1))
168
- rhs_val = x[i][j]
169
- viol = lhs_val - rhs_val
170
- if viol > TOL:
171
- record(violations, magnitudes, 3,
172
- f"Min up-time violated: gen {i+1}, period {j+1}: "
173
- f"sum(y)={lhs_val} > x={rhs_val}",
174
- lhs_val, rhs_val, viol)
175
-
176
- # =====================================================================
177
- # Constraint 4 (1e): Minimum down-time
178
- # sum_{j'=j-ell+1}^{j} ybar_{j'}^i <= 1 - x_j^i
179
- # for j in {ell^i,...,T} (1-indexed) => j in {ell-1,...,T-1} (0-indexed)
180
- # =====================================================================
181
- for i in range(n_gen):
182
- ell = gens[i]["ell"]
183
- for j in range(ell - 1, T):
184
- lhs_val = sum(y_bar[i][jp] for jp in range(j - ell + 1, j + 1))
185
- rhs_val = 1 - x[i][j]
186
- viol = lhs_val - rhs_val
187
- if viol > TOL:
188
- record(violations, magnitudes, 4,
189
- f"Min down-time violated: gen {i+1}, period {j+1}: "
190
- f"sum(ybar)={lhs_val} > 1-x={rhs_val}",
191
- lhs_val, rhs_val, viol)
192
-
193
- # =====================================================================
194
- # Constraints 5-9 require p, p_bar (per scenario)
195
- # =====================================================================
196
- if p is not None and p_bar is not None:
197
- for s in range(n_scen):
198
- for i in range(n_gen):
199
- RU = gens[i]["RU"]
200
- SU = gens[i]["SU"]
201
- RD = gens[i]["RD"]
202
- SD = gens[i]["SD"]
203
- m_cap = gens[i]["m"]
204
- M_cap = gens[i]["M"]
205
-
206
- for j in range(T):
207
- x_prev = x[i][j - 1] if j > 0 else 0
208
- p_ij = p[i][s][j]
209
- p_prev = p[i][s][j - 1] if j > 0 else 0.0
210
- pbar_ij = p_bar[i][s][j]
211
-
212
- # =================================================
213
- # Constraint 5 (1f): Ramp-up
214
- # p_j - p_{j-1} <= RU * x_{j-1} + SU * y_j
215
- # =================================================
216
- lhs_val = p_ij - p_prev
217
- rhs_val = RU * x_prev + SU * y[i][j]
218
- viol = lhs_val - rhs_val
219
- if viol > TOL:
220
- record(violations, magnitudes, 5,
221
- f"Ramp-up violated: gen {i+1}, period {j+1}, "
222
- f"scenario {s+1}: LHS={lhs_val:.4f} > RHS={rhs_val:.4f}",
223
- lhs_val, rhs_val, viol)
224
-
225
- # =================================================
226
- # Constraint 6 (1g): Ramp-down
227
- # p_{j-1} - p_j <= RD * x_j + SD * ybar_j
228
- # =================================================
229
- lhs_val = p_prev - p_ij
230
- rhs_val = RD * x[i][j] + SD * y_bar[i][j]
231
- viol = lhs_val - rhs_val
232
- if viol > TOL:
233
- record(violations, magnitudes, 6,
234
- f"Ramp-down violated: gen {i+1}, period {j+1}, "
235
- f"scenario {s+1}: LHS={lhs_val:.4f} > RHS={rhs_val:.4f}",
236
- lhs_val, rhs_val, viol)
237
-
238
- # =================================================
239
- # Constraint 7 (1h): Capacity bounds
240
- # m*x <= p <= pbar <= M*x
241
- # =================================================
242
- # 7a: m*x <= p (p >= m*x)
243
- lhs_val_7a = m_cap * x[i][j]
244
- viol_7a = lhs_val_7a - p_ij
245
- if viol_7a > TOL:
246
- record(violations, magnitudes, 7,
247
- f"Capacity lower bound violated: gen {i+1}, "
248
- f"period {j+1}, scenario {s+1}: "
249
- f"m*x={lhs_val_7a:.4f} > p={p_ij:.4f}",
250
- p_ij, lhs_val_7a, viol_7a)
251
-
252
- # 7b: p <= pbar
253
- viol_7b = p_ij - pbar_ij
254
- if viol_7b > TOL:
255
- record(violations, magnitudes, 7,
256
- f"p > pbar violated: gen {i+1}, period {j+1}, "
257
- f"scenario {s+1}: p={p_ij:.4f} > pbar={pbar_ij:.4f}",
258
- p_ij, pbar_ij, viol_7b)
259
-
260
- # 7c: pbar <= M*x
261
- rhs_val_7c = M_cap * x[i][j]
262
- viol_7c = pbar_ij - rhs_val_7c
263
- if viol_7c > TOL:
264
- record(violations, magnitudes, 7,
265
- f"Capacity upper bound violated: gen {i+1}, "
266
- f"period {j+1}, scenario {s+1}: "
267
- f"pbar={pbar_ij:.4f} > M*x={rhs_val_7c:.4f}",
268
- pbar_ij, rhs_val_7c, viol_7c)
269
-
270
- # =================================================
271
- # Constraint 8 (1i): Demand satisfaction
272
- # sum_i p_j^i >= D_j
273
- # =================================================
274
- for j in range(T):
275
- lhs_val = sum(p[i][s][j] for i in range(n_gen))
276
- rhs_val = demand[s][j]
277
- viol = rhs_val - lhs_val # >= constraint: violation if rhs > lhs
278
- if viol > TOL:
279
- record(violations, magnitudes, 8,
280
- f"Demand not met: period {j+1}, scenario {s+1}: "
281
- f"sum(p)={lhs_val:.4f} < D={rhs_val:.4f}",
282
- lhs_val, rhs_val, viol)
283
-
284
- # =================================================
285
- # Constraint 9 (1j): Spinning reserve
286
- # sum_i pbar_j^i >= D_j + R_j
287
- # =================================================
288
- for j in range(T):
289
- lhs_val = sum(p_bar[i][s][j] for i in range(n_gen))
290
- rhs_val = demand[s][j] + reserve[s][j]
291
- viol = rhs_val - lhs_val
292
- if viol > TOL:
293
- record(violations, magnitudes, 9,
294
- f"Spinning reserve not met: period {j+1}, scenario {s+1}: "
295
- f"sum(pbar)={lhs_val:.4f} < D+R={rhs_val:.4f}",
296
- lhs_val, rhs_val, viol)
297
-
298
- # =====================================================================
299
- # Constraint 10 (1k): Binary variable domains
300
- # x, y, ybar in {0, 1}
301
- # =====================================================================
302
- for i in range(n_gen):
303
- for j in range(T):
304
- for var_name, var_val in [("x", x[i][j]), ("y", y[i][j]),
305
- ("ybar", y_bar[i][j])]:
306
- dist = min(abs(var_val - 0), abs(var_val - 1))
307
- if dist > TOL:
308
- record(violations, magnitudes, 10,
309
- f"Binary domain violated: gen {i+1}, period {j+1}: "
310
- f"{var_name}={var_val}",
311
- var_val, round(var_val), dist)
312
-
313
- # =====================================================================
314
- # Constraint 11 (1l): Non-negativity q, p, pbar >= 0
315
- # =====================================================================
316
- for i in range(n_gen):
317
- for j in range(T):
318
- if q[i][j] < -TOL:
319
- record(violations, magnitudes, 11,
320
- f"Non-negativity violated: gen {i+1}, period {j+1}: "
321
- f"q={q[i][j]:.6f} < 0",
322
- q[i][j], 0.0, -q[i][j])
323
-
324
- if p is not None and p_bar is not None:
325
- for i in range(n_gen):
326
- for s in range(n_scen):
327
- for j in range(T):
328
- if p[i][s][j] < -TOL:
329
- record(violations, magnitudes, 11,
330
- f"Non-negativity violated: gen {i+1}, "
331
- f"scenario {s+1}, period {j+1}: "
332
- f"p={p[i][s][j]:.6f} < 0",
333
- p[i][s][j], 0.0, -p[i][s][j])
334
- if p_bar[i][s][j] < -TOL:
335
- record(violations, magnitudes, 11,
336
- f"Non-negativity violated: gen {i+1}, "
337
- f"scenario {s+1}, period {j+1}: "
338
- f"pbar={p_bar[i][s][j]:.6f} < 0",
339
- p_bar[i][s][j], 0.0, -p_bar[i][s][j])
340
-
341
- # =====================================================================
342
- # Constraint 12 (Tier C): Objective consistency
343
- # Recompute the expected total cost from the solution variables
344
- # (formula 1a; second-stage component averaged across scenarios)
345
- # and reject solutions whose reported objective_value disagrees.
346
- # Defends against LLM exploits that lie about objective_value
347
- # (e.g. obj=0 or obj=sys.float_info.max on otherwise feasible
348
- # commitment+dispatch decisions).
349
- # =====================================================================
350
- reported_obj = solution.get("objective_value")
351
- if reported_obj is not None and p is not None:
352
- try:
353
- reported = float(reported_obj)
354
- if not math.isfinite(reported):
355
- raise ValueError("non-finite")
356
- ok = True
357
- except (TypeError, ValueError):
358
- ok = False
359
- if ok:
360
- # First-stage cost: sum_{i,j} (c_f^i * x_j^i + q_j^i)
361
- first_stage = 0.0
362
- for i in range(n_gen):
363
- cf = gens[i]["c_f"]
364
- for j in range(T):
365
- first_stage += cf * x[i][j] + q[i][j]
366
- # Expected second-stage production cost:
367
- # (1/n_scen) * sum_{i,j,s} c_g^i * p[i][s][j]
368
- second_stage = 0.0
369
- if n_scen > 0:
370
- acc = 0.0
371
- for i in range(n_gen):
372
- cg = gens[i]["c_g"]
373
- for s in range(n_scen):
374
- for j in range(T):
375
- acc += cg * p[i][s][j]
376
- second_stage = acc / n_scen
377
- true_obj = first_stage + second_stage
378
- abs_diff = abs(reported - true_obj)
379
- # 0.1% relative tolerance with a 1e-3 absolute floor.
380
- tol_obj = max(1e-3, 1e-3 * abs(true_obj))
381
- if abs_diff > tol_obj:
382
- record(violations, magnitudes, 12,
383
- f"Objective consistency violated: reported "
384
- f"objective_value={reported} differs from recomputed "
385
- f"sum_{{i,j}}(c_f*x+q) + (1/S)*sum_{{i,j,s}}(c_g*p)="
386
- f"{true_obj} (|diff|={abs_diff:.3g}, tol={tol_obj:.3g})",
387
- reported, true_obj, abs_diff)
388
-
389
- # --- Aggregate results ---
390
- violated_indices = sorted(set(v[0] for v in violations))
391
- violation_msgs = []
392
- for idx in violated_indices:
393
- msgs = [v[1] for v in violations if v[0] == idx]
394
- # Aggregate: take first message and count
395
- if len(msgs) == 1:
396
- violation_msgs.append(msgs[0])
397
- else:
398
- violation_msgs.append(f"{msgs[0]} (and {len(msgs)-1} more)")
399
-
400
- feasible = len(violated_indices) == 0
401
- return feasible, violated_indices, violation_msgs, magnitudes
402
-
403
-
404
- def main():
405
- parser = argparse.ArgumentParser(
406
- description="Feasibility checker for the stochastic UCP (Salemi & Davarnia 2022)."
407
- )
408
- parser.add_argument("--instance_path", type=str, required=True,
409
- help="Path to the JSON instance file.")
410
- parser.add_argument("--solution_path", type=str, required=True,
411
- help="Path to the JSON solution file.")
412
- parser.add_argument("--result_path", type=str, required=True,
413
- help="Path to write the JSON feasibility result.")
414
- args = parser.parse_args()
415
-
416
- instance = load_json(args.instance_path)
417
- solution = load_json(args.solution_path)
418
-
419
- feasible, violated_constraints, violation_msgs, magnitudes = \
420
- check_feasibility(instance, solution)
421
-
422
- result = {
423
- "feasible": feasible,
424
- "violated_constraints": violated_constraints,
425
- "violations": violation_msgs,
426
- "violation_magnitudes": magnitudes,
427
- }
428
-
429
- with open(args.result_path, "w") as f:
430
- json.dump(result, f, indent=2)
431
-
432
- print(f"Feasible: {feasible}")
433
- if violated_constraints:
434
- print(f"Violated constraints: {violated_constraints}")
435
- for msg in violation_msgs:
436
- print(f" - {msg}")
437
- print(f"Result written to {args.result_path}")
438
-
439
-
440
- if __name__ == "__main__":
441
- main()
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
salemi2022/gurobi_code.py DELETED
@@ -1,306 +0,0 @@
1
- """
2
- Stochastic Unit Commitment Problem (UCP) from Salemi and Davarnia (2022).
3
-
4
- Two-stage stochastic program:
5
- First stage: commitment (x, y, y_bar binary) + startup cost (q continuous)
6
- Second stage: production (p, p_bar continuous) per demand scenario
7
-
8
- Minimises first-stage costs plus expected second-stage production costs.
9
- """
10
-
11
- import argparse
12
- import json
13
- import time
14
-
15
- import gurobipy as gp
16
- from gurobipy import GRB
17
- import os as _os, sys as _sys
18
- # Walk up from this file's directory to find repo root (containing scripts/).
19
- _repo = _os.path.dirname(_os.path.abspath(__file__))
20
- while _repo != _os.path.dirname(_repo) and not _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')):
21
- _repo = _os.path.dirname(_repo)
22
- if _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')):
23
- _sys.path.insert(0, _repo)
24
- try:
25
- from scripts.utils.gurobi_log_helper import install_gurobi_logger
26
- except ImportError:
27
- def install_gurobi_logger(log_path): # no-op fallback when scripts/ unavailable
28
- pass
29
-
30
-
31
- def build_model(instance):
32
- n_gen = instance["n_generators"]
33
- n_scen = instance["n_scenarios"]
34
- T = instance["T"]
35
- gens = instance["generators"]
36
- demand = instance["demand_scenarios"] # [s][j]
37
- reserve = instance["reserve_scenarios"] # [s][j]
38
-
39
- model = gp.Model("StochasticUCP")
40
- model.setParam("Threads", 1)
41
-
42
- # Index ranges
43
- periods = range(T) # 0-indexed: period j corresponds to j+1 in paper
44
- generators = range(n_gen)
45
- scenarios = range(n_scen)
46
-
47
- # ---- First-stage variables ----
48
- # x[i,j]: commitment status of generator i in period j
49
- x = model.addVars(generators, periods, vtype=GRB.BINARY, name="x")
50
- # y[i,j]: start-up indicator
51
- y = model.addVars(generators, periods, vtype=GRB.BINARY, name="y")
52
- # y_bar[i,j]: shut-down indicator
53
- y_bar = model.addVars(generators, periods, vtype=GRB.BINARY, name="ybar")
54
- # q[i,j]: start-up cost
55
- q = model.addVars(generators, periods, lb=0.0, name="q")
56
-
57
- # ---- Second-stage variables ----
58
- # p[i,j,s]: production of generator i in period j under scenario s
59
- p = model.addVars(generators, periods, scenarios, lb=0.0, name="p")
60
- # p_bar[i,j,s]: available capacity
61
- p_bar = model.addVars(generators, periods, scenarios, lb=0.0, name="pbar")
62
-
63
- # Initial conditions: x_0^i = 0 for all i (all generators start OFF)
64
- # We handle this inline when j-1 or j-h references period 0 or earlier.
65
-
66
- # ---- Objective (1a) ----
67
- first_stage = gp.quicksum(
68
- gens[i]["c_f"] * x[i, j] + q[i, j]
69
- for i in generators
70
- for j in periods
71
- )
72
- second_stage = (1.0 / n_scen) * gp.quicksum(
73
- gens[i]["c_g"] * p[i, j, s]
74
- for i in generators
75
- for j in periods
76
- for s in scenarios
77
- )
78
- model.setObjective(first_stage + second_stage, GRB.MINIMIZE)
79
-
80
- # ---- Constraint (1b): Start-up cost linearization ----
81
- # q_j^i >= K_k^i * (x_j^i - sum_{h=1}^{k} x_{j-h}^i)
82
- # for k in {1, ..., j} (paper says k in {1,...,j-1} but j is 1-indexed;
83
- # here j is 0-indexed so k in {1, ..., j+1-1} = {1,...,j} when j>=1,
84
- # and for j=0 (first period), k can be 1 only with x_{-1}=0).
85
- # Actually: paper j in {1,...,T}, k in {1,...,j-1}. With 0-indexing:
86
- # j in {0,...,T-1}, k in {1,...,j} (since paper's j-1 maps to our j).
87
- # For j=0 in paper (j=1), k in {1,...,0} is empty. But we also need k
88
- # up to T for the full startup cost schedule.
89
- # Corrected: paper says k in {1,...,j-1} for period j (1-indexed).
90
- # 0-indexed: for period j, k in {1,...,j}. (paper j -> our j+1, so k <= j+1-1 = j)
91
- # Also k is bounded by len(startup_costs_K) = T.
92
- for i in generators:
93
- K = gens[i]["startup_costs_K"] # K[k-1] = cost after k inactive periods
94
- for j in periods:
95
- # k ranges from 1 to j (0-indexed j corresponds to 1-indexed j+1)
96
- # In paper: k in {1,...,j-1} for 1-indexed j, i.e. k in {1,...,j} for 0-indexed j
97
- max_k = j # since paper's j-1 = our j when converting
98
- # Also k <= T (length of K array)
99
- max_k = min(max_k, len(K))
100
- for k in range(1, max_k + 1):
101
- # sum_{h=1}^{k} x_{j-h}^i, where x for negative index = 0
102
- past_sum = gp.quicksum(
103
- x[i, j - h] for h in range(1, k + 1) if j - h >= 0
104
- )
105
- model.addConstr(
106
- q[i, j] >= K[k - 1] * (x[i, j] - past_sum),
107
- name=f"startup_{i}_{j}_{k}"
108
- )
109
-
110
- # ---- Constraint (1c): Logical relation ----
111
- # y_j^i - y_bar_j^i = x_j^i - x_{j-1}^i, x_0^i = 0
112
- for i in generators:
113
- for j in periods:
114
- x_prev = x[i, j - 1] if j >= 1 else 0
115
- model.addConstr(
116
- y[i, j] - y_bar[i, j] == x[i, j] - x_prev,
117
- name=f"logical_{i}_{j}"
118
- )
119
-
120
- # ---- Constraint (1d): Minimum up-time ----
121
- # sum_{j'=j-L+1}^{j} y_{j'}^i <= x_j^i, for j >= L-1 (0-indexed)
122
- for i in generators:
123
- L = gens[i]["L"]
124
- for j in range(L - 1, T):
125
- model.addConstr(
126
- gp.quicksum(y[i, jp] for jp in range(j - L + 1, j + 1)) <= x[i, j],
127
- name=f"minup_{i}_{j}"
128
- )
129
-
130
- # ---- Constraint (1e): Minimum down-time ----
131
- # sum_{j'=j-ell+1}^{j} y_bar_{j'}^i <= 1 - x_j^i, for j >= ell-1 (0-indexed)
132
- for i in generators:
133
- ell = gens[i]["ell"]
134
- for j in range(ell - 1, T):
135
- model.addConstr(
136
- gp.quicksum(y_bar[i, jp] for jp in range(j - ell + 1, j + 1))
137
- <= 1 - x[i, j],
138
- name=f"mindown_{i}_{j}"
139
- )
140
-
141
- # ---- Per-scenario constraints ----
142
- for s in scenarios:
143
- for i in generators:
144
- RU = gens[i]["RU"]
145
- SU = gens[i]["SU"]
146
- RD = gens[i]["RD"]
147
- SD = gens[i]["SD"]
148
- m_cap = gens[i]["m"]
149
- M_cap = gens[i]["M"]
150
-
151
- for j in periods:
152
- x_prev = x[i, j - 1] if j >= 1 else 0
153
- p_prev = p[i, j - 1, s] if j >= 1 else 0
154
-
155
- # (1f) Ramp-up
156
- model.addConstr(
157
- p[i, j, s] - p_prev <= RU * x_prev + SU * y[i, j],
158
- name=f"rampup_{i}_{j}_{s}"
159
- )
160
-
161
- # (1g) Ramp-down
162
- model.addConstr(
163
- p_prev - p[i, j, s] <= RD * x[i, j] + SD * y_bar[i, j],
164
- name=f"rampdown_{i}_{j}_{s}"
165
- )
166
-
167
- # (1h) Generation capacity bounds
168
- model.addConstr(
169
- p[i, j, s] >= m_cap * x[i, j],
170
- name=f"pmin_{i}_{j}_{s}"
171
- )
172
- model.addConstr(
173
- p[i, j, s] <= p_bar[i, j, s],
174
- name=f"p_le_pbar_{i}_{j}_{s}"
175
- )
176
- model.addConstr(
177
- p_bar[i, j, s] <= M_cap * x[i, j],
178
- name=f"pbarmax_{i}_{j}_{s}"
179
- )
180
-
181
- # (1i) Demand satisfaction
182
- for j in periods:
183
- model.addConstr(
184
- gp.quicksum(p[i, j, s] for i in generators) >= demand[s][j],
185
- name=f"demand_{j}_{s}"
186
- )
187
-
188
- # (1j) Spinning reserve
189
- for j in periods:
190
- model.addConstr(
191
- gp.quicksum(p_bar[i, j, s] for i in generators)
192
- >= demand[s][j] + reserve[s][j],
193
- name=f"reserve_{j}_{s}"
194
- )
195
-
196
- model.update()
197
- return model, x, y, y_bar, q, p, p_bar
198
-
199
-
200
- def solve_and_save(model, instance, x, y, y_bar, q, p, p_bar, solution_path):
201
- n_gen = instance["n_generators"]
202
- n_scen = instance["n_scenarios"]
203
- T = instance["T"]
204
-
205
- model.optimize()
206
-
207
- result = {
208
- "status": model.Status,
209
- "status_name": {
210
- GRB.OPTIMAL: "OPTIMAL",
211
- GRB.INFEASIBLE: "INFEASIBLE",
212
- GRB.INF_OR_UNBD: "INF_OR_UNBD",
213
- GRB.UNBOUNDED: "UNBOUNDED",
214
- GRB.TIME_LIMIT: "TIME_LIMIT",
215
- }.get(model.Status, str(model.Status)),
216
- "objective_value": None,
217
- "best_bound": None,
218
- "mip_gap": None,
219
- "solve_time": model.Runtime,
220
- "x": None,
221
- "y": None,
222
- "y_bar": None,
223
- "q": None,
224
- "p": None,
225
- "p_bar": None,
226
- }
227
-
228
- if model.SolCount > 0:
229
- result["objective_value"] = model.ObjVal
230
- result["best_bound"] = model.ObjBound
231
- result["mip_gap"] = model.MIPGap
232
-
233
- # Extract first-stage solution
234
- result["x"] = [
235
- [int(round(x[i, j].X)) for j in range(T)]
236
- for i in range(n_gen)
237
- ]
238
- result["y"] = [
239
- [int(round(y[i, j].X)) for j in range(T)]
240
- for i in range(n_gen)
241
- ]
242
- result["y_bar"] = [
243
- [int(round(y_bar[i, j].X)) for j in range(T)]
244
- for i in range(n_gen)
245
- ]
246
- result["q"] = [
247
- [q[i, j].X for j in range(T)]
248
- for i in range(n_gen)
249
- ]
250
-
251
- # Extract second-stage solution
252
- result["p"] = [
253
- [
254
- [p[i, j, s].X for j in range(T)]
255
- for s in range(n_scen)
256
- ]
257
- for i in range(n_gen)
258
- ]
259
- result["p_bar"] = [
260
- [
261
- [p_bar[i, j, s].X for j in range(T)]
262
- for s in range(n_scen)
263
- ]
264
- for i in range(n_gen)
265
- ]
266
-
267
- with open(solution_path, "w") as f:
268
- json.dump(result, f, indent=2)
269
-
270
- return result
271
-
272
-
273
- def main():
274
- parser = argparse.ArgumentParser(
275
- description="Solve the stochastic UCP (Salemi & Davarnia 2022) with Gurobi."
276
- )
277
- parser.add_argument("--instance_path", type=str, required=True,
278
- help="Path to instance JSON file.")
279
- parser.add_argument("--solution_path", type=str, required=True,
280
- help="Path to write the solution JSON file.")
281
- parser.add_argument("--time_limit", type=float, default=3600,
282
- help="Gurobi time limit in seconds (default: 3600).")
283
- parser.add_argument("--log_path", type=str, default=None, help="Path to log incumbent solutions")
284
- args = parser.parse_args()
285
- install_gurobi_logger(args.log_path)
286
-
287
- with open(args.instance_path, "r") as f:
288
- instance = json.load(f)
289
-
290
- model, x, y, y_bar, q, p, p_bar = build_model(instance)
291
- model.setParam("TimeLimit", args.time_limit)
292
-
293
- result = solve_and_save(model, instance, x, y, y_bar, q, p, p_bar,
294
- args.solution_path)
295
-
296
- print(f"Status: {result['status_name']}")
297
- if result["objective_value"] is not None:
298
- print(f"Objective: {result['objective_value']:.4f}")
299
- print(f"Best bound: {result['best_bound']:.4f}")
300
- print(f"MIP gap: {result['mip_gap']:.6f}")
301
- print(f"Solve time: {result['solve_time']:.2f}s")
302
- print(f"Solution written to {args.solution_path}")
303
-
304
-
305
- if __name__ == "__main__":
306
- main()
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
salemi2022/gurobi_feasi_result/large_feasi_result_1.json DELETED
@@ -1,6 +0,0 @@
1
- {
2
- "feasible": true,
3
- "violated_constraints": [],
4
- "violations": [],
5
- "violation_magnitudes": []
6
- }
 
 
 
 
 
 
 
salemi2022/gurobi_feasi_result/large_feasi_result_2.json DELETED
@@ -1,6 +0,0 @@
1
- {
2
- "feasible": true,
3
- "violated_constraints": [],
4
- "violations": [],
5
- "violation_magnitudes": []
6
- }
 
 
 
 
 
 
 
salemi2022/gurobi_feasi_result/large_feasi_result_3.json DELETED
@@ -1,6 +0,0 @@
1
- {
2
- "feasible": true,
3
- "violated_constraints": [],
4
- "violations": [],
5
- "violation_magnitudes": []
6
- }
 
 
 
 
 
 
 
salemi2022/gurobi_feasi_result/large_feasi_result_4.json DELETED
@@ -1,6 +0,0 @@
1
- {
2
- "feasible": true,
3
- "violated_constraints": [],
4
- "violations": [],
5
- "violation_magnitudes": []
6
- }
 
 
 
 
 
 
 
salemi2022/gurobi_feasi_result/large_feasi_result_5.json DELETED
@@ -1,6 +0,0 @@
1
- {
2
- "feasible": true,
3
- "violated_constraints": [],
4
- "violations": [],
5
- "violation_magnitudes": []
6
- }
 
 
 
 
 
 
 
salemi2022/gurobi_feasi_result/tiny_feasi_result.json DELETED
@@ -1,6 +0,0 @@
1
- {
2
- "feasible": true,
3
- "violated_constraints": [],
4
- "violations": [],
5
- "violation_magnitudes": []
6
- }
 
 
 
 
 
 
 
salemi2022/gurobi_solution/large_solution_1.json DELETED
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salemi2022/gurobi_solution/large_solution_2.json DELETED
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salemi2022/gurobi_solution/tiny_solution.json DELETED
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salemi2022/gurobi_solution_log/large_log_1.jsonl DELETED
@@ -1,12 +0,0 @@
1
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2
- {"time": 1362.383, "objective_value": 5734056.397344253}
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- {"time": 3613.468, "objective_value": 5561886.668307355}
 
 
 
 
 
 
 
 
 
 
 
 
 
salemi2022/gurobi_solution_log/large_log_2.jsonl DELETED
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1
- {"time": 1318.602, "objective_value": 7137441.283418336}
2
- {"time": 1328.923, "objective_value": 7036029.967775351}
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- {"time": 2804.531, "objective_value": 6984191.305113643}
 
 
 
 
 
 
 
 
 
 
 
 
 
salemi2022/gurobi_solution_log/large_log_3.jsonl DELETED
@@ -1,12 +0,0 @@
1
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- {"time": 2514.936, "objective_value": 6645535.785259293}
 
 
 
 
 
 
 
 
 
 
 
 
 
salemi2022/gurobi_solution_log/large_log_4.jsonl DELETED
@@ -1,10 +0,0 @@
1
- {"time": 1081.639, "objective_value": 5426582.506201321}
2
- {"time": 1198.405, "objective_value": 5303716.135109444}
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salemi2022/gurobi_solution_log/large_log_5.jsonl DELETED
@@ -1,17 +0,0 @@
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- {"time": 3076.943, "objective_value": 4818066.142453771}
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
salemi2022/gurobi_solution_log/tiny_log.jsonl DELETED
@@ -1,4 +0,0 @@
1
- {"time": 1.546, "objective_value": 660367.8037799965}
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- {"time": 3.082, "objective_value": 486695.5263240005}
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- {"time": 6.101, "objective_value": 457720.714676001}
 
 
 
 
 
salemi2022/instance/large_instance_1.json DELETED
@@ -1 +0,0 @@
1
- {"instance_id": 11, "scale": "large", "n_generators": 100, "n_scenarios": 100, "T": 24, "seed": 210200, "generators": [{"id": 1, "M": 105, "m": 16, "L": 2, "ell": 1, "RU": 32, "SU": 96, "RD": 47, "SD": 141, "c_g": 77.82, "c_f": 581.97, "hot_start_cost": 160.49, "cold_start_cost": 582.35, "tau": 1.08, "startup_costs_K": [512.13, 651.44, 706.63, 728.5, 737.16, 740.59, 741.95, 742.49, 742.7, 742.78, 742.82, 742.83, 742.84, 742.84, 742.84, 742.84, 742.84, 742.84, 742.84, 742.84, 742.84, 742.84, 742.84, 742.84]}, {"id": 2, "M": 36, "m": 7, "L": 1, "ell": 1, "RU": 14, "SU": 42, "RD": 11, "SD": 33, "c_g": 51.49, "c_f": 552.71, "hot_start_cost": 100.19, "cold_start_cost": 662.48, "tau": 2.83, "startup_costs_K": [297.39, 435.89, 533.17, 601.48, 649.46, 683.16, 706.83, 723.45, 735.13, 743.33, 749.08, 753.13, 755.97, 757.96, 759.36, 760.35, 761.04, 761.52, 761.87, 762.11, 762.27, 762.39, 762.47, 762.53]}, {"id": 3, "M": 207, "m": 32, "L": 4, "ell": 4, "RU": 59, "SU": 177, "RD": 45, "SD": 135, 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