Initial commit: cuGenOpt GPU optimization solver

python/cugenopt/include/problems/schedule.cuh

@@ -0,0 +1,101 @@
+/**
+ * schedule.cuh - 排班问题
+ * 
+ * 继承 ProblemBase，使用 ObjDef 目标注册机制
+ * 2 个目标：总成本（min）+ 不公平度（min，权重更高）
+ */
+
+#pragma once
+#include "types.cuh"
+#include "cuda_utils.cuh"
+#include "operators.cuh"
+
+struct ScheduleProblem : ProblemBase<ScheduleProblem, 8, 16> {
+    const float* d_cost;
+    int days, emps, required;
+    
+    // ---- 目标计算 ----
+    __device__ float calc_total_cost(const Sol& sol) const {
+        float total = 0.0f;
+        for (int d = 0; d < days; d++)
+            for (int e = 0; e < emps; e++)
+                if (sol.data[d][e]) total += d_cost[d * emps + e];
+        return total;
+    }
+    
+    __device__ float calc_unfairness(const Sol& sol) const {
+        int workdays[D2];
+        for (int e = 0; e < emps; e++) workdays[e] = 0;
+        for (int d = 0; d < days; d++)
+            for (int e = 0; e < emps; e++)
+                if (sol.data[d][e]) workdays[e]++;
+        int max_w = 0, min_w = days;
+        for (int e = 0; e < emps; e++) {
+            if (workdays[e] > max_w) max_w = workdays[e];
+            if (workdays[e] < min_w) min_w = workdays[e];
+        }
+        return (float)(max_w - min_w);
+    }
+    
+    // ---- 目标定义（OBJ_DEFS 与 compute_obj 必须一一对应）----
+    static constexpr ObjDef OBJ_DEFS[] = {
+        {ObjDir::Minimize, 1.0f, 0.0f},   // case 0: calc_total_cost
+        {ObjDir::Minimize, 5.0f, 0.0f},   // case 1: calc_unfairness
+    };
+    __device__ float compute_obj(int idx, const Sol& sol) const {
+        switch (idx) {
+            case 0: return calc_total_cost(sol);     // OBJ_DEFS[0]
+            case 1: return calc_unfairness(sol);     // OBJ_DEFS[1]
+            default: return 0.0f;
+        }
+    }
+    
+    __device__ float compute_penalty(const Sol& sol) const {
+        float penalty = 0.0f;
+        for (int d = 0; d < days; d++) {
+            int count = 0;
+            for (int e = 0; e < emps; e++)
+                if (sol.data[d][e]) count++;
+            int diff = count - required;
+            penalty += (diff > 0) ? (float)diff : (float)(-diff);
+        }
+        return penalty;
+    }
+    
+    ProblemConfig config() const {
+        ProblemConfig cfg;
+        cfg.encoding = EncodingType::Binary;
+        cfg.dim1 = days;  cfg.dim2_default = emps;
+        cfg.row_mode = RowMode::Fixed;
+        fill_obj_config(cfg);
+        return cfg;
+    }
+    
+    // 默认回退全量（基类行为）— 不需要覆盖 evaluate_move
+    
+    // ---- shared memory 接口 ----
+    size_t shared_mem_bytes() const {
+        return (size_t)days * emps * sizeof(float);
+    }
+    
+    __device__ void load_shared(char* smem, int tid, int bsz) {
+        float* sc = reinterpret_cast<float*>(smem);
+        int total = days * emps;
+        for (int i = tid; i < total; i += bsz) sc[i] = d_cost[i];
+        d_cost = sc;
+    }
+    
+    static ScheduleProblem create(const float* hc, int days, int emps, int req) {
+        ScheduleProblem prob;
+        prob.days = days; prob.emps = emps; prob.required = req;
+        float* dc;
+        CUDA_CHECK(cudaMalloc(&dc, sizeof(float)*days*emps));
+        CUDA_CHECK(cudaMemcpy(dc, hc, sizeof(float)*days*emps, cudaMemcpyHostToDevice));
+        prob.d_cost = dc;
+        return prob;
+    }
+    
+    void destroy() {
+        if (d_cost) { cudaFree(const_cast<float*>(d_cost)); d_cost = nullptr; }
+    }
+};