cuGenOpt/python/cugenopt/include/problems/bin_packing.cuh

/**
 * bin_packing.cuh - 一维装箱问题（Integer 编码 + 约束）
 * 
 * N 个物品，每个重量 w[i]，装入最多 B 个箱子，每个箱子容量 C。
 * 决策变量：data[0][i] ∈ [0, B-1]，表示物品 i 放入的箱子编号。
 * 目标：最小化使用的箱子数。
 * 约束：每个箱子总重不超过 C，超出部分作为 penalty。
 * 
 * 验证实例：8 物品 weights=[7,5,3,4,6,2,8,1], C=10, 最优=4 箱
 *   箱0={7,3}=10, 箱1={5,4,1}=10, 箱2={6,2}=8, 箱3={8}=8
 */

#pragma once
#include "types.cuh"
#include "cuda_utils.cuh"

struct BinPackingProblem : ProblemBase<BinPackingProblem, 1, 64> {
    const float* d_weights;
    int n;              // 物品数
    int max_bins;       // 最大箱子数 B
    float capacity;     // 箱子容量 C
    
    __device__ float calc_bins_used(const Sol& sol) const {
        bool used[32] = {};
        int size = sol.dim2_sizes[0];
        for (int i = 0; i < size; i++) {
            int b = sol.data[0][i];
            if (b >= 0 && b < max_bins) used[b] = true;
        }
        int count = 0;
        for (int b = 0; b < max_bins; b++)
            if (used[b]) count++;
        return (float)count;
    }
    
    static constexpr ObjDef OBJ_DEFS[] = {
        {ObjDir::Minimize, 1.0f, 0.0f},
    };
    __device__ float compute_obj(int idx, const Sol& sol) const {
        switch (idx) {
            case 0: return calc_bins_used(sol);
            default: return 0.0f;
        }
    }
    
    __device__ float compute_penalty(const Sol& sol) const {
        float penalty = 0.0f;
        float load[32] = {};
        int size = sol.dim2_sizes[0];
        for (int i = 0; i < size; i++) {
            int b = sol.data[0][i];
            if (b >= 0 && b < max_bins)
                load[b] += d_weights[i];
        }
        for (int b = 0; b < max_bins; b++) {
            float over = load[b] - capacity;
            if (over > 0.0f) penalty += over * 10.0f;
        }
        return penalty;
    }
    
    ProblemConfig config() const {
        ProblemConfig cfg;
        cfg.encoding = EncodingType::Integer;
        cfg.dim1 = 1;  cfg.dim2_default = n;
        cfg.value_lower_bound = 0;
        cfg.value_upper_bound = max_bins - 1;
        fill_obj_config(cfg);
        return cfg;
    }
    
    size_t shared_mem_bytes() const {
        return (size_t)n * sizeof(float);
    }
    
    __device__ void load_shared(char* smem, int tid, int bsz) {
        float* sw = reinterpret_cast<float*>(smem);
        for (int i = tid; i < n; i += bsz) sw[i] = d_weights[i];
        d_weights = sw;
    }
    
    static BinPackingProblem create(const float* h_weights, int n,
                                     int max_bins, float capacity) {
        BinPackingProblem prob;
        prob.n = n; prob.max_bins = max_bins; prob.capacity = capacity;
        float* dw;
        CUDA_CHECK(cudaMalloc(&dw, sizeof(float) * n));
        CUDA_CHECK(cudaMemcpy(dw, h_weights, sizeof(float) * n, cudaMemcpyHostToDevice));
        prob.d_weights = dw;
        return prob;
    }
    
    void destroy() {
        if (d_weights) cudaFree(const_cast<float*>(d_weights));
        d_weights = nullptr;
    }
};