为解决传统A^(*)寻路算法在搜索过程中会产生大量冗余节点,导致算法整体搜索效率低,运算内存消耗大等问题,从A^(*)算法的两个重要决策点出发,改进算法的代价评估函数与邻节点搜索策略,提出一种改进融合算法。首先,采用向量叉积与尺度平...为解决传统A^(*)寻路算法在搜索过程中会产生大量冗余节点,导致算法整体搜索效率低,运算内存消耗大等问题,从A^(*)算法的两个重要决策点出发,改进算法的代价评估函数与邻节点搜索策略,提出一种改进融合算法。首先,采用向量叉积与尺度平衡因子相结合的方法优化传统A^(*)算法的启发函数,减少A^(*)算法寻路过程中在最优路径周围产生的具有相同代价值的冗余节点,减少了对称路径的搜索;其次,融合跳点搜索(Jump point search, JPS)策略,通过逻辑判断实现路径的变步长跳跃搜索,避免了A^(*)算法逐层搜索效率低的弊端。在不同尺寸的栅格地图中进行仿真分析,发现改进融合算法相比于传统A^(*)算法,在路径长度基本相等的情况下,节点搜索数量约减少95%,且与传统JPS寻路算法相比,有效过滤了路径周围复杂形状障碍物产生的大量冗余跳点。最后,将改进融合算法应用于ROS移动机器人并进行对比实验以验证算法的可行性。实验结果表明:改进融合算法在获得高效安全的路径基础上,搜索效率相比于A^(*)算法可提高约94%。展开更多
This paper studies a two stage supply chain with a dominant upstream partner. Manufacturer is the dominant partner and operates in a Just-in-Time environment. Production is done in a single manufacturing line capable ...This paper studies a two stage supply chain with a dominant upstream partner. Manufacturer is the dominant partner and operates in a Just-in-Time environment. Production is done in a single manufacturing line capable of producing two products without stopping the production for switching from one product to the other. The manufacturer imposes constraints on the distributor by adhering to his favorable production schedule which minimizes his manufacturing cost. Distributor on the other hand caters to retailers＇ orders without incurring any shortages and is responsible for managing the inventory of finished goods. Adhering to manufacturer＇s schedule may lead to high inventory carrying costs for the distributor. Distributor＇s problem, which is to find an optimal distribution sequence which minimizes the distributor＇s inventory cost under the constraint imposed by the manufacturer is proved NP-Hard by Manoj et al. （2008）. Therefore, solving large size problems require efficient heuristics. We develop algorithms for the distribution problem by exploiting its structural properties. We propose two heuristics and use their solutions in the initial population of a genetic algorithm to arrive at solutions with an average deviation of less than 3.5% from the optimal solution for practical size problems.展开更多
文摘为解决传统A^(*)寻路算法在搜索过程中会产生大量冗余节点,导致算法整体搜索效率低,运算内存消耗大等问题,从A^(*)算法的两个重要决策点出发,改进算法的代价评估函数与邻节点搜索策略,提出一种改进融合算法。首先,采用向量叉积与尺度平衡因子相结合的方法优化传统A^(*)算法的启发函数,减少A^(*)算法寻路过程中在最优路径周围产生的具有相同代价值的冗余节点,减少了对称路径的搜索;其次,融合跳点搜索(Jump point search, JPS)策略,通过逻辑判断实现路径的变步长跳跃搜索,避免了A^(*)算法逐层搜索效率低的弊端。在不同尺寸的栅格地图中进行仿真分析,发现改进融合算法相比于传统A^(*)算法,在路径长度基本相等的情况下,节点搜索数量约减少95%,且与传统JPS寻路算法相比,有效过滤了路径周围复杂形状障碍物产生的大量冗余跳点。最后,将改进融合算法应用于ROS移动机器人并进行对比实验以验证算法的可行性。实验结果表明:改进融合算法在获得高效安全的路径基础上,搜索效率相比于A^(*)算法可提高约94%。
文摘This paper studies a two stage supply chain with a dominant upstream partner. Manufacturer is the dominant partner and operates in a Just-in-Time environment. Production is done in a single manufacturing line capable of producing two products without stopping the production for switching from one product to the other. The manufacturer imposes constraints on the distributor by adhering to his favorable production schedule which minimizes his manufacturing cost. Distributor on the other hand caters to retailers＇ orders without incurring any shortages and is responsible for managing the inventory of finished goods. Adhering to manufacturer＇s schedule may lead to high inventory carrying costs for the distributor. Distributor＇s problem, which is to find an optimal distribution sequence which minimizes the distributor＇s inventory cost under the constraint imposed by the manufacturer is proved NP-Hard by Manoj et al. （2008）. Therefore, solving large size problems require efficient heuristics. We develop algorithms for the distribution problem by exploiting its structural properties. We propose two heuristics and use their solutions in the initial population of a genetic algorithm to arrive at solutions with an average deviation of less than 3.5% from the optimal solution for practical size problems.
