Abstract: Aiming at the demands for accurate and timely material delivery in dynamic workshop environment, intelligent sensing devices are deployed to build a real-time sensing network. According to the sensed real-time production state, delivery time windows of workstations are adjusted dynamically. The material delivery dynamic optimization model based on intelligent sensing network is set up with the minimum material delivery cost as the optimization goal. An improved tabu-search-based Ant Colony Optimization（ACO） algorithm is designed to solve the problem. To improve the search rate and stability, the algorithm merges the memory function of tabu search into ACO. The 2-opt local optimization method and the maximum-minimum pheromone strategy are introduced. A case study is conducted to verify the feasibility and effectiveness of the proposed model and algorithm.

Key words: intelligent sensing network, material delivery, dynamic optimization, tabu search, improved ACO algorithm

摘要： 针对动态环境下的车间物料配送准确性和及时性需求，在车间部署智能感知设备组建智能感知网。以工位实时生产状态为依据，动态调整各工位配送时间窗，以最小物料配送成本为优化目标，建立基于智能感知网的物料配送动态优化模型，并设计一种基于禁忌搜索的改进蚁群算法。该算法将禁忌搜索的记忆功能融进蚁群算法，引入2-opt局部优化方法，并设置最大最小信息素浓度，以提高算法的搜索速率和求解稳定性。最后通过案例和算法对比验证了该模型和算法的可行性和有效性。

关键词: 智能感知网, 物料配送, 动态优化, 禁忌搜索, 改进蚁群算法