分布式供应链系统的协调优化方法研究
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摘要
分布式供应链管理已成为供应链管理研究中的热点和难点。本文论述了协同计划为分布式供应链管理最适合的协调机制,然后分别针对含有单属性目标和多属性目标的分布式供应链协同计划在不同前提条件下的优化问题进行研究。论文有如下的主要研究内容。
(1)研究了分布式供应链管理决策的特性,提出协同计划为分布式供应链协调优化的最佳机制。特别地,当分布式决策含有多属性目标时,需要对协同计划进行实时调整,对模型实时重新进行计算求解最优值。分析了在此情况下供应链管理决策可能用到的各种算法技术。
(2)针对含有单属性目标的分布式供应链协同计划的同步协调优化问题,建立了分布式同步协调优化模型,为对其求解,提出一种混合了拉格朗日松弛算法和遗传算法的协调优化算法。对优于次梯度算法的模糊次梯度算法进一步进行修正,用它来迭代拉格朗日算子以提高整个协调策略的效率,并证明了修正后算法的收敛性。在此基础上,提出一种基于内部惩罚的协调策略,并对协调效果加以改善。
(3)针对分布式供应链成员目标为单属性且供应链中不存在协调中心时的协调优化问题,建立了协同计划的异步协调优化模型。利用代理次梯度算法迭代拉格朗日算子,并将遗传算法和交互式启发式算法相结合,提出了一种基于拉格朗日松弛算法的异步协调策略。
(4)针对含有多属性目标的分布式供应链协同计划,分析了存在协调中心和允许信息私有两种不同情况下的协调优化问题,分别对它们建模,并使用分解法对含有整数变量的模型提出相应的协调优化算法。当存在协调中心时,所采用的协调优化策略是基于原始分解法的,此时会导致各个成员局部信息的披露。相对的,如果要保持成员的局部信息私有,就需改而采用对偶分解法。此时协调优化策略也会发生变化,不再是将各种约束条件从成员推向供应链整体,而是将供应链中各个成员间的链接约束条件从供应链整体下推到成员的局部子问题。为改进协调优化效率,提出使用原-对偶分解法对协调优化流程加以改善。
Decentralized supply chain management has become the key character of the supply chain management. This dissertation first illustrates that collaborative planning is the most suitable coordination mechanism of decentralized supply chain. It then studies the optimization of collaborative planning of decentralized supply chain, with single-attribute objective and multi-attribute objective respectively. The main contributions of the dissertation consist of the following four parts.
(1) After researching the features of decentralized supply chain management decision making, this dissertation suggests that collaborative planning is the most suitable coordination mechanism of decentralized supply chain. Especially when it comes with multi-attribute objective, collaborative planning needs to be adapted on time and the model needs to be rerun to get an optimum solution. Different techniques working under such circumstance in supply chain management are shown in the dissertation.
(2) For the single-attribute objective decentralized supply chain system, a synchronous coordination model is constructed. To solve this model, an optimal method, combining Lagrangian relaxation algorithm with genetic algorithm, is presented. This dissertation modifies a fuzzy subgradient algorithm, which is superior to subgradient algorithm, and uses the modified algorithm, whose convergence is proved, to update Lagrangian multiplier to increase the efficiency of entire coordination strategy. Based on the internal punishment, a coordination strategy is proposed and the coordination optimization is improved.
(3) For collaborative planning of decentralized supply chain system without a coordinator and with its members all having single-attribute objective, an asynchronous coordination model is presented. The surrogate subgradient algorithm is used to update the Lagrangian multipliers. Combining the genetic algorithm with the interactive heuristics algorithm, the dissertation develops an asynchronous coordination strategy based on the Lagrangian relaxation algorithm.
(4) For multi-attribute objective collaborative planning of the decentralized supply chain, coordination optimization problems under two circumstances: one has a mediator and the other allows local information private, are researched. Coordination models are constructed for these two circumstances and decomposition methods are used to solve the models with integer variables to reach a global Pareto optimal solution. When there exists a mediator, the solution approach is based on Primal Decomposition methods. In this approach, members of the supply chain have to disclose their local information. On the other hand, if they want to keep local information private, Dual Decomposition methods should be adopted. Therefore, the coordination optimization strategy also changes. Instead of pushing restricting constraints upwards from local sub-problems to a global master problem, Dual Decomposition methods push linking constraints down to local problems. In order to make the coordination optimization more efficient, Primal-Dual Decomposition is suggested to improve the optimizing approach.
(1)研究了分布式供应链管理决策的特性,提出协同计划为分布式供应链协调优化的最佳机制。特别地,当分布式决策含有多属性目标时,需要对协同计划进行实时调整,对模型实时重新进行计算求解最优值。分析了在此情况下供应链管理决策可能用到的各种算法技术。
(2)针对含有单属性目标的分布式供应链协同计划的同步协调优化问题,建立了分布式同步协调优化模型,为对其求解,提出一种混合了拉格朗日松弛算法和遗传算法的协调优化算法。对优于次梯度算法的模糊次梯度算法进一步进行修正,用它来迭代拉格朗日算子以提高整个协调策略的效率,并证明了修正后算法的收敛性。在此基础上,提出一种基于内部惩罚的协调策略,并对协调效果加以改善。
(3)针对分布式供应链成员目标为单属性且供应链中不存在协调中心时的协调优化问题,建立了协同计划的异步协调优化模型。利用代理次梯度算法迭代拉格朗日算子,并将遗传算法和交互式启发式算法相结合,提出了一种基于拉格朗日松弛算法的异步协调策略。
(4)针对含有多属性目标的分布式供应链协同计划,分析了存在协调中心和允许信息私有两种不同情况下的协调优化问题,分别对它们建模,并使用分解法对含有整数变量的模型提出相应的协调优化算法。当存在协调中心时,所采用的协调优化策略是基于原始分解法的,此时会导致各个成员局部信息的披露。相对的,如果要保持成员的局部信息私有,就需改而采用对偶分解法。此时协调优化策略也会发生变化,不再是将各种约束条件从成员推向供应链整体,而是将供应链中各个成员间的链接约束条件从供应链整体下推到成员的局部子问题。为改进协调优化效率,提出使用原-对偶分解法对协调优化流程加以改善。
Decentralized supply chain management has become the key character of the supply chain management. This dissertation first illustrates that collaborative planning is the most suitable coordination mechanism of decentralized supply chain. It then studies the optimization of collaborative planning of decentralized supply chain, with single-attribute objective and multi-attribute objective respectively. The main contributions of the dissertation consist of the following four parts.
(1) After researching the features of decentralized supply chain management decision making, this dissertation suggests that collaborative planning is the most suitable coordination mechanism of decentralized supply chain. Especially when it comes with multi-attribute objective, collaborative planning needs to be adapted on time and the model needs to be rerun to get an optimum solution. Different techniques working under such circumstance in supply chain management are shown in the dissertation.
(2) For the single-attribute objective decentralized supply chain system, a synchronous coordination model is constructed. To solve this model, an optimal method, combining Lagrangian relaxation algorithm with genetic algorithm, is presented. This dissertation modifies a fuzzy subgradient algorithm, which is superior to subgradient algorithm, and uses the modified algorithm, whose convergence is proved, to update Lagrangian multiplier to increase the efficiency of entire coordination strategy. Based on the internal punishment, a coordination strategy is proposed and the coordination optimization is improved.
(3) For collaborative planning of decentralized supply chain system without a coordinator and with its members all having single-attribute objective, an asynchronous coordination model is presented. The surrogate subgradient algorithm is used to update the Lagrangian multipliers. Combining the genetic algorithm with the interactive heuristics algorithm, the dissertation develops an asynchronous coordination strategy based on the Lagrangian relaxation algorithm.
(4) For multi-attribute objective collaborative planning of the decentralized supply chain, coordination optimization problems under two circumstances: one has a mediator and the other allows local information private, are researched. Coordination models are constructed for these two circumstances and decomposition methods are used to solve the models with integer variables to reach a global Pareto optimal solution. When there exists a mediator, the solution approach is based on Primal Decomposition methods. In this approach, members of the supply chain have to disclose their local information. On the other hand, if they want to keep local information private, Dual Decomposition methods should be adopted. Therefore, the coordination optimization strategy also changes. Instead of pushing restricting constraints upwards from local sub-problems to a global master problem, Dual Decomposition methods push linking constraints down to local problems. In order to make the coordination optimization more efficient, Primal-Dual Decomposition is suggested to improve the optimizing approach.
引文
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