Resource allocation for contingency planning: An inexact proximal bundle method for stochastic optimization
Resource allocation for contingency planning: An inexact proximal bundle method for stochastic optimization
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DOI：
10.1016/j.ejor.2020.10.008
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
S. Moazeni;Ricardo A. Collado
中科院分区：
文献类型：
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作者：
S. Moazeni;Ricardo A. Collado
资源应急计划旨在减轻供应链中意外中断的影响。虽然这些故障很少发生，但往往会造成灾难性的后果。本文将应急计划中的资源分配问题描述为具有风险规避补偿函数的两阶段随机优化问题。所提出的解决方案的方法依赖于一个不精确的近端束方法与次梯度近似，通过一个场景减少机制。本文将不精确预言机推广到更一般的风险厌恶环境，并证明了它满足不精确捆绑方法中预言机的要求，保证收敛到最优解。我们的资源分配问题的风险规避下的开发不精确束方法的实际性能进行了研究。我们创建了一个测试问题库，并通过应用精确束方法获得其最优值。从发达国家的不精确束方法的计算解决方案进行比较，对这些最佳值，在不同的相干风险措施。我们的分析表明，我们的不精确的捆绑方法显着减少了计算时间的解决资源分配问题相比，精确的捆绑方法，并能够在更短的时间内实现高比例的最优性。
Resource contingency planning aims to mitigate the effects of unexpected disruptions in supply chains. While these failures occur infrequently, they often have disastrous consequences. This paper formulates the resource allocation problem in contingency planning as a two-stage stochastic optimization problem with a risk-averse recourse function. The solution method proposed relies on an inexact proximal bundle method with subgradient approximations through a scenario reduction mechanism. The paper extends the inexact oracle to a more general risk-averse setting, and proves that it meets the requirements of the oracle in the inexact bundle method, ensuring convergence to an optimal solution. The practical performance of the developed inexact bundle method under risk aversion is investigated for our resource allocation problem. We create a library of test problems and obtain their optimal values by applying the exact bundle method. The computed solutions from the developed inexact bundle method are compared against these optimal values, under different coherent risk measures. Our analyses indicate that our inexact bundle method significantly reduces the computational time of solving the resource allocation problem in comparison to the exact bundle method, and is capable of achieving a high percentage of optimality within a much shorter time.