数学优化
启发式
分解
随机规划
利用
计算机科学
过程(计算)
二进制数
分解法(排队论)
整数规划
工作(物理)
线性规划
数学
工程类
算术
离散数学
操作系统
生物
机械工程
计算机安全
生态学
作者
Robert M. Apap,Ignacio E. Grossmann
标识
DOI:10.1016/j.compchemeng.2016.11.011
摘要
In this work, we address the modeling and solution of mixed-integer linear multistage stochastic programming problems involving both endogenous and exogenous uncertain parameters. We first propose a composite scenario tree that captures both types of uncertainty, and we exploit its unique structure to derive new theoretical properties that can drastically reduce the number of non-anticipativity constraints (NACs). Since the reduced model is often still intractable, we discuss two special solution approaches. The first is a sequential scenario decomposition heuristic in which we sequentially solve endogenous MILP subproblems to determine the binary investment decisions, fix these decisions to satisfy the first-period and exogenous NACs, and then solve the resulting model to obtain a feasible solution. The second is Lagrangean decomposition. We present numerical results for a process network and an oilfield development planning problem. The results clearly demonstrate the efficiency of the special solution methods over solving the reduced model directly.
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