Most real-life optimization models inherently contain uncertain model parameters. Stochastic programming provides a natural way of addressing such formulations, and has been extensively used in the process systems engineering literature [1-5].

This work presents a software framework for solving two-stage stochastic programs with recourse, whose deterministic equivalent form can be written as:

where , , , and functions , , and are continuous.
The uncertainties are modeled using a finite number, *s*, of scenarios
indexed by *h*, each with a positive probability of occurrence . The binary and
continuous first-stage decisions *y* and *z*, respectively, are made
before the realization of the uncertainties, while the mixed-integer
second-stage decisions, , are made after
the realization of scenario *h*. Joint polyhedral constraints in *y*
and *z* are assumed (purely for notational convenience) to be modeled by
the scenario constraints in Problem (P).

Duality-based decomposition techniques such as Benders decomposition [6], and its nonlinear variant generalized Benders decomposition [7] can be used to solve Problem (P) in a decomposable manner under certain restrictions on the participating functions. These methods, however, rely on strong duality for convergence, which is not guaranteed for most nonconvex problems. When the first-stage variables in Problem (P) correspond purely to 0-1 decisions, nonconvex generalized Benders decomposition (NGBD) can be used [2-4, 8]. Nonconvex generalized Benders decomposition is an extension of generalized Benders decomposition that rigorously handles nonconvexities in the participating functions in Problem (P). A popular decomposition technique that can handle the general form of Problem (P) is Lagrangian relaxation [9, 10]. The lower bounding problem in the conventional Lagrangian relaxation technique involves the (partial) solution of the Lagrangian dual problem, which is usually computationally intensive. Our recent work [11] proposed a modified Lagrangian relaxation technique which combines Lagrangian relaxation with NGBD and bounds tightening techniques to solve Problem (P) when continuous first-stage decisions are to be made.

In this work, we provide a computational framework for solving Problem (P) which implements versions of all of the aforementioned decomposition techniques. We demonstrate the capabilities of our framework via several process systems engineering-related case studies.

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