Multiple stage stochastic linear programming with multiple objectives: flexible decision making (2407.04602v1)

Abstract: Optimization problems with random data have a wide range of applications. A typical feature of many such problems is that some variables have to be optimized before certain random coefficients have been realized and for other variables it is sufficient to decide on them afterwards. This leads to a multiple stage decision process. To optimize the variables in the first of two subsequent stages the stochastic problem is transformed into a deterministic program, called the (deterministic equivalent of the) recourse problem. In case of stochastic linear programs with finitely distributed random data this non-stochastic substitute is just a linear program. In the same way a multiple objective linear program is obtained if the original problem has multiple objective functions. In the first of the two stages, a decision maker usually would chose a feasible point out of the set of all Pareto-optimal points. This choice however has consequences to later stage decisions. We claim that the decision process in the earlier of the two stages is not fully transparent if a classical multi-objective decision process is applied: in addition to the original objectives of the problem a decision maker may have a preference for largest possible flexibility in later stage decisions. This additional objective is taken into account if the recourse problem in case of multiple objectives is taken to be a polyhedral convex set optimization problem instead of a multi-objective linear program only. We also discuss several surrogate problems to the recourse problem such as the wait-and-see problem and the expected valued problem for the multi-objective case. The new approach based on set optimization is illustrated by an example, the multi-objective newsvendor problem.