Ensuring a consistent, continuous, and efficient spare parts supply is critical issue that must be addressed in the equipment support system. In order to effectively improve coverage level handle common asymmetry information present practical applications, transport vehicle routing scheduling model was further optimized. We integrated supportability requirements uncertainty theory into better d...

The standard approach to deal with uncertainty in dynamic optimization is to tak e a conservativ e stand. Measurement-based optimization schemes allow reducing this conservatism by using measurements to compensate for the uncertainty. On the example of productivity optimization of a batch distillation column with a terminal quality constraint, various measurement-based optimization schemes are ...

Compared to center-based hemodialysis (HD), peritoneal dialysis (PD) has many advantages among which cost effectiveness and comfort of patients are the most important ones. On the other hand the number of PD patients is so small and even decreasing worldwide due to difficulties of this mode of dialysis. Therefore to encourage dialysis patients to choose PD, health system must provide a proper s...

This paper introduces a method to minimize norms on nonlinear trajectory sensitivities during open-loop trajectory optimization. Specifically, we derive new parametric sensitivity terms that measure the variation in nonlinear (continuous-time) trajectories due to variations in model parameters, and hybrid sensitivities, which account for variations in trajectory caused by sudden transitions fro...

We propose a general approach for nding minmax regret solutions for a class of combinatorial optimization problems with an objective function of minimax type and uncertain objective function coe cients. The approach is based on reducing a problem with uncertainty to a number of problems without uncertainty. The method is illustrated on bottleneck combinatorial optimization problems, minimax mul...

in this paper, integrated direct and reverse logistics considering production, distribution, customer, devastation, retrieval centers under uncertainty are developed. in this model, cost parameters are not certain, thus the scenario-based robust optimization method is applied. the aim of this model is to minimize the total cost and obtain a robust solution. finally, a practical case study is pr...

Robust optimization methods have shown practical advantages in a wide range of decision-making applications under uncertainty. Recently, their efficacy has been extended to multi-period settings. Current approaches model uncertainty either independent the past or an implicit fashion by budgeting aggregate In many applications, however, realizations directly influence future uncertainties. For t...