the present paper aims to investigate the effects of modularity and the layout of subsystems and parts of a complex system on its maintainability. for this purpose, four objective functions have been considered simultaneously: i) maximizing the level of accordance between system design and optimum modularity design,ii) maximizing the level of accessibility and the maintenance space required,iii...

Bayesian optimization is a popular tool for optimizing time-consuming objective functions with limited number of function evaluations. In real-life applications like engineering design, the designer often wants to take multiple objectives as well input uncertainty into account find set robust solutions. While this an active topic in single-objective optimization, it less investigated multi-obje...

time-dependent vehicle routing problem is one of the most applicable but least-studied variants of routing and scheduling problems. in this paper, a novel mathematical formulation of time-dependent vehicle routing problems with heterogeneous fleet, hard time widows and multiple depots, is proposed. to deal with the traffic congestions, we also considered that the vehicles are not forced to come...

this paper presents a methodology for design of instrumentation sensor networks in non-linear chemical plants. the method utilizes a robust extended kalman filter approach to provide an efficient dynamic data reconciliation. a weighted objective function has been introduced to enable the designer to incorporate each individual process variable with its own operational importance. to enhance the...

in this paper a new strategy is proposed to design a fixed-structure robust controller for a flexible beam.  robust controller designed by the conventional  loop­ shaping method is not appropriate for a beam because of its high order and complicated form. fixed-structure  loop shaping control in conjunction with particle swarm optimization (pso)algorithm  is used to overcome this drawback. the ...

Robust discrete optimization is a technique for structuring uncertainty in the decision-making process. The objective is to find a robust solution that has the best worst-case performance over a set of possible scenarios. However, this is a difficult optimization problem. This paper proposes a two-space genetic algorithm as a general technique to solve minimax optimization problems. This algori...

In this paper, we define a distributionally robust k-sum optimization problem as the problem of finding a solution that minimizes the worst-case expected sum of up to the k largest costs of the elements in the solution. The costs are random with a joint probability distribution that is not completely specified but rather assumed to be known to lie in a set of probability distributions. For k = ...