Simulation optimization of operator allocation problem with learning effects and server breakdown under uncertainty

Joint optimization of pricing and capacity allocation for two competitive airlines under demand uncertainty

Nowadays, airline industries should overcome different barriers regarding the fierce competition and changing consumer behavior. Thus, they attempt to focus on joint decision making which enables them to set pricing and capacity allocation to maximize their profits. In this research, we develop a model to optimize pricing and capacity allocation in a duopoly of single-flight leg for two competi...

robust optimization approach in production planning problem considering rework, backlogging and breakdown under conditions of uncertainty: an evolutionary approach

in this paper, we consider a multi-site production planning problem subject to uncertainty in demand and workforce expenses. in our new mathematical model, we presented a production planning system considering failure in rework and breakdown. we also survey human workforce allocation and its expenses which are considered uncertain due to some tradeoff between company’s benefits and workforce :u...

Robust uncapacitated multiple allocation hub location problem under demand uncertainty: minimization of cost deviations

The hub location–allocation problem under uncertainty is a real-world task arising in the areas such as public and freight transportation and telecommunication systems. In many applications, the demand is considered as inexact because of the forecasting inaccuracies or human’s unpredictability. This study addresses the robust uncapacitated multiple allocation hub location problem with a set of ...

Bi-objective optimization of multi-server intermodal hub-location-allocation problem in congested systems: modeling and solution

A new multi-objective intermodal hub-location-allocation problem is modeled in this paper in which both the origin and the destination hub facilities are modeled as an M/M/m queuing system. The problem is being formulated as a constrained bi-objective optimization model to minimize the total costs as well as minimizing the total system time. A small-size problem is solved on the GAMS software t...

Simulation Optimization: Improving decisions under uncertainty