A demand-based simultaneous inspection and preventive maintenance planning with Markovian deteriorating machine conditions: An application in Wind Turbine

In this paper, a single-product, single-machine system under Markovian deterioration of machine condition and demand uncertainty is studied. The objective is to find the optimal intervals for inspection and preventive maintenance (PM) activities in a condition-based maintenance planning with discrete monitoring (CBMDM) framework. At first, a stochastic dynamic programming model whose state variable is the machine status is presented. This model whose objective is minimizing inspection, preventive maintenance and lost production costs due to the difference in actual capacity with nominal capacity, does not take into account demand. Then, demand is appended to the state variable in the second model and the average cost of lost production which is due to the difference in actual production capacity with demand is replaced in the first model correspondingly. Finally, to validate and analyze the proposed models, an application of the models in wind turbines is prepared. The numerical results show that replacing demand by nominal capacity in simultaneous inspection and preventive maintenance planning, the average total costs consist of inspection and preventive maintenance in the planning horizon are reduced in the planning horizon

Eliminating bullwhip effect in supply chain stock systems using smart controllers

Several alternative approaches have been proposed for supply chain modeling majority of which steady-state models. These models cannot adequately deal with dynamic characteristics of supply chain system affected by lead time, demand fluctuation, sale prediction and so forth. Static models in particular cannot describe, analyze and provide solutions for a key issue in supply chains called bullwhip effect. The bullwhip effect is information deviation from one end of the supply chain to the other which intensifies fluctuation and change in demand from downstream to upstream. This issue leads to major deficiencies. One of the approaches used to cope with dynamic issues is control systems approach. In present study, a predicting model controller was developed to minimize the bullwhip effect in supply chain. In addition, a prediction methodology is integrated into predicting model control framework to predict uncertainty in distorting demand behavior. Integration of a prediction methodology in predicting model control framework improved the controlling system's performance. The main feature of demand signal used in model design is its fluctuation and distortion. One of the main factors behind bullwhip effect is demand signals processing and in fact, the predicting model used

Using Mixed Integer Linear Programming Model For Beam Angle And Fluence Map Optimization In Intensity- Modulated Radiation Therapy

Introdution: Intensity- modulated radiation therapy is one of the treatment methods for cancer tumors. The effectiveness of this method is dependent on the accuracy and treatment planning quality. Therefore, there is a need for a plan to select the angle and intensity simultaneous optimum of radiation. Methods: In this study, an mixed integer linear programming model was proposed for simultaneous optimization of angles and intensity in the GAMS programming environment.To implement the model, after the patient's CT was prepared, the organ cantoring was performed by CERR software and the Influence Matrix was obtained for each organ. After collecting the inputs of the problem, in order to obtain the desired outputs, was used  from The GAMS software from the CPLEX solver. Results: Finally, the actual case of head and neck cancer is analyzed to demonstrate the effectiveness of the model. From the angle of the candidate, ، is chosen as the optimal radiation angles. The maximum dose received by the brainstem was 3. 999, Mandible 70, LeftOrbit 0.026, RightOrbit 0.440, Parotid Gland 0.881, OpticChiasm 0.177, OpticNerves 0.167, spinalcord 9.929 Gray and the minimum dose received by the tumor is 70 Gray. Also, the optimal amount of intensity for implementing the treatment plan on the patient is achieved. Conclusion: The dose received by each organ was significantly improved compared to prescribing doses. Similarly, the comparison of the Dose Volume Histogram obtained by solving a common problem with the model and software CERR, Represents the optimal performance of the model, which improves the security rate and reduces the cost for healthy tissues

Project scheduling based on resource leveling using fuzzy ranking and genetic algorithm: Examination and analysis

Project planning and scheduling is often addressed in the paradigm of combined and fuzzy optimization, which is largely owing to the inherent features of the combination and the ensuing uncertainty in determining the variables, none more significant than the timetables and deadlines of activities. As such, the purpose of the current study is to present a novel metaheuristic model for the aforementioned problem by applying fuzzy ranking and genetic algorithm on the resource leveling indicator. This method will be case-studied on fuzzy numbers needed to express uncertain variables in the real world. Project planning and scheduling using resource leveling and the fuzzy approach is of paramount significance to the industry, as it has shown to greatly ensure the proper and effective use of resources. This research seeks to propose a new model for project scheduling in which the uncertainty of the timetable of the activities and resource levelling are examined at the same time. To generate the initial population in the genetic algorithm, parallel fuzzy prioritization method is used to optimally level the resources, while fuzzy theory is further employed to model the uncertainty of the duration of activities.&nbsp

Mathematical modeling of a bi-objective hub location-routing problem for rapid transit networks

This paper aims to develop a mathematical model for rapid transit networks based on a hub and spoke model, comprising stopovers (stations) in the hub and non-hub (spoke) alignments. Due to the use of rapid transit systems in both the hub-level sub-network (i.e., the network among the hub nodes) and the spoke-level sub-network (i.e., the network which connect the spoke nodes to each other and to the hub nodes), the proposed model relaxes some of the usual assumptions in classical hub location models. In the proposed model, the transshipment of flows among the spoke nodes is possible, the setup costs of all the hub and spoke nodes and edges are considerable, and both hub and spoke edges have capacity constraints. In addition to the network infrastructure designed through decisions about the locations of the hub and spoke nodes and edges, the hub and spoke rapid transit lines are determined along with the routes of demands in those lines. The model incorporates profit and service time criteria. An adaptive large neighborhood search solution algorithm is developed whose efficiency is proved by the computational results. Some managerial insight is also provided through the analysis of the resulting networks under various parameter settings