AN INTEGRATED MULTI-CRITERIA SCREENING FRAMEWORK TO ANALYZE FLEXIBILITY IN ENGINEERING SYSTEMS DESIGN: APPLICATIONS IN LNG INFRASTRUCTURES

Ph.DDOCTOR OF PHILOSOPH

An integrated screening framework to analyze flexibility in engineering systems design

Proceedings of the International Conference on Engineering Design, ICEDDS75-09135-14

Chaos synchronization for a class of uncertain chaotic supply chain and its control by ANFIS

[EN] In this paper, modelling of a three-level chaotic supply chain network. This model has the uncertainty of the retailer in the manufacturer. An adaptive neural fuzzy method has been proposed to synchronize the two chaotic supply chain networks. To train adaptive neural fuzzy controller, first, a nonlinear feedback control method is designed. Then, using Lyapanov theory, it is proved that the nonlinear feedback controller can reduce the synchronization error to zero in a finite time. The simulation results show that the proposed neural fuzzy controller architecture well controls the synchronization of the two chaotic supply chain networks. In the other part of the simulation, a comparison is made between the performance of the nonlinear controller and the adaptive neural fuzzy. Also, in the simulation results, the controller signal is depicted. 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Improving the lifecycle performance of engineering projects with flexible strategies: Example of on-shore LNG production design

10.1002/sys.21301Systems Engineering183253-26

A decision-making tool to design a flexible liquefied natural gas system under uncertainty

IEEE International Conference on Systems, Man and Cybernetics, Hong Kong, China, 201

Novel modifications of social engineering optimizer to solve a truck scheduling problem in a cross-docking system

The truck scheduling problem is one of the most challenging and important types of scheduling with a large number of real-world applications in the area of logistics and cross-docking systems. This problem is formulated to find an optimal condition for both receiving and shipping trucks sequences. Due to the difficulty of the practicality of the truck scheduling problem for large-scale cases, the literature has shown that there is a chance, even with low possibility, for a new optimizer to outperform existing algorithms for this optimization problem. Already applied successfully to solve similar complicated optimization problems, the Social Engineering Optimizer (SEO) inspired by social engineering phenomena, has been never applied to the truck scheduling problem. This motivates us to develop a set of novel modifications of the recently-developed SEO. To validate these optimizers, they are evaluated by solving a set of standard benchmark functions. All the algorithms have been calibrated by the Taguchi experimental design approach to further enhance their optimization performance. In addition to some benchmarks of truck scheduling, a real case study to prove the proposed problem is utilized to show the high-efficiency of the developed optimizers in a real situation. The results indicate that the proposed modifications of SEO considerably outperform the state of the art algorithms and provide very competitive results

A robust bi-objective mathematical model for disaster rescue units allocation and scheduling with learning effect

This paper proposes a novel bi-objective mixed-integer linear programming (MILP) model for allocating and scheduling disaster rescue units considering the learning effect. When a natural phenomenon (e.g., earthquake or flood) occurs, the presented decision support model is expected to help decision-makers of emergency relief centers to provide efficient planning for rescue units to minimize the total weighted completion time of rescue operations, as well as the total delay in rescue operations. The problem has some features in common with unrelated parallel machine scheduling (UPMS) problem and traveling salesman problem (TSP). To deal with the inherent uncertainty and bi-objective nature of the problem, an uncertainty-set based robust optimization technique and multi-choice goal programming (MCGP) with utility functions are applied. To demonstrate the applicability of the proposed model, a real case study in Mazandaran province in Iran is presented. The computational results confirm the high complexity of the problem and the significant impacts of the uncertainty on the solution. Moreover, the analytical results provide useful insights to decision-makers for disastrous situations

Quantifying the Value of Flexibility in Design and Management of Onshore LNG Production Systems

10.1002/j.2334-5837.2014.tb03181.xINCOSE International Symposium241775-79