The Interval Branch and Bound (IBB) method is a widely used approach for solving nonlinear programming problems where a rigorous solution is required. The method uses Interval Arithmetic (IA) to handle rounding errors...
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This paper seeks to develop a reliable network of cross-docks by taking in to account disruption and reliability issues to hedge against heterogeneous risk of cross-docking failure. In real environments, applying a re...
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This paper seeks to develop a reliable network of cross-docks by taking in to account disruption and reliability issues to hedge against heterogeneous risk of cross-docking failure. In real environments, applying a recovery policy can be a feasible strategy to handle disruptions. Hence, in this study, a recovery policy has been addressed in the form of reallocating suppliers to alternative cross-docks or altering the transportation strategy to move shipments. In addition to cross-dock location design, the optimum capacity of opened cross-docks will be determined considering the loads that will be served by each cross-docking center under regular and disruption conditions. A mixed integer nonlinear programming formulation is presented for the problem and is then linearized to present an efficient model. In order to solve it, two Lagrangian relaxation algorithms are designed and tested on 40 problem instances with different values of parameters. The results achieved by GAMS/CPLEX are compared with those of two algorithms and some analyses are performed on the solutions. Moreover, as the case study, the focus has been placed on logistic part of a car-manufacturing company with a vast supply chain network, containing more than 600 suppliers. The logistic strategies have been applied in order to reduce the transportation cost through the supply chain network and diminish the disruption subsequences in such a network. Based on the results, some managerial recommendations are presented.
This paper helps cyclists obtain optimal pacing strategies. This paper establishes objective optimization models under various conditions and obtain optimal power output distribution which provides reference for cycli...
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This paper first discusses the design requirements of the simulator, and determines the modular design idea based on discrete event-driven. Then, the overall structure of the software is expounded, the function defini...
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In this study, we propose a new protection level computation algorithm, where a nonlinear optimization problem derived from conventional receiver autonomous integrity monitoring (RAIM) is just once solved for a bias v...
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Most production environments are stochastic in nature, due to the randomness inherent in the production processes. One important engineering problem commonly faced by practitioners is to determine optimal engineering ...
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Most production environments are stochastic in nature, due to the randomness inherent in the production processes. One important engineering problem commonly faced by practitioners is to determine optimal engineering tolerances to be used in production. This article develops optimization models for determining tolerance sets to maximize the long-run average net profit on a production line with processing and rework stations, as well as instantaneous inspection and scrap operations. We assume that only one server works at the rework station, and the service times at the processing and rework stations are uncertain, thus, a stochastic queueing system is embedded into the manufacturing process. We also consider the trade-off between the overall production cost and the cost associated with a quality loss in the final product. Our work is the first to introduce the concept of double-tolerance sets to the tolerance design optimization literature. By comparing the proposed double-tolerance model with a single-tolerance model, we investigate the impact of different parameter settings and modeling assumptions on the optimal tolerances through numerical examples and a sensitivity analysis.
Integrating Multiple Input Multiple Output (MIMO) into Multi-access Edge Computing (MEC) as a new computing paradigm can provide users with higher quality of services. In this paper, a cloud-edge-end three-layer colla...
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This paper is devoted to the problem of digital control design to keep the output variables of the controlled process in a given range. Such a problem is of particular importance in control practice if it is only nece...
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Many industrial systems demand equipment with high levels of reliability. Companies and academia have been developing, over the years, mathematical methods and advancing engineering techniques to assist in the mainten...
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Many industrial systems demand equipment with high levels of reliability. Companies and academia have been developing, over the years, mathematical methods and advancing engineering techniques to assist in the maintenance of active and reliable systems. There are different optimization problems in this context, highlighting (1) the redundancy allocation problem (RAP), (2) the reliability allocation problem, and (3) the reliability-redundancy allocation problem (RRAP). Many solving methods have already been applied to these problems, e.g., dynamic, linear, integer, and nonlinear programming, as well as classical metaheuristics based on evolutionary algorithms, such as the Genetic Algorithm (GA). Either way, these methods are modeled according to the specificities of the systems. However, these approaches can be very computationally expensive depending on the problem instances. Meanwhile, quantum computing has gained ground for combinatorial optimization problems. It is expected that problems with a high level of complexity can be solved more efficiently using these new methods than classical ones. Optimization methods have attempted to improve their efficiency by adding quantum concepts, as is the case of Quantum-inspired Evolutionary Algorithms (QEA). The QEA has a better diversity and convergence rate than other EAs because it uses qubit representation instead of numerical, binary, or symbolic representations. In this context, this paper aims to develop a systematic review of the literature through keyword filtering, article reading, and bibliometric analysis on applying purely quantum and quantum-inspired methods in system reliability optimization problems, specifically in RAP, the reliability allocation problem, and the RRAP. Our goal is to identify quantum-based techniques' advantages, limitations, and potential in such a context and suggest a research plan based on the observed literature gaps. We observed few studies in the context, especially in classical qua
In this paper, we study the frequency coexistence problem between NGSO constellation systems of global distributed earth stations, and propose a specific method to mitigate uplink interference of NGSO constellation sy...
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