The bi-leveloptimization Problem (BLOP) is defined as a mathematical program with two nested optimization tasks. Although many applications fit the bi-level framework, however, existing resolution methods were most p...
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The bi-leveloptimization Problem (BLOP) is defined as a mathematical program with two nested optimization tasks. Although many applications fit the bi-level framework, however, existing resolution methods were most proposed to solve single-objectivebi-level problems. Regarding multi-objective BLOPs (MBLOPs), there do not exist too many previous studies because of the difficulties associated with solving these complex problems. Additionally, a recently proposed metaheuristic, called Non-dominated sorting Chemical Reaction optimization (NCRO), has been successfully applied to solve single-levelmulti-objective Problems (MOPs). NCRO applies a quick-non-dominated sorting technique that makes it one of the most powerful search algorithms in solving MOPs. Based on these observations, a new bi-levelmulti-objective CRO method, called BMCRO, is proposed in this article for solving MBLOPs. The main idea behind BMCRO is to come up with good solutions in an acceptable execution time within the bi-level framework. Experimental results on well-established benchmarks reveal the outperformance of the proposed algorithm against a bi-level variant of the Non-dominated Sorting Genetic Algorithm (NSGA-II) which is developed for this purpose.
Compression Resin Transfer Moulding is a popular method for high volume production of superior quality fibre-reinforced polymer composite parts. However, the process involves a large number of design variables that mu...
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ISBN:
(纸本)9781509006229
Compression Resin Transfer Moulding is a popular method for high volume production of superior quality fibre-reinforced polymer composite parts. However, the process involves a large number of design variables that must be carefully chosen in order to reduce cycle time, capital layout and running costs, while maximizing final part quality. These objectives are principally governed by two separate phases of the manufacturing cycle, namely the resin filling and curing phases. It turns out that independently optimizing either phase (which is the general practice) may often lead to conditions that significantly restrict or even adversely affect the progress of the other. In light of this fact, a novel approach of modelling the entire composites manufacturing problem as bi-level program, one that assimilates both phases, has been adopted in this paper. In particular, an efficient multi-objectivebi-level evolutionary algorithm is designed to effectively deal with the computationally expensive simulation-based optimization problem. The unique feature of the algorithm is that it incorporates a Pareto Rank Learning scheme, together with surrogate assistance for the upper level problem, in order to eliminate several expensive but redundant objective function evaluations. The optimization process is therefore considerably accelerated, assisting manufacturers in making improved decisions for this complex engineering design problem.
This Article Develops a Novel multi-Microgrids (MMGs) Participation Framework in the Day-Ahead Energy and Ancillary Services, i.e. Services of Reactive Power and Reserve Regulation, Markets Incorporating the Smart Dis...
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This Article Develops a Novel multi-Microgrids (MMGs) Participation Framework in the Day-Ahead Energy and Ancillary Services, i.e. Services of Reactive Power and Reserve Regulation, Markets Incorporating the Smart Distribution Network (SDN) objectives Based on Two-Layer Power Management System (PMS). A bi-leveloptimization Structure Is Introduced Wherein the Upper level Models Optimal Scheduling of SDN in the Presence of MMGs While Considering the bilateral Coordination Between Microgrids (MGs) and SDN's Operators, i.e. Second Layer's PMS. This Layer Is Responsible for Minimizing Energy Loss, Expected Energy Not-Supplied, and Voltage Security as the Sum of Weighted Functions. In Addition, the Proposed Problem Is Subject to Linearized AC Optimal Power Flow (LAC-OPF), Reliability and Security Constraints to Make It More Practical. Lower level Addresses Participation of MGs in the Competitive Market Based on bilateral Coordination Among Sources, Active Loads and MGs' Operator (First Layer's PMS). The Problem Formulation Then Tries to Minimize the Difference Between MGs' Cost and Revenue in Markets While Satisfying Constraints of LAC-OPF Equations, Reliability, Security, and Flexibility of the MGs. Karush-Kuhn-Tucker Method Is Exploited to Achieve a Single-level Model. Moreover, a Stochastic Programming Model Is Introduced to Handle the Uncertainties of Load, Renewable Power, Energy Price, the Energy Demand of Mobile Storage, and Availability of Network Equipment. The Simulation Results Confirm the Capabilities of the Suggested Stochastic Two-Layer Scheme in Simultaneous Evaluation of the Optimal Status of Different Technical and Economic Indices of the SDN and MGs.
This paper focuses on a stone industrial park location problem with a hierarchical structure consisting of a local government and several stone enterprises under a random environment. In contrast to previous studies, ...
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This paper focuses on a stone industrial park location problem with a hierarchical structure consisting of a local government and several stone enterprises under a random environment. In contrast to previous studies, conflicts between the local authority and the stone enterprises are considered. The local government, being the leader in the hierarchy, aims to minimize both total pollution emissions and total development and operating costs. The stone enterprises, as the followers in the hierarchy, only aim to minimize total costs. In addition, unit production cost and unit transportation cost are considered random variables. This complicated multi-objective bi-level optimization problem poses several challenges, including randomness, two-level decision making, conflicting objectives, and difficulty in searching for the optimal solutions. Various approaches are employed to tackle these challenges. In order to make the model trackable, expected value operator is used to deal with the random variables in the objective functions and a chance constraint-checking method is employed to deal with such variables in the constraints. The problem is solved using a bi-level interactive method based on a satisfactory solution and Adaptive Chaotic Particle Swarm optimization (ACPSO). Finally, a case study is conducted to demonstrate the practicality and efficiency of the proposed model and solution algorithm. The performance of the proposed bi-level model and ACPSO algorithm was highlighted by comparing to a single-level model and basic PSO and GA algorithms. (C) 2014 Elsevier Ltd. All rights reserved.
The aim of this paper is to present an administrative and market-based optimization method for solving a problem of regional water resources allocation by considering a hierarchical structure under multiple uncertaint...
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The aim of this paper is to present an administrative and market-based optimization method for solving a problem of regional water resources allocation by considering a hierarchical structure under multiple uncertainties. To accomplish this, a multi-objectivebi-level programming model is developed based on the water right distribution in a river basin. In this model, the stream flow (i.e., water supply) and water demand are considered as a fuzzy random variable and a random fuzzy variable, respectively. The regional authority, the leader in the hierarchy, seeks to maximize the total:benefit to society while simultaneously minimizing pollution emissions. The sub-areas, the followers in the hierarchy, seek to maximize their own economic benefits. To deal with the inherent uncertainty, a transformation of variables into fuzzy variables is done, and through the expected value operation, the fuzzy variables are subsequently transformed into determined ones. For solving the complex non-linear bi-level programming model, a bi-level interactive method based on satisfactory solution with global local neighbor adaptive particle swarm optimization (GLN-aPSO) is designed as a combined solution method. A case study is presented to demonstrate the applicability and efficiency of this method. The interactive solutions associated with different minimal satisfactory degrees of the two objectives in the upper level have been generated. They can help the regional authority and the sub-areas to identify desired water allocation schemes according to their preferences and practical conditions, as well as facilitate in-depth analyses of tradeoffs between the objectives in the two levels. Finally, to verify that it is reasonable to use bi-level programming the results are compared with those of using single level programming. (C) 2014 Elsevier B.V. All rights reserved.
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