The design of supply chain network usually directly influences the performance of location-allocation of facilities, especially for the main parties. This paper firstly addresses the tri-level location-allocation desi...
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The design of supply chain network usually directly influences the performance of location-allocation of facilities, especially for the main parties. This paper firstly addresses the tri-level location-allocation design problem which considers the forward and reverse network, simultaneously. The proposed problem is formulated on the static Stackelberg game between the Distribution Centers (DCs), Customer Zones (CZs) and Recover Centers (RCs) in the framework. The literature reports that most of previous works have utilized the various exact approaches which are not efficient and are so complex. In this study, three old and successful methods consist of Variable Neighborhood Search (VNS), Tabu Search (TS) and Particle Swarm Optimization (PSO), as well as two recent nature-inspired algorithms;Keshtel Algorithm (KA) and Water Wave Optimization (WWO) are utilized. Besides, according the nature of the problem, this study proposes a simple nested approach named as tri-level metaheuristic for the first time in order to solve the large scale problems. The performances of the algorithms are probed by using Taguchi experimental method to set the proper values for the parameters. Eventually, the efficiency of the algorithms is compared by different criteria and validated through a real case study. The obtained results show that tri-level metaheuristics are effective approaches to solve the underlying tri-level models in large scale network. (C) 2017 Elsevier B.V. All rights reserved.
In this paper, a novel inverse approach to the p-median problem is introduced in which the locations of p facilities and their supplies are known, while the demands of client nodes and the fraction allocated to each f...
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In this paper, a novel inverse approach to the p-median problem is introduced in which the locations of p facilities and their supplies are known, while the demands of client nodes and the fraction allocated to each facility need to be estimated. To achieve this purpose, a tri-level programming problem is proposed. The primary objective of the first-level model is to minimize the sum of the squared differences between the estimated demand values and the observed target values. The second and third level problems together form a bi-level p-median model that incorporates the minimum information approach into the allocation phase. By substituting the optimality conditions of the third-level problem into the second one, a nonlinear bi-level mixed-integer model is obtained, which is addressed by using a particle swarm optimization algorithm. The added value of the tri-level model and the proposed method is verified by some small and large-sized examples.
Facilities are the backbone of many supply systems and are always vulnerable to potential attacks. Considering that when disruptions occur, limits on transportation distances are essential for many important supply sy...
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Facilities are the backbone of many supply systems and are always vulnerable to potential attacks. Considering that when disruptions occur, limits on transportation distances are essential for many important supply systems like emergency items and perishable items, this paper presents fortification plan of capacitated facilities with maximum distance limits. The problem is formulated as a tri-level mixed integer problem, which considers capacitated facilities, interdiction, and fortification budget limit simultaneously. Then it is simplified to a bi-level problem and solved by implicit enumeration algorithm performed on a binary tree. Computational results obtained from 64 randomly generated test instances are reported along with the benefit of maximum distance limits.
Developing efficient strategies for defending electric power systems against attacks is a major concern, especially when uncertainties are involved. This paper addresses the allocation of the defensive resource to min...
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ISBN:
(纸本)9781538622124
Developing efficient strategies for defending electric power systems against attacks is a major concern, especially when uncertainties are involved. This paper addresses the allocation of the defensive resource to minimize the damage when there are uncertainties regarding the resource the attacker has. A Multiple-Attack-Scenario (MAS) defender attacker-defender model is proposed by extending the conventional tri-level defender-attacker-defender model. The proposed model considers the uncertainties related to the offensive resource and the interactions involving the security personnel at the top-level, the attacker at the middle-level and the power system operator at the bottom-level. The Column and-Constraint Generation (C&CG) algorithm is implemented by decomposing the MAS defender-attacker-defender model into an upper-level problem for the security personnel, and a lower-level problem for the attacker involving the optimal power flow analysis-based corrective power re-dispatch implemented by the power system operator. Case studies are performed based on the IEEE RTS79 system, and the results validate that the proposed method is able to minimize the damage when uncertainties are involved in the offensive resource. This work can offer meaningful insights into power system protection involving uncertainties in a cyber-physical environment.
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