Disaster logistics management is vital in planning and organizing humanitarian assistance distribution. The planning problem faces challenges, such as coordinating the allocation and distribution of essential resource...
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Disaster logistics management is vital in planning and organizing humanitarian assistance distribution. The planning problem faces challenges, such as coordinating the allocation and distribution of essential resources while considering the severity of the disaster, population density, and accessibility. This study proposes an optimized disaster relief management model, including distribution center placement, demand point prediction, prohibited route mapping, and efficient relief goods distribution. A dynamic model predicts the location of post-disaster distribution centers using the K-Means method based on impacted demand points' positions. Artificial Neural Networks (ANN) aid in predicting assistance requests around formed distribution centers. The forbidden route model maps permitted and prohibited routes while considering constraints to enhance relief supply distribution efficacy. The objective function aims to minimize both cost and time in post-disaster aid distribution. The model deep location routing problem (DLRP) effectively handles mixednonlinear multi-objective programming, choosing the best forbidden routes. The combination of these models provides a comprehensive framework for optimizing disaster relief management, resulting in more effective and responsive disaster handling. Numerical examples show the model's effectiveness in solving complex humanitarian logistics problems with lower computation time, which is crucial for quick decision making during disasters.
The provision of self-cleaning velocities has been shown to reduce the risk of discolouration in water distri-bution networks (WDNs). Despite these findings, control implementations continue to be focused primarily on...
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The provision of self-cleaning velocities has been shown to reduce the risk of discolouration in water distri-bution networks (WDNs). Despite these findings, control implementations continue to be focused primarily on pressure and leakage management. This paper considers the control of diurnal flow velocities to maximize the self-cleaning capacity (SCC) of WDNs. We formulate a new optimal design-for-control problem where locations and operational settings of pressure control and automatic flushing valves are jointly optimized. The problem formulation includes a nonconvex objective function, nonconvex hydraulic conservation law constraints, and binary variables for modelling valve placement, resulting in a nonconvex mixed integer nonlinear programming (MINLP) optimization problem. Considering the challenges with solving nonconvex MINLP problems, we propose a heuristic algorithm which combines convex relaxations (with domain reduction), a randomization technique, and a multi-start strategy to compute feasible solutions. We evaluate the proposed algorithm on case study networks with varying size and degrees of complexity, including a large-scale operational network in the UK. The convex multi-start algorithm is shown to be a more robust solution method compared to an off-the-shelf genetic algorithm, finding good-quality feasible solutions to all design-for-control numerical experiments. Moreover, we demonstrate the implemented multi-start strategy to be a fast and scalable method for computing feasible solutions to the nonlinear SCC control problem. The proposed method extends the control capabilities and benefits of dynamically adaptive networks to improve water quality in WDNs.
In the coming sixth-generation mobile communication era, the intensive deployment of Internet of Things (IoT) devices and cellular networks is an irresistible trend, leading to system energy consumption and network tr...
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In the coming sixth-generation mobile communication era, the intensive deployment of Internet of Things (IoT) devices and cellular networks is an irresistible trend, leading to system energy consumption and network traffic increasing sharply. Fortunately, edge caching as a promising technology to reduce system energy consumption and transmission latency is attracting wide attention. Although simply deploying cache in edge network and merely shutting down the idle base stations (BSs) during the idle periods can save certain energy to a certain extent, in this case, the contents with important mission cached in idle BSs cannot be accessed by users that will affect users' experience. In this paper, we employ coded caching encoded by maximum distance separable (MDS) codes at the network edge, and we propose a joint coded caching and BS sleeping strategy, which utilizes the reconstruction feature of MDS codes to alleviate the impact of BS sleeping. Furthermore, the problem of minimizing energy consumption is studied, and we also design a discrete particle swarm optimization (DPSO) algorithm that is suitable to solve this mixed integer nonlinear programming problem. Simulation results reveal that energy consumption of the joint coded caching and BS sleeping strategy can be significantly decreased over 15.2% when compared with the current state-of-art strategy. Meanwhile, our proposed strategy can also improve the cache hit rate up to a maximum 11.1% compared with the existing strategies.
This paper is concerned with the search for the optimal inventory policy for a finite horizon inventory model with time-varying demand, non-instantaneous deteriorating items, and permissible delay in payment. The opti...
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This paper is concerned with the search for the optimal inventory policy for a finite horizon inventory model with time-varying demand, non-instantaneous deteriorating items, and permissible delay in payment. The optimal inventory policy consists of the determination of the number and timing of replenishment schedules that minimise some total inventory costs. The search for such policy is formulated as a mixed integer nonlinear programming problem (MINLP). It turns out that the non-instantaneous deterioration phenomenon coupled with permissible delay in payment introduce non-smoothness in the objective function of the MINLP. This leads to failure of direct applications of known solution methods to the MINLP. To circumvent this problem, a methodology is proposed for solving fully the MINLP problem. It is shown, through careful mathematical analysis, that earlier results on similar problems can be adapted to solve this problem. Conditions under which the solution of the MINLP is unique are identified. Moreover, convexity with respect to the number of replenishment orders is established. This makes the search for the optimal inventory policy handy. Numerical experiments are also conducted to test the applicability, to identify the key elements and to provide managerial insights to the model.
In this paper, we propose a linearization algorithm for solving a mixedintegernonlinear Problem (MINLP) for Intersection Management (IM) of Connected Autonomous Vehicles (CAVs). The objective of such problem is to m...
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ISBN:
(纸本)9789897585135
In this paper, we propose a linearization algorithm for solving a mixedintegernonlinear Problem (MINLP) for Intersection Management (IM) of Connected Autonomous Vehicles (CAVs). The objective of such problem is to minimize the time it takes to clear a given arbitrary intersection for all vehicles in the consideration. We treat the IM problem as a bi-level optimization problem. On the lower level we solve an Optimal Control Problem (OCP) for each individual vehicle, whereas on the higher level we deal with an optimization problem of finding the optimal sequence and starting times for every car, which essentially yields a MINLP. An intuitive linearization technique is presented to solve the emerging MINLP in a reasonable time. The actual controls, if necessary, are computed a posteriori by minimizing the L 2 -norm of control variables. The algorithm is tested in different intersection scenarios. Numerical results show that it is suitable for real-time applications.
Optimal transmission switching (OTS) is a new practice in power systems and can improve the economics of electric power systems integrated with renewable resources such as wind. In OTS modeling binary decision variabl...
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ISBN:
(纸本)9781450383509
Optimal transmission switching (OTS) is a new practice in power systems and can improve the economics of electric power systems integrated with renewable resources such as wind. In OTS modeling binary decision variables are added to the optimal power flow (OPF) problem to represent on and off switching status of lines. This extension to alternative current optimal power flow (ACOPF) problem results in a mixedintegernonlinear program (MINLP) which is not guaranteed to be solved optimally by existing solution methods and also requires excessive computation times for large real systems. In this paper we develop a genetic algorithm (GA) for ACOPF based OTS problem. In our GA approach we benefit from the structure of power transmission network and develop a line scoring method and a graphical distance based local improvement technique to better search the solution space. We compare our proposed genetic algorithm with two greedy heuristics on test power systems with renewable resources of energy. The results show that our proposed approach finds more economic solutions especially in larger power systems.
To handle the detrimental effects brought by leakage of radioactive gases at nuclear power station, we propose a bus based evacuation optimization problem. The proposed model incorporates the following four constraint...
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ISBN:
(纸本)9789881563804
To handle the detrimental effects brought by leakage of radioactive gases at nuclear power station, we propose a bus based evacuation optimization problem. The proposed model incorporates the following four constraints, 1) the maximum dose of radiation per evacuee, 2) the limitation of bus capacity, 3) the number of evacuees at demand node (bus pickup stop), 4) evacuees balance at demand and shelter nodes, which is formulated as a mixed integer nonlinear programming (MINLP) problem. Then, to eliminate the difficulties of choosing a proper M value in Big-M method, a Big-M free method is employed to linearize the nonlinear terms of the MINLP problem. Finally, the resultant mixedinteger linear program (MILP) problem is solvable with efficient commercial solvers such as CPLEX or Gurobi, which guarantees the optimal evacuation plan obtained. To evaluate the effectiveness of proposed evacuation model, we test our model on two different scenarios (a random one and a practical scenario). For both scenarios, our model attains executable evacuation plan within given 3600 seconds computation time.
In this paper, a new method is proposed for the optimal allocation of Flexible AC Transmission System (FACTS) devices for voltage stability. It aims at maximizing a margin between current power system conditions and t...
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ISBN:
(纸本)9781728190488
In this paper, a new method is proposed for the optimal allocation of Flexible AC Transmission System (FACTS) devices for voltage stability. It aims at maximizing a margin between current power system conditions and the nose point of voltage stability with FACTS devices. The location and output variables of FACTS might be expressed as binary and conditions variables, respectively. Thus, the mathematical formulation results in one of mixed integer nonlinear programming problems. This paper proposes a new method that makes use of two-stage Brain Storm Optimization (TSBSO) as evolutional computation. The goal of optimization is to minimize a margin to the nose point of the PV /QV-curve of the continuation power flow calculation. As a practical optimization method, the idea of Robust Optimization is introduced into TSBSO to consider uncertainties in power systems. The effectiveness of the proposed method is demonstrated in the IEEE 30-node system.
This paper introduces a demand-side integration (DSI) framework that upholds the efficacious electrical energy consumption to attain the aims of smart grid as well as the customers' requirement. The proposed DSI f...
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This paper introduces a demand-side integration (DSI) framework that upholds the efficacious electrical energy consumption to attain the aims of smart grid as well as the customers' requirement. The proposed DSI framework is based on a pre-paid orderly energy consumption strategy for a smart microgrid. The interaction between aggregators and end-use customers is captured as a Nash Bargaining Game. A dynamic electricity pricing scheme is implemented to derive the profitable daily electricity tariffs considering an elasticity-based model of price responsive load. The resources caused by proper response of small-scale consumers are integrated into day ahead energy scheduling problem. In order to cope the real-time deviations, the DSI program is accompanied by a supplementary pay-off module. The DSI framework is formulated as a stochastic optimization problem in the form of mixed integer nonlinear programming involving probabilistic representation of uncertainty in generation pattern of renewable energy resources. According to the simulation results, in contrast with the normal consumption paradigm, the load factor improves at least 1.36%, the net profit of unified entity enhances 0.27%, and the total gas emissions are mitigated about 10.9%. The outcomes demonstrate the accuracy and merit of the proposed method.
We present a review of available tools for solving mixed integer nonlinear programming problems. Our aim is to give the reader a flavor of the difficulties one could face and to discuss the tools one could use to try ...
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We present a review of available tools for solving mixed integer nonlinear programming problems. Our aim is to give the reader a flavor of the difficulties one could face and to discuss the tools one could use to try to overcome such difficulties.
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