Water distribution networks are important systems that provide citizens with an essential public service which is crucial for the normal development of most basic activities of life. We address a challenging problem i...
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Water distribution networks are important systems that provide citizens with an essential public service which is crucial for the normal development of most basic activities of life. We address a challenging problem in the design of the isolation system for water distribution networks concerning the optimal location of valves in a water network. The aim of an isolation valve system is to separate a portion of the water distribution network for maintenance, pipe breaks or protection against contaminants. Despite many water distribution network problems have been extensively investigated in the literature, the presence of uncertainty in the data has often been neglected. We address this shortcoming presenting a stochastic programming approach for the strategic valve locations problem in water distribution networks by considering different failure scenarios. Numerical results on case studies taken from the literature are presented.
In the design of green ports, the strategic decision on what types of container transportation equipment are appropriate is extremely important. Yard trucks (YTs) are indispensable in container transportation. In this...
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In the design of green ports, the strategic decision on what types of container transportation equipment are appropriate is extremely important. Yard trucks (YTs) are indispensable in container transportation. In this paper, we propose a YT retrofitting and deployment problem that considers hazardous material transportation in green ports. A stochastic mixed-integer programming model is developed to minimize the costs of purchasing, retrofitting, and chartering YTs and the operation costs during the planning horizon. An enhanced Benders decomposition based on a Lagrangian relaxation algorithm is developed to solve the model. We conduct numerical experiments to verify the effectiveness of the proposed algorithms. We find that the larger free carbon emission quotas provided to enterprises by the government are not always an optimum solution. This research also provides suggestions that can inform decisions about YT retrofitting and deployment and that can contribute to the sustainable development of green ports.
With the rapid development of battery electric buses (BEBs) in urban public traffic, it arises the problem of BEB charging scheduling, which aims to supply electric power for all the BEBs to meet the bus timetable in ...
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With the rapid development of battery electric buses (BEBs) in urban public traffic, it arises the problem of BEB charging scheduling, which aims to supply electric power for all the BEBs to meet the bus timetable in the smallest cost. Practical experience reports that both weather temperature and accumulative battery using time have a non-negligible impact on battery charging efficiency, and bring about the uncertainty of charging time of a battery. It may cause a negative influence to the departure schedule of the BEBs. Motivated by the above observation, this work investigates a BEB charging scheduling problem with uncertain charging time. The objective is to minimize the expected total charging cost, which consists of in-service cost, energy consumption cost and penalty cost due to over-low charging. We first prove the strong NP-hardness of the considered problem. A stochastic linear programming model is then established. A scenario-reduction based enhanced sample average approximation approach and an improved genetic algorithm are proposed to solve large-scale instances of the considered problem. Numerical experiments and comparisons with adapted previous algorithms are conducted to demonstrate the effectiveness of the proposed approaches.
The transition to the zero-carbon power system is underway accelerating recently. Hydrogen energy and electric vehicles (EVs) are promising solutions on the supply and demand sides. This brief presents a novel archite...
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The transition to the zero-carbon power system is underway accelerating recently. Hydrogen energy and electric vehicles (EVs) are promising solutions on the supply and demand sides. This brief presents a novel architecture that includes hydrogen production stations, fast-charging stations, and commercial EVs. The proposed architecture jointly optimizes the hydrogen energy dispatch and the EV charging location selection and is formulated by a time-varying bilevel bipartite graph (T-BBG) model. We develop a bilevel iteration optimization method combining the linear programming (LP) and the Kuhn-Munkres (KM) algorithm to solve the joint problem whose optimality is proved theoretically. The effectiveness of the proposed architecture on reducing the operating cost is verified via case studies in Shanghai. The proposed method outperforms other strategies and improves the performance by at least 13%, which shows the potential economic benefits of the joint architecture. The convergence and impact of parameters are assessed.
The research on traveling salesman problem (TSP) is important in logistics distribution. Because many stochastic cost factors affect logistics distribution in real life, a class of stochastic programming models is con...
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This paper presents a stochastic optimization program for multi-objective cost and environmental pollution optimization in a network over a one-year time horizon. In this context, the system operator, in addition to p...
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ISBN:
(纸本)9798350310665
This paper presents a stochastic optimization program for multi-objective cost and environmental pollution optimization in a network over a one-year time horizon. In this context, the system operator, in addition to planning equipment utilization, mitigates the effects of existing fluctuations in the uncertain input parameters by employing demand response programs. Random functions are used to model the oscillatory behavior of wind turbine speed. Furthermore, planning is performed for different seasons of the year to examine the sensitivity of the obtained response to weather conditions. To simulate the proposed model, a mixed-integer linear programming (MILP) approach is employed, and the GAMS software is used to solve it. Considering uncertainty in wind turbine generation, the proposed model is applied to a test microgrid consisting of multiple energy carriers. Simulation results demonstrate the impact of demand response programs while increasing the cost of planning due to the consideration of uncertainty in the system.
Admission scheduling in an intensive care unit (ICU) poses a complex challenge that requires balancing the optimization of resources for the post-operative care of patients while managing the risk of capacity overload...
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During the COVID-19 pandemic, it is imperative to distribute daily supplies to residents in lockdown communities and collect their household garbage. Unmanned Aerial Vehicle (UAV) logistics offers a contactless soluti...
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ISBN:
(纸本)9781665455336
During the COVID-19 pandemic, it is imperative to distribute daily supplies to residents in lockdown communities and collect their household garbage. Unmanned Aerial Vehicle (UAV) logistics offers a contactless solution, but the limitations of load and maintenance costs must be considered. Therefore, it is crucial to plan UAV transportation routes effectively to minimize overall transportation costs. Additionally, the stochasticity of household garbage quality further complicates UAV delivery. We address this issue by formulating the problem as vehicle routing problem with simultaneous stochastic pickups and deliveries (VRPSSPD) and develop a range of swarm intelligent optimization algorithms (SIOAs) to solve it. Our experiments demonstrate that the Artificial Bee Colony (ABC) algorithm and Shuffled Frog Leaping Algorithm (SFLA) are well-suited to this practical problem. Moreover, to address the long convergence time of SFLA, we propose the General Center SFLA based on Roulette Wheel Selection (RWS-GC-SFLA) to improve it. Our experimental results show that RWS-GC-SFLA can improve the convergence time while ensuring solution quality. Finally, we apply RWS-GC-SFLA to a practical instance case of UAV logistics in Guangzhou communities and achieved satisfactory results.
We consider so called 2-stage stochastic integer programs (IPs) and their generalized form, so calledmulti-stage stochastic IPs. A2-stage stochastic IP is an integer program of the form max{c(T) x vertical bar Ax = b,...
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We consider so called 2-stage stochastic integer programs (IPs) and their generalized form, so calledmulti-stage stochastic IPs. A2-stage stochastic IP is an integer program of the form max{c(T) x vertical bar Ax = b, l <= x <= u, x epsilon Z(s+nt)} where the constraint matrix A epsilon Z(rnxs+nt) consists roughly of n repetitions of a matrix A epsilon Z(rxs) on the vertical line and n repetitions of a matrix B epsilon Z(rxt) on the diagonal. In this paper we improve upon an algorithmic result by Hemmecke and Schultz from 2003 [Hemmecke and Schultz, Math. Prog. 2003] to solve 2-stage stochastic IPs. The algorithm is based on the Graver augmentation framework where our main contribution is to give an explicit doubly exponential bound on the size of the augmenting steps. The previous bound for the size of the augmenting steps relied on non-constructive finiteness arguments from commutative algebra and therefore only an implicit bound was known that depends on parameters r, s, t and Delta, where Delta is the largest entry of the constraint matrix. Our new improved bound however is obtained by a novel theorem which argues about intersections of paths in a vector space. As a result of our new bound we obtain an algorithm to solve 2-stage stochastic IPs in time f (r, s, Delta) . poly(n, t), where f is a doubly exponential function. To complement our result, we also prove a doubly exponential lower bound for the size of the augmenting steps.
In shale gas field development, the major endogenous uncertainty comes from the production profile of candidate wells and its realization is dependent on the development decision. In this work, we apply multistage sto...
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In shale gas field development, the major endogenous uncertainty comes from the production profile of candidate wells and its realization is dependent on the development decision. In this work, we apply multistage stochastic programming to address the shale gas field development planning problem under production profile uncertainty. Business realities such as budget limit and uncertainty resolution delay are also considered in the proposed model. To solve the multistage stochastic model, a Lagrangean decomposition method and a heuristic method are proposed. Computational results demonstrate the efficiency of the proposed methods on examples that are otherwise intractable. The optimal development strategy from the proposed model is analyzed under different levels of uncertainties. Different cases are further tested to figure out the impact of budget and resolution delay on development decisions.
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