Drone logistics is a novel method of distribution that will become prevalent.The advantageous location of the logistics hub enables quicker customer deliveries and lower fuel consumption,resulting in cost savings for ...Drone logistics is a novel method of distribution that will become prevalent.The advantageous location of the logistics hub enables quicker customer deliveries and lower fuel consumption,resulting in cost savings for the company’s transportation operations.Logistics firms must discern the ideal location for establishing a logistics hub,which is challenging due to the simplicity of existing models and the intricate delivery factors.To simulate the drone logistics environment,this study presents a new mathematical model.The model not only retains the aspects of the current models,but also considers the degree of transportation difficulty from the logistics hub to the village,the capacity of drones for transportation,and the distribution of logistics hub locations.Moreover,this paper proposes an improved particle swarm optimization(PSO)algorithm which is a diversity-based hybrid PSO(DHPSO)algorithm to solve this model.In DHPSO,the Gaussian random walk can enhance global search in the model space,while the bubble-net attacking strategy can speed convergence.Besides,Archimedes spiral strategy is employed to overcome the local optima trap in the model and improve the exploitation of the algorithm.DHPSO maintains a balance between exploration and exploitation while better defining the distribution of logistics hub locations Numerical experiments show that the newly proposed model always achieves better locations than the current model.Comparing DHPSO with other state-of-the-art intelligent algorithms,the efficiency of the scheme can be improved by 42.58%.This means that logistics companies can reduce distribution costs and consumers can enjoy a more enjoyable shopping experience by using DHPSO’s location selection.All the results show the location of the drone logistics hub is solved by DHPSO effectively.展开更多
In equipment integrated logistics support(ILS), the supply capability of spare parts is a significant factor. There are lots of depots in the traditional support system, which makes too many redundant spare parts and ...In equipment integrated logistics support(ILS), the supply capability of spare parts is a significant factor. There are lots of depots in the traditional support system, which makes too many redundant spare parts and causes high cost of support. Meanwhile,the inconsistency among depots makes it difficult to manage spare parts. With the development of information technology and transportation, the supply network has become more efficient. In order to further improve the efficiency of supply-support work and the availability of the equipment system, building a system of one centralized depot with multiple depots becomes an appropriate way.In this case, location selection of the depots including centralized depots and multiple depots becomes a top priority in the support system. This paper will focus on the location selection problem of centralized depots considering ILS factors. Unlike the common location selection problem, depots in ILS require a higher service level. Therefore, it becomes desperately necessary to take the high requirement of the mission into account while determining location of depots. Based on this, we raise an optimal depot location model. First, the expected transportation cost is calculated.Next, factors in ILS such as response time, availability and fill rate are analyzed for evaluating positions of open depots. Then, an optimization model of depot location is developed with the minimum expected cost of transportation as objective and ILS factors as constraints. Finally, a numerical case is studied to prove the validity of the model by using the genetic algorithm. Results show that depot location obtained by this model can guarantee the effectiveness and capability of ILS well.展开更多
develop a mentation This paper considers the priority facility primal-dual 3-approximation algorithm for procedure, the authors further improve the location problem with penalties： The authors this problem. Combining...develop a mentation This paper considers the priority facility primal-dual 3-approximation algorithm for procedure, the authors further improve the location problem with penalties： The authors this problem. Combining with the greedy aug- previous ratio 3 to 1.8526.展开更多
In this paper,a novel location inventory routing(LIR)model is proposed to solve cold chain logistics network problem under uncertain demand environment. The goal of the developed model is to optimize costs of location...In this paper,a novel location inventory routing(LIR)model is proposed to solve cold chain logistics network problem under uncertain demand environment. The goal of the developed model is to optimize costs of location,inventory and transportation.Due to the complex of LIR problem( LIRP), a multi-objective genetic algorithm(GA), non-dominated sorting in genetic algorithm Ⅱ( NSGA-Ⅱ) has been introduced. Its performance is tested over a real case for the proposed problems. Results indicate that NSGA-Ⅱ provides a competitive performance than GA,which demonstrates that the proposed model and multi-objective GA are considerably efficient to solve the problem.展开更多
The differential evolution algorithm is an evolutionary algorithm for global optimization and the un-capacitated facility location problem (UFL) is one of the classic NP-Hard problems. In this paper, combined with the...The differential evolution algorithm is an evolutionary algorithm for global optimization and the un-capacitated facility location problem (UFL) is one of the classic NP-Hard problems. In this paper, combined with the specific characteristics of the UFL problem, we introduce the activation function to the algorithm for solving UFL problem and name it improved adaptive differential evolution algorithm (IADEA). Next, to improve the efficiency of the algorithm and to alleviate the problem of being stuck in a local optimum, an adaptive operator was added. To test the improvement of our algorithm, we compare the IADEA with the basic differential evolution algorithm by solving typical instances of UFL problem respectively. Moreover, to compare with other heuristic algorithm, we use the hybrid ant colony algorithm to solve the same instances. The computational results show that IADEA improves the performance of the basic DE and it outperforms the hybrid ant colony algorithm.展开更多
To address the poor performance of commonly used intelligent optimization algorithms in solving location problems—specifically regarding effectiveness,efficiency,and stability—this study proposes a novel location al...To address the poor performance of commonly used intelligent optimization algorithms in solving location problems—specifically regarding effectiveness,efficiency,and stability—this study proposes a novel location allocation method for the delivery sites to deliver daily necessities during epidemic quarantines.After establishing the optimization objectives and constraints,we developed a relevant mathematical model based on the collected data and utilized traditional intelligent optimization algorithms to obtain Pareto optimal solutions.Building on the characteristics of these Pareto front solutions,we introduced an improved clustering algorithm and conducted simulation experiments using data from Changchun City.The results demonstrate that the proposed algorithm outperforms traditional intelligent optimization algorithms in terms of effectiveness,efficiency,and stability,achieving reductions of approximately 12%and 8%in time and labor costs,respectively,compared to the baseline algorithm.展开更多
As global air transportation as well as supply chains face unprecedented challenges,optimizing hub locations is crucial for enhancing efficiency,reducing costs,and mitigating environmental impacts.This paper reviews t...As global air transportation as well as supply chains face unprecedented challenges,optimizing hub locations is crucial for enhancing efficiency,reducing costs,and mitigating environmental impacts.This paper reviews the state-of-the-art in hub location problems,with a particular focus on air transportation,driven by the increasing complexity and importance of networks in today’s interconnected world.Our review offers two major contributions:a meta-review of existing surveys to synthesize current knowledge and identify gaps,and the proposal of ten critical research challenges encapsulated in the acronym DISRUPTIVE.These challenges include the need for high-quality datasets,deeper analytical insights,sustainability considerations,robustness against disruptions,addressing uncertainty,leveraging parallelization techniques,incorporating temporal dynamics,embracing interdisciplinary approaches,designing versatile hubs,and exploring emerging transportation modes.By addressing these challenges,researchers can drive innovation in hub location problems,paving the way for more resilient and efficient logistical networks that meet the demands of a rapidly evolving global landscape.展开更多
Capacitated facility location problem(CFLP)is a classical combinatorial optimization problem that has various applications in operations research,theoretical computer science,and management science.In the CFLP,we have...Capacitated facility location problem(CFLP)is a classical combinatorial optimization problem that has various applications in operations research,theoretical computer science,and management science.In the CFLP,we have a potential facilities set and a clients set.Each facility has a certain capacity and an open cost,and each client has a spliitable demand that need to be met.The goal is to open some facilities and assign all clients to these open facilities so that the total cost is as low as possible.The CFLP is NP-hard(non-deterministic polynomial-hard),and a large amount of work has been devoted to designing approximation algorithms for CFLP and its variants.Following this vein,we introduce a new variant of CFLP called capacitated uniform facility location problem with soft penalties(CUFLPSP),in which the demand of each client can be partially rejected by paying penalty costs.As a result,we present a linear programming-rounding(LP-rounding)based 5.5122-approximation algorithm for the CUFLPSP.展开更多
Multi-project multi-site location problems are multi-objective combinational optimization ones with discrete variables which are hard to solve. To do so, the case of particle swarm optimization is considered due to it...Multi-project multi-site location problems are multi-objective combinational optimization ones with discrete variables which are hard to solve. To do so, the case of particle swarm optimization is considered due to its useful char- acteristics such as easy implantation, simple parameter settings and fast convergence. First these problems are trans- formed into ones with continuous variables by defining an equivalent probability matrix in this paper, then multi-objective particle swarm optimization based on the minimal particle angle is used to solve them. Methods such as continuation of discrete variables, update of particles for matrix variables, normalization of particle position and evalua- tion of particle fitness are presented. Finally the efficiency of the proposed method is validated by comparing it with other methods on an eight-project-ten-site location problem.展开更多
We study the two-stage stochastic facility location problem(2-SFLP)by proposing an LP(location problem)-rounding approximation algorithm with 2.3613 per-scenario bound for this problem,improving the previously best pe...We study the two-stage stochastic facility location problem(2-SFLP)by proposing an LP(location problem)-rounding approximation algorithm with 2.3613 per-scenario bound for this problem,improving the previously best per-scenario bound of 2.4957.展开更多
In this paper, we study the dynamic facility location problem with submodular penalties （DFLPSP）. We present a combinatorial primal-dual 3-approximation algorithm for the DFLPSP.
In this paper, we consider the fault-tolerant concave facility location problem （FTCFL） with uniform requirements. By investigating the structure of the FTCFL, we obtain a modified dual-fitting bifactor approximatio...In this paper, we consider the fault-tolerant concave facility location problem （FTCFL） with uniform requirements. By investigating the structure of the FTCFL, we obtain a modified dual-fitting bifactor approximation algorithm. Combining the scaling and greedy argumentation technique, the approximation factor is proved to be 1.52.展开更多
In this paper,we study a stochastic version of the fault-tolerant facility location problem.By exploiting the stochastic structure,we propose a 5-approximation algorithm which uses the LP-rounding technique based on t...In this paper,we study a stochastic version of the fault-tolerant facility location problem.By exploiting the stochastic structure,we propose a 5-approximation algorithm which uses the LP-rounding technique based on the revised optimal solution to the linear programming relaxation of the stochastic fault-tolerant facility location problem.展开更多
In this paper, the continuously optimal location problem is considered. The strong convexity of the objective function, the Lipschitz continuity of the gradient of the objective function are proved. Furthermore, a var...In this paper, the continuously optimal location problem is considered. The strong convexity of the objective function, the Lipschitz continuity of the gradient of the objective function are proved. Furthermore, a variant of conjugate gradient method for continuously optimal location problem is presented and its global convergence is analyzed.展开更多
Purpose–The purpose of this paper is to solve the capacitated location routing problem(CLRP),which is an NP-hard problem that involves making strategic decisions as well as tactical and operational decisions,using a ...Purpose–The purpose of this paper is to solve the capacitated location routing problem(CLRP),which is an NP-hard problem that involves making strategic decisions as well as tactical and operational decisions,using a hybrid particle swarm optimization(PSO)algorithm.Design/methodology/approach–PSO,which is a population-based metaheuristic,is combined with a variable neighborhood strategy variable neighborhood search to solve the CLRP.Findings–The algorithm is tested on a set of instances available in the literature and gave good quality solutions,results are compared to those obtained by other metaheuristic,evolutionary and PSO algorithms.Originality/value–Local search is a time consuming phase in hybrid PSO algorithms,a set of neighborhood structures suitable for the solution representation used in the PSO algorithm is proposed in the VNS phase,moves are applied directly to particles,a clear decoding method is adopted to evaluate a particle(solution)and there is no need to re-encode solutions in the form of particles after applying local search.展开更多
The Euclidean single facility location problem (ESFL) and the Euclidean multiplicity lo-cation problem (EMFL) are two special nonsmooth convex programming problems which haveattracted a largr literature. For the ESFL ...The Euclidean single facility location problem (ESFL) and the Euclidean multiplicity lo-cation problem (EMFL) are two special nonsmooth convex programming problems which haveattracted a largr literature. For the ESFL problem. there are algorithms which converge bothglobally and quadratically For the EMFL problem, there are some quadratically convergentalgorithms. but for global convergencel they all need nontrivial assumptions on the problem.In this paper, we present an algorithm for EMFL. With no assumption on the problem, it isproved that from any initial point, this algorithm generates a sequence of points which convergesto the closed convex set of optimal solutions of EMFL.展开更多
Urban air mobility(UAM)extends urban transportation to low-altitude airspace using electric vertical take-off and landing(eVTOL)vehicle to reduce traffic congestion.The vertical take-off and landing(VTOL)site connecti...Urban air mobility(UAM)extends urban transportation to low-altitude airspace using electric vertical take-off and landing(eVTOL)vehicle to reduce traffic congestion.The vertical take-off and landing(VTOL)site connecting ground and air transport is the critical infrastructure of the UAM.Determining its locations is essential for the design and operation of the air route.This study focuses on the problem of the location of the VTOL site,using Shenzhen as the study area,and establishes an integer programming model with the objective of maximizing travel cost savings to identify the optimal locations of the VTOL sites.This study is different from existing ones in that it explicitly considers the three-dimensional spatial availability of VTOL sites.Geographic information system(GIS)tools are used to identify locations that satisfy two-dimensional(2D)planar availability,and an obstacle assessment model of the approach/departure and transitional surfaces of the VTOL site is built to further screen the locations.The selected potential sites are used as input to the integer programming model,ensuring that the locations identified to establish the VTOL site are optimal.The impact of the number of VTOL sites,the user's transfer time at the VTOL sites,and the eVTOL pricing on the model solution is also discussed.Although this study uses Shenzhen as a research object,the proposed methodology is generalized and applicable to any other city or region,providing recommendations and references for initial planning and related operations of the UAM in selected areas.展开更多
Green shipping and electrification have been the main topics in the shipping industry.In this process,the pure battery-powered ship is developed,which is zero-emission and well-suited for inland shipping.Currently,bat...Green shipping and electrification have been the main topics in the shipping industry.In this process,the pure battery-powered ship is developed,which is zero-emission and well-suited for inland shipping.Currently,battery swapping stations and ships are being explored since battery charging ships may not be feasible for inland long-distance trips.However,improper infrastructure planning for battery swapping stations and ships will increase costs and decrease operation efficiency.Therefore,a bilevel optimal infrastructure planning method is proposed in this paper for battery swapping stations and ships.First,the energy consumption model for the battery swapping ship is established considering the influence of the sailing environment.Second,a bilevel optimization model is proposed to minimize the total cost.Specifically,the battery swapping station(BSS)location problem is investigated at the upper level.The optimization of battery size in each battery swapping station and ship and battery swapping scheme are studied at the lower level based on speed and energy optimization.Finally,the bilevel self-adaptive differential evolution algorithm(BlSaDE)is proposed to solve this problem.The simulation results show that total cost could be reduced by 5.9%compared to the original results,and the effectiveness of the proposed method is confirmed.展开更多
A novel model for the charging station planning problem of plug-in electric vehicles is proposed in this paper considering the users＇ daily travel. With the objective of minimizing the total cost, including the charg...A novel model for the charging station planning problem of plug-in electric vehicles is proposed in this paper considering the users＇ daily travel. With the objective of minimizing the total cost, including the charging stations＇ cost （including installing cost and management cost） and the users＇ cost （including station access cost and charging cost）, the proposed model simultaneously handles the problems where to locate the charging stations and how many chargers to be established in each charging station. Considering that different users may have different perception of station access cost and charging cost, two cases （i.e., homogeneous users and heterogeneous users） are typically investigated. The impacts of different discount rates, operating period of the charging stations, number of electric vehicles and number of charging stations on the location of the charging station are also studied. The simulation results not only show that it is very important to locate the charging stations according to the traveling behavior of users, but also verify the validity of the proposed model.展开更多
The artificial bee colony(ABC)algorithm is an evolutionary optimization algorithm based on swarm intelligence and inspired by the honey bees’food search behavior.Since the ABC algorithm has been developed to achieve ...The artificial bee colony(ABC)algorithm is an evolutionary optimization algorithm based on swarm intelligence and inspired by the honey bees’food search behavior.Since the ABC algorithm has been developed to achieve optimal solutions by searching in the continuous search space,modification is required to apply it to binary optimization problems.In this study,we modify the ABC algorithm to solve binary optimization problems and name it the improved binary ABC(IbinABC).The proposed method consists of an update mechanism based on fitness values and the selection of different decision variables.Therefore,we aim to prevent the ABC algorithm from getting stuck in a local minimum by increasing its exploration ability.We compare the IbinABC algorithm with three variants of the ABC and other meta-heuristic algorithms in the literature.For comparison,we use the well-known OR-Library dataset containing 15 problem instances prepared for the uncapacitated facility location problem.Computational results show that the proposed algorithm is superior to the others in terms of convergence speed and robustness.The source code of the algorithm is available at https://github.com/rafetdurgut/ibinABC.展开更多
基金supported by the NationalNatural Science Foundation of China(No.61866023).
文摘Drone logistics is a novel method of distribution that will become prevalent.The advantageous location of the logistics hub enables quicker customer deliveries and lower fuel consumption,resulting in cost savings for the company’s transportation operations.Logistics firms must discern the ideal location for establishing a logistics hub,which is challenging due to the simplicity of existing models and the intricate delivery factors.To simulate the drone logistics environment,this study presents a new mathematical model.The model not only retains the aspects of the current models,but also considers the degree of transportation difficulty from the logistics hub to the village,the capacity of drones for transportation,and the distribution of logistics hub locations.Moreover,this paper proposes an improved particle swarm optimization(PSO)algorithm which is a diversity-based hybrid PSO(DHPSO)algorithm to solve this model.In DHPSO,the Gaussian random walk can enhance global search in the model space,while the bubble-net attacking strategy can speed convergence.Besides,Archimedes spiral strategy is employed to overcome the local optima trap in the model and improve the exploitation of the algorithm.DHPSO maintains a balance between exploration and exploitation while better defining the distribution of logistics hub locations Numerical experiments show that the newly proposed model always achieves better locations than the current model.Comparing DHPSO with other state-of-the-art intelligent algorithms,the efficiency of the scheme can be improved by 42.58%.This means that logistics companies can reduce distribution costs and consumers can enjoy a more enjoyable shopping experience by using DHPSO’s location selection.All the results show the location of the drone logistics hub is solved by DHPSO effectively.
基金supported by the Science Challenge Project(TZ2018007)the National Natural Science Foundation of China(71671009+2 种基金 61871013 61573041 61573043)
文摘In equipment integrated logistics support(ILS), the supply capability of spare parts is a significant factor. There are lots of depots in the traditional support system, which makes too many redundant spare parts and causes high cost of support. Meanwhile,the inconsistency among depots makes it difficult to manage spare parts. With the development of information technology and transportation, the supply network has become more efficient. In order to further improve the efficiency of supply-support work and the availability of the equipment system, building a system of one centralized depot with multiple depots becomes an appropriate way.In this case, location selection of the depots including centralized depots and multiple depots becomes a top priority in the support system. This paper will focus on the location selection problem of centralized depots considering ILS factors. Unlike the common location selection problem, depots in ILS require a higher service level. Therefore, it becomes desperately necessary to take the high requirement of the mission into account while determining location of depots. Based on this, we raise an optimal depot location model. First, the expected transportation cost is calculated.Next, factors in ILS such as response time, availability and fill rate are analyzed for evaluating positions of open depots. Then, an optimization model of depot location is developed with the minimum expected cost of transportation as objective and ILS factors as constraints. Finally, a numerical case is studied to prove the validity of the model by using the genetic algorithm. Results show that depot location obtained by this model can guarantee the effectiveness and capability of ILS well.
基金supported by the National Natural Science Foundation of China under Grant No.11371001
文摘develop a mentation This paper considers the priority facility primal-dual 3-approximation algorithm for procedure, the authors further improve the location problem with penalties： The authors this problem. Combining with the greedy aug- previous ratio 3 to 1.8526.
基金Natural Science Foundation of Shanghai,China(No.15ZR1401600)the Fundamental Research Funds for the Central Universities,China(No.CUSF-DH-D-2015096)
文摘In this paper,a novel location inventory routing(LIR)model is proposed to solve cold chain logistics network problem under uncertain demand environment. The goal of the developed model is to optimize costs of location,inventory and transportation.Due to the complex of LIR problem( LIRP), a multi-objective genetic algorithm(GA), non-dominated sorting in genetic algorithm Ⅱ( NSGA-Ⅱ) has been introduced. Its performance is tested over a real case for the proposed problems. Results indicate that NSGA-Ⅱ provides a competitive performance than GA,which demonstrates that the proposed model and multi-objective GA are considerably efficient to solve the problem.
文摘The differential evolution algorithm is an evolutionary algorithm for global optimization and the un-capacitated facility location problem (UFL) is one of the classic NP-Hard problems. In this paper, combined with the specific characteristics of the UFL problem, we introduce the activation function to the algorithm for solving UFL problem and name it improved adaptive differential evolution algorithm (IADEA). Next, to improve the efficiency of the algorithm and to alleviate the problem of being stuck in a local optimum, an adaptive operator was added. To test the improvement of our algorithm, we compare the IADEA with the basic differential evolution algorithm by solving typical instances of UFL problem respectively. Moreover, to compare with other heuristic algorithm, we use the hybrid ant colony algorithm to solve the same instances. The computational results show that IADEA improves the performance of the basic DE and it outperforms the hybrid ant colony algorithm.
基金National Natural Science Foundation of China(62202477)。
文摘To address the poor performance of commonly used intelligent optimization algorithms in solving location problems—specifically regarding effectiveness,efficiency,and stability—this study proposes a novel location allocation method for the delivery sites to deliver daily necessities during epidemic quarantines.After establishing the optimization objectives and constraints,we developed a relevant mathematical model based on the collected data and utilized traditional intelligent optimization algorithms to obtain Pareto optimal solutions.Building on the characteristics of these Pareto front solutions,we introduced an improved clustering algorithm and conducted simulation experiments using data from Changchun City.The results demonstrate that the proposed algorithm outperforms traditional intelligent optimization algorithms in terms of effectiveness,efficiency,and stability,achieving reductions of approximately 12%and 8%in time and labor costs,respectively,compared to the baseline algorithm.
基金supported by the National Natural Science Foundation of China(Grant No.U2233214).
文摘As global air transportation as well as supply chains face unprecedented challenges,optimizing hub locations is crucial for enhancing efficiency,reducing costs,and mitigating environmental impacts.This paper reviews the state-of-the-art in hub location problems,with a particular focus on air transportation,driven by the increasing complexity and importance of networks in today’s interconnected world.Our review offers two major contributions:a meta-review of existing surveys to synthesize current knowledge and identify gaps,and the proposal of ten critical research challenges encapsulated in the acronym DISRUPTIVE.These challenges include the need for high-quality datasets,deeper analytical insights,sustainability considerations,robustness against disruptions,addressing uncertainty,leveraging parallelization techniques,incorporating temporal dynamics,embracing interdisciplinary approaches,designing versatile hubs,and exploring emerging transportation modes.By addressing these challenges,researchers can drive innovation in hub location problems,paving the way for more resilient and efficient logistical networks that meet the demands of a rapidly evolving global landscape.
基金supported by the National Natural Science Foundation of China(Nos.11971349,12071442,12371320,and 12371318).
文摘Capacitated facility location problem(CFLP)is a classical combinatorial optimization problem that has various applications in operations research,theoretical computer science,and management science.In the CFLP,we have a potential facilities set and a clients set.Each facility has a certain capacity and an open cost,and each client has a spliitable demand that need to be met.The goal is to open some facilities and assign all clients to these open facilities so that the total cost is as low as possible.The CFLP is NP-hard(non-deterministic polynomial-hard),and a large amount of work has been devoted to designing approximation algorithms for CFLP and its variants.Following this vein,we introduce a new variant of CFLP called capacitated uniform facility location problem with soft penalties(CUFLPSP),in which the demand of each client can be partially rejected by paying penalty costs.As a result,we present a linear programming-rounding(LP-rounding)based 5.5122-approximation algorithm for the CUFLPSP.
基金Project 60304016 supported by the Nationa Natural Science Foundation of China
文摘Multi-project multi-site location problems are multi-objective combinational optimization ones with discrete variables which are hard to solve. To do so, the case of particle swarm optimization is considered due to its useful char- acteristics such as easy implantation, simple parameter settings and fast convergence. First these problems are trans- formed into ones with continuous variables by defining an equivalent probability matrix in this paper, then multi-objective particle swarm optimization based on the minimal particle angle is used to solve them. Methods such as continuation of discrete variables, update of particles for matrix variables, normalization of particle position and evalua- tion of particle fitness are presented. Finally the efficiency of the proposed method is validated by comparing it with other methods on an eight-project-ten-site location problem.
基金supported by National Natural Science Foundation of China (Grant No. 11371001)the Natural Sciences and Engineering Research Council of Canada (Grant No. 283106)China Scholarship Council
文摘We study the two-stage stochastic facility location problem(2-SFLP)by proposing an LP(location problem)-rounding approximation algorithm with 2.3613 per-scenario bound for this problem,improving the previously best per-scenario bound of 2.4957.
基金Supported in part by Hebei Province Department of Education Fund under Grant No.Z2012017the National Natural Science Foundation of China under Grant No.11371001 and 11201013
文摘In this paper, we study the dynamic facility location problem with submodular penalties （DFLPSP）. We present a combinatorial primal-dual 3-approximation algorithm for the DFLPSP.
基金Supported by the National Natural Science Foundation of China (No. 60773185, 11071268, 10871144)Beijing Natural Science Foundation (No. 1102001)
文摘In this paper, we consider the fault-tolerant concave facility location problem （FTCFL） with uniform requirements. By investigating the structure of the FTCFL, we obtain a modified dual-fitting bifactor approximation algorithm. Combining the scaling and greedy argumentation technique, the approximation factor is proved to be 1.52.
基金C.Wu was supported by National Natural Science Foundation of China(Grant No.11071268)D.Xu was supported by National Natural Science Foundation of China(Grant No.11371001)+2 种基金Scientific Research Common Program of Beijing Municipal Commission of Education(Grant No.KM201210005033)China Scholarship Council.J.Shu was supported by National Natural Science Foundation of China(Grant Nos.70801014,71171047,and 71222103)The authors would like to thank the two anonymous referees for many helpful suggestions.
文摘In this paper,we study a stochastic version of the fault-tolerant facility location problem.By exploiting the stochastic structure,we propose a 5-approximation algorithm which uses the LP-rounding technique based on the revised optimal solution to the linear programming relaxation of the stochastic fault-tolerant facility location problem.
基金The subject is supported by Natural Science Foundation of China( No
文摘In this paper, the continuously optimal location problem is considered. The strong convexity of the objective function, the Lipschitz continuity of the gradient of the objective function are proved. Furthermore, a variant of conjugate gradient method for continuously optimal location problem is presented and its global convergence is analyzed.
文摘Purpose–The purpose of this paper is to solve the capacitated location routing problem(CLRP),which is an NP-hard problem that involves making strategic decisions as well as tactical and operational decisions,using a hybrid particle swarm optimization(PSO)algorithm.Design/methodology/approach–PSO,which is a population-based metaheuristic,is combined with a variable neighborhood strategy variable neighborhood search to solve the CLRP.Findings–The algorithm is tested on a set of instances available in the literature and gave good quality solutions,results are compared to those obtained by other metaheuristic,evolutionary and PSO algorithms.Originality/value–Local search is a time consuming phase in hybrid PSO algorithms,a set of neighborhood structures suitable for the solution representation used in the PSO algorithm is proposed in the VNS phase,moves are applied directly to particles,a clear decoding method is adopted to evaluate a particle(solution)and there is no need to re-encode solutions in the form of particles after applying local search.
基金This research is supported in part by the Air Force Office of Scientific Research Grant AFOSR-87-0127, the National Science Foundation Grant DCR-8420935 and University of Minnesota Graduate School Doctoral Dissertation Fellowship awarded to G.L. Xue
文摘The Euclidean single facility location problem (ESFL) and the Euclidean multiplicity lo-cation problem (EMFL) are two special nonsmooth convex programming problems which haveattracted a largr literature. For the ESFL problem. there are algorithms which converge bothglobally and quadratically For the EMFL problem, there are some quadratically convergentalgorithms. but for global convergencel they all need nontrivial assumptions on the problem.In this paper, we present an algorithm for EMFL. With no assumption on the problem, it isproved that from any initial point, this algorithm generates a sequence of points which convergesto the closed convex set of optimal solutions of EMFL.
基金National Natural Science Foundation of China(Grant Nos.U2033203 and 52272333)Fundamental Research Funds for Central Universities of Nanjing University of Aeronautics and Astronautics(Grant No.3082022NS2022067).
文摘Urban air mobility(UAM)extends urban transportation to low-altitude airspace using electric vertical take-off and landing(eVTOL)vehicle to reduce traffic congestion.The vertical take-off and landing(VTOL)site connecting ground and air transport is the critical infrastructure of the UAM.Determining its locations is essential for the design and operation of the air route.This study focuses on the problem of the location of the VTOL site,using Shenzhen as the study area,and establishes an integer programming model with the objective of maximizing travel cost savings to identify the optimal locations of the VTOL sites.This study is different from existing ones in that it explicitly considers the three-dimensional spatial availability of VTOL sites.Geographic information system(GIS)tools are used to identify locations that satisfy two-dimensional(2D)planar availability,and an obstacle assessment model of the approach/departure and transitional surfaces of the VTOL site is built to further screen the locations.The selected potential sites are used as input to the integer programming model,ensuring that the locations identified to establish the VTOL site are optimal.The impact of the number of VTOL sites,the user's transfer time at the VTOL sites,and the eVTOL pricing on the model solution is also discussed.Although this study uses Shenzhen as a research object,the proposed methodology is generalized and applicable to any other city or region,providing recommendations and references for initial planning and related operations of the UAM in selected areas.
基金supported by the Foundation of National Key Laboratory of Science and Technology(No.614221722040401)Green Intelligent Ship Standardization Leading Project(No.CBG4N21-4-2).
文摘Green shipping and electrification have been the main topics in the shipping industry.In this process,the pure battery-powered ship is developed,which is zero-emission and well-suited for inland shipping.Currently,battery swapping stations and ships are being explored since battery charging ships may not be feasible for inland long-distance trips.However,improper infrastructure planning for battery swapping stations and ships will increase costs and decrease operation efficiency.Therefore,a bilevel optimal infrastructure planning method is proposed in this paper for battery swapping stations and ships.First,the energy consumption model for the battery swapping ship is established considering the influence of the sailing environment.Second,a bilevel optimization model is proposed to minimize the total cost.Specifically,the battery swapping station(BSS)location problem is investigated at the upper level.The optimization of battery size in each battery swapping station and ship and battery swapping scheme are studied at the lower level based on speed and energy optimization.Finally,the bilevel self-adaptive differential evolution algorithm(BlSaDE)is proposed to solve this problem.The simulation results show that total cost could be reduced by 5.9%compared to the original results,and the effectiveness of the proposed method is confirmed.
文摘A novel model for the charging station planning problem of plug-in electric vehicles is proposed in this paper considering the users＇ daily travel. With the objective of minimizing the total cost, including the charging stations＇ cost （including installing cost and management cost） and the users＇ cost （including station access cost and charging cost）, the proposed model simultaneously handles the problems where to locate the charging stations and how many chargers to be established in each charging station. Considering that different users may have different perception of station access cost and charging cost, two cases （i.e., homogeneous users and heterogeneous users） are typically investigated. The impacts of different discount rates, operating period of the charging stations, number of electric vehicles and number of charging stations on the location of the charging station are also studied. The simulation results not only show that it is very important to locate the charging stations according to the traveling behavior of users, but also verify the validity of the proposed model.
文摘The artificial bee colony(ABC)algorithm is an evolutionary optimization algorithm based on swarm intelligence and inspired by the honey bees’food search behavior.Since the ABC algorithm has been developed to achieve optimal solutions by searching in the continuous search space,modification is required to apply it to binary optimization problems.In this study,we modify the ABC algorithm to solve binary optimization problems and name it the improved binary ABC(IbinABC).The proposed method consists of an update mechanism based on fitness values and the selection of different decision variables.Therefore,we aim to prevent the ABC algorithm from getting stuck in a local minimum by increasing its exploration ability.We compare the IbinABC algorithm with three variants of the ABC and other meta-heuristic algorithms in the literature.For comparison,we use the well-known OR-Library dataset containing 15 problem instances prepared for the uncapacitated facility location problem.Computational results show that the proposed algorithm is superior to the others in terms of convergence speed and robustness.The source code of the algorithm is available at https://github.com/rafetdurgut/ibinABC.
