Using Improved Particle Swarm Optimization Algorithm for Location Problem of Drone Logistics Hub

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.

Improved Adaptive Differential Evolution Algorithm for the Un-Capacitated Facility Location Problem

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.

Location and allocation problem for spare parts depots on integrated logistics support

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.

Approximation Algorithms for the Priority Facility Location Problem with Penalties

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.

Models for Location Inventory Routing Problem of Cold Chain Logistics with NSGA-Ⅱ Algorithm

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.

Optimization of Multi-Project Multi-Site Location Based on MOPSOs

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.

Bilevel Optimal Infrastructure Planning Method for the Inland Battery Swapping Stations and Battery-Powered Ships

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.

CHARGING STATION PLANNING FOR PLUG-IN ELECTRIC VEHICLES

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.

Improved binary artificial bee colony algorithm

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.

An improved per-scenario bound for the two-stage stochastic facility location problem

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.

Projection-based High-dimensional Sign Test

This article is concerned with the high-dimensional location testing problem.For highdimensional settings,traditional multivariate-sign-based tests perform poorly or become infeasible since their Type I error rates are far away from nominal levels.Several modifications have been proposed to address this challenging issue and shown to perform well.However,most of modified sign-based tests abandon all the correlation information,and this results in power loss in certain cases.We propose a projection weighted sign test to utilize the correlation information.Under mild conditions,we derive the optimal direction and weights with which the proposed projection test possesses asymptotically and locally best power under alternatives.Benefiting from using the sample-splitting idea for estimating the optimal direction,the proposed test is able to retain type-I error rates pretty well with asymptotic distributions,while it can be also highly competitive in terms of robustness.Its advantage relative to existing methods is demonstrated in numerical simulations and a real data example.

The Pos/neg-weighted 2-medians in Balanced Trees with Subtree-shaped Customers

This paper deals with the pos/neg-weighted p-median problem on tree graphs where all customers are modeled as subtrees. We present a polynomial algorithm for the 2-median problem on an arbitrary tree. Then we improve the time complexity to O（n logn） for the problem on a balanced tree, where n is the number of the vertices in the tree.

An Approximation Algorithm for the Dynamic Facility Location Problem with Submodular Penalties

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.

A Convergent Algorithm for Continuously Optimal Location Problem

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.

Fault-tolerant Concave Facility Location Problem with Uniform Requirements

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.

An Approximation Algorithm for the Stochastic Fault-Tolerant Facility Location Problem

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.

A hybrid particle swarm optimization algorithm for the capacitated location routing problem

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.

A GLOBALLY CONVERGENT ALGORITHM FOR THE EUCLIDEAN MULTIPLICITY LOCATION PROBLEM

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.