Logistics Distribution Center Location Problem Research Articles

Location strategy for logistics distribution centers utilizing improved whale optimization algorithm

Abstract An enhanced whale optimization algorithm is introduced to address the challenges associated with the logistics distribution center location problem, aiming to overcome the traditional heuristic algorithm’s susceptibility to local optima and enhance the overall efficiency of the logistics system. This improvement involves the integration of stochastic sinusoidal inertia weights and a variational strategy to augment the global search capability and convergence accuracy of the conventional algorithm. The results of experimental simulations indicate that the upgraded whale algorithm exhibits superior computational performance compared to other heuristic algorithms. Furthermore, it demonstrates the ability to effectively compute optimal distribution center locations, thereby enhancing the delivery efficiency of the logistics distribution system.

A New Hybrid Algorithm for Cold Chain Logistics Distribution Center Location Problem

According to the perishable characteristics of refrigerated food and the objective of minimizing the total cost, the mathematical optimization model of cold chain logistics distribution center location problem is established by introducing such constraints as the freshness and time window. In order to solve the problems of slow convergence and easy to fall into local optimal solution in the process of the traditional wolf colony optimization, an immune wolf colony hybrid algorithm is proposed to solve the location problem of distribution center. In this hybrid algorithm, the idea of vaccination of immune algorithm is introduced into the wolf colony algorithm. By adjusting the antibody concentration and selecting immune operator, the diversity of the wolf colony algorithm is improved, and then the search space of the solution is expanded; the convergence speed and solution accuracy of the wolf colony algorithm are improved by using immune memory cells and immune vaccine. The simulation results show that the immune wolf colony algorithm can quickly converge to the global optimal solution and optimize the location model of logistics distribution center. The algorithm has good feasibility and robustness.

A Hybridization of Cuckoo Search and Differential Evolution for the Logistics Distribution Center Location Problem

The location selection of logistics distribution centers is a crucial issue in the modern urban logistics system. In order to achieve a more reasonable solution, an effective optimization algorithm is indispensable. In this paper, a new hybrid optimization algorithm named cuckoo search‐differential evolution (CSDE) is proposed for logistics distribution center location problem. Differential evolution (DE) is incorporated into cuckoo search (CS) to improve the local searching ability of the algorithm. The CSDE evolves with a coevolutionary mechanism, which combines the Lévy flight of CS with the mutation operation of DE to generate solutions. In addition, the mutation operation of DE is modified dynamically. The mutation operation of DE varies under different searching stages. The proposed CSDE algorithm is tested on 10 benchmarking functions and applied in solving a logistics distribution center location problem. The performance of the CSDE is compared with several metaheuristic algorithms via the best solution, mean solution, and convergence speed. Experimental results show that CSDE performs better than or equal to CS, ICS, and some other metaheuristic algorithms, which reveals that the proposed CSDE is an effective and competitive algorithm for solving the logistics distribution center location problem.

Solving logistics distribution center location problem using a wolf pack algorithm

Logistics distribution center is crucial to the whole logistics system. The location of the distribution is the key of the logistics system analysis. In this paper, it is changed to a constrained optimization problem using assumptions firstly. An improved wolf pack algorithm is then proposed, which changes the aggregating step length from certainty into uncertainty, and applying it solves the optimization problem. Experiments are conducted on the problem of the logistics distribution center location. What’s more, the compared simulation experiments with other intelligent algorithms show that the proposed method is effective and feasible.

An Uncertain Programming Model for Competitive Logistics Distribution Center Location Problem

We employ uncertain programming to investigate the competitive logistics distribution center location problem in uncertain environment, in which the demands of customers and the setup costs of new distribution centers are uncertain variables. This research was studied with the assumption that customers patronize the nearest distribution center to satisfy their full demands. Within the framework of uncertainty theory, we construct the expected value model to maximize the expected profit of the new distribution center. In order to seek for the optimal solution, this model can be transformed into its deterministic form by taking advantage of the operational law of uncertain variables. Then we can use mathematical software to obtain the optimal location. In addition, a numerical example is presented to illustrate the effectiveness of the presented model.

Study on Decision-Making System and its Data Mining Application of Logistics Intelligent

The most useful function of data mining technology is extracting valuable information and knowledge from irregular data. This paper briefly described the steps of data mining technology, elaborated logistics intelligent decision-making system components, proposed an improved genetic algorithm, took logistics distribution center location problem as argumentation. Applied data mining technology to logistics smart decisions, it can improve service quality, and establish long-term friendly and cooperative relations with customers.

An Improved Bat Algorithm and its Application in the Logistics Distribution Center Location Problem

In this paper, according to the characteristics and influence factors of the distribution logistics and distribution center problem, a mathematical model of the distribution center of the LRTWP (Location and Routing with Time Window Problem) was established. An improved bat algorithm was used to solve the model, the parameter and selection operator in the algorithm is set reasonably. Simulations and results indicate that the improved bat algorithm has better feasibility and validity for solving the LRTWP.

An Improved Simulated Annealing Algorithm and its Application in the Logistics Distribution Center Location Problem

In this paper, according to the characteristics and influence factors of the distribution logistics and distribution center problem, a mathematical model of the distribution center of the LRTWP (Location and Routing with Time Window Problem) was established. An improved simulated annealing algorithm was used to solve the model, the parameter and selection operator in the algorithm is setted reasonably. Simulations and results indicate that the improved simulated annealing algorithm has better feasibility and validity for solving the LRTWP.

A multi-objective chance-constrained network optimal model with random fuzzy coefficients and its application to logistics distribution center location problem

The problem of the distribution center is concerned with how to select distribution centers from a potential set in order to minimize the total relevant cost comprising of fixed costs of the distri...