scholarly journals Research on Logistics Center Location Problem Based on p-Median Model

2016 ◽  
Vol 05 (02) ◽  
pp. 276-281
Author(s):  
旭 童
Keyword(s):  
Location Problem ◽  
Logistics Center ◽  
Center Location

scholarly journals A Hybrid Strategic Oscillation with Path Relinking Algorithm for the Multiobjective k-Balanced Center Location Problem

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 853
Author(s):  
Jesús Sánchez-Oro ◽  
Ana D. López-Sánchez ◽  
Anna Martínez-Gavara ◽  
Alfredo G. Hernández-Díaz ◽  
Abraham Duarte
Keyword(s):  
Location Problem ◽  
State Of The Art ◽  
Path Relinking ◽  
Multiobjective Evolutionary Algorithms ◽  
Demand Point ◽  
High Quality ◽  
Nondominated Solutions ◽  
Center Location ◽  
Strategic Oscillation ◽  
N Demand

This paper presents a hybridization of Strategic Oscillation with Path Relinking to provide a set of high-quality nondominated solutions for the Multiobjective k-Balanced Center Location problem. The considered location problem seeks to locate k out of m facilities in order to serve n demand points, minimizing the maximum distance between any demand point and its closest facility while balancing the workload among the facilities. An extensive computational experimentation is carried out to compare the performance of our proposal, including the best method found in the state-of-the-art as well as traditional multiobjective evolutionary algorithms.

scholarly journals A Multicriteria Decision Making Approach Based on Fuzzy Theory and Credibility Mechanism for Logistics Center Location Selection

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Bowen Wang ◽  
Haitao Xiong ◽  
Chengrui Jiang
Keyword(s):  
Decision Making ◽  
Fuzzy Theory ◽  
Multicriteria Decision Making ◽  
Decision Makers ◽  
Location Selection ◽  
Logistics Center ◽  
Multicriteria Decision ◽  
Model Based ◽  
Center Location ◽  
Decision Making Model

As a hot topic in supply chain management, fuzzy method has been widely used in logistics center location selection to improve the reliability and suitability of the logistics center location selection with respect to the impacts of both qualitative and quantitative factors. However, it does not consider the consistency and the historical assessments accuracy of experts in predecisions. So this paper proposes a multicriteria decision making model based on credibility of decision makers by introducing priority of consistency and historical assessments accuracy mechanism into fuzzy multicriteria decision making approach. In this way, only decision makers who pass the credibility check are qualified to perform the further assessment. Finally, a practical example is analyzed to illustrate how to use the model. The result shows that the fuzzy multicriteria decision making model based on credibility mechanism can improve the reliability and suitability of site selection for the logistics center.

Sustainability benchmarking for logistics center location decision

2019 ◽  
Vol 31 (5) ◽  
pp. 1239-1260 ◽  
Author(s):  
Yiğit Kazançoğlu ◽  
Melisa Özbiltekin ◽  
Yeşim Deniz Özkan-Özen
Keyword(s):  
Design Methodology ◽  
Fuzzy Ahp ◽  
Preference Ranking ◽  
Location Decision ◽  
Content Type ◽  
Location Selection ◽  
Logistics Center ◽  
Center Location ◽  
Emerging Country ◽  
Managerial Implications

Purpose As in line with eco benchmarking, the purpose of this paper is to solve a location selection problem in an emerging country by applying sustainability benchmarking principles. Design/methodology/approach A hybrid multi-criteria decision-making method, fuzzy AHP and Preference Ranking Organization METHod for Enrichment Evaluations (PROMETHEE), is used as methodology to make sustainability benchmarking for logistics center location selection. Findings It is revealed that according to AHP and PROMETHEE calculations, Kemalpasa is determined as the most appropriate location from the sustainable perspectives. Torbali is specified as the worst location to construct a logistics center in terms of benchmarking criteria based on sustainability concerns. Based on these numerical results, managerial implications are presented with a sustainability benchmarking view. Originality/value The main originality of this study is integrating one of the relatively new topics, sustainability benchmarking, with a popular area, logistics center location selection.

Research on Mixed Intelligent Arithmetic of Reuse Reverse Logistics Centers' Location Model

2014 ◽  
Vol 945-949 ◽  
pp. 3246-3251 ◽  
Author(s):  
Lu Feng Dai ◽  
Xi Fu Wang
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Stochastic Simulation ◽  
Reverse Logistics ◽  
Random Variables ◽  
Location Model ◽  
Logistics Center ◽  
Center Location

The paper builds the model of direct reuse reverse logistics center location followed by the uncertainty random variables of the demand of retailers and the recovery of collection points. This model assumes that the enterprises are expanding on the traditional network. For the random variables, they will be solved by using stochastic simulation, genetic algorithm and linear programming, and numericai exampie is presented.

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

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Rui Chi ◽  
Yixin Su ◽  
Zhijian Qu ◽  
Xuexin Chi
Keyword(s):  
Differential Evolution ◽  
Optimization Algorithm ◽  
Location Problem ◽  
Cuckoo Search ◽  
Distribution Center ◽  
Metaheuristic Algorithms ◽  
Logistics Distribution ◽  
Center Location ◽  
Mutation Operation ◽  
Distribution Center Location

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.

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