Optimal mathematical programming for the warehouse location problem with Euclidean distance linearization

Meng, You; Xiao, Yiyong; Zhang, Siyue; Yang, Pei

doi:10.1016/j.cie.2019.07.020

Cited by 36 publications

(20 citation statements)

References 28 publications

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“…That is, even if the type of warehouse is differentiated, the criteria considered are similar. On the other hand, the most commonly used success factors are distance to demand center/consumer etc [4], [10], [18], [23]- [25], [27], [29], [33], [39], [41], [45], [49], [51], accessibility and flexibility (location, transportation connection, land availability [4]- [7], [10], [12], [13], [18], [20], [30], [31], [40], [43], [44], [47]- [49], costs (transportation, warehouse management, distribution, shipping, order, fixed, labor, handling, storage, settlement, tax incentives and structure) [6]- [9], [11]- [14], [19]- [22], [26]- [28], [31], [32], [34], [36], [38], [39], [42]- [49], infrastructure (logistics, information technology, capacity, storage conditions etc.) [10], [15], [17],…”

Section: Discussionmentioning

confidence: 99%

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Literature Review on Success Factors and Methods Used in Warehouse Location Selection

Gergin¹,

Peker²

2019

Pamukkale J Eng Sci

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ÖzThe fact that the production and consumption amounts are different from each other gives a dynamic feature to the concept of storage. In addition, an effective storage activity hinges upon the selection of the right warehouse location that takes into account capital and labor investments; minimizes the transportation cost and time. The aim of this study is to investigate the preliminary researches in the selection of the warehouse location selection and to present information on which success factors and methods are preferred. Within this scope, many academic studies have been examined by filtering various databases. According to the results of the study, the most used methods in warehouse location selection are multi-criteria decision making methods such as AHP, ANP and TOPSIS; and the most commonly used success factor is determined as the cost-based factors. Dünya üzerinde üretim ve tüketim miktarlarının birbirlerinden farklı olması depolama kavramına dinamik bir özellik kazandırmaktadır.Ayrıca etkin bir depolama faaliyeti; sermaye ve işçilik yatırımlarını dikkate alacak, taşıma maliyet ve zamanını minimize edecek doğru depo yerinin seçilmesinden geçmektedir. Bu doğrultuda çalışmanın amacı, depo yeri seçiminde yapılmış olan öncül araştırmaların incelenmesi ve depo yer seçiminde hangi başarı faktörleri ve yöntemlerin tercih edildiğine dair bir bilgi sunmaktır. Bu amaç kapsamında çok sayıda akademik çalışma çeşitli veri tabanları süzülerek incelenmiştir. Çalışmanın sonuçlarına göre depo yer seçiminde en fazla kullanılan yöntemler AHP, ANP ve TOPSIS'in başını çektiği Çok Kriterli Karar Verme yöntemleri, en çok kullanılan başarı faktörü ise Maliyet içerikli faktörler olarak belirlenmiştir.

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Section: Discussionmentioning

confidence: 99%

“…This study considered a limited number of criteria. [51] utilized Euclidean Distance Linearization to determine the optimal location of warehouses. The model has two advantages; first, it is easy to trap into local optimal and second, it is sensitive to initial locations.…”

Section: Review Papersmentioning

confidence: 99%

Literature Review on Success Factors and Methods Used in Warehouse Location Selection

Gergin¹,

Peker²

2019

Pamukkale J Eng Sci

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“…A heuristic alternates a phase facilities location with a phase of assignment of users to facilities using k-mean clustering. In [25], the authors compare, for the same continuous facility location problemm, k-mean heuristics to a MILP formulation based on euclidean distance linearization. However, they did not use k-mean clustering to reduce the size of the resulting MILP.…”

Section: Voronoï Polyhedra and K-means Clustering For Disjoint Beam Center Domainsmentioning

confidence: 99%

MILP formulation improvement with k-means clustering for the beam layout optimization in multibeam satellite systems

Camino

Artigues

Houssin

et al. 2021

Computers & Industrial Engineering

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This paper addresses an NP-hard design optimization problem in a multibeam satellite communication system. This problem consists in designing irregular beam layouts to satisfy non-uniform user traffic demands over the satellite service area, under antenna constraints, satellite payload constraints and a telecommunication mission criterion. Efficiently solving this problem is of crucial importance due to its impact on the system performance and cost. We compare three mixed-integer linear programming formulations. The first one, issued from previous work, is based on a linearization of both convex and non-convex Euclidean distance constraints. The two other aim at reducing the solution space size and at breaking symmetry inherent to the first formulation. For that purpose, we introduce a new process to interface k-means clustering with mixed-integer linear programming. We examine an exact and a heuristic approach for exploiting these principles that yield two new formulations. The heuristic approach outperforms the others based on our tests on a set of large-scale realistic problem instances, allowing to use mixed-integer linear programming in the industrial context.

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“…Firstly, it was necessary to find a concentration set according to some rules, and secondly, the optimal solution in the concentration set could be solved every time by generating a concentration set. Based on Rosing and Hodgson's heuristic concentration procedure, the MILP-based neighborhood searching algorithms by Xiao et al [24,25] and You et al [27,28] were introduced, and the concept of the partial set was also presented. For this paper, we developed a heuristic algorithm for large-sized problems following the steps showed above.…”

Section: A Heuristic Partial Optimization Algorithmmentioning

confidence: 99%

“…Sustainability 2020, 12, x FOR PEER REVIEW 9 of 17 searching algorithms by Xiao et al [24,25] and You et al [27,28] were introduced, and the concept of the partial set was also presented. For this paper, we developed a heuristic algorithm for large-sized problems following the steps showed above.…”

Section: A Heuristic Partial Optimization Algorithmmentioning

confidence: 99%

Dynamic Gaming Case of the R-Interdiction Median Problem with Fortification and an MILP-Based Solution Approach

et al. 2020

Self Cite

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This paper studies the cyclic dynamic gaming case of the r-interdiction median problem with fortification (CDGC-RIMF), which is important for strengthening a facility’s reliability and invulnerability under various possible attacks. We formulated the CDGC-RIMF as a bi-objective mixed-integer linear programming (MILP) model with two opposing goals to minimize/maximize the loss from both the designer (leader) and attacker (follower) sides. The first goal was to identify the most cost-effective plan to build and fortify the facility considering minimum loss, whereas the attacker followed the designer to seek the most destructive way of attacking to cause maximum loss. We found that the two sides could not reach a static equilibrium with a single pair of confrontational plans in an ordinary case, but were able to reach a dynamically cyclic equilibrium when the plan involved multiple pairs. The proposed bi-objective model aimed to discover the optimal cyclic plans for both sides to reach a dynamic equilibrium. To solve this problem, we first started from the designer’s side with a design and fortification plan, and then the attacker was able to generate their worst attack plan based on that design. After that, the designer changed their plan again based on the attacker’s plan in order to minimize loss, and the attacker correspondingly modified their plan to achieve maximum loss. This game looped until, finally, a cyclic equilibrium was reached. This equilibrium was deemed to be optimal for both sides because there was always more loss for either side if they left the equilibrium first. This game falls into the subgame of a perfect Nash equilibrium—a kind of complete game. The proposed bi-objective model was directly solved by the CPLEX solver to achieve optimal solutions for small-sized problems and near-optimal feasible solutions for larger-sized problems. Furthermore, for large-scale problems, we developed a heuristic algorithm that implemented dynamic iterative partial optimization alongside MILP (DIPO-MILP), which showed better performance compared with the CPLEX solver when solving large-scale problems.

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Optimal mathematical programming for the warehouse location problem with Euclidean distance linearization

Cited by 36 publications

References 28 publications

Literature Review on Success Factors and Methods Used in Warehouse Location Selection

Literature Review on Success Factors and Methods Used in Warehouse Location Selection

MILP formulation improvement with k-means clustering for the beam layout optimization in multibeam satellite systems

Dynamic Gaming Case of the R-Interdiction Median Problem with Fortification and an MILP-Based Solution Approach

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