Determining an optimal warehouse location, capacity, and product allocation in a multi-product, multi-period distribution network: a case study

Le, Thi Diem Chau; Buddhakulsomsiri, Jirachai; Jeenanunta, Chawalit; Dumrongsiri, Aussadavut

doi:10.1504/ijlsm.2019.103517

Cited by 3 publications

(3 citation statements)

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“…Cortinhal et al (2015) formulated an MILP model that minimizes the overall logistics cost of a four-stage supply chain, in which outsourcing opportunities are also considered. Le et al (2019) developed an MILP model to determine an optimal warehouse location, capacity and product allocation such that the transportation and warehouse operational costs in a network of factories, warehouses and customers are minimized.…”

Section: Optimization Problem With Deterministic Model Parametersmentioning

confidence: 99%

“…Methods for solving optimization problems include mathematical programming and heuristics. Several mathematical programming modeling approaches consist of single-objective mixed-integer linear programming or MILP (Sadjady and Davoudpour, 2012; Askin et al , 2013; Cortinhal et al , 2015; Santosa and Kresna, 2015; Le et al , 2019), multi-objective mixed-integer linear programming (MOMILP) (Paksoy et al , 2010; Boronoos et al , 2021), normalized normal constraint (Wang et al , 2011), evolutionary multiobjective algorithm (Bhattacharya and Bandyopadhyay, 2010; Harris et al , 2014), weighted sum method (Amin and Zhang, 2013), goal programming (Yaghin et al , 2012; Mohammed and Wang, 2017; Yaghin and Sarlak, 2020), LP-metric (Mohammed and Wang, 2017; Khalilzadeh and Derikvand, 2018), min–max approach (Kannan et al , 2013; Mohammed and Wang, 2017; Olapiriyakul et al , 2019; Jinawat and Buddhakulsomsiri, 2021), ε -constraint (Amin and Zhang, 2013; Jindal and Sangwan, 2017; Mohammed and Wang, 2017; Mohammed et al , 2019) and augmented ε -constraint (Mohebalizadehgashti et al , 2020). In addition, several studies, including that of Liang (2006), Paksoy et al (2012), Pishvaee and Razmi (2012), Amin and Zhang (2013), Dey et al (2015), Gholamian et al (2015), Jindal and Sangwan (2017), Mohammed and Wang (2017), Mohammed et al (2019), Yaghin and Sarlak (2020) and Asim et al (2021), consider uncertainty in their model parameters.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The optimization problems, whose model parameters are deterministic, can be grouped into single- and multiple-objective problems. For single-objective problems, minimizing of the total cost of a supply chain is the most common objective function (Sadjady and Davoudpour, 2012; Askin et al , 2013; Cortinhal et al , 2015; Santosa and Kresna, 2015; and Le et al , 2019). Santosa and Kresna (2015) dealt with a warehouse location problem, which minimizes transportation and fixed costs by designing a simulated annealing algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

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A fuzzy multiobjective linear programming approach for green multiechelon distribution network design

Tai,

Jinawat,

Buddhakulsomsiri

2023

JM2

Self Cite

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Purpose Distribution network design involves a set of strategic decisions in supply chains because of their long-term impacts on the total logistics cost and environment. To incorporate a trade-off between financial and environmental aspects of these decisions, this paper aims to determine an optimal location, among candidate locations, of a new logistics center, its capacity, as well as optimal network flows for an existing distribution network, while concurrently minimizing the total logistics cost and gas emission. In addition, uncertainty in transportation and warehousing costs are considered. Design/methodology/approach The problem is formulated as a fuzzy multiobjective mathematical model. The effectiveness of this model is demonstrated using an industrial case study. The problem instance is a four-echelon distribution network with 22 products and a planning horizon of 20 periods. The model is solved by using the min–max and augmented ε-constraint methods with CPLEX as the solver. In addition to illustrating model’s applicability, the effect of choosing a new warehouse in the model is investigated through a scenario analysis. Findings For the applicability of the model, the results indicate that the augmented ε-constraint approach provides a set of Pareto solutions, which represents the ideal trade-off between the total logistics cost and gas emission. Through a case study problem instance, the augmented ε-constraint approach is recommended for similar network design problems. From a scenario analysis, when the operational cost of the new warehouse is within a specific fraction of the warehousing cost of third-party warehouses, the solution with the new warehouse outperforms that without the new warehouse with respective to financial and environmental objectives. Originality/value The proposed model is an effective decision support tool for management, who would like to assess the impact of network planning decisions on the performance of their supply chains with respect to both financial and environmental aspects under uncertainty.

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Section: Optimization Problem With Deterministic Model Parametersmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

A fuzzy multiobjective linear programming approach for green multiechelon distribution network design

Tai,

Jinawat,

Buddhakulsomsiri

2023

JM2

Self Cite

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The Capacitated Product Portfolio Mix-and-Allocation problem with integrity constraints

Menezes,

Ruiz-Hernandez,

Pinto

2024

Computers & Industrial Engineering

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The location routing problem with facility sizing decisions

Tordecilla

Montoya‐Torres

Quintero-Araújo

et al. 2022

Int Trans Operational Res

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The location routing problem (LRP) integrates operational decisions on vehicle routing operations with strategic decisions on the location of the facilities or depots from which the distribution will take place. In other words, it combines the well‐known vehicle routing problem (VRP) with the facility location problem (FLP). Hence, the LRP is an NP‐hard combinatorial optimization problem, which justifies the use of metaheuristic approaches whenever large‐scale instances need to be solved. In this paper, we explore a realistic version of the LRP in which facilities of different capacities are considered, i.e., the manager has to consider not only the location but also the size of the facilities to open. In order to tackle this optimization problem, three mixed‐integer linear formulations are proposed and compared. As expected, they have been proved to be cost‐ and time‐ inefficient. Hence, a biased‐randomized iterated local search algorithm is proposed. Classical instances for the LRP with homogeneous facilities are naturally extended to test the performance of our approach.

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Determining an optimal warehouse location, capacity, and product allocation in a multi-product, multi-period distribution network: a case study

Cited by 3 publications

References 0 publications

A fuzzy multiobjective linear programming approach for green multiechelon distribution network design

A fuzzy multiobjective linear programming approach for green multiechelon distribution network design

The Capacitated Product Portfolio Mix-and-Allocation problem with integrity constraints

The location routing problem with facility sizing decisions

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