Competitive facility location on decentralized supply chains

Meng, Qiang; Huang, Yikai; Cheu, Ruey Long

doi:10.1016/j.ejor.2008.03.030

Cited by 55 publications

(21 citation statements)

References 32 publications

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“…Despite that the present generalized ABM formulation refers to a heuristic rule-based search process for finding an optimal location, some advanced optimization techniques from the field of operational research could be further employed to determine the optimal asset allocation and decentralized supply chain organization of firms among regions under a variety of (capacity, production, etc.) constraints (e.g., [31]). …”

Section: Model Overviewmentioning

confidence: 99%

Spatial agent-based modeling of household and firm location with endogenous transport costs

Tsekeris

Vogiatzoglou

2011

Netnomics

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Section: Model Overviewmentioning

confidence: 99%

Spatial agent-based modeling of household and firm location with endogenous transport costs

Tsekeris

Vogiatzoglou

2011

Netnomics

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“…[3][4][5]7,9,10,12,16,18,19]. Taking into account the common knowledge that it is more practical to solve the involved LQP systems approximately, the majority of existing LQP-based algorithms actually allow for inexact solutions of (2) subject to certain criteria.…”

Section: Introductionmentioning

confidence: 99%

An improved LQP-based method for solving nonlinear complementarity problems

Yuan

2009

Front. Math. China

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The well-known logarithmic-quadratic proximal (LQP) method has motivated a number of efficient numerical algorithms for solving nonlinear complementarity problems (NCPs). In this paper, we aim at improving one of them, i.e., the LQP-based interior prediction-correction method proposed in [He, Liao and Yuan, J. Comp. Math., 2006, 24(1): 33-44], via identifying more appropriate step-sizes in the correction steps. Preliminary numerical results for solving some NCPs arising in traffic equilibrium problems are reported to verify the theoretical assertions.

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“…To implement LQP-PC method effectively, it is essential to transform the constraint set into the form of partitioned matrix firstly, and then carry out the prediction and correction procedures while adjusting the step size adaptively. We refer the readers to the work such as Meng, Huang and Cheu (2009) to reach a comprehensive understanding.…”

Section: Network Equilibrium Algorithmmentioning

confidence: 99%

Research on closed-loop supply chain network equilibrium with two-type suppliers, risk-averse manufacturers and capacity constraints

Sun

Zhang

2015

JIEM

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Abstract:Purpose: The aim of this paper is to investigate the closed-loop supply chain (CLSC) network equilibrium with the consideration of three practical factors: two complementary types of suppliers, risk-averse character of the manufacturer and capacity constraints of the suppliers.Design/methodology/approach: The equilibrium of various decision makers including the suppliers, the manufacturers, the retailers, the collectors and the demand markets are modeled via finite-dimensional variational inequality, respectively. Then the governing CLSC network equilibrium model is established. The logarithmic-quadratic proximal prediction-correction algorithm is designed to solve the variational inequality model. Numerical examples are given to analyze the impact of return rate, risk-averse degree and capacity constraints on the network equilibrium under different product BOMs. Findings:With the increase of return rate, the profits of various channel members and the performance of the CLSC system will improve. There is a contradiction between profit maximization and risk minimization for the manufacturers. Moreover, the economic behavior of the CLSC is likely to be limited by the capacity constraints of the suppliers. Originality/value: Prior to this paper, few papers have addressed with the CLSC network equilibrium considering some practical factors. They assume all the suppliers are identical and -509-Journal of Industrial Engineering and Management -http://dx.doi.org/10.3926/jiem.1336 all the decision-makers are risk neutral. Furthermore, the production capacities of all suppliers are assumed to be infinite or large enough. To fill the gap, this paper examines the influences of two-type suppliers, risk aversion and capacity constraints upon the CLSC network equilibrium.

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Competitive facility location on decentralized supply chains

Cited by 55 publications

References 32 publications

Spatial agent-based modeling of household and firm location with endogenous transport costs

Spatial agent-based modeling of household and firm location with endogenous transport costs

An improved LQP-based method for solving nonlinear complementarity problems

Research on closed-loop supply chain network equilibrium with two-type suppliers, risk-averse manufacturers and capacity constraints

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