An integrated supply chain problem: a nested lagrangian relaxation approach

Diabat, Ali; Richard, Jean‐Philippe P.

doi:10.1007/s10479-015-1818-4

Cited by 36 publications

(8 citation statements)

References 38 publications

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“…[22]. Multi-objective nonlinear programming [22], general Lagrangian architecture to analyze build-to-order SCNs [44,45], have been designed to handle such demand uncertainties. In particular, the SCN constraints for build-to-order kernels have been enacted through Lagrangian multipliers, a more robust version of which has been adapted in this present study.…”

Section: Social and Economic Uncertaintymentioning

confidence: 99%

“…In order to reduce the energy (often related to the optimized entropy of a chaotic system) of the system (i.e., the supply chain network uncertainties represented by the mathematical model), the dominant dynamic variables are only considered at the final stage (section 3.3.1) based on subjective (industrial partners) inputs. To understand the dynamics of the system, Lagrange Multipliers are introduced to develop a constrained system (section 3.3.2) in line with [44,45]. In both cases, the cost function takes a modified form, which are detailed in the latter sections.…”

Section: Inter-dependency Of the Variablesmentioning

confidence: 99%

See 1 more Smart Citation

Time evolution of a Supply Chain Network: Kinetic modeling

Debnath

El-Hassani

Chattopadhyay

et al. 2022

Physica A: Statistical Mechanics and its Applications

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Section: Social and Economic Uncertaintymentioning

confidence: 99%

Section: Inter-dependency Of the Variablesmentioning

confidence: 99%

Time evolution of a Supply Chain Network: Kinetic modeling

Debnath

El-Hassani

Chattopadhyay

et al. 2022

Physica A: Statistical Mechanics and its Applications

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“…Currently, there is an emerging trend to integrate strategic with tactical decisions through the development of joint location-inventory models [10]. Facility location decisions are focused on maximizing the efficiency of a supply chain, while inventory control models have focused on improving the responsiveness of the supply chain [11]. In many cases, integrating strategic decisions with inventory control policies in a closed-loop supply chain can help overcome short-sighted sub-optimal network designs [12].…”

Section: Introductionmentioning

confidence: 99%

A hybrid differential evolution algorithm for a location-inventory problem in a closed-loop supply chain with product recovery

Guo

Liu

Zhang

et al. 2022

Complex Intell. Syst.

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Product recovery is an important business because of its great economic, social, and environmental benefits in practice. In this paper, a location-inventory problem (LIP) in a closed-loop supply chain (CLSC) is investigated to optimize facility location and inventory control decisions by considering product recovery. The objective is to optimize facility location and inventory control decisions to minimize the total cost of business operations in a closed-loop supply chain system. We formulate this problem as a mixed-integer nonlinear programming model and design a modified hybrid differential evolution algorithm (MHDE) to solve it efficiently. Finally, numerical results are presented to validate the performance of the new algorithm. The results show that MHDE is more efficient and effective than Lingo and other algorithms for the research problem under study. Managerial insights are also derived for business managers to improve their supply chain performance.

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“…Such is the case in the works of Chan, Chung, and Wadhwa (2005), Jayaraman and Ross (2003), Diabat, Aouam, and Ozsen (2009), Shiguemoto and Armentano (2010), and Diabat (2014) who developed metaheuristics such as genetic algorithms, simulated annealing and tabu search to solve similar problems. Furthermore, Lagrangian relaxation has been implemented by Chen and Chu (2003), Diabat and Richard (2015), and Eskigun, Uzsoy, Preckel, Beaujon, Krishnan, and Tew (2005). Benders decomposition method is used by Santibanez-Gonzalez and for solving a reverse logistics problem.…”

Section: Introductionmentioning

confidence: 99%