Applied Fuzzy Multi-Objective with α-Cut Analysis for Optimizing Supply Chain Master Planning Problem

Sutthibutr, Noppasorn; Chiadamrong, Navee

doi:10.1145/3335550.3335571

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…Fuzzy sets are used to express information ambiguity and uncertainty. Membership functions can take many forms such as trapezoidal and triangular membership functions (Sutthibutr and Chiadamrong, 2019). As the variety of products on the market increases and the product life cycle decreases, manufacturers mostly confront fluctuations in demand of product, which has led to a dynamic production environment.…”

Section: Problem Definitionmentioning

confidence: 99%

Optimization of virtual closed-loop supply chain under uncertainty: application of IoT

Shambayati

Nikabadi

Firouzabadi

et al. 2022

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PurposeSupply chains (SCs) have been growingly virtualized in response to the market challenges and opportunities that are presented by new and cost-effective internet-based technologies today. This paper designed a virtual closed-loop supply chain (VCLSC) network based on multiperiod, multiproduct and by using the Internet of Things (IoT). The purpose of the paper is the optimization of the VCLSC network.Design/methodology/approachThe proposed model considers the maximization of profit. For this purpose, costs related to virtualization such as security, energy consumption, recall and IoT facilities along with the usual costs of the SC are considered in the model. Due to real-world demand fluctuations, in this model, demand is considered fuzzy. Finally, the problem is solved using the Grey Wolf algorithm and Firefly algorithm. A numerical example and sensitivity analysis on the main parameters of the model are used to describe the importance and applicability of the developed model.FindingsThe findings showed that the Firefly algorithm performed better and identified more profit for the SC in each period. Also, the results of the sensitivity analysis using the IoT in a VCLSC showed that the profit of the virtual supply chain (VSC) is higher compared to not using IoT due to tracking defective parts and identifying reversible products. In proposed model, chain members can help improve chain operations by tracking raw materials and products, delivering products faster and with higher quality to customers, bringing a new level of SC efficiency to industries. As a result, VSCs can be controlled, programmed and optimized remotely over the Internet based on virtual objects rather than direct observation.Originality/valueThere are limited researches on designing and optimizing the VCLSC network. This study is one of the first studies that optimize the VSC networks considering minimization of virtual costs and maximization of profits. In most researches, the theory of VSC and its advantages have been described, while in this research, mathematical optimization and modeling of the VSC have been done, and it has been tried to apply SC virtualization using the IoT. Considering virtual costs in VSC optimization is another originality of this research. Also, considering the uncertainty in the SC brings the issue closer to the real world. In this study, virtualization costs including security, recall and energy consumption in SC optimization are considered.HighlightsInvestigates the role of IoT for virtual supply chain profit optimization and mathematical optimization of virtual closed-loop supply chain (VCLSC) based on multiperiod, multiproduct with emphasis on using the IoT under uncertainty.Considering the most important costs of virtualization of supply chain include: cost of IoT information security, cost of IoT energy consumption, cost of recall the production department, cost of IoT facilities.Selection of the optimal suppliers in each period and determination of the price of each returned product in virtual supply chain.Solving and validating the proposed model with two meta-heuristic algorithms (the Grey Wolf algorithm and Firefly algorithm).

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Section: Problem Definitionmentioning

confidence: 99%

Optimization of virtual closed-loop supply chain under uncertainty: application of IoT

Shambayati

Nikabadi

Firouzabadi

et al. 2022

View full text Add to dashboard Cite

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“…Grillo et al [56] use the fuzzy set theory to model uncertainty and propose a metaheuristic particle swarm optimization (PSO) technique as a solution method. A method to achieve an optimal MPS in an uncertain environment is that proposed by Sutthibutr and Chiadamrong [57]. It is based on a multi-objective linear fuzzy model with an α-cut analysis to ensure decision makers.…”

Section: Content Analysismentioning

confidence: 99%

Smart Master Production Schedule for the Supply Chain: A Conceptual Framework

Serrano-Ruiz

Mula

2021

Computers

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Risks arising from the effect of disruptions and unsustainable practices constantly push the supply chain to uncompetitive positions. A smart production planning and control process must successfully address both risks by reducing them, thereby strengthening supply chain (SC) resilience and its ability to survive in the long term. On the one hand, the antidisruptive potential and the inherent sustainability implications of the zero-defect manufacturing (ZDM) management model should be highlighted. On the other hand, the digitization and virtualization of processes by Industry 4.0 (I4.0) digital technologies, namely digital twin (DT) technology, enable new simulation and optimization methods, especially in combination with machine learning (ML) procedures. This paper reviews the state of the art and proposes a ZDM strategy-based conceptual framework that models, optimizes and simulates the master production schedule (MPS) problem to maximize service levels in SCs. This conceptual framework will serve as a starting point for developing new MPS optimization models and algorithms in supply chain 4.0 (SC4.0) environments.

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“…Grillo et al [14] use the fuzzy set theory to model uncertainty and propose a metaheuristic particle swarm optimisation (PSO) technique as a solution method. A method to achieve an optimal MPS in an uncertain environment is proposed by Sutthibutr and Chiadamrong [15], based on a multi-objective linear fuzzy model with an α-cut analysis. Arani and Torabi [16] integrate physical/material tactical plans with financial ones to account for their reciprocal effects.…”

Section: Literature Reviewmentioning

confidence: 99%

Digital Twin for Supply Chain Master Planning in Zero-Defect Manufacturing

Serrano

Mula

Poler

2021

IFIP Advances in Information and Communication Technology

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Recently, many novel paradigms, concepts and technologies, which lay the foundation for the new revolution in manufacturing environments, have emerged and make it faster to address critical decisions today in supply chain 4.0 (SC4.0), with flexibility, resilience, sustainability and quality criteria. The current power of computational resources enables intelligent optimisation algorithms to process manufacturing data in such a way, that simulating supply chain (SC) planning performance in real time is now possible, which allows relevant information to be acquired so that SC nodes are digitally interconnected. This paper proposes a conceptual framework based on a digital twin (DT) to model, optimise and prescribe a SC's master production schedule (MPS) in a zero-defect environment. The proposed production technologies focus on the scientific development and resolution of new models and optimisation algorithms for the MPS problem in SC4.0.

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Applied Fuzzy Multi-Objective with α-Cut Analysis for Optimizing Supply Chain Master Planning Problem

Cited by 5 publications

References 13 publications

Optimization of virtual closed-loop supply chain under uncertainty: application of IoT

Optimization of virtual closed-loop supply chain under uncertainty: application of IoT

Smart Master Production Schedule for the Supply Chain: A Conceptual Framework

Digital Twin for Supply Chain Master Planning in Zero-Defect Manufacturing

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