Artificial intelligence in supply chain management: theory and applications

Min, Hokey

doi:10.1080/13675560902736537

Cited by 305 publications

(168 citation statements)

References 107 publications

(82 reference statements)

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“…Artificial intelligence systems such as agent-based systems, genetic algorithms and expert systems are gaining popularity in the field of supply chain management (Min, 2010).…”

Section: Introductionmentioning

confidence: 99%

Role of technological dimensions of green supply chain management practices on firm performance

Bag

Gupta

Kumar

et al. 2020

JEIM

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PurposeThe research study aims to investigate green supply chain management (GSCM) elements as part of a complete system. It aims to understand the special properties of the GSCM system under the moderating effects of product complexity and purchasing structure.Design/methodology/approachA thorough literature review led to the building of the conceptual framework. Six constructs were identified using systems theory. These constructs include green supply chain technological dimensions (particularly, Artificial Intelligence (AI) based), green supply chain strategy, green supply chain process, product complexity, purchasing structure, and firm performance. The instrument was scientifically developed for gathering survey responses using complete design test methods. The conceptual model was eventually tested based on survey data collected from 250 automotive components and allied manufacturers in the emerging economy of South Africa.FindingsThe results indicate that GSCM technological dimensions (AI-based) positively influence GSCM strategy. Further, GSCM strategy was found to positively influence the GSCM process. The GSCM processes have significant effects on environmental performance, social performance, and financial performance. The product complexity has a significant moderation effect on the paths GSCM strategy and GSCM process.Originality/valueThe findings from multivariate data analysis provide a better understanding of GSCM system dynamics and are helpful to key decision-makers. This unique model has elevated GSCM theory to a new level. There are limited studies available in the existing GSCM literature using systems theory. This study will offer an advanced/comprehensive understanding to readers in this relatively new concept.

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“…Artificial intelligence systems such as agent-based systems, genetic algorithms and expert systems are gaining popularity in the field of supply chain management (Min, 2010).…”

Section: Introductionmentioning

confidence: 99%

Role of technological dimensions of green supply chain management practices on firm performance

Bag

Gupta

Kumar

et al. 2020

JEIM

View full text Add to dashboard Cite

show abstract

“…Recent developments in artificial intelligence and machine learning have provided tools with which a computer can now outperform the analytic capability of a human, particularly when data sets are large or when a system relies on many free parameters [1]. The application of machine learning methods has led to dramatic advances in many scientific fields and contexts, such as supply chain forecasting and healthcare [2,3]. Machine learning is also well suited to the optimization of a complex experimental apparatus [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Applying machine learning optimization methods to the production of a quantum gas

Barker

Style

Luksch

et al. 2020

Mach. Learn.: Sci. Technol.

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We apply three machine learning strategies to optimize the atomic cooling processes utilized in the production of a Bose-Einstein condensate (BEC). For the first time, we optimize both laser cooling and evaporative cooling mechanisms simultaneously. We present the results of an evolutionary optimization method (differential evolution), a method based on non-parametric inference (Gaussian process regression) and a gradient-based function approximator (artificial neural network). Online optimization is performed using no prior knowledge of the apparatus, and the learner succeeds in creating a BEC from completely randomized initial parameters. Optimizing these cooling processes results in a factor of four increase in BEC atom number compared to our manually-optimized parameters. This automated approach can maintain close-to-optimal performance in long-term operation. Furthermore, we show that machine learning techniques can be used to identify the main sources of instability within the apparatus.

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“…Furuhata et al (2009) presented an experimental approach to design and evaluate the market mechanisms with different sets of market policies. Similarly, the potential use of agent-based systems for logistics applications was highlighted in a recent review article by Min (2010). Hence, it is evident from the review that an integrated multi-agent system with an auction mechanism to solve MBPP has not been addressed so far, and it is a major gap that this study attempts to solve.…”

Section: Literature Reviewmentioning

confidence: 96%

Multi-agent system with iterative auction mechanism for master bay plan problem in marine logistics

Parthibaraj¹,

Palaniappan²,

Gunasekaran

et al. 2017

Maritime Policy & Management

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Nachiappan (2017) Multi-agent system with iterative auction mechanism for master bay plan problem in marine logistics. Maritime Policy and Management, 44 (6). pp. 705-726. Abstract:The support of containerization to trade development demands an efficient solution method for the container loading problem in order to reduce shipment and handling time. Hence, the stowage planning of containers is critical to providing speedy delivery of resources from the area of supply to the area of demand. Moreover, information on container terminal activities, structure of ship, and characteristics of containers is distributed among stowage planners. This information imposes constraints, and so the master bay plan problem (MBPP) becomes NP-hard. Therefore, a multi-agent systems (MAS) methodology is designed to effectively communicate the information and solve the MBPP sustainably.In the designed MAS methodology, an information exchange system (IES) is created for stowage planners to bid for ship slots in each experimental iterative combinatorial auction (ICA) market. The winner in the ICA experiments is provided with the ship slots, and the entire bay plan is prepared.Further, the ship-turnaround time is validated using the data obtained from the benchmark problem of Wilson and Roach (1999). Keywords:Master bay plan problem (MBPP), multi-agent system (MAS), information exchange system (IES), iterative combinatorial auction (ICA).

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Artificial intelligence in supply chain management: theory and applications

Cited by 305 publications

References 107 publications

Role of technological dimensions of green supply chain management practices on firm performance

Role of technological dimensions of green supply chain management practices on firm performance

Applying machine learning optimization methods to the production of a quantum gas

Multi-agent system with iterative auction mechanism for master bay plan problem in marine logistics

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