Advanced supplier selection: A hybrid multi-agent negotiation protocol supporting supply chain dyadic collaboration

Nejma, Maryam; Zair, Firdaous; Cherkaoui, Abdelghani; Fourka, Mohamed

doi:10.5267/j.dsl.2018.7.001

Cited by 9 publications

(2 citation statements)

References 22 publications

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“…Many of them were developed a model based on the specifications of the problem or assumptions which are used such as linear programming which is a sufficiently simple model containing a linear objective function and linear constraints occurred in the model [7], [8]. For a few advanced problem, a model was developed with considering negotiation process on multi-agent condition [9] and a decision making model with multi-objective approach [10]. Another important part in supply chain is inventory management which contributes to the holding cost and raw material/product stock management.…”

Section: Introductionmentioning

confidence: 99%

Determining the best optimization method for large scale probabilistic supplier selection problem integrated with inventory management

2020

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In logistics and supply chain management, a problem of supplier selection is an optimization problem where the number of variables is growing exponentially which will produce a large-scale optimization problem. A right choice of the used method to solve is needed according to the performance of the method. This paper is considered to compare and analyse how the performance of some classic numerical optimization methods which are interior point, SQP, SQP-legacy and active-set to solve a large-scale optimization problem of a probabilistic supplier selection problem with inventory management. Word “probabilistic” in this case is referring to that the problem is involving some uncertain parameters approached by random variable (probabilistic parameter). We used the existing mathematical model of probabilistic supplier selection problem with inventory management provided in our previous works that only considering few numbers of decision variable then the occurred optimization problem is a small-scale problem that can be solved efficiently by analytical method or numerical method. Then, in this paper we resolved this model with huge number of decision variable indicated by the number of the supplier and time period that is large by using an existing numerical optimization method to analyse how the decision variable, is it reliable to be used or not. We generate some randomly data to simulate the problem and the results. From our computational experiment, the optimal decision variables obtained by the used methods are acceptable to be used as the decision that can be used to be applied by the decision maker. Based on the relative error given by these methods, the active set was given the best performance which means that active-set method is the best choice to solve.

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Section: Introductionmentioning

confidence: 99%

Determining the best optimization method for large scale probabilistic supplier selection problem integrated with inventory management

2020

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“…Distribution of supply chain automation articles within the SCOR modelThe second largest category of articles have studied the use of agents in Sourcing. Sourcing studies mostly involved automated supplier selection such asGuneri et al (2009),Yu and Wong (2015),Cavalcante et al (2019) andNejma et al (2019) Zhang et al (2015). created an agent-based peer-topeer architecture for semantic discovery of manufacturing services across virtual enterprises, which was then used for supplier selection.The third category involved Planning which involves SC processes that balance aggregate demand and supply to develop a best course of actions to meet sourcing, production, and delivery requirements.…”

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confidence: 99%

Will bots take over the supply chain? Revisiting agent-based supply chain automation

Mak

Brintrup

2021

International Journal of Production Economics

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Agent-based systems have the capability to fuse information from many distributed sources and create better plans faster. This feature makes agent-based systems naturally suitable to address the challenges in Supply Chain Management (SCM). Although agent-based supply chains systems have been proposed since the early 2000s; industrial uptake of them has been lagging. The reasons quoted include the immaturity of technology, a lack of interoperability with supply chain information systems, and a lack of trust in Artificial Intelligence (AI). In this paper, we revisit the agent-based supply chain and review the state of the art. We find that agent-based technology has matured, and other supporting technologies that are penetrating supply chains; are filling in gaps, leaving the concept applicable to a wider range of functions. For example, the ubiquity of IoT technology helps agents "sense" the state of affairs in a supply chain and opens up new possibilities for automation. Digital ledgers help securely transfer data between third parties, making agent-based information sharing possible, without the need to integrate Enterprise Resource Planning (ERP) systems. Learning functionality in agents enables agents to move beyond automation and towards autonomy. We note this convergence effect through conceptualising an agent-based supply chain framework, reviewing its components, and highlighting research challenges that need to be addressed in moving forward.

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Smarter Relationships? The Present and Future Scope of AI Application in Buyer-Supplier Relationships

Nitsche

Bürger

Arlinghaus

et al. 2021

Lecture Notes in Computer Science

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Advanced supplier selection: A hybrid multi-agent negotiation protocol supporting supply chain dyadic collaboration

Cited by 9 publications

References 22 publications

Determining the best optimization method for large scale probabilistic supplier selection problem integrated with inventory management

Determining the best optimization method for large scale probabilistic supplier selection problem integrated with inventory management

Will bots take over the supply chain? Revisiting agent-based supply chain automation

Smarter Relationships? The Present and Future Scope of AI Application in Buyer-Supplier Relationships

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