Agent-based Supply Chain Research Articles

Enhancing the Efficiency of Enterprise Shutdowns for Environmental Protection: An Agent-Based Modeling Approach with High Spatial–Temporal Resolution Data

Top-down environmental policies aim to mitigate environmental risks but inevitably lead to economic losses due to the market entry or exit of enterprises. This study developed a universal dynamic agent-based supply chain model to achieve tradeoffs between environmental risk reduction and economic sustainability. The model was used to conduct high-resolution daily simulations of the dynamic shifts in enterprise operations and their cascading effects on supply chain networks. It includes production, consumption, and transportation agents, attributing economic features to supply chain components and capturing their interactions. It also accounts for adaptive responses to daily external shocks and replicates realistic firm behaviors. By coupling high spatial–temporal resolution firm-level data from 18 916 chemical enterprises, this study investigates the economic and environmental impacts of an environmental policy resulting in the closure of 1800 chemical enterprises over three years. The results revealed a significant economic loss of 25.8 billion USD, ranging from 23.8 billion to 31.8 billion USD. Notably, over 80% of this loss was attributed to supply chain propagation. Counterfactual analyses indicated that implementing a staggered shutdown strategy prevented 18.8% of supply chain losses, highlighting the importance of a gradual policy implementation to prevent abrupt supply chain disruptions. Furthermore, the study highlights the effectiveness of a multi-objective policy design in reducing economic losses (about 29%) and environmental risks (about 40%), substantially enhancing the efficiency of the environmental policy. The high-resolution simulations provide valuable insights for policy designers to formulate strategies with staggered implementation and multiple objectives to mitigate supply chain losses and environmental risks and ensure a sustainable future.

Identifying Key Drivers for a National Transition to Low Carbon Energy using Agent-based Supply Chain Models

Understanding robust pathways to achieve affordable, reliable, and equitable energy transitions to a decarbonized society has received growing attention. While existing energy transition literature emphasizes the role of government policy and technological innovation, little has been written to integrate “top-down” approaches encompassing government policy and “bottom-up” individual enterprise-level decisions. Therefore, here we develop a hybrid top-down, bottom-up production consumption model towards intra-national energy transition planning using a decentralized agent-based approach, including a detailed geographic coverage utilizing GIS to map individual energy units. This enables considering SC-level dynamics of end-users pursuing economic objectives subject to regulatory constraints. Two different case studies are reported. The first study reveals that green hydrogen consumption in the Indian state of Tamil Nadu may grow only up to 3% by 2042 from the current nil usage, even with aggressive technological cost reductions and a fixed carbon price of $20/ton CO2, but demand could grow more rapidly if stringent criteria pollutant emissions limits are imposed. In the second case study, which considers natural gas usage across India, the power and city gas sectors are found to be the consistent demand drivers, with their share rising from ∼40% of the total in 2022 to ∼60% by 2032. This is consistent with IEA and IRENA projections. The proposed agent-based model thus provides multiple benefits: It enables the identification of key sectors driving the energy transition; it helps analyze critical factors like resource availability, supply chain infrastructure, and economics; and it can be used to develop decision support tools to investigate clean energy policies.

Risk assessment of power outages to inter-regional supply chain networks in China

The power industry is one of the most important infrastructures in the modern supply chain. Due to the deep interdependence of enterprises on the power industry and between enterprises, power outages will cause serious cascading risks propagating through the supply chain. To assess the risk and establish a more targeted and reliable electricity security policy, we developed a dynamic adaptive agent-based supply chain model to simulate the superimposed direct and cascading indirect economic loss risks of multiple small-scale power outages. We apply this model to assess the economic loss risks of China's power outage in 2018 as a case study. With a day-by-day simulation in a whole year, the results showed that total economic loss risks caused by power outages accounted for 0.49% (ranging from 0.32% to 0.63%) of the national GDP in 2018, while the indirect economic loss risks were 2.35 times (ranging from 1.18 to 3.28) the direct loss. From the perspective of two different mechanisms of material flow and information flow, the results showed different impacts of power outages on the upstream and downstream supply chains. In addition, scenario simulations were conducted to identify 20 priority control regions to provide policy implications for securing the stability of the power supply. Overall, our model can be used to evaluate the economic loss risks caused by any historical outage events with available data and enables the early-stage warning of the financial dangers by arising power intervention policy.

Retraction Note: Agent-based supply chain allocation model and its application in smart manufacturing enterprises

Retraction Note: Agent-based supply chain allocation model and its application in smart manufacturing enterprises

Mitigating bullwhip effect in an agent-based supply chain through a fuzzy reverse ultimatum game negotiation module

Supply Chain Management is frequently regarded as a distributed system whose productivity is mainly influenced by healthy interaction and cooperation among members. Examples of such inefficiencies can be seen in “Demand amplification” referring to asymmetric increase of demands among supply echelons due to both operational and behavioral causes. This paper addresses defective loops of non-cooperation as well as absence of information to manage the dynamics of demand amplification in a four-echelon supply chain; a new agent-based structure is suggested to facilitate the cooperation and coordination among major components and provide a structured context for interactive information sharing. Adequate motivation to share the required information is derived from an automated negotiation between retailer and manufacturer echelons in a four-echelon serial supply model. In doing so, the retailer agent would be encouraged to share customer demand information using Token-Based ordering policy, through a Reverse Ultimatum Game negotiation module. A novel fuzzy approach is suggested in order to cope with ambiguities involved in this negotiation; Numerical experiments prove that the proposed fuzzy negotiation mechanism can warrant the agreement among negotiating parties in nearly half the number of RUG iterations with 30% agreement share. This success in bringing the negotiation parties to an agreement results in the bullwhip effect decreasing by 30% in a four-echelon agent-based supply system.The proposed agent-based supply model which is designed to facilitate this cooperative decision-making process includes some unique innovative features: the knowledge base of the proposed system is able to retrieve and reuse the previous negotiation outcomes, through a gradual learning module. A combination of Case-Based Reasoning and Rule-Based inference mechanisms are applied to facilitate this, so prior cases will be stored in a Frame-Based structure. Eventually, the integrity of the proposed agent-based system is examined through combining Top-Down and Subsets integration approaches and some numerical experiments are provided to confirm the efficiency of the proposed agent-based structure in bullwhip effect management.

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

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.

Criticality analysis of a country’s transport network via an agent-based supply chain model

Building resilience against shocks has become a pillar of sustainability. By understanding how the different components of an economy interact in times of crisis, we can design resilience strategies that go beyond building walls or dams. We formulate an original agent-based model to explore a crucial pathway through which a disaster affects the economy: the transport–supply chain nexus. The model simulates the behaviour of firms facing transport and supply disruptions and estimates the resulting indirect losses. As an illustration, the model is used to assess the criticality of Tanzanian roads, which are vulnerable to floods. We report three main results. First, the model generates maps that identify the transport infrastructure assets that are most critical for specific supply chains: roads that are most important for food security are different from those supporting international trade, for instance. Second, economic losses from transport disruptions increase non-linearly with the duration of disruptions, highlighting the benefit from fast repairs. Third, by combining economic and transport modelling, we can consider a broader spectrum of interventions. Beyond strengthening the transport system, it is also possible to make supply chains more resilient to disruption with, for instance, sourcing decisions or inventory management. Assessing the performance of an economy in times of crisis is a difficult task. This study presents an agent-based model capturing the behaviour of firms facing transport and supply disruptions due to natural disasters, and shows possible paths to reinforce infrastructure and supply chain resilience.

Supply Chain Service Quality Improvement by E-marketplace Automation

E-marketplaces have become an essential part of e-commerce. In our research, a decentralized agent-based e-marketplace platform was devised. Although there are significant agent-based supply chain models in the literature, measuring quality performance using agents is still a subject of investigation. In order to improve overall supply chain service quality by allowing companies' agents to evaluate the service quality of their partners through the history of their transactions, this article proposes a service quality agent model. The model is designed using MCDM tools to suit different approaches to supply chain management. Consequently, since more informed procurement decisions are taking place continuously and autonomously at each node of a supply chain, supply chain service quality is being improved along the whole supply chain. At the end, the service quality valuation model of the supply chain is empirically evaluated.

Production scheduling in agent-based supply chain for manufacturing efficiency improvement

Manufacturing efficiency improvement can be achieved by different means, e.g., effective load of equipment, reduced manufacturing time, production cost decrease and Lean manufacturing. The paper reviews the mentioned improvements and focuses on production scheduling aimed to reduce the overall production time. The sequence of simultaneous launch for production is often not considered, but it affects the overall production time. The elaborated production sequence eliminates waste waiting time, reduces production cost and manufacturing time, and thus improves the manufacturing efficiency.The authors propose to apply a software agent, which is engaged with production scheduling and production process improvement that includes the following tasks: effective workload of equipment, production lead-time decrease, organization of continuous product flow on production department. The agent uses such priority levels, as order deadline and class affiliation of ABC analysis in its production scheduling.The paper presents a case study on elaborated production scheduling for manufacturing efficiency improvement.

RETRACTED ARTICLE: Agent-based supply chain allocation model and its application in smart manufacturing enterprises

Supply chain (SC) management plays a crucial role in modern enterprises, especially for smart manufacturing enterprises. It refers to the coordination and integration with upstream suppliers and downstream customers, aiming at optimizing the performance of the entire SC of smart manufacturing enterprises. Owing to the complexity and dynamics of the SC system and the multitudinous uncertainties of the changing business environment, it is necessary for smart manufacturing enterprises to have a good SC organization and operation model to coordinate the benefits of actors involving in the network system, to improve the information flow and logistics, and to make rapid response to the changing market and industry. For these aspects, this paper attempts to utilize the agent technology to optimize the SC, promoting its capability of dynamic allocation planning. Further, smart manufacturing enterprises will be taken into account in this paper to make specific analysis. The results show that the application of agent technology will contribute to the coordination and dynamic allocation planning, and the agent-based SC dynamic allocation planning will be conducive to the comprehensive performance of the smart manufacturing enterprises, which has extensive managerial significance in practices.

Decentralized Agent-based Electronic Marketplace Supply Chain Ecosystem

Electronic marketplaces have become an essential part of the business to business e-commerce. Supply chain concept is fully taking advantage of using the network effects in electronic marketplaces due to augmented added value from each participant. However, highly desirable network effects lead to one company monopoly over the whole marketplace. To prevent this effect, authors are proposing and devising a decentralized agent-based electronic marketplace for supply chain ecosystem. The goal of our research is to improve overall supply chain service quality by allowing companies’ agents to evaluate the service quality of their partners through the history of their transactions. Consequently, since in their procurement activities more informed decisions are being made instantaneously, continuously and autonomously at each node of a supply chain, supply chain service quality is being improved along the whole supply chain.

A hybrid agent-based computational economics and optimization approach for supplier selection problem

Abstract Supplier evaluation and selection problem is among the most important of logistics decisions that have been addressed extensively in supply chain management. The same logistics decision is also important in freight transportation since it identifies trade relationships between business establishments and determines commodity flows between production and consumption points. The commodity flows are then used as input to freight transportation models to determine cargo movements and their characteristics including mode choice and shipment size. Various approaches have been proposed to explore this latter problem in previous studies. Traditionally, potential suppliers are evaluated and selected using only price/cost as the influential criteria and the state-of-practice methods. This paper introduces a hybrid agent-based computational economics and optimization approach for supplier selection. The proposed model combines an agent-based multi-criteria supplier evaluation approach with a multi-objective optimization model to capture both behavioral and economical aspects of the supplier selection process. The model uses a system of ordered response models to determine importance weights of the different criteria in supplier evaluation from a buyers’ point of view. The estimated weights are then used to calculate a utility for each potential supplier in the market and rank them. The calculated utilities are then entered into a mathematical programming model in which best suppliers are selected by maximizing the total accrued utility for all buyers and minimizing total shipping costs while balancing the capacity of potential suppliers to ensure market clearing mechanisms. The proposed model, herein, was implemented under an operational agent-based supply chain and freight transportation framework for the Chicago Metropolitan Area.

Agent-based Supply Chain Management Simulator の開発

Agent-based Supply Chain Management Simulator の開発

Application of an Agent-based Supply Chain to Mitigate Supply Chain Disruptions

With the advent of globalization, mitigating supply chain disruptions is the differential for any company. However, mitigating supply chain disruption-related risks in companies is a difficult task. There are some ISO norms that provide standards that can make systems to deal with supply chain disruptions and there are some modeling techniques that ensure that supply chain disruptions be mitigated. Although both approaches do not eliminate supply chain disruptions, they have a fundamental importance for supply chain mitigation. In this context, this paper presents an agent-based with the support of some supply chain and risk norms to mitigate supply chain disruptions. The objective of the proposed method is to identify possible hidden supply chain risks, often highlighting previous hidden or overlooked areas of high risk. An example application for a manufacturing firm demonstrates that this model is feasible and can be replicated to different sectors.

Distributed supply chain network modelling and simulation: integration of agent-based distributed simulation and improved SCOR model

Agent-based distributed simulation is an efficient methodology for modelling and analysing such complex adaptive systems as dynamic supply chain networks. However, it lacks an acceptable generic standard. Supply chain operations reference (SCOR) model is a cross-functional framework widely accepted as an industry standard. It provides the standard processes, performance metrics, best practices and associated software functionalities for modelling, evaluating and improving supply chain networks. However, it is a static tool. Integration of agent-based distributed simulation and SCOR model can exploit their advantages to form a generic methodology for modelling and simulation of a wide range of supply chain networks. Therefore, this paper proposes a methodology for distributed supply chain network modelling and simulation by means of integration of agent-based distributed simulation and an improved SCOR model. The methodology contains two components: a hierarchical framework for modelling supply chain network based on the improved SCOR model and agent building blocks integrating the standard processes from the SCOR model. The hierarchical framework provides an approach for structure modelling in any level with different granularities based on the improved SCOR model, and allows rapidly mapping a supply chain network into the structure model of a multi-agent system; while agent building blocks are quite useful and convenient to fill the structure model to fulfil its function modelling. With the approach of structure modelling and function filling, not only can the process of agent-based supply chain network modelling be accelerated, but also the built models can be reused and expanded. Because the hierarchical framework is based on the conceptual framework of SCOR model and agent building blocks integrate the standard processes from SCOR model, the proposed methodology is more generic. In addition, the issues of sub-model synchronisation and data distribution management in the agent-based distributed simulation implementation are taken into consideration and the corresponding solutions for these issues are proposed. Finally, an example of a supply chain network is modelled and implemented to illustrate the proposed methodology and related solutions.

Intelligent System for Customer Oriented Design and Supply Chain Management

Due to the intensely increasing individualized and rapidly changing demand in global market, customer oriented design has been the top priority for modern enterprises to quickly respond to the volatile personalized requirements. This leads to the great change of mass customization in supply chain management, in which the agility, efficiency and cost have become the main impediment to improve supply chain’s performance. In this paper, we first propose a new method of adaptive clustering for the orientation and analysis of target customers that is originally inspired from the swarm intelligence of ant colony, then the recommendation system for the design of LCD-TV is illustrated that applied by this method to test the validity of the algorithm. After that, we present a supply chain management based on mobile agents to satisfy the customization, especially, detailed description of the agent-based supply chain process is also expounded. Finally, an integrated intelligent system to support the customer oriented design as well as its supply chain management is discussed.

Integrated methodological frameworks for modelling agent-based advanced supply chain planning systems: A systematic literature review

Purpose: The objective of this paper is to provide a systematic literature review of recent developments in methodological frameworks for the modelling and simulation of agent-based advanced supply chain planning systems. Design/methodology/approach: A systematic literature review is provided to identify, select and make an analysis and a critical summary of all suitable studies in the area. It is organized into two blocks: the first one covers agent-based supply chain planning systems in general terms, while the second one specializes the previous search to identify those works explicitly containing methodological aspects. Findings: Among sixty suitable manuscripts identified in the primary literature search, only seven explicitly considered the methodological aspects. In addition, we noted that, in general, the notion of advanced supply chain planning is not considered unambiguously, that the social and individual aspects of the agent society are not taken into account in a clear manner in several studies and that a significant part of the works are of a theoretical nature, with few real-scale industrial applications. An integrated framework covering all phases of the modelling and simulation process is still lacking in the literature visited. Research limitations/implications: The main research limitations are related to the period covered (last four years), the selected scientific databases, the selected language (i.e. English) and the use of only one assessment framework for the descriptive evaluation part. Practical implications: The identification of recent works in the domain and discussion concerning their limitations can help pave the way for new and innovative researches towards a complete methodological framework for agent-based advanced supply chain planning systems. Originality/value: As there are no recent state-of-the-art reviews in the domain of methodological frameworks for agent-based supply chain planning, this paper contributes to systematizing and consolidating what has been done in recent years and uncovers interesting research gaps for future studies in this emerging field.

Negotiation among autonomous computational agents: principles, analysis and challenges

Automated negotiation systems with software agents representing individuals or organizations and capable of reaching agreements through negotiation are becoming increasingly important and pervasive. Examples, to mention a few, include the industrial trend toward agent-based supply chain management, the business trend toward virtual enterprises, and the pivotal role that electronic commerce is increasingly assuming in many organizations. Artificial intelligence (AI) researchers have paid a great deal of attention to automated negotiation over the past decade and a number of prominent models have been proposed in the literature. These models exhibit fairly different features, make use of a diverse range of concepts, and show performance characteristics that vary significantly depending on the negotiation context. As a consequence, assessing and relating individual research contributions is a difficult task. Currently, there is a need to build a framework to define and characterize the essential features that are necessary to conduct automated negotiation and to compare the usage of key concepts in different publications. Furthermore, the development of such a framework can be an important step to identify the core elements of autonomous negotiating agents, to provide a coherent set of concepts related to automated negotiation, to assess progress in the field, and to highlight new research directions. Accordingly, this paper introduces a generic framework for automated negotiation. It describes, in detail, the components of the framework, assesses the sophistication of the majority of work in the AI literature on these components, and discusses a number of prominent models of negotiation. This paper also highlights some of the major challenges for future automated negotiation research.

An architecture for agent-based mobile Supply Chain Event Management

Supply Chain Event Management (SCEM) is an approach aiming to fill the gap between Supply Chain Planning and Supply Chain Execution. The goal of SCEM applications is to monitor the states of supply chains by observing specific events and exceptions in real-time and alerting managers if problems occur. This paper presents an architecture for a mobile SCEM system based on software agents, Auto-ID technologies, and mobile computing. It introduces the main layers and components of this architecture. A special focus is placed on web services, mobile user interfaces, and integration of a multi-agent platform as innovative solutions to improve SCEM.

Agent-based modeling of supply chains for distributed scheduling

This paper considers a supply chain that comprises multiple independent and autonomous enterprises (project managers) that seek and select various contractors to complete operations of their project. Both the project managers and contractors jointly determine the schedules of their operations while no single enterprise has complete information of other enterprises. The centralized scheduling approach that can usually obtain good global performance but must share nearly complete information that is difficult or even impractical due to the distributed nature of real-life supply chains. This paper proposes an agent-based supply chain model to support distributed scheduling. A modified contract-net protocol (MCNP) is proposed to enable more information sharing among the enterprises than conventional CNP. Experimental simulation studies are conducted to compare and contrast the performances of the centralized [centralized heuristic (CTR)], conventional CNP, and MNCP approaches. The results show that MCNP outperforms CNP and performs comparably with CTR when project complexity is high in terms of the total supply chain operating cost. Moreover, it is found that although CTR is better than MCNP in terms of global performance, MCNP yields good schedule stability when facing unexpected disturbances.