Exploring the Integration of Agent-Based Modelling, Process Mining, and Business Process Management through a Text Analytics–Based Literature Review

Agent-based modelling and business process management are two interrelated yet distinct concepts. To explore the relationship between these two fields, we conducted a systematic literature review to investigate existing methods and identify research gaps in the integration of agent-based modelling, process mining, and business process management. Our search yielded 359 research papers, which were evaluated using predefined criteria and quality measures. This resulted in a final selection of forty-two papers. Our findings reveal several research gaps, including the need for enhanced validation methods, the modelling of complex agents and environments, and the integration of process mining and business process management with emerging technologies. Existing agent-based approaches within process mining and business process management have paved the way for identifying the validation methods for performance evaluation. The addressed research gaps primarily concern validation before delving deeper into specific research topics. These include improved validation methods, modelling of complex agents and environments, and a preliminary exploration of integrating process mining and business process management with emerging technologies

Agents for Smart Power Grids

The future of electricity systems will compose of small-scale generation and distribution where end-users will be active participants with localized energy management systems that are able to interact on a free energy market. Software agents will most likely control power assets and interact together to decide the best and safest configuration of the power grid system. This paper presents a design of agents that can be deployed in real-time with capabilities that include optimization of resources, intensive computation, and appropriate decision-making. Jordan 51-bus system has been used for simulation with a total generation capacity of 4050 MW of which 230 MW represents renewable energy. The economic analyses demonstrated the use of smart grid technologies with 2016 generation—load profiles for nominal liquified gas (NLG) prices and ±20% sensitivity analysis. The results have shown variations in the range of 1% in the price of MWh with smart grid technologies. These variations are mainly driven by the fact that agents shift power generation to renewable power plants to produce maximum power at peak hours. As a result, there is a positive economic impact in both NLG ± 20% sensitivity analysis, due to the fact that agents coordinate to better displace expensive thermal generation with renewable generation. It is evident that renewable resources compensate for power at peak times and provide economic benefits and savings

Multi-objective optimization for preemptive & predictive supply chain operation

At present, the manufacturing industry has undergone a tremendous change in its operating principle with respect to the supply chain management system where the demands of consumers are dynamically and exponentially rising. Although Industry 4.0 offers a significant solution to this principle with the aid of its predictive automated operating process, till date there is less number of fault tolerant model that can effectively meet the standard demands of supply chain planning. Therefore, the proposed system introduces an analytical model where predictive optimization is carried out towards bridging the gap between supply and demands in supply chain 4.0. An analytical framework is a design from constraints derived from practical environment in order to offer better applicability of it. The study outcome shows that the proposed model could offer better performance in comparison to the existing optimization method with respect to the better budget control system for offering predictive and preemptive model design

Human-Computer Collaboration for Visual Analytics: an Agent-based Framework

The visual analytics community has long aimed to understand users better and assist them in their analytic endeavors. As a result, numerous conceptual models of visual analytics aim to formalize common workflows, techniques, and goals leveraged by analysts. While many of the existing approaches are rich in detail, they each are specific to a particular aspect of the visual analytic process. Furthermore, with an ever-expanding array of novel artificial intelligence techniques and advances in visual analytic settings, existing conceptual models may not provide enough expressivity to bridge the two fields. In this work, we propose an agent-based conceptual model for the visual analytic process by drawing parallels from the artificial intelligence literature. We present three examples from the visual analytics literature as case studies and examine them in detail using our framework. Our simple yet robust framework unifies the visual analytic pipeline to enable researchers and practitioners to reason about scenarios that are becoming increasingly prominent in the field, namely mixed-initiative, guided, and collaborative analysis. Furthermore, it will allow us to characterize analysts, visual analytic settings, and guidance from the lenses of human agents, environments, and artificial agents, respectively

Solution strategies for a supply chain deterministic model

To most firms, intelligent supply chain decisions are essential to achieve competitiveness in an environment characterized with increasing globalization and relentless changes. As the supply chain concept largely evolved from traditional logistics management, practitioners and researchers have historically focused on the individual processes of a supply chain. A wide array of mathematical models have been developed to tackle such functional issues as inventory level, lead-time performance, delivery performance, customer satisfaction and so on. This research presents a model that aims to evaluate and optimize the overall performance of the supply chain. The key nodes and variables in the model are composed of plant location and production volume, storage location and volume, transportation mode and volume. Optimization of the model is to minimize the total supply chain operation cost, expressed as the sum of production cost, storage cost, transportation cost and lost-sale cost. Our methodology is a three-phased approach. First, we build a mixed integer-programming model of 3-tier supply chain with multi-plant, multi-warehouse, and multi-retailer, multi-period and restricted capacity. This mathematical model is solved by CPLEX/OPL. Due to excessive computation time to reach the Optimal Solution, we introduce the second phase to develop heuristic solutions to reduce the computation time. In the final phase, we evaluate the proposed heuristic solutions. Result analysis shows that the computation time is generally exponentially correlated to the data size in seeking Optimal Solutions, whereas it generally follows the polynomial distribution when the Heuristic Solutions are applied. Consequently, Heuristic Solution is preferred for models with large size data

Optimisation de l’approvisionnement en vieux papiers par la simulation à base d’agents

RÉSUMÉ : La gestion de la chaîne d’approvisionnement est un savoir-faire essentiel à la réussite des entreprises. Cela demande de plus en plus de précision, d’adaptation et de flexibilité. S’imbriquant dans ce contexte complexe et évolutif, ce mémoire propose d'utiliser un modèle de simulation à base d'agents pour étudier la performance de différentes politiques d'approvisionnement et de production dans l'industrie du vieux papier. Développé en partenariat avec un grand producteur de pâte à papier recyclée en Amérique du Nord, le modèle de simulation détaille un producteur de pâte recyclée et ses fournisseurs de vieux papiers. Plus précisément, il émule le comportement de gestionnaires et de processus de production et d'approvisionnement. Suivant l’objectif d’optimiser la stratégie d'approvisionnement de l’entreprise partenaire tout en respectant sa stratégie de production, une série d’expériences composée de diverses politiques d’achat et opérationnelles a été conduite. La principale conclusion portant sur la flexibilité de la production semble indiquer que cette dernière a un impact négatif sur les coûts, l'inventaire et la qualité. Il est possible de réduire partiellement ces effets avec l'introduction d’une politique de contrats flexibles, mais son effet est limité. Une stratégie plus efficace pour réduire les coûts est de réduire la cadence planifiée au minimum requis pour répondre à la demande. Enfin, un inventaire cible élevé permet de réduire les coûts d'achat, mais sa performance globale dépend des coûts d'entreposage.----------ABSTRACT : The coordination of procurement and production activities is an essential part of business success. It requires accurate information and flexibility to adapt to complex and constantly changing business conditions. In this general context, this paper proposes to use an agent-based simulation model to study and analyze the performance of various procurement and production policies in the waste paper industry, between a recycled pulp producer and its waste paper suppliers. A detailed simulation model developed in partnership with a large recycled pulp producer in North America was developed in order to emulate the managers' behaviour and the production and procurement processes. A series of experiments was carried out in order to optimize of the procurement and production policies, in several productions contexts. Results show that production flexibility has a negative impact on costs, inventory and quality. However, it is possible to partially reduce these issues with the introduction of flexible contracts, although only a limited effect has been observed in our experiments. A more significant strategy to improve costs consists in reducing production rate to the minimum required to meet demand

Dynamic mutual adjustment search for supply chain operations planning coordination

RÉSUMÉ Les chaînes d’approvisionnement sont des systèmes complexes comprenant plusieurs organisations indépendantes avec des objectifs différents dans un environnement incertain et dynamique. Une question clé dans la gestion de chaîne d’approvisionnement (Supply Chain Management) est la coordination des décisions de planification des opérations. Les systèmes de planification de chaîne d’approvisionnement introduits dans la littérature peuvent être classés en deux systèmes de planification principaux: les systèmes de planification centralisés et les systèmes de planification décentralisés. Les systèmes centralisés peuvent théoriquement optimiser les performances de la chaîne d’approvisionnement bien que leur mise en œuvre nécessite un haut degré d’échange d’informations entre les partenaires de la chaîne d’approvisionnement. Cela conduit à des difficultés lorsque des partenaires indépendants ne veulent pas partager l’information. Afin de répondre à ces difficultés, les systèmes décentralisés de planification des opérations sont conçus dans lesquels chaque membre est une entité économique distincte qui prend ses décisions opérationnelles de manière indépendante, mais avec un niveau minimal d’échange d’information. Dans cette thèse, nous étudions dans un premier temps les méthodes de coordination des processus de planification des opérations dans les chaînes d’approvisionnement proposées dans la littérature. Ensuite, nous proposons un cadre de classification de ces méthodes basée sur la technologie mise en œuvre, et identifions des opportunités de recherches. Dans un deuxième temps, nous proposons une approche de coordination décentralisée qui consiste en un ajustement mutuel des décisions de planification basé sur la programmation mathématique et l’échange d’incitatifs financiers. Ce mécanisme, contrairement à un système centralisé traditionnel, implique deux entreprises, qui interagissent l’une avec l’autre afin d’améliorer leur performance. Dans le cadre de cette approche, seul un petit sous ensemble des solutions de coordination sont considérées, et l’expérimentation montre que cette approche de coordination a un potentiel d’amélioration du profit global tout en préservant l’équité en termes de partage des bénéfices de l’amélioration. Enfin, afin de proposer une méthode de coordination capable d’être utilisable dans le contexte dynamique des chaînes d’approvisionnement, cette thèse propose dans un premier temps une stratégie performante de négociation du fournisseur adaptée à l’approche de coordination proposée, ainsi qu’une stratégie de partage des revenus appliquée à un contexte d’horizon roulant. L’analyse de la performance de cette méthode particulière montre également que l’approche proposée produit une stratégie gagnante-gagnante pour les deux partenaires de la chaîne d’approvisionnement et améliore les résultats de planification.----------ABSTRACT Supply chains are complex systems, which include several independent organizations with different objectives, in dynamic uncertain environment. A key issue in supply chain management (SCM) is the coordination of supply chain operations planning decisions. Supply chain planning systems introduced in the literature can be classified into two main planning systems: centralized and decentralized planning systems. Centralized systems can theoretically optimize supply chain performance although its implementation requires a high degree of information exchange among supply chain partners. This leads to difficulties when independent partners do not want to share information. In order to address these difficulties, decentralized systems are designed for supply chains where each member is a separate economic entity that makes its operational decisions independently, yet with some minimal level of information sharing. In this thesis, we first review supply chain operations planning coordination methods from centralized to decentralized approaches proposed in the literature. Next, we propose a classification scheme of these approaches based on the technology used by the authors. Finally, we identify research opportunities. Second, we propose a decentralized operations planning coordination mechanism referred to as mutual adjustment search (MAS), which is based on a negotiation-like mutual adjustment of planning decisions with financial incentives and rooted in mathematical programming. This mechanism, unlike traditional centralized system, involves two independent enterprises linked by material and non-strategic information flows, which interact with each other in order to coordinate their operations planning, and to improve their individual and collective performance. In this approach, only a few coordination solutions (pairs of coordinated operations plans) are considered and computational analysis shows that this coordination mechanism has the potential to improve global profit, while maintaining fairness in terms of revenue sharing. Finally, in order to develop an approach capable of supporting the dynamic coordination of operations planning in a rolling horizon context, this thesis first proposes a negotiation strategy for the supplier, as well as a revenue sharing protocol. Computational analysis shows that the proposed approach produces a win-win strategy for two partners of supply chain and improves the results of upstream planning

An Intelligent Multi-Agent Based Model for Collaborative Logistics Planning

Efficient freight distribution is indispensable for sustaining customer demand in modern times. In recent years, there has been a steady growth in the use of information systems in the logistics domain towards facilitating an agile distribution process. This study investigates the problem of collaboration planning in logistics and proposes an agent based approach for better management of collaborative logistics. Based on the approach, a decision support system is designed that utilizes RFID technology for ensuring inventory accuracy and monitoring carriers’ delivery movements. The proposed approach involves three steps. In the first step, a conceptual framework is designed. Afterwards, a simulation agent based model is developed including six autonomous agents namely (RFIDG, Supplier, Retailer, Carrier, Network, and City Administrator) interacting with each other, as well as, with the surrounding environment. In the second step, game theory is utilized to study and analyze suppliers’ collaboration and carriers’ collaboration behavior in detail. Modeled games are solved using Nash Equilibrium. Finally, correctness of the games is verified by formulating them mathematically. Developed optimization equations are fundamental to the operations research field. They employ the simplex and goal algorithms of linear programming. Results prove that there are plethora of advantages such as automatism and real time response, cost reduction, increased suppliers’ profits, time management, and a collaborative framework for implementing the proposed agent based model where suppliers, retailers, and carriers will receive immediate benefits. Major contributions of the thesis stems from considering future technologies such as RFID and agent oriented strategies to provide fast quality services to customers

How to Make the Most Productive Intervention in a Complex Economic System

Information about supply and demand propagates through supply chains in a queueing network with people and computers as batch information processors. As each batch processor delays propagation of information whilst pursuing optimal local decisions, the effect is delay and distortion of the information that is used to commit resources to actions in the supply chain. This thesis investigates the effect of delay and imperfect information as a source of error, to establish the case for change in research focus from optimal exploitation of physical constraints to optimal exploitation of information. In the context of real world supply chains, the thesis asks "How does one make the most productive intervention in a complex economic system?" and pursues a meta-intervention which perpetually minimises the discovered error-term. Evidence from literature indicates that agent-based modelling permits real-time peer-to-peer communication and distributed optimisation. Based on the literature the research project designs and develops an agent-based model which operates in real-time without batch-processes and can perform incremental multi-objective optimisation under realistic (chronologically progressive) conditions for decision making. The agent based model is then used to investigate two real-world supply chains, as case studies, which reveals a significant improvement of profitability and order-fulfilment. The thesis concludes that agent-based modelling is a very promising direction for "making the most productive intervention" as it reduces delay to a minimum. Finally it recommends that continuous improvement of decision making methods is a role better suited for humans, rather than operational decision making where computers cope much better with the high amount of detailed information

Colaboração em sistemas multiagentes na roteirização dinâmica de veículos: um método para avaliação de estratégias em empresas OEM

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Produção, Florianópolis, 2014O gerenciamento integrado dos processos de transporte é um campo promissor para o uso de agentes, uma vez que a distribuição geográfica dos envolvidos e a existência de diferentes decisores são características inerentes desses processos. Tais agentes, atuando conjuntamente, podem ser vistos como um sistema multiagentes (MAS) que permite aos veículos lidar com novas informações percebidas durante a operação das rotas. A esse tipo de problema, onde as informações utilizadas para o planejamento das rotas mudam durante a operação, chama-se problema de roteirização dinâmica de veículos (DVRP). Os trabalhos que aplicam agentes a DVRP comumente dedicam-se ao tratamento de novas demandas, desconhecidas durante o planejamento das rotas; entretanto, ignoram outras questões dinâmicas que podem estar envolvidas nessas operações, como a existência de congestionamentos e a remoção de tarefas no roteiro. A presente pesquisa propõe um método para avaliar estratégias de colaboração em sistemas multiagentes para DVRP, de maneira que os veículos envolvidos são capazes de tratar, de modo autônomo, a ocorrência de eventos não planejados em suas operações. Para isto, foram estudados os processos logísticos relacionados com empresas montadoras. Nesta modelagem, foram utilizados métodos heurísticos para roteirização de veículos, permitindo assim, que os agentes utilizassem tais métodos a fim de resolver conflitos durante a operação das rotas. A modelagem e o desenvolvimento da ferramenta multiagentes viabilizou a avaliação da colaboração entre agentes nas operações de coleta de componentes para uma empresa montadora. Com isto, foram simulados diferentes experimentos a fim de avaliar algumas estratégias em cenários específicos. Os resultados obtidos permitiram verificar que o método proposto serve para avaliar diferentes estratégias de colaboração. Dentre outras coisas, percebeu-se ainda que a utilização de veículos auxiliares pode melhorar o nível de serviço prestado e, que o seu dimensionamento depende diretamente da demanda inicialmente atribuída.Abstract: The management of transportation is a promising field for the agent based approach, once the geographical distribution and the presence of dierent decision makers are inherent characteristics of these activities. Such agents, acting together, can be seen as a multi-agent system (MAS) that enables vehicles to handle new information perceived after the start of the routes. These issues are known in the literature as Dynamic Vehicle Routing Problem (DVRP). The works in this field usually focus on the processing of new demands, unknown during the planning of routes; however, ignore other dynamic issues that may affect the route accomplishment, as the presence of traffic congestion and the removal of tasks from route. This work propose a method to evaluate collaboration strategies for DVRP using MAS, by this way, vehicles involved are able to deal the occurrence of unplanned events autonomously. So, were studied the logistics processes related with companies assemblers, different heuristics for vehicle routing and how agents can make use of such methods in order to solve conflicts in the operations. After that, the modelling and the development of multi-agent tool were made; this allowed the simulation of collaborative strategies among agents in the operations of components collection for an assembler company. Finally, some experiments were simulated to evaluate some strategies in different contexts. The results obtained proved that the proposed method achieve the objectives of evaluate collaboration strategies. Among other conclusions, were perceived that the fleet sizing depends directly of the initial demand and which these strategies could improve the service level