Hidden order: how adaptation builds complexity
Hidden order: how adaptation builds complexity
Dynamics of complex systems
Emergence: From Chaos to Order
Emergence: From Chaos to Order
Computer Simulation in Management Science
Computer Simulation in Management Science
Feedback Thought in Social Science and Systems Theory
Feedback Thought in Social Science and Systems Theory
Business Dynamics
Simulation for the Social Scientist
Simulation for the Social Scientist
Introduction to Multiagent Systems
Introduction to Multiagent Systems
Agent Based and System Dynamics Modeling: A Call for Cross Study and Joint Research
HICSS '01 Proceedings of the 34th Annual Hawaii International Conference on System Sciences ( HICSS-34)-Volume 3 - Volume 3
Emergent Structures in Supply Chains " A Study Integrating Agent-Based and System Dynamics Modeling
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 3 - Volume 3
Introduction to Logistics Systems Planning and Control
Introduction to Logistics Systems Planning and Control
Investigating Ontologies for Simulation Modeling
ANSS '04 Proceedings of the 37th annual symposium on Simulation
Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation
Generative Social Science: Studies in Agent-Based Computational Modeling (Princeton Studies in Complexity)
Complex Adaptive Systems: An Introduction to Computational Models of Social Life (Princeton Studies in Complexity)
System and actor perspectives on sociotechnical systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Diversity and Complexity
Design of Agent-Based Models
Cross-paradigm simulation modeling: challenges and successes
Proceedings of the Winter Simulation Conference
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Each simulation paradigm is characterized by a set of core assumptions and some underlying concepts to describe the world. These assumptions, in fact, constrain the development of a conceptual model for the system of study. Consequently, the choice of appropriate simulation paradigm is an important step in the model development process. In this paper, selection of a simulation approach for supply chain modeling is discussed. For this purpose, the supply chain is described from perspective of two well-established system theories. Firstly, supply chains are defined as socio-technical systems. Afterwards, they are described from complex adaptive systems perspective. This study gives a set of features for supply chains as complex socio-technical systems which is subsequently used to compare three simulation paradigms for supply chain modeling -- namely, system dynamics, discrete-even simulation and agent-based simulation.