| Abstract: | Modern technologies, such as RFID, offer never-before seen learning abilities to parts moving in supply chains. Logistics systems may be understood as complex adaptive logistics systems (CALS). They also may be conceived as electronic auction markets as ‘smart parts’ bid for the best routing and pricing from transportation firms. To ensure the world-wide functionality and efficiency of CALS transportation markets, we suggest the utility of an agent-based computational market design based on Blake LeBaron's stock-market model. Given that parts may be more or less smart, markets more or less complex, and self-organizing CALS systems probabilistically subject to the bullwhip effect, we suggest nine different computational CALS market-design options, offering more adaptivity to unexpected environmental contingencies. |
