Circulation of autonomous agents in production and service networks

Abstract

a b s t r a c tThe inherent complexity characterizing production and/or service networks strongly favors decentralized and self-organizing mechanisms to regulate the flows of matter and information in circulation. This basic observation motivates us to study the flow dynamics in queueing networks roamed by autonomous agents which, at a given time and at a given vertex location, select their routing according to (individual) historical data (such as waiting times) collected during their past progression in the network. For several simple network configurations and despite the intrinsically non-Markovian character of the dynamics, we are able to discuss analytically the emerging collective dynamics that such a circulation of autonomous agents generates. Feedback loops in the network topology coupled with the presence of delays in the routing selection mechanisms produce a wealth of dynamical phenomena like self-sustained generically stable oscillations, spatio-temporal patterns, stabilization by noise phenomena and oscillator synchronization that are explicitly discussed in this paper.

Operations management of smart logistics: A literature review and future research

“…Researchers have proven that a logistics system, especially a smart one, should be a complex adaptive system (CAS). In the CAS, internal units can realize the evolution of a logistics system from disorder to order by themselves under the control of certain rules (Gallay and Hongler, 2009). To identify these rules, stylized models are applied to explore the internal mechanisms and underlying implications of the system behavior with the most essential system elements (Hongler et al, 2010).…”

Strategic Customer Behavior in an $M/M/1$ Feedback Queue

“…This transmission style is similar to segmented message transmission with the number of segments in a message geometrically distributed with mean 1/q. Filliger and Hongler (2005), and Gallay and Hongler (2009) studied queueing networks with feedback loops and intelligent customers. However, the customers in their model are not strategic in the sense that their decisions do not depend on the others' decisions, which is different from our setting.…”

Managing service facilities with endogenous arrival and service rates

“…See Law et al. () for a general review of the literature and Gallay and Hongler () for a review focusing on agent‐based models. A review of more recent work can be found in Arango‐Aramburo et al.…”

“…Their aim is to endow service providers with information about customers' attitudes to enable them to redesign their service facility accordingly. In other areas, authors have modeled customers' decisions by assuming that customers base their decisions on their most recent experience (e.g., Gallay and Hongler, 2009;Stidham, 1985), on their adaptive expectation (e.g., Rump and Stidham, 1998; van Ackere and Larsen, 2004;Zohar et al, 2002), or on their preferences and prices for service (e.g., Naor, 1969). See Law et al (2004) for a general review of the literature and Gallay and Hongler (2009) for a review focusing on agent-based models.…”