Interaction between intelligent agent strategies for real-time transportation planning

In this paper we study the real-time scheduling of time-sensitive full truckload pickup-and-delivery jobs. The problem involves the allocation of jobs to a fixed set of vehicles which might belong to dfferent collaborating transportation agencies. A recently proposed solution methodology for this problem is the use of a multi-agent system where shipper agents other jobs through sequential auctions and vehicle agents bid on these jobs. In this paper we consider such a multi-agent system where both the vehicle agents and the shipper agents are using profit maximizing look-ahead strategies. Our main contribution is that we study the interrelation of these strategies and their impact on the system-wide logistical costs. From our simulation results, we conclude that the system-wide logistical costs (i) are always reduced by using the look-ahead policies instead of a myopic policy (10-20%) and (ii) the joint effect of two look-ahead policies is larger than the effect of an individual policy. To provide an indication of the savings that might be realized with a central solution methodology, we benchmark our results against an integer programming approach

Multi Agent Systems in Logistics: A Literature and State-of-the-art Review

Based on a literature survey, we aim to answer our main question: “How should we plan and execute logistics in supply chains that aim to meet today’s requirements, and how can we support such planning and execution using IT?†Today’s requirements in supply chains include inter-organizational collaboration and more responsive and tailored supply to meet specific demand. Enterprise systems fall short in meeting these requirements The focus of planning and execution systems should move towards an inter-enterprise and event-driven mode. Inter-organizational systems may support planning going from supporting information exchange and henceforth enable synchronized planning within the organizations towards the capability to do network planning based on available information throughout the network. We provide a framework for planning systems, constituting a rich landscape of possible configurations, where the centralized and fully decentralized approaches are two extremes. We define and discuss agent based systems and in particular multi agent systems (MAS). We emphasize the issue of the role of MAS coordination architectures, and then explain that transportation is, next to production, an important domain in which MAS can and actually are applied. However, implementation is not widespread and some implementation issues are explored. In this manner, we conclude that planning problems in transportation have characteristics that comply with the specific capabilities of agent systems. In particular, these systems are capable to deal with inter-organizational and event-driven planning settings, hence meeting today’s requirements in supply chain planning and execution.supply chain;MAS;multi agent systems

Interaction between intelligent agent strategies for real-time transportation planning

In this paper we study the real-time scheduling of time-sensitive full truckload pickup-and-delivery jobs. The problem involves the allocation of jobs to a fixed set of vehicles which might belong to different collaborating transportation agencies. A recently proposed solution methodology for this problem is the use of a multi-agent system where shipper agents offer jobs through sequential auctions and vehicle agents bid on these jobs. In this paper we consider such a system where both the vehicle agents and the shipper agents are using profit maximizing look-ahead strategies. Our main contribution is that we study the interrelation of these strategies and their impact on the system-wide logistical costs. From our simulation results, we conclude that the system-wide logistical costs (i) are always reduced by using the look-ahead strategies instead of a myopic strategy (10–20%) and (ii) the joint effect of two look-ahead strategies is larger than the effect of an individual strategy. To provide an indication of the savings that might be realized under centralized decision making, we benchmark our results against an integer programming approach

Towards cooperative urban traffic management: Investigating voting for travel groups

In den letzten Jahrzehnten haben intelligente Verkehrssysteme an Bedeutung gewonnen. Wir betrachten einen Teilbereich des kooperativen Verkehrsmanagements, nämlich kollektive Entscheidungsfindung in Gruppen von Verkehrsteilnehmern. In dem uns interessierenden Szenario werden Touristen, die eine Stadt besuchen, gebeten, Reisegruppen zu bilden und sich auf gemeinsame Besuchsziele (Points of Interest) zu einigen. Wir konzentrieren uns auf Wählen als Gruppenentscheidungsverfahren. Unsere Fragestellung ist, wie sich verschiedene Algorithmen zur Bildung von Reisegruppen und zur Bestimmung gemeinsamer Reiseziele hinsichtlich der System- und Benutzerziele unterscheiden, wobei wir als Systemziel große Gruppen und als Benutzerziele hohe präferenzbasierte Zufriedenheit und geringen organisatorischen Aufwand definieren. Wir streben an, einen Kompromiss zwischen System- und Benutzerzielen zu erreichen. Neu ist, dass wir die inhärenten Auswirkungen verschiedener Wahlregeln, Wahlprotokolle und Gruppenbildungsalgorithmen auf Benutzer- und Systemziele untersuchen. Altere Arbeiten zur kollektiven Entscheidungsfindung im Verkehr konzentrieren sich auf andere Zielgrößen, betrachten nicht die Gruppenbildung, vergleichen nicht die Auswirkungen mehrerer Wahlalgorithmen, benutzen andere Wahlalgorithmen, berücksichtigen nicht klar definierte Gruppen von Verkehrsteilnehmern, verwenden Wahlen für andere Anwendungen oder betrachten andere Algorithmen zur kollektiven Entscheidungsfindung als Wahlen. Wir untersuchen in der Hauptsimulationsreihe verschiedene Gruppenbildungsalgorithmen, Wahlprotokolle und Komiteewahlregeln. Wir betrachten sequentielle Gruppenbildung vs. koordinierte Gruppenbildung, Basisprotokoll vs. iteratives Protokoll und die Komiteewahlregeln Minisum-Approval, Minimax-Approval und Minisum-Ranksum. Die Simulationen wurden mit dem neu entwickelten Simulationswerkzeug LightVoting durchgef¨uhrt, das auf dem Multi-Agenten-Framework LightJason basiert. Die Experimente der Hauptsimulationsreihe zeigen, dass die Komiteewahlregel Minisum-Ranksum in den meisten Fällen bessere oder ebenso gute Ergebnisse erzielt wie die Komiteewahlregeln Minisum-Approval und Minimax-Approval. Das iterative Protokoll tendiert dazu, eine Verbesserung hinsichtlich der präferenzbasierten Zufriedenheit zu erbringen, auf Kosten einer deutlichen Verschlechterung hinsichtlich der Gruppengröße. Die koordinierte Gruppenbildung tendiert dazu, eine Verbesserung hinsichtlich der präferenzbasierten Zufriedenheit zu erbringen bei relativ geringen Kosten in Bezug auf die Gruppengröße. Dies führt uns dazu, die Komiteewahlregel Minisum-Ranksum, das Basisprotokoll und die koordinierte Gruppenbildung zu empfehlen, um einen Kompromiss zwischen System- und Benutzerzielen zu erreichen. Wir demonstrieren auch die Auswirkungen verschiedener Kombinationen von Gruppenbildungsalgorithmen und Wahlprotokollen auf die Reisekosten. Hier bietet die Kombination aus Basisprotokoll und koordinierter Gruppenbildung einen Kompromiss zwischen der präferenzbasierten Zufriedenheit und den Reisekosten. Zusätzlich zur Hauptsimulationsreihe bieten wir ein erweitertes Modell an, das die Präferenzen der Reisenden generiert, indem es die Attraktivität der möglichen Ziele und Distanzkosten, basierend auf den Entfernungen zwischen den möglichen Zielen, kombiniert. Als weiteren Anwendungsfall von Wahlverfahren betrachten wir ein Verfahren zur Treffpunktempfehlung, bei dem eine Bewertungs-Wahlregel und eine Minimax-Wahlregel zur Bestimmung von Treffpunkten verwendet werden. Bei kleineren Gruppen ist die durchschnittliche maximale Reisezeit unter der Bewertungs-Wahlregel deutlich höher. Bei größeren Gruppen nimmt der Unterschied ab. Bei kleineren Gruppen ist die durchschnittliche Verspätung für die Gruppe unter der Minimax-Wahlregel hoch, bei größeren Gruppen nimmt sie ab. Es ist also sinnvoll für kleinere Gruppen, die Minimax-Wahlregel zu verwenden, wenn man eine fairere Verteilung der Reisezeiten anstrebt, und die Bewertungs-Wahlregel zu verwenden, wenn das Ziel stattdessen ist, Verzögerungen für die Gruppe zu vermeiden. Für zukünftige Arbeiten wäre es sinnvoll, das Simulationskonzept anzupassen, um reale Bedingungen und Anforderungen berücksichtigen zu können. Weitere Möglichkeiten für zukünftige Arbeiten wären die Betrachtung zusätzlicher Algorithmen und Modelle, wie zum Beispiel die Betrachtung kombinatorischer Wahlen oder die Durchführung von Simulationen auf der Grundlage des erweiterten Modells, die Berücksichtigung der Rolle finanzieller Anreize zur Förderung von Ridesharing oder Platooning und die Nutzung des LightVoting-Tools für weitere Forschungsanwendungen.In the last decades, intelligent transport systems have gained importance. We consider a subarea of cooperative traffic management, namely collective decision-making in groups of traffic participants. In the scenario we are studying, tourists visiting a city are asked to form travel groups and to agree on common points of interest. We focus on voting as a collective decision-making process. Our question is how different algorithms for the formation of travel groups and for determining common travel destinations differ with respect to system and user goals, where we define as system goal large groups and as user goals high preference satisfaction and low organisational effort. We aim at achieving a compromise between system and user goals. What is new is that we investigate the inherent effects of different voting rules, voting protocols and grouping algorithms on user and system goals. Older works on collective decision-making in traffic focus on other target quantities, do not consider group formation, do not compare the effects of several voting algorithms, use other voting algorithms, do not consider clearly defined groups of vehicles, use voting for other applications or use other collective decision-making algorithms than voting. In the main simulation series, we examine different grouping algorithms, voting protocols and committee voting rules. We consider sequential grouping vs. coordinated grouping, basic protocol vs. iterative protocol and the committee voting rules Minisum-Approval, Minimax-Approval and Minisum-Ranksum. The simulations were conducted using the newly developed simulation tool LightVoting, which is based on the multi-agent framework LightJason. The experiments of the main simulation series show that the committee voting rule Minisum-Ranksum in most cases yields better than or as good results as the committee voting rules Minisum-Approval and Minimax-Approval. The iterative protocol tends to yield an improvement regarding preference satisfaction, at the cost of strong deterioriation regarding the group size. The coordinated grouping tends to yield an improvement regarding the preference satisfaction at relative small cost regarding the group size. This leads us to recommend the committee voting rule Minisum-Ranksum, the basic protocol and coordinated grouping in order to achieve a compromise between system and user goals. We also demonstrate the effect of different combinations of grouping algorithms and voting protocols on travel costs. Here, the combination of the basic protocol and coordinated grouping yields a compromise between preference satisfaction and traveller costs. Additionally to the main simulation series, we provide an extended model which generates traveller preferences by combining attractiveness of the points of interest and distance costs based on the distances between the points of interest. As further application of voting, we consider a meeting-point scenario where a range voting rule and a minimax voting rule are used to agree on meeting points. For smaller groups, the average maximum travel time is clearly higher for range voting. For larger groups, the difference decreases. For smaller groups, the average lateness for the group using minimax voting is high, for larger groups it decreases. Hence, it makes sense for smaller groups to use the minimax voting rule if one aims at fairer distribution of travel times, and to use the range voting rule if the goal is instead to avoid delay for the group. For future work, it would be useful to adapt the simulation concept to take real-world conditions and requirements into account. Further possibilities for future work would be considering additional algorithms and models, such as considering combinatorial voting or running simulations based on the extended model, considering the role of financial incentives to encourage ridesharing or platooning and using the LightVoting tool for further research applications

Multi Agent Systems in Logistics: A Literature and State-of-the-art Review

Based on a literature survey, we aim to answer our main question: “How should we plan and execute logistics in supply chains that aim to meet today’s requirements, and how can we support such planning and execution using IT?” Today’s requirements in supply chains include inter-organizational collaboration and more responsive and tailored supply to meet specific demand. Enterprise systems fall short in meeting these requirements The focus of planning and execution systems should move towards an inter-enterprise and event-driven mode. Inter-organizational systems may support planning going from supporting information exchange and henceforth enable synchronized planning within the organizations towards the capability to do network planning based on available information throughout the network. We provide a framework for planning systems, constituting a rich landscape of possible configurations, where the centralized and fully decentralized approaches are two extremes. We define and discuss agent based systems and in particular multi agent systems (MAS). We emphasize the issue of the role of MAS coordination architectures, and then explain that transportation is, next to production, an important domain in which MAS can and actually are applied. However, implementation is not widespread and some implementation issues are explored. In this manner, we conclude that planning problems in transportation have characteristics that comply with the specific capabilities of agent systems. In particular, these systems are capable to deal with inter-organizational and event-driven planning settings, hence meeting today’s requirements in supply chain planning and execution

Modeling and optimizing network infrastructure for autonomous vehicles

Autonomous vehicle (AV) technology has matured sufficiently to be in testing on public roads. However, traffic models of AVs are still in development. Most previous work has studied AV technologies in micro-simulation. The purpose of this dissertation is to model and optimize AV technologies for large city networks to predict how AVs might affect city traffic patterns and travel behaviors. To accomplish these goals, we construct a dynamic network loading model for AVs, consisting of link and node models of AV technologies, which is used to calculate time-dependent travel times in dynamic traffic assignment. We then study several applications of the dynamic network loading to predict how AVs might affect travel demand and traffic congestion. AVs admit reduced perception-reaction times through technologies such as (cooperative) adaptive cruise control, which can reduce following headways and increase capacity. Previous work has studied these in micro-simulation, but we construct a mesoscopic simulation model for analyses on large networks. To study scenarios with both autonomous and conventional vehicles, we modify the kinematic wave theory to include multiple classes of flow. The flow-density relationship also changes in space and time with the class proportions. We present multiclass cell transmission model and prove that it is a Godunov approximation to the multiclass kinematic wave theory. We also develop a car-following model to predict the fundamental diagram at arbitrary proportions of AVs. Complete market penetration scenarios admit dynamic lane reversal -- changing lane direction at high frequencies to more optimally allocate road capacity. We develop a kinematic wave theory in which the number of lanes changes in space and time, and approximately solve it with a cell transmission model. We study two methods of determining lane direction. First, we present a mixed integer linear program for system optimal dynamic traffic assignment. Since this program is computationally difficult to solve, we also study dynamic lane reversal on a single link with deterministic and stochastic demands. The resulting policy is shown to significantly reduce travel times on a city network. AVs also admit reservation-based intersection control, which can make greater use of intersection capacity than traffic signals. AVs communicate with the intersection manager to reserve space-time paths through the intersection. We create a mesoscopic node model by starting with the conflict point variant of reservations and aggregating conflict points into capacity-constrained conflict regions. This model yields an integer program that can be adapted to arbitrary objective functions. To motivate optimization, we present several examples on theoretical and realistic networks demonstrating that naïve reservation policies can perform worse than traffic signals. These occur due to asymmetric intersections affecting optimal capacity allocation and/or user equilibrium route choice behavior. To improve reservations, we adapt the decentralized backpressure wireless packet routing and P0 traffic signal policies for reservations. Results show significant reductions in travel times on a city network. Having developed link and node models, we explore how AVs might affect travel demand and congestion. First, we study how capacity increases and reservations might affect freeway, arterial, and city networks. Capacity increases consistently reduced congestion on all networks, but reservations were not always beneficial. Then, we use dynamic traffic assignment within a four-step planning model, adding the mode choice of empty repositioning trips to avoid parking costs. Results show that allowing empty repositioning to encourage adoption of AVs could reduce congestion. Also, once all vehicles are AVs, congestion will still be significantly reduced. Finally, we present a framework to use the dynamic network loading model to study shared AVs. Results show that shared AVs could reduce congestion if used in certain ways, such as with dynamic ride-sharing. However, shared AVs also cause significant congestion. To summarize, this dissertation presents a complete mesoscopic simulation model of AVs that could be used for a variety of studies of AVs by planners and practitioners. This mesoscopic model includes new node and link technologies that significantly improve travel times over existing infrastructure. In addition, we motivate and present more optimal policies for these AV technologies. Finally, we study several travel behavior scenarios to provide insights about how AV technologies might affect future traffic congestion. The models in this dissertation will provide a basis for future network analyses of AV technologies.Civil, Architectural, and Environmental Engineerin

A Survey on Aerial Swarm Robotics

The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas