Centralized versus market-based approaches to mobile task allocation problem: State-of-the-art

Centralized approach has been adopted for finding solutions to resource allocation problems (RAPs) in many real-life applications. On the other hand, market-based approach has been proposed as an alternative to solve the problem due to recent advancement in ICT technologies. In spite of the existence of some efforts to review the pros and cons of each approach in RAPs, the studies cannot be directly applied to specific problem domains like mobile task allocation problem which is characterised with high level of uncertainty on the availability of resources (workers). This paper aims to review existing studies on task allocation problems(TAPs) focusing on those two approaches and their comparison and identify major issues that need to be resolved for comparing the two approaches in mobile task allocation problems. Mobile Task Allocation Problem (MTAP) is defined and its problematic structures are explained in relation with task allocation to mobile workers. Solutions produced by each approach to some applications and variations of MTAP are also discussed and compared. Finally, some future research directions are identified in order to compare both approaches in function of uncertainty emerging from the mobile nature of the MTAP

Multi-Agent Environment for Modelling and Solving Dynamic Transport Problems

The transport requirements in modern society are becoming more and more important. Thus, offered transport services need to be more and more advanced and better designed to meet users demands. Important cost factors of many goods are transport costs. Therefore, a reduction of costs, a better adjustment of strategies to the demand as well as a better planning and scheduling of available resources are important for the transport companies. This paper is aimed at modelling and simulation of transport systems, involving a dynamic Pickup and Delivery problem with Time Windows and capacity constraints (PDPTW). PDPTW is defined by a set of transport requests which should be performed while minimising costs expressed by the number of vehicles, total distance and total travel time. Each request is described by two locations: pickup and delivery, periods of time when the operations of pickup or delivery can be performed and a load to be transported. The nature of this problem, its distribution and the possibility of using a lot of autonomous planning modules, lead us to use a multi-agent approach. Our approach allows the modeling of entities which do not appear in the classical PDPTW such as company organisation, communication among vehicles, interactions between vehicles and company dispatcher or different strategies of requests acceptation by different vehicles. This paper presents also a software environment and experimentations to validate the proposed approach

Analysis and evaluation of multi-agent systems for digital production planning and control

Industrial manufacturing companies have different IT control functions that can be represented with a so-called hierarchical automation pyramid. While these conventional software systems especially support the mass production with consistent demand, the future project “Industry 4.0” focuses on customer-oriented and adaptable production processes. In order to move from conventional production systems to a factory of the future, the control levels must be redistributed. With the help of cyber-physical production systems, an interoperable architecture must be, implemented which removes the hierarchical connection of the former control levels. The accompanied digitalisation of industrial companies makes the transition to modular production possible. At the same time, the requirements for production planning and control are increasing, which can be solved with approaches such as multi-agent systems (MASs). These software solutions are autonomous and intelligent objects with a distinct collaborative ability. There are different modelling methods, communication and interaction structures, as well as different development frameworks for these new systems. Since multi-agent systems have not yet been established as an industrial standard due to their high complexity, they are usually only tested in simulations. In this bachelor thesis, a detailed literature review on the topic of MASs in the field of production planning and control is presented. In addition, selected multi-agent approaches are evaluated and compared using specific classification criteria. In addition, the applicability of using these systems in digital and modular production is assessed

Multi-Agent Systems

This Special Issue ""Multi-Agent Systems"" gathers original research articles reporting results on the steadily growing area of agent-oriented computing and multi-agent systems technologies. After more than 20 years of academic research on multi-agent systems (MASs), in fact, agent-oriented models and technologies have been promoted as the most suitable candidates for the design and development of distributed and intelligent applications in complex and dynamic environments. With respect to both their quality and range, the papers in this Special Issue already represent a meaningful sample of the most recent advancements in the field of agent-oriented models and technologies. In particular, the 17 contributions cover agent-based modeling and simulation, situated multi-agent systems, socio-technical multi-agent systems, and semantic technologies applied to multi-agent systems. In fact, it is surprising to witness how such a limited portion of MAS research already highlights the most relevant usage of agent-based models and technologies, as well as their most appreciated characteristics. We are thus confident that the readers of Applied Sciences will be able to appreciate the growing role that MASs will play in the design and development of the next generation of complex intelligent systems. This Special Issue has been converted into a yearly series, for which a new call for papers is already available at the Applied Sciences journal’s website: https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019

Task Recovery in Self-Organised Multi-Agent Systems for Distributed Domains

Grid computing and cloud systems are distributed systems which provide substantial widely-accessible services to resources. Quality of service is affected by the issues around resource allocation, sharing, task execution and node failure. The focus of this research is on task execution in distributed environments and the effects of node failure on service provision. Most methods in the literature which provide fault tolerance, use reactive techniques; these provide solutions to failure only after its occurrence. In contrast, this research argues that using multi-agent systems with self-organising capabilities can provide a proactive methodology which can improve task execution in open, dynamic and distributed environments. We have modelled a system of autonomous agents with heterogeneous resources and proposed a new delegation protocol for executing tasks within their time constraints. This helps avoid the loss of tasks and to improve efficiency. However, this method on its own is not sufficient in terms of task execution throughput, especially in the presence of agent failure. Hence, we propose, a self-organisation technique. This is represented in this research by two different mechanisms for creating organisations of agents with a certain structure; we suggest, in addition, the adoption of task delegation within the organisations. Adding an organisation structure with agent roles to the network enables smoother performance, increases task execution throughput and copes with agent failures. In addition, we study the failure problem as it manifests within the organisations and we suggest an improvement to the organisation structure which involves the use of another protocol and adding a new role. An exploratory study of dynamic, heterogeneous organisations of agents has also been conducted to understand the formation of organisations in a dynamic environment where agents may fail and new agents may join organisations. These conditions mean that new organisations may evolve and existing organisations may change

Multi-Agent Systems

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains

CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms

How to optimally dispatch orders to vehicles and how to tradeoff between immediate and future returns are fundamental questions for a typical ride-hailing platform. We model ride-hailing as a large-scale parallel ranking problem and study the joint decision-making task of order dispatching and fleet management in online ride-hailing platforms. This task brings unique challenges in the following four aspects. First, to facilitate a huge number of vehicles to act and learn efficiently and robustly, we treat each region cell as an agent and build a multi-agent reinforcement learning framework. Second, to coordinate the agents from different regions to achieve long-term benefits, we leverage the geographical hierarchy of the region grids to perform hierarchical reinforcement learning. Third, to deal with the heterogeneous and variant action space for joint order dispatching and fleet management, we design the action as the ranking weight vector to rank and select the specific order or the fleet management destination in a unified formulation. Fourth, to achieve the multi-scale ride-hailing platform, we conduct the decision-making process in a hierarchical way where a multi-head attention mechanism is utilized to incorporate the impacts of neighbor agents and capture the key agent in each scale. The whole novel framework is named as CoRide. Extensive experiments based on multiple cities real-world data as well as analytic synthetic data demonstrate that CoRide provides superior performance in terms of platform revenue and user experience in the task of city-wide hybrid order dispatching and fleet management over strong baselines.Comment: CIKM 201