Contract Net Protocol Research Articles

On rescheduling in holonic manufacturing systems

This paper treats a topical issue for present manufacturing systems, which is the needed adaptability when manufacturing orders are received in an unpredictable way and/or a perturbation appears with respect to the used resources. To face such cases, a rescheduling mechanism is necessary and such a possibility is investigated for a holonic system that materializes its coordination through a combination between the Contract Net Protocol and Distributed Constraint Satisfaction Problem. The analysis is carried out for two of the most used distributed algorithms – Asynchronous Backtracking and Weak Commitment Search. Moreover, the improvement of searching through a learning process is also examined. The proposed method is investigated for a case study, by doing simulation experiments with the system colored Petri net model. The results are analyzed for cases when a new command received during execution is more or less important than the ongoing ones and for situations when a resource becomes unavailable.

Dynamic configuration and collaborative scheduling in supply chains based on scalable multi-agent architecture

Due to diversified and frequently changing demands from customers, technological advances and global competition, manufacturers rely on collaboration with their business partners to share costs, risks and expertise. How to take advantage of advancement of technologies to effectively support operations and create competitive advantage is critical for manufacturers to survive. To respond to these challenges, development of a dynamic scheme to better manage collaborative workflows is urgent. In this paper, we will study how to develop a flexible and scalable framework to dynamically and coherently configure workflows that can meet order requirements based on multi-agent systems (MAS). Configuring and scheduling collaborative workflows is a challenging problem due to the computational complexity involved, distributed architecture and dependency among different partners’ workflows. To achieve flexibility and reduce the cost and time involved in configuration of a supply chain network, we propose an approach that combines MAS, contract net protocol, workflow models and automated transformation of the workflow models to dynamically formulate the scheduling problem. To attain scalability, we develop a solution algorithm to solve the optimization problem by a collaborative and distributed computation scheme. We implement a software system based on industrial standards, including FIPA and the Petri net workflow specification model. In addition, we also illustrate effectiveness and analyze scalability of our approach by examples. Our approach facilitates collaboration between partners and provides a scalable solution for the increasing size of supply chain networks.

Efficiency of task allocation based on contract net protocol with audience restriction in a manufacturing control application

ABSTRACTThe contract net protocol (CNP) serves as a framework for cooperative distributed problem-solving agents to allocate tasks among themselves. Multi-agent systems (MAS) based on CNP are, however, susceptible to message congestion which hampers the efficiency of agents in making their task allocation decisions. Various attempts have been made to alleviate the problem. This article focuses on a particular solution, namely, audience restriction (AR). While AR has been incorporated into several CNP-based MAS, there is a lack of empirical evidence on its effectiveness in reducing message congestion and whether its contribution could be outweighed by its overheads. Furthermore, AR has a direct impact on the participation rate of agents in making task allocation decisions which has not been properly considered. By comparing the performance of a CNP-based task allocation algorithm with its AR variants in a simulation study in manufacturing control, our results show that, while AR can effectively be applied to reduce message traffic under heavy load conditions, there is also evidence that if audience size is only marginally reduced, message traffic can increase as well as decrease. The new findings contribute to more efficient CNP-based manufacturing control algorithms.

A multi-agent system for sharing distributed manufacturing resources

This paper considers a shared scheduling environment for distributed manufacturing resources. In this paper, we proposed a multi-agent system based approach to promote competition and cooperation among multi-agents and to achieve global optimal scheduling. We first build two multi-agent system (MAS) architectures. One is enterprise multi-agent subsystem (Sub-EMAS) architecture comprises job agents, resource agents and manager agents; the other one is enterprise alliance multi-agent system (EA-MAS) architecture involves the addition of a mediator agent and a scheduling agent. Then we design a Shared Contract Net Protocol (SCNP) to support both the Sub-EMAS and EA-MAS. We propose two heuristic algorithms to solve the scheduling model. The computational experiments show that the EA-MAS, with the integration of shared resource information, is able to provide shared scheduling scheme for distributed manufacturing with a good performance.

Toward Dynamic Resources Management for IoT-Based Manufacturing

The cyber-physical production system (CPPS), which combines information communication technology, cyberspace virtual technology, and intelligent equipment technology, is accelerating the path of Industry 4.0 to transform manufacturing from traditional to intelligent. The Industrial Internet of Things integrates the key technologies of industrial communication, computing, and control, and is providing a new way for a wide range of manufacturing resources to optimize management and dynamic scheduling. In this article, OLE for process control technology, software defined industrial network, and device-to-device communication technology are proposed to achieve efficient dynamic resource interaction. Additionally, the integration of ontology modeling with multiagent technology is introduced to achieve dynamic management of resources. We propose a load balancing mechanism based on Jena reasoning and Contract-Net Protocol technology that focuses on intelligent equipment in the smart factory. Dynamic resources management for IoT-based manufacturing provides a solution for complex resource allocation problems in current manufacturing scenarios, and provides a technical reference point for the implementation of intelligent manufacturing in Industry 4.0.

Developing an Agent-Based Simulation System for Post-Earthquake Operations in Uncertainty Conditions: A Proposed Method for Collaboration among Agents

Agent-based modeling is a promising approach for developing simulation tools for natural hazards in different areas, such as during urban search and rescue (USAR) operations. The present study aimed to develop a dynamic agent-based simulation model in post-earthquake USAR operations using geospatial information system and multi agent systems (GIS and MASs, respectively). We also propose an approach for dynamic task allocation and establishing collaboration among agents based on contract net protocol (CNP) and interval-based Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods, which consider uncertainty in natural hazards information during agents’ decision-making. The decision-making weights were calculated by analytic hierarchy process (AHP). In order to implement the system, earthquake environment was simulated and the damage of the buildings and a number of injuries were calculated in Tehran’s District 3: 23%, 37%, 24% and 16% of buildings were in slight, moderate, extensive and completely vulnerable classes, respectively. The number of injured persons was calculated to be 17,238. Numerical results in 27 scenarios showed that the proposed method is more accurate than the CNP method in the terms of USAR operational time (at least 13% decrease) and the number of human fatalities (at least 9% decrease). In interval uncertainty analysis of our proposed simulated system, the lower and upper bounds of uncertain responses are evaluated. The overall results showed that considering uncertainty in task allocation can be a highly advantageous in the disaster environment. Such systems can be used to manage and prepare for natural hazards.

Distributed Dynamic Scheduling for Cyber-Physical Production Systems Based on a Multi-Agent System

The world is experiencing a new industrial revolution characterized by intelligent manufacturing. Cyber-physical production systems (CPPSs) have become a research focus due to their proposed use as a solution to the development of flexible and reactive systems. The application of current centralized scheduling methods is difficult because of the enhanced precision control mode of a CPPS. Therefore, this paper focuses on distributed optimal scheduling based on multi-agent systems. First, the goals and constraints of the system are set, a two-layer decision model and the required indicators are designed to ensure the overall optimization effect, and the roles and functions of different agents are then set. Second, the dynamic decision cycle and the multistage negotiation mechanism based on the contract net protocol are studied to ensure the quality of negotiation. A rescheduling algorithm is designed to guarantee adaptability in the case of disturbance in the system. Finally, the applicability and superiority of the strategies are demonstrated via experiments and case studies.

Multi-agent-based scheduling in cloud manufacturing with dynamic task arrivals

Scheduling is a critical means for providing on-demand manufacturing services in cloud manufacturing. Multi-agent technologies provide an effective approach for addressing scheduling issues in cloud manufacturing, which, however, have rarely been used for solving the issue. This paper addresses scheduling issues in cloud manufacturing using multi-agent technologies. A multi-agent architecture for scheduling in cloud manufacturing is proposed firstly. Then, a corresponding multi-agent model is presented, which incorporates many-to-many negotiations based on an extended contract net protocol and takes into account dynamic task arrivals. Simulation results indicate the feasibility of the model and approach proposed.

Optimal Operation of Microgrids Considering Auto-Configuration Function Using Multiagent System

Monitoring the status of existing devices and identification of newly added devices is required in microgrids to adjust the operation schedule followed by any event or integration of a new device. Therefore, in this paper, automatic reconfiguration of microgrids is considered after the addition of a new device or change in the operation status of an existing device by using a multiagent system. This capability of the microgrid is named as auto-configuration function, which is performed by the auto-configurator agent. In case of addition of a new device, the auto-configurator agent is responsible for authorization and registration of the newly added device. In case of change in status of any existing device, the status information is updated. After integration of a new device or change in status of an existing device, re-optimization is carried out by the energy management system (EMS) agent. Agent communication language (ACL) is used to develop a modified contract net protocol (MCNP) for communication among different agents. EMS agent and auto-configurator agent exchange information for economic rescheduling of the microgrid components. Simulation results have shown that the proposed method can be used for optimal operation of microgrids when the configuration changes due to the addition/removal of a device.

When do agents outperform centralized algorithms?

Multi-agent systems (MAS) literature often assumes decentralized MAS to be especially suited for dynamic and large scale problems. In operational research, however, the prevailing paradigm is the use of centralized algorithms. Present paper empirically evaluates whether a multi-agent system can outperform a centralized algorithm in dynamic and large scale logistics problems. This evaluation is novel in three aspects: (1) to ensure fairness both implementations are subject to the same constraints with respect to hardware resources and software limitations, (2) the implementations are systematically evaluated with varying problem properties, and (3) all code is open source, facilitating reproduction and extension of the experiments. Existing work lacks a systematic evaluation of centralized versus decentralized paradigms due to the absence of a real-time logistics simulator with support for both paradigms and a dataset of problem instances with varying properties. We extended an existing logistics simulator to be able to perform real-time experiments and we use a recent dataset of dynamic pickup-and-delivery problem with time windows instances with varying levels of dynamism, urgency, and scale. The OptaPlanner constraint satisfaction solver is used in a centralized way to compute a global schedule and used as part of a decentralized MAS based on the dynamic contract-net protocol (DynCNET) algorithm. The experiments show that the DynCNET MAS finds solutions with a relatively lower operating cost when a problem has all following three properties: medium to high dynamism, high urgency, and medium to large scale. In these circumstances, the centralized algorithm finds solutions with an average cost of 112.3% of the solutions found by the MAS. However, averaged over all scenario types, the average cost of the centralized algorithm is 94.2%. The results indicate that the MAS performs best on very urgent problems that are medium to large scale.

Agent-based framework to individual tracking in unconstrained environments

A framework to detect, identify and track individuals is proposed.Agents communication language complies with FIPA standards using cloud storage.Manager, face detector and face tracker agents interact by contract net protocol.Face detection uses Scale Invariant Feature Transform/Speeded Up Robust Features.The proposed framework tracks individuals in unconstrained environments. Agents with intelligent perceptual capabilities are considered state-of-the-art in advanced intelligent systems. In addition, a multiagent system is considered an enabling technology for applications that rely on distributed and parallel processing, including data, information and knowledge in complex computing environments. With the aim of creating advanced intelligent systems with visual perception, this paper presents an agent-based framework to individual tracking in unconstrained environments. The framework has three types of agents that interact using the Contract Net Protocol. The face detector and tracker agents perform fully automatic single-sample face recognition using the Viola-Jones and the Scale Invariant Feature Transform/Speeded Up Robust Features algorithms. The experimental results show that the framework adequately recognizes and tracks individuals in unconstrained environments, indicating the path the individuals have taken and the time they spent in the field of view of the surveillance agents. Some of the open source framework advantages are the distribution in heterogeneous infrastructures, the expansion with new agents using different face recognition algorithms (e.g., Eigenfaces), and the individual tracking logs that can be used in different ways, e.g., improve security in surveillance areas such as automated teller machines, self-paying kiosks, movie box offices, and malls.

Agent-based task allocation under uncertainties in disaster environments: An approach to interval uncertainty

The uncertainty of task allocation in disaster environments is challenging both practically and theoretically. In real environments, agents encounter uncertain factors for the selection and execution of tasks. The present methods for task allocation seem inadequate in such environments. This paper aims to provide an efficient approach to improving the task allocation, despite the uncertainty in disaster environments. Therefore, after deduction of the major uncertainties in disaster environments, we propose a method for the agents’ decision-making about the task allocation. The allocation procedure includes four phases of ordering the tasks, choosing the coordinating agent, implementing the auction by considering the uncertainties, performing tasks and observing the real environment. The main innovation of this research is using the concepts of interval uncertainty in the task ordering as well as in the auction implementing. The results were obtained by comparing the proposed method with the contract net protocol (CNP) at three scales. In addition, the results were evaluated in the presence of uncertainties at different ranges. On average, the proposed method was better than the CNP in terms of search and rescue (SAR) operation time (124min), the number of dead people (8) and the number of incorrect allocations (180 tasks). The uncertainties range in the tasks’ decision-making procedure affected SAR operation time by more than 26%. Therefore, considering that uncertainty in task allocation can be a great advantage in the disaster environment, and considering these factors in the decision-making procedure, we have improved confidence in allocating tasks with fewer errors.

Adaptive service composition in an ambient environment with a multi-agent system

The ambient intelligence consists of the integration of computer science technologies in the objects of the environment around us in order to create a smart one. Each object in the environment performs a different service as part of achieving a task. The adaptive Service composition is a context-aware system used in the situation where the user’s needs cannot be satisfied by a single service but by the combination of several ones. One of the main and principal characteristics of ambient environments is the adaptability to the context. The multi-agent systems with their characteristics of autonomy, proactivity, mobility and sociability are one of the most promising techniques of service composition and they became a major and significant paradigm for developping ambient systems. In this paper, we present a model of adaptive service composition in ambient environment based on multi-agent system named Multi Agent System for Adaptive Service Composition (MAS-ASC). To each object of the environment is associated an agent. The set of the agents coordinate their competences taking into account the context in order to perform an activity. MAS-ASC is based on the Contract Net Protocol in which the agents exchange their proposals in the form of call for proposal. It is implemented on Java Agent DEvelopment framework platform and has been tested to economize the consumption of energy in an intelligent lighting system example.

A hybrid and scalable multi-agent approach for patient scheduling based on Petri net models

Scheduling patients in a hospital is a challenging issue due to distributed organizational structure, dynamic medical workflows, variability of resources and the computational complexity involved. It calls for a sustainable architecture and a flexible scheduling scheme that can dynamically allocate available resources to promptly react to patients in a hospital and deliver healthcare services timely. The objectives of this paper are to propose a viable and systematic approach to develop a scalable and sustainable scheduling system based on multi-agent system (MAS) to shorten patient stay in a hospital and plan schedules based on the medical workflows and available resources. To develop a patient scheduling system, we combine MAS architecture, contract net protocol (CNP), workflow specification models based on Petri nets and the cooperative distributed problem solving concept. To achieve interoperability and sustainability, Petri Net Markup Language (PNML) and XML are used to specify precedence constraints of operations in medical workflows and capabilities of resource agents, respectively. Agent communication language (ACL) and CNP are used to achieve communication and negotiation/mutual selection of agents. A collaborative algorithm is invoked by individual agents to optimize the schedules locally based on a problem formulation automatically obtained by Petri net models. We have developed a scheduling system based on a FIPA compliant MAS platform to solve the dynamic patient scheduling problem. To illustrate the benefit of our approach, we compare the performance of our method with a heuristic rule commonly used in practice. In addition, we also analyze and verify scalability of our approach by experiments.

Optimizing agents with genetic programming: an evaluation of hyper-heuristics in dynamic real-time logistics

Dynamic pickup and delivery problems (PDPs) require online algorithms for managing a fleet of vehicles. Generally, vehicles can be managed either centrally or decentrally. A common way to coordinate agents decentrally is to use the contract-net protocol (CNET) that uses auctions to allocate tasks among agents. To participate in an auction, agents require a method that estimates the value of a task. Typically, this method involves an optimization algorithm, e.g. to calculate the cost to insert a customer. Recently, hyper-heuristics have been proposed for automated design of heuristics. Two properties of automatically designed heuristics are particularly promising: (1) a generated heuristic computes quickly, it is expected therefore that hyper-heuristics perform especially well for urgent problems, and (2) by using simulation-based evaluation, hyper-heuristics can create a ‘rule of thumb’ that anticipates situations in the future. In the present paper we empirically evaluate whether hyper-heuristics, more specifically genetic programming (GP), can be used to improve agents decentrally coordinated via CNET. We compare several GP settings and compare the resulting heuristic with existing centralized and decentralized algorithms based on the OptaPlanner optimization library. The tests are conducted in real-time on a dynamic PDP dataset with varying levels of dynamism, urgency, and scale. The results indicate that the evolved heuristic always outperforms the optimization algorithm in the decentralized multi-agent system (MAS) and often outperforms the centralized optimization algorithm. Our paper demonstrates that designing MASs using genetic programming is an effective way to obtain competitive performance compared to traditional operational research approaches. These results strengthen the relevance of decentralized agent based approaches in dynamic logistics.

A Multi-Agent System based simulation approach for planning procurement operations and scheduling with multiple cross-docks

Reducing food wastage during procurement, collection and storage remain understudied in the context of the developing world that faces unique challenges not seen in the developed world. In order to achieve this objective, a simulation-based framework is needed for evaluation of decision-making policies in procurement context. In this research we propose a Multi-Agent System framework, specifically considering the Indian scenario of paddy procurement operations. We formally define procurement, allocation, milling and scheduling agents under this context and explicitly state the interaction protocols and related algorithms. Procurement agents solve the problem of allocation and maximum coverage to strategically determine their locations. An Improvised Contract Net Protocol is implemented by allocation agents to either reorganize excess procurement quantities among procurement agents or tag to milling agents who implicitly engender disturbance in the system. Scheduling agents solve a Vehicle Routing Problem with Multiple Cross-Docks to determine near optimal routing using a Particle Swarm Optimization Approach. All these agents are entities of a homogeneous system and collectively co-operate and communicate on behalf of a single superior entity. Simulations were performed to identify results such as the percentage of procurement covered, the number of tasks generated, the number of tasks not assigned to any agent.

The Command Decision Method of Multiple UUV Cooperative Task Assignment Based on Contract Net Protocol

Abstract With the help of multiple UCAV cooperative task control model, the mathematical model of multiple UUV cooperative task control is made. Variables related to decision are broken into goals, guidelines and programs levels by Analytical Hierarchy Process (AHP), on this basis; the command decision of multiple UUV task assignment is achieved. The correctness of task allocation algorithm is verified by case analysis. Time calculation formulas for a task assignment are given. The changes of overall effectiveness in the process of task allocation are analyzed, the time changes of each sub task allocation time in one task assignment are analyzed, the time changes of the number of tasks and platforms respectively fixed in task allocation are also discussed.

Stochastic Petri net-based modeling and formal analysis of fault tolerant Contract Net Protocol

Stochastic Petri net-based modeling and formal analysis of fault tolerant Contract Net Protocol

Multi-agent-based hierarchical collaborative scheduling in re-entrant manufacturing systems

Production scheduling problems in re-entrant manufacturing systems are complex due to their features of large-scale complexity, unbalanced workload and dynamic uncertainty. The aim of this paper is thus to develop an effective way of formulating production schedules for designing RMSs. First, a multi-agent-based hierarchical collaborative system consisting of a system layer, a machine layer, and a material handling device layer is developed to improve the efficiency of RMSs. The objective of the system layer is to maximise the total processing profit, and the objective of the machine layer is to determine the winning bid. Second, a contract net protocol scheduling algorithm is applied to solve capacity planning problems for key machine groups in the system layer. Third, a generalised partial global planning-contract net collaborative mechanism is adopted to allocate tasks to machines within each machine group in the machine layer. Finally, the performance of the proposed approach is validated through a case study, and the results demonstrate that the proposed approach outperforms the first-come first-serve rule in combination with the minimised batch size rule in terms of daily movement and machine utilisation.

Agent-Based Dynamic Scheduling for Earth-Observing Tasks on Multiple Airships in Emergency

Airships are becoming promising platforms for Earth observing in emergency (e.g., nature disaster surveillance). Dynamic scheduling plays a very critical role in dealing with emergent tasks. In this paper, we devise a novel agent-based scheduling mechanism. In contrast to the traditional contract net protocol, our mechanism has a bidirectional announcement mechanism, and the collaborative process consists of forward announcements from the perspective of tasks and backward announcements from the perspective of resources to jointly accomplish the scheduling. Additionally, we devise calculation rules of the bidding values in both forward and backward announcements and two heuristics for selecting contractors. Based on the bidirectional announcement mechanism, we propose an agent-based dynamic scheduling (ABDS) strategy for scheduling Earth-observing tasks on multiple airships. The ABDS scheme employs the fair competition principle of a roulette wheel and the dynamic adjustment principle of a buffer pool to solve the problem of local searching and to improve the load balancing of resources. Extensive experiments were carried out to evaluate the performance of ABDS by comparing it with a unidirectional announcement (UA) scheduling algorithm and a genetic algorithm. In addition, the sensitivity of the priority parameter to the system performance is evaluated. Experimental results show that ABDS significantly outperforms the scheduling quality of UA and that it is suitable for the Earth-observing task scheduling on multiple airships in emergency.