It has been argued that the use of constraint-based techniques and tools enables the implementation of precise, flexible, efficient and extensible scheduling systems: precise and flexible as the system can take into account any constraint expressible in the constraint language; efficient inasmuch as highly optimized constraint propagation procedures are now available; extensible as the consideration of a new type of constraint may require (especially in an object-oriented framework) only an extension to the constraint system or, in the worst case, the implementation of additional decision-making modules (without needs for modification of the existing code). The following paper presents ILOG SCHEDULE, a C++ library enabling the representation of a wide collection of scheduling constraints in terms of "resources" and "activities." ILOG SCHEDULE is based on SOLVER, the generic software tool for object-oriented constraint programming developed and marketed by ILOG. SOLVER variables and constraints can be accessed from SCHEDULE activities and resources. As a result, the user of SCHEDULE can make use of SOLVER to represent specific constraints, and implement and combine the specific problem-solving strategies that are the most appropriate for the scheduling application under consideration. It is hoped --- and expected --- that object-oriented constraint programming tools like SCHEDULE will enable the industry to make decisive steps toward the implementation of "state of the art," highly flexible, constraint-based scheduling applications.

The allocation of resources within a system of autonomous agents, that not only have preferences over alternative allocations of resources but also actively participate in computing an allocation, is an exciting area of research at the interface of Computer Science and Economics. This paper is a survey of some of the most salient issues in Multiagent Resource Allocation. In particular, we review various languages to represent the preferences of agents over alternative allocations of resources as well as different measures of social welfare to assess the overall quality of an allocation. We also discuss pertinent issues regarding allocation procedures and present important complexity results. Our presentation of theoretical issues is complemented by a discussion of software packages for the simulation of agent-based market places. We also introduce four major application areas for Multiagent Resource Allocation, namely industrial procurement, sharing of satellite resources, manufacturing control, and grid computing.

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Abstract. Agent-based approaches to manufacturing scheduling and control have gained increasing attention in recent years. Such approaches are attractive because they offer increased robustness against the unpredictability of factory operations. But the specification of local coordination policies that give rise to efficient global performance and effectively adapt to changing circumstances remains an interesting challenge. In this paper, we present a new approach to this coordination problem, drawing on various aspects of a computational model of how wasp colonies coordinate individual activities and allocate tasks to meet the collective needs of the nest. We focus specifically on the problem of configuring parallel multi-purpose machines in a factory to best satisfy product demands over time. Wasp-like computational agents that we call routing wasps act as overall machine proxies. These agents use a model of wasp task allocation behavior, coupled with a model of wasp dominance hierarchy formation, to determine which new jobs should be accepted into the machine’s queue. If you view our system from a market-oriented perspective, the policies that the routing wasps independently adapt for their respective machines can be likened to policies for deciding when to bid and when not to bid for arriving jobs. We benchmark the performance of our system on the real-world problem of assigning trucks to paint

Multi-machine scheduling, that is, the assigment of jobs to machines such that certain performance demands like cost and time effectiveness are fulfilled, is a ubiquitous and complex activity in everyday life. This paper presents an approach to multi-machine scheduling that follows the multiagent learning paradigm known from the field of Distributed Artificial Intelligence. According to this approach the machines collectively and as a whole learn and iteratively refine appropriate schedules. The major characteristic of this approach is that learning is distributed over several machines, and that the individual machines carry out their learning activities in a parallel and asynchronous way.

Agent-based approaches to manufacturing scheduling and control have gained increasing attention in recent years. Such approaches are attractive because they offer increased robustness against the unpredictability of factory operations. But the specification of local coordination policies that give rise to efficient global performance and effectively adapt to changing circumstances remains an interesting challenge. In this paper, we introduce a new approach to this coordination problem, drawing on various aspects of a computational model of how wasp colonies coordinate individual activities and allocate tasks to meet the collective needs of the nest. We focus specifically on the problem of configuring machines in a factory to best satisfy (potentially changing) product demands over time. Our system models the set of jobs queued in front of any given machine as a wasp nest, wherein wasp-like agents interact to form a social hierarchy and prioritize the jobs that they represent. Other was...

Abstract. Open environments are characterized by their uncertainty and non-determinism. Agents need to adapt their task processing to available resources, deadlines, the goal criteria specified by the clients as well their current problem solving context in order to survive in these environments. If there were no resource constraints, then an optimal Markov Decision Process based policy would obviously be the best way for complex problem solving agents to make scheduling decisions. However in many agent systems, these scheduling decisions have to be made on-line or in soft real-time, making the off-line policy computationally infeasible in open environments. The hybrid planner/scheduler used to control TÆMS agents is the Design-to-Criteria (DTC) agent scheduler. Design-to-Criteria scheduling is the soft real-time process of custom building a plan/schedule to meet an agent’s current objectives which are expressed as dynamic goal criteria (including real-time deadlines), using task models that describe alternate ways to achieve tasks and subtasks. Recent advances in Design-to-Criteria control include the addition of uncertainty to the TÆMS computational task models analyzed by the scheduler and the incorporation of uncertainty in the scheduling process. As we show, the use of uncertainty in TÆMS and Design-to-Criteria enables agents to make better control decisions in uncertain environments. Design-to-Criteria uses a heuristic approach for on-line scheduling of medium granularity tasks. It approximates the analysis used to generate an optimal policy by heuristically reasoning about the implications of uncertainty in task execution. The addition of uncertainty has also spawned a post-scheduling contingency analysis step for situations in which an agent must produce a result by a given deadline (deadline critical situations) and where the added computational cost is worth the expense. We describe the

Consumer demand and current computational capabilities are driving the manufacturing complex from mass production to mass customization. Current barriers to mass customization have less to do with manufacturing machinery and more to do with the manual and computerized information systems currently used to control that machinery. On-line commerce offers potential benefits and functionality far beyond the automated catalogs that characterize today's cybermarkets. The AARIA project (Autonomous Agents for Rock Island Arsenal) demonstrates how agent technologies and Internet communications can support this expanded vision. This paper outlines new directions that we expect trade on the Internet to take, and shows how AARIA's architecture, scheduling approach, and simulation capabilities support these new directions.

This paper presents a framework for the design of the planning and controlling components in a flexible manufacturing system. A hierarchical planning structure is presented which consists of six layers: the layer of the production planning and control system, the layer of the shop floor control system, the task coordination layer, the task planning layer, the task execution layer and the machine control layer. The last four layers mentioned are present separately for each manufacturing device which is able to execute tasks autonomously. For the abstract system which consists of the task coordination component, the task planner, the task execution component, the machine control component, and the flexible manufacturing device itself the term autonomous system is introduced. Special emphasis is placed on the description of the shop floor control system, the task coordination and the task planning layer. The implementation of the planning and controlling components of the hierarchical pla...