Integrating planning and scheduling

Planning and scheduling research is becoming an increasingly interesting topic in the artificial intelligence area because of its immediate application to real problems. Although the last few years have seen dramatic advances in planning systems, they have not seen the same advances in the methods of solving planning and scheduling problems. In this paper, an intuitive way of integrating independent planning and scheduling processes is presented which achieves better performance in the process of solving planning and scheduling problems. The integrated system has the advantage of obtaining a final plan that is executable and optimal. Moreover, the system discards any partial plan as soon as the plan becomes invalid, improving its performance. Experience demonstrates the validity of this system for tackling planning and scheduling problems.     

Verification and validation of knowledge-based systems

Knowledge-based systems (KBSs) are being used in many applications areas where their failures can be costly because of losses in services, property or even life. To ensure their reliability and dependability, it is therefore important that these systems are verified and validated before they are deployed. This paper provides perspectives on issues and problems that impact the verification and validation (V&V) of KBSs. Some of the reasons why V&V of KBSs is difficult are presented. The paper also provides an overview of different techniques and tools that have been developed for performing V&V activities. Finally, some of the research issues that are relevant for future work in this field are discussed     

Verification and validation of Bayesian knowledge-bases

Knowledge-base V&V primarily addresses the question: “Does my knowledge-base contain the right answer and can I arrive at it?” One of the main goals of our work is to properly encapsulate the knowledge representation and allow the expert to work with manageable-sized chunks of the knowledge-base. This work develops a new methodology for the verification and validation of Bayesian knowledge-bases that assists in constructing and testing such knowledge-bases. Assistance takes the form of ensuring that the knowledge is syntactically correct, correcting “imperfect” knowledge, and also identifying when the current knowledge-base is insufficient as well as suggesting ways to resolve this insufficiency. The basis of our approach is the use of probabilistic network models of knowledge. This provides a framework for formally defining and working on the problems of uncertainty in the knowledge-base.

In this paper, we examine the project which is concerned with assisting a human expert to build knowledge-based systems under uncertainty. We focus on how verification and validation are currently achieved in .     

Collision-free motion planning and scheduling

The positioning, motion coordination and test ordering procedures of new testing equipment for printed circuit boards is presented. The equipment structure consists of four mobile probes whose movements must be coordinated to avoid collisions both with obstacles and with each other. This paper explains the algorithm used to obtain the optimum path between two points in a space with obstacles based on traditional methods but including new ideas to reduce the computation times. A new method for motion coordination that obtains the optimum sequence of consecutive or simultaneous probe movements is also introduced and is based on the decomposition of the sequence of movements into stages in which only movements without interferences are permitted. Finally, the ordering of the tests can be viewed as a traveling salesman problem, and the paper presents the results of various methods when applied to the specific characteristics of the equipment involved.     

Heuristic factory planning algorithm for advanced planning and scheduling

This study focuses on solving the factory planning (FP) problem for product structures with multiple final products. In situations in which the capacity of the work center is limited and multiple job stages are sequentially dependent, the algorithm proposed in this study is able to plan all the jobs, while minimizing delay time, cycle time, and advance time. Though mixed integer programming (MIP) is a popular way to solve supply chain factory planning problems, the MIP model becomes insolvable for complex FP problems, due to the time and computer resources required. For this reason, this study proposes a heuristic algorithm, called the heuristic factory planning algorithm (HFPA), to solve the supply chain factory planning problem efficiently and effectively. HFPA first identifies the bottleneck work center and sorts the work centers according to workload, placing the work center with the heaviest workload ahead of the others. HFPA then groups and sorts jobs according to various criteria, for example, dependency on the bottleneck work center, the workload at the bottleneck work center, and the due date. HFPA plans jobs individually in three iterations. First, it plans jobs without preempting, advancing, and/or delaying. Jobs that cannot be scheduled under these conditions are scheduled in the second iteration, which allows preemption. In the final iteration, which allows jobs to be preempted, advanced, and delayed, all the remaining jobs are scheduled. A prototype was constructed and tested to show HFPA's effectiveness and efficiency. This algorithm's power was demonstrated using computational and complexity analysis.     

OVA技术在接口时序验证中的应用

本文主要阐述了基于断言技术并结合动态仿真,形式测试,测试激励自动化等方法的新硬件验证平台,着重介绍了OVA的特点及运用,并以LCD controller的sharp接口为实例讲解了该方法的优点.     

Verification and validation of an intelligent tutorial system

This paper presents the results of a verification and validation process for an intelligent system. The system being studied is an Intelligent Tutorial that employs fuzzy logic and multiagent systems. Software engineering techniques were used in the verification process, while the validation exploited both qualitative and quantitative techniques.     

Constraint satisfaction techniques in planning and scheduling

Over the last few years constraint satisfaction, planning, and scheduling have received increased attention, and substantial effort has been invested in exploiting constraint satisfaction techniques when solving real life planning and scheduling problems. Constraint satisfaction is the process of finding a solution to a set of constraints. Planning is the process of finding a sequence of actions that transfer the world from some initial state to a desired state. Scheduling is the problem of assigning a set of tasks to a set of resources subject to a set of constraints. In this paper, we introduce the main definitions and techniques of constraint satisfaction, planning and scheduling from the Artificial Intelligence point of view.     

A featured-based dynamic process planning and scheduling

The paper presents a dynamic featured-based process planning and scheduling system which is currently under development utilizing a commercial CAD system.

In this system, the product features are extracted using an AI-based feature extractor according to the shop floor capabilities. Using this system, the products can be dynamically process planned and scheduled.     

Acquisition planning and scheduling of computing resources

Cloud computing has been attracting considerable attention since the last decade. This study considers a decision problem formulated from the use of computing services over the Internet. An agent receives orders of computing tasks from his/her clients and on the other hand he/she acquires computing resources from computing service providers to fulfill the requirements of the clients. The processors are bundled as packages according to their speeds and the business strategies of the providers. The packages are rated at a certain pricing scheme to provide flexible purchasing options to the agent. The decision of the agent is to select the packages which can be acquired from the service providers and then schedule the tasks of the clients onto the processors of the acquired packages such that the total cost, including acquisition cost and scheduling cost (total weighted tardiness), is minimized. In this study, we present an integer programming model to formulate the problem and propose several solution methods to produce acquisition and scheduling plans. Ten well-known heuristics of parallel-machine scheduling are adapted to fit into the studied problem so as to provide initial solutions. Tabu search and genetic algorithm are tailored to reflect the problem nature for improving upon the initial solutions. We conduct a series of computational experiments to evaluate the effectiveness and efficiency of all the proposed algorithms. The results of the numerical experiments reveal that the proposed tabu search and genetic algorithm can attain significant improvements.     

一种推拉结合的生产计划与调度模型*

通过分析混合流程企业生产计划管理的实际需求特点,提出生产工艺流程图和一种推拉结合的生产计划与调度模型.模型以订单、库存和预投为综合需求起点,以拉式补货的方式制订有限产能的主生产计划,依据工艺流程图分解生成可行的流程作业计划和离散作业计划,以及车间级物料需求计划等.在生产执行中则结合工序流转卡等方式进行推式生产,以保证生...     

Integration of process planning and scheduling functions

Automated process planning (APP) has received a considerable amount of attention in recent years. This has mainly been due to APP allowing for the bridging of the gap between design and manufacturing, and for its automation of the related tedious human task. While there have been several efforts in developing interfaces between computer-aided design (CAD) and APP, little attention has been given to the integration of APP and scheduling, which is likely to be of even higher importance. This paper addresses the basic issues involved in the integration of the two functions, demonstrates a methodology and the potential benefits of the integrated approach by an example, and presents directions for future research.     

Constraint satisfaction for planning and scheduling problems

The areas of planning and scheduling (from the Artificial Intelligence point of view) have seen important advances thanks to application of constraint satisfaction techniques. Currently, many important real-world problems require efficient constraint handling for planning, scheduling and resource allocation to competing goal activities over time in the presence of complex state-dependent constraints. Solutions to these problems require integration of resource allocation and plan synthesis capabilities. Hence to manage such complex problems planning, scheduling and constraint satisfaction must be interrelated. This special issue on Constraint Satisfaction for Planning and Scheduling Problems compiles a selection of papers dealing with various aspects of applying constraint satisfaction techniques in planning and scheduling. The core of submitted papers was formed by the extended versions of papers presented at COPLAS??2009: ICAPS 2009 Workshop on Constraint Satisfaction Techniques for Planning and Scheduling Problems. This issue presents novel advances on planning, scheduling, constraint programming/constraint satisfaction problems (CSPs) and many other common areas that exist among them. On the whole, this issue mainly focus on managing complex problems where planning, scheduling, constraint satisfaction and search must be combined and/or interrelated, which entails an enormous potential for practical applications and future research.     

进程调度模型的Spin验证分析与改进

Spin不仅可以用于通信协议的正确性验证,也可以对其进行有效的安全性分析.对用Promela语言描述的进程调度模型进行分析研究,并对其存在的不足加以改进.增加了LTL式描述,用spin进行模型检测验证,使用Xspin得到了反例状态迁移轨迹图.     

Production planning & scheduling: Computational feasibility of multi-criteria models of production, planning and scheduling

This paper surveys the applications of multi-criteria decision making (MCDM) methods to production planning, scheduling, and sequencing problems. The basic structure of the decision models are described by their objectives and the resulting models are classified by decision variables into the areas of Aggregate Production Planning, Disaggregate Production Planning, Production Scheduling, and Single Machine Sequencing. The problem sizes that have been solved are summarized to determine how practical it is to use MCDM.     

Integrated pulp and paper mill planning and scheduling

This article describes a real-world production planning and scheduling problem occurring at an integrated pulp and paper mill (P&P) which manufactures paper for cardboard out of produced pulp. During the cooking of wood chips in the digester, two by-products are produced: the pulp itself (virgin fibers) and the waste stream known as black liquor. The former is then mixed with recycled fibers and processed in a paper machine. Here, due to significant sequence-dependent setups in paper type changeovers, sizing and sequencing of lots have to be made simultaneously in order to efficiently use capacity. The latter is converted into electrical energy using a set of evaporators, recovery boilers and counter-pressure turbines. The planning challenge is then to synchronize the material flow as it moves through the pulp and paper mills, and energy plant, maximizing customer demand (as backlogging is allowed), and minimizing operation costs. Due to the intensive capital feature of P&P, the output of the digester must be maximized. As the production bottleneck is not fixed, to tackle this problem we propose a new model that integrates the critical production units associated to the pulp and paper mills, and energy plant for the first time. Simple stochastic mixed integer programming based local search heuristics are developed to obtain good feasible solutions for the problem. The benefits of integrating the three stages are discussed. The proposed approaches are tested on real-world data. Our work may help P&P companies to increase their competitiveness and reactiveness in dealing with demand pattern oscillations.     

Introduction to planning, scheduling and constraint satisfaction

Planning, scheduling and constraint satisfaction are important areas in artificial intelligence (AI). Many real-world problems are known as AI planning and scheduling problems, where resources must be allocated so as to optimize overall performance objectives. Therefore, solving these problems requires an adequate mixture of planning, scheduling and resource allocation to competing goal activities over time in the presence of complex state-dependent constraints. Constraint satisfaction plays also an important role to solve real-life problems, so that integrated techniques that manage planning and scheduling with constraint satisfaction remains necessary. This special issue on Planning, Scheduling and Constraint Satisfaction compiles a selection of papers of CAEPIA’2007 workshop on Planning, Scheduling and Constraint Satisfaction and COPLAS’2007: CP/ICAPS 2007 Joint Workshop on Constraint Satisfaction Techniques for Planning and Scheduling Problems. This issue presents novel advances on planning, scheduling, constraint programming/constraint satisfaction problems (CSPs) and many other common areas that exist among them. On the whole, this issue mainly focus on managing complex problems where planning, scheduling, constraint satisfaction and search must be combined and/or interrelated, which entails an enormous potential for practical applications and future research. Furthermore, this issue also includes a complete survey about constraint satisfaction, planning, scheduling and integration among these areas.