乘客运输的车辆路径规划策略

针对乘客运输问题,提出一种基于粒子群算法的乘客运输车辆路径规划策略。初始化阶段对n个站点、m辆车的乘客运输问题编码成一个（n+2m）维的粒子。迭代阶段对粒子进行解码,将一个（n+2m）维的粒子解码为m辆车的行走路径,对路径进行“移除－插入额外站点”优化。实验结果表明,该策略能有效地解决乘客运输车辆路径规划问题,达到总路程最短、车辆数目最少、服务的乘客数多,减少运输成本的目的。     

运钞车车辆路径规划策略

针对实际运钞网点数每天动态变化问题,提出一种先划分、再优化的动态运钞车路线规划策略。第一阶段先采用Dijkstra算法求出两点之间的最短路径,再利用最近邻算法和均衡工作量因子求出动态需求车辆的车辆数和每条路径上的网点;第二阶段利用前置交叉的改进遗传算法,分别优化每条路径并求出每条路径上的网点顺序,获得距离最短和时间最少的路径。实验结果表明,该策略能有效解决车辆数目和路径根据需求动态变化的问题,达到节约和合理利用资源的目的。     

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.     

基于分散自律体系的铁路运输计划与调度信息综合系统研发

对ADS技术进行了探讨,并结合在此基础上丌发的铁路运输计划与调度信息综合系统的软件设计过程与系统的最后实施应用,得出基于ADS的列车调度指挥系统具有在线可维护性、可扩展性与容错性等优点,并且将成为我国现代化行车调度指挥系统的理想解决模式。     

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.     

旅游路线规划研究综述

旅游业的快速发展和用户分享内容的激增使得旅游领域的信息过载问题日益突出，如何帮助游客在快速制定个性化游览路线的同时提升旅行体验，成为当前旅游路线规划问题研究的关键。首先，给出旅游路线规划问题的形式化定义；然后，将文献中的旅游路线规划求解方法分为基于精确数学建模的求解、基于用户生成内容的求解两大类，对各类方法的关键技术和存在的主要问题进行了较为详细的考察；最后，给出一个旅游路线规划系统整体架构，对其中存在的重点和难点问题进行了分析，为旅游路线规划问题的研究提供理论支持的同时指明了下一步的研究方向。     

Improved genetic algorithm based on multi-layer encoding approach for integrated process planning and scheduling problem

Integrated process planning and scheduling (IPPS) is of great significance for modern manufacturing enterprises to achieve high efficiency in manufacturing and maximize resource utilization. In this paper, the integration strategy and solution method of IPPS problem are deeply studied, and an improved genetic algorithm based on multi-layer encoding (IGA-ML) is proposed to solve the IPPS problem. Firstly, considering the interaction ability between the two subsystems and the multi-flexibility characteristics of the IPPS problem, a new multi-layer integrated encoding method is designed. The encoding method includes feature layer, operation layer, machine layer and scheduling layer, which respectively correspond to the four sub-problems of IPPS problem, which provides a premise for a more flexible and deeper exploration in the solution space. Then, based on the coupling characteristics of process planning and shop scheduling, six evolutionary operators are designed to change the four-layer coding interdependently and independently. Two crossover operators change the population coding in the unit of jobs, and search the solution space globally. The four mutation operators change the population coding in the unit of gene and search the solution space locally. The six operators are used in series and iteratively optimized to ensure a fine balance between the global exploration ability and the local exploitation ability of the algorithm. Finally, performance of IGA-ML is verified by testing on 44 examples of 14 benchmarks. The experimental results show that the proposed algorithm can find better solutions (better than the optimal solutions found so far) on some problems, and it is an effective method to solve the IPPS problem with the maximum completion time as the optimization goal.     

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.     

On personalized and sequenced route planning

Online trip planning is a popular service that has facilitated a lot of people greatly. However, little attention has been paid to personalized trip planning which is even more useful. In this paper, we define a highly expressive personalized route planning query-the Personalized and Sequenced Route (PSR) Query which considers both personalization and sequenced constraint, and propose a novel framework to deal with the query. The framework consists of three phases: guessing, crossover and refinement. The guessing phase strives to obtain one high quality route as the baseline to bound the search space into a circular region. The crossover phase heuristically improve the quality of multiple guessed routes via a modified genetic algorithm, which further narrows the radius of the search space. The refinement phase backwardly examines each candidate point and partial route to rule out impossible ones. The combination of these phases can efficiently and effectively narrow our search space via a few iterations. In the experiment part, we firstly show our evaluation results of each phase separately, proving the effectiveness of each phase. Then, we present the evaluation results of the combination of them, which offers insight into the merits of the proposed framework.     

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.     

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.     

Railway route planning in Australia

A software company and major travel agent have jointly developed an expert/information retrieval system. The system maps out itineries through Australia's complex rail network. Queries on this subject had previously always been passed to the human expert.     

Verification and validation meet planning and scheduling

A planning and scheduling (P&S) system takes as input a domain model and a goal, and produces a plan of actions to be executed, which will achieve the goal. A P&S system typically also offers plan execution and monitoring engines. Due to the non-deterministic nature of planning problems, it is a challenge to construct correct and reliable P&S systems, including, for example, declarative domain models. Verification and validation (V&V) techniques have been applied to address these issues. Furthermore, V&V systems have been applied to actually perform planning, and conversely, P&S systems have been applied to perform V&V of more traditional software. This article overviews some of the literature on the fruitful interaction between V&V and P&S.     

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.     

一种改进型Min-Min调度算法

依据经典Min-Min调度算法,针对网格任务本身以及资源的属性,采取了具体化属性参数的方法,选取具有代表性的相关属性,设计了约束函数和优先权模型,从不同角度改进算法,提出了P-Min-Min调度算法。将调度算法仿真结果中的若干数据进行了系统的比较和分析,从而验证了改进的调度算法在解决类似问题的有效性。     

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...     

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.