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.     

A system framework for OKP product planning in a cloud-based design environment

Nowadays, one-of-a-kind (OKP) companies, which generally operate in an 'engineer-to-order' business mode, strive to deliver individualized products with quality to achieve customer satisfaction. Thus, an accurate and prompt analysis of customer requirements (CRs) in the early design stage is critical to its success. However, most OKP companies are small or medium-sized enterprises (SMEs). Due to the limited resources and low product planning budget, they often cannot obtain abundant CR information nor can they afford the expense of complicated planning process. To address these issues, a system framework is proposed in support of OKP product planning process in a cloud-based design (CBD) environment. The challenges and future market niches of OKP companies are presented. The comparison of typical distributed systems shows that CBD, which utilizes advanced information technologies and business model, has advantages in providing sufficient resources, decreasing product development time span for OKP companies in a cost-efficient way. This article describes the proposed system architecture, the business interaction process and the information communication among customers, designers and marketing analysts at the product planning stage. To validate the proposed framework, a prototype system module MyProduct is under development in the CBD environment with an illustrative example.     

Evolutionary algorithm for advanced process planning and scheduling in a multi-plant

Integration of process planning and scheduling is one of the most important functions to support flexible planning in a multi-plant. The planning and scheduling are actually interrelated and should be solved simultaneously. In this paper, we propose an advanced process planning and scheduling model for the multi-plant. The objective of the model is to decide the schedules for minimizing makespan and operation sequences with machine selections considering precedence constraints, flexible sequences, and alternative machines. The problem is formulated as a mathematical model, and an evolutionary algorithm is developed to solve the model. Numerous experiments are carried out to demonstrate the efficiency of the proposed approach.     

基于高级计划排程系统与制造执行系统的电网运检计划管控系统

针对传统运检计划的编制与执行依靠工作人员人工编制与现场管控,存在运检计划编制缓慢、执行管控疏漏等问题,为提升运检工作质效、保障电网本质安全,搭建了运检计划数字化管控系统.系统基于制造业应用成熟的高级计划排程(APS)系统以及制造执行系统(MES),通过对比运检业务与制造业业务异同点,建立映射模型.系统在电网资源业务中台的基础上进行微应用改造,明确了运检计划数字化管控系统八大基本功能与全业务工作流程.通过对比数字化管控系统的应用,可减少90％信息收集工作,避免因检修不到位造成设备质量事件.通过分析运检承载能力、辨识运检风险、制定差异化运检策略,切实保障人身、设备、电网安全.     

卫星的智能规划与调度

以对地观测卫星为例，分析卫星的结构功能和飞行任务的特点，并在此基础上建立卫星智能规划与调度系统。规划与调度系统由卫星模型和推理机组成，其中模型描述卫星结构功能和各种约束条件，推理机分析并解决这些约束条件，形成一个没有冲突的飞行计划。规划与调度系统还具有修正计划的能力，能满足任务删除、更改和新任务插入等需求。     

炼钢-连铸生产计划调度系统开发

针对炼钢-连铸生产以及钢厂现有信息化系统的特点,开发了面向钢厂MES的炼钢-连铸生产计划调度系统。系统通过与MES的数据接口收集生产作业计划、调度和生产运行实绩相关信息;设计和构建系统关系数据库,将通过MES接收的自ERP系统下达的生产批量计划和来自过程信息系统的实时生产状态信息分类集成并显示;设计基于生产过程信息的扰动自动识别和人工扰动自主设置的调度触发机制,按分类扰动处理机制进行重计划和重调度,调用智能优化算法编制生产作业计划,确定可行的生产调度方案,实现对调度方案的物流和工位的多种可视化表达方式;重计划和重调度信息可反馈至MES信息系统,为调度人员提供决策参考。通过在钢厂的在线测试表明,该系统可以满足实际生产需要,通过辅助调度决策可提高调度员决策的科学性和调度水平,提升和充分发挥MES的性能。     

基于改进型批决策与调度建模的生产计划调度系统的研究

热轧型钢生产工艺复杂,其生产中极易出现由于计划调度安排不当而产生的交货期延误、库存超负荷等问题。针对以上问题研究设计了MES生产计划调度系统,改进了批决策调度策略用于数学建模,利用自适应遗传算法求解生产调度计划。以此为基础,为某热轧企业设计实现了生产计划调度系统,并通过真实的热轧型钢订单、原料、设备等数据,对模型改进前后的计划编制方法进行模拟与比较,验证了利用该改进型批决策与调度模型编制的热轧型钢生产调度计划可节省生产时间、降低设备调度时间,以此来指导热轧型钢的生产可切实减少交货延误和减少库存占用率,并提高企业利润率。     

A two-level advanced production planning and scheduling model for RFID-enabled ubiquitous manufacturing

Radio frequency identification (RFID) technology has been used in manufacturing industries to create a RFID-enabled ubiquitous environment, in where ultimate real-time advanced production planning and scheduling (APPS) will be achieved with the goal of collective intelligence. A particular focus has been placed upon using the vast amount of RFID production shop floor data to obtain more precise and reasonable estimates of APPS parameters such as the arrival of customer orders and standard operation times (SOTs). The resulting APPS model is based on hierarchical production decision-making principle to formulate planning and scheduling levels. A RFID-event driven mechanism is adopted to integrate these two levels for collective intelligence. A heuristic approach using a set of rules is utilized to solve the problem. The model is tested through four dimensions, including the impact of rule sequences on decisions, evaluation of released strategy to control the amount of production order from planning to scheduling, comparison with another model and practical operations, as well as model robustness. Two key findings are observed. First, release strategy based on the RFID-enabled real-time information is efficient and effective to reduce the total tardiness by 44.46% averagely. Second, it is observed that the model has the immune ability on disturbances like defects. However, as the increasing of the problem size, the model robustness against emergency orders becomes weak; while, the resistance to machine breakdown is strong oppositely. Findings and observations are summarized into a number of managerial implications for guiding associated end-users for purchasing collective intelligence in practice.     

A distributed agent system for port planning and scheduling

This paper describes a distributed agent system for dynamic port planning and scheduling. The proposed system comprises four agents, namely a port planning manager (PPM), a berth control agent (BCA), a shuttle allocation agent (SAA) and a yard storage agent (YSA). These agents communicate and cooperate with one another to work out the schedule for berth allocation and requirements for shuttles. Basically, the PPM maintains all the necessary information and provides a negotiation and communication locale for the BCA, SAA and YSA, which represent the berths, the shuttles and the container yard, respectively, to resolve their conflicts under its supervision. With the help of a GA-enhanced dynamic scheduler, the BCA attempts to optimise the berth allocation schedule using the data such as ship arrival information retrieved from the PPM. As soon as the SAA receives the schedule from the BCA, it proceeds to check for the availability of shuttles and work out the requirement for shuttles. On the other hand, the YSA generates the yard storage allocation schedule and the truck/train schedule for the transportation of containers.A case study is conducted to illustrate the capability of the distributed agent system. It has been shown that the proposed system is able to successfully generate the schedules for both the BCA and the SAA.     

A hierarchic approach to production planning and scheduling of a flexible manufacturing system

The paper deals with the problem of improving the machine utilization of a flexible manufacturing cell. Limited tool magazine space of the machines turns out to be a relevant bottleneck. A hierarchic approach for this problem is proposed. At the upper level, sets of parts that can be concurrently processed (batches) are determined. At the lower levels, batches are sequenced, linked, and scheduled. Methods taken from the literature are used for the solution of the latter subproblems, and an original mixed integer programming model is formulated to determine batches. The proposed methods are discussed on the basis of computational experience carried out on real instances.     

A prototype of a feature-based multiple-alternative process planning system with scheduling verification

The primary objective of this research was to develop a prototype feature-based multiple-alternative process planning system in which the process plan would be generated directly from design and available factory facility information. An overall removable volume is generated by graphically comparing the 3D part and 3D workpiece blank. The manufacturing features are decomposed into a series of general manufacturing features by using a mixed graph-based and rule-based algorithm. The multiple-alternative process plan generation is based on recognized manufacturing features and various production rules. After generating multiple process plans, each process plan is allocated the possible manufacturing scheduling time and the candidate process plans are retrieved based on the required due day. An example problem is presented to illustrate the functionality of the prototype system. This research presents an alternative method that provides useful information to the factory planner and controller to facilitate production.     

A genetic algorithm for dynamic advanced planning and scheduling (DAPS) with a frozen interval

Since the opening of Chinese economic, a great amount of Taiwanese merchants enter Mainland China for investment. The annual production of bamboo flutes in China is over 4 millions to provide domestic and international consumers. Owing to the great amount of demand, folk instrument industry is one of the attractive investment items. In order to increase the product manufacturing process and promote the product quality, the implementation and application of an automatic timbre evaluation system with spectrum analysis and cluster algorithm to increase the flute quality becomes an important method for Taiwanese businessmen to earn more profits. This research is to implement an automatic timbre quality system to evaluate the timbre quality of Chinese flute in flute businesses. From research data displays that the automatic timbre evaluation system does help the traditional handicraft manufacture classify the timbre quality of flute more effectively.     

A genetic algorithm for dynamic advanced planning and scheduling (DAPS) with a frozen interval

This paper investigates a dynamic advanced planning and scheduling (DAPS) problem where new orders arrive on a continuous basis. A periodic policy with a frozen interval is adopted to increase stability on the shop floor. A genetic algorithm is developed to find a schedule such that both production idle time and penalties on tardiness and earliness of both original orders and new orders are minimized at each rescheduling point. The proposed methodology is tested on a series of examples. A representative example is illustrated to indicate that the suggested approach can improve the schedule stability while retaining efficiency.     

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.     

OPTIMUM-AIV: A planning and scheduling system for spacecraft AIV

This paper presents a project undertaken for the European Space Agency (ESA). The project is developing a knowledge based system for planning and scheduling of activities for spacecraft assembly, integration and verification (AIV). The system extends to the monitoring of plan execution and the plan repair phases.

The objectives of the contract are to develop an operational kernel of a planning, scheduling and plan repair tool, called OPTIMUM-AIV, and to provide facilities which will allow individual projects to customize the kernel to suit its specific needs. The kernel shall consist of a set of software functionalities for assistance in the initial specification of the AIV plan, in the verification and generation of valid plans and schedules for the AIV activities, and in interactive monitoring and execution problem recovery for the detailed AIV plans. Embedded in OPTIMUM-AIV are external interfaces which allow integration with alternative scheduling systems and project databases.

The current status of the OPTIMUM-AIV project, as of May 1991, is that the architectural design of the system has been agreed on by ESTEC/ESA and detailed design and implementation is now underway, expecting a final delivery in October of 1991.     

Toward a cloud-based manufacturing execution system for distributed manufacturing

This paper illustrates the needs and challenges for the management of distributed manufacturing in a multi-company supply chain and processes these further as features of new IT systems. Requirements are collected from manufacturing companies and combined with insights from literature in the field of current ERP/MES system drawbacks, advantages, needs and challenges. The findings show that the needs and challenges in data integration inside SME networks are closely related to the limitations of current supply chain solutions. Current ERP-solutions lack extended enterprise support and a shared cloud-based approach. On the other hand, current MES solutions can operate the manufacturing process, but not for distributed manufacturing. As an answer to the requirements, we made a proposal for the core of architecture for next generation of MES solution in this position paper. Moreover, a pilot software tool has been developed to support the needs related to real time, cloud-based, light weight operation.     

A multi-objective algorithm for task scheduling and resource allocation in cloud-based disassembly

Some manufacturers outsource their disassembly tasks to professional factories, each factory of them has specialized in its disassembly ability. Different disassembly facilities are usually combined to execute disassembly tasks. This study proposes the cloud-based disassembly that abstracts ability of the disassembly factory as the disassembly resource, the disassembly resource is then able to be allocated to execute disassembly tasks. Based on this concept, the cloud-based disassembly system is proposed, which provides the disassembly service according to the user requirement. The disassembly service is the execution plan for disassembly tasks, which is the result of scheduling disassembly tasks and allocating disassembly resources. To formally describe the disassembly service, this paper builds a mathematical model that considers the uncertainty nature of the disassembly process and precedence relationships of disassembly tasks. Two objectives including minimizing the expected total makespan and minimizing the expected total cost of the disassembly service are also discussed. The mathematical model is NP-complete, a multi-objective genetic algorithm based on non-dominated sorting genetic algorithm II is designed to address the problem. Computation results show that the proposed algorithm performs well, the algorithm generates a set of Pareto optimal solutions. The user can choose a preferred disassembly service among Pareto optimal solutions.     

基于非线性工艺规划思想的车间动态调度系统

描述了一种基于非线性工艺规划思想的车间动态调度系统的集成框架结构，其核心模块为计划调度模块，该模块调用由遗传算法和启发式调度相结合的调度算法生成动态调度方案。其中所提出的遗传编码的设计充分考虑工艺路径的柔性，并根据此编码提出调度方案的构造方法，同时相应地改进了遗传操作，从而实现了调度的全局最优性和可行性。     

An energy-efficient multi-objective integrated process planning and scheduling for a flexible job-shop-type remanufacturing system

This study considers an energy-efficient multi-objective integrated process planning and scheduling (IPPS) problem for the remanufacturing system (RMS) integrating parallel disassembly, flexible job-shop-type reprocessing, and parallel reassembly shops with the goal of realizing the minimization of both energy cost and completion time. The multi-objective mixed-integer programming model is first constructed with consideration of operation, sequence, and process flexibilities in the RMS for identifying this scheduling issue mathematically. An improved spider monkey optimization algorithm (ISMO) with a global criterion multi-objective method is developed to address the proposed problem. By embedding dynamic adaptive inertia weight and various local neighborhood searching strategies in ISMO, its global and local search capabilities are improved significantly. A set of simulation experiments are systematically designed and conducted for evaluating ISMO’s performance. Finally, a case study from the real-world remanufacturing scenario is adopted to assess ISMO’s ability to handle the realistic remanufacturing IPPS problem. Simulation results demonstrate ISMO’s superiority compared to other baseline algorithms when tackling the energy-aware IPPS problem regarding solution accuracy, computing speed, solution stability, and convergence behavior. Meanwhile, the case study results validate ISMO’s supremacy in solving the real-world remanufacturing IPPS problem with relatively lower energy usage and time cost.