Finite capacity scheduling systems: Performance issues and comparisons

This study examines the flowtime, schedule stability and delivery performance results for finite capacity scheduling (FCS) systems. Two common schedule construction approaches, blocked-time and event-driven, are compared. A production shop simulation model facilitates the testing of these two approaches using either internally or externally specified due dates. Also, various due-date dependent loading rules are used in schedule construction. The results show that using the event-driven schedule construction approach along with internally specified due dates works best. Flowtime, flowtime variability and mean tardiness are lower when comparing the event-driven approach to the blocked-time approach as well as when comparing internally to externally set due dates. Schedule stability results are less consistent, with performance being largely a function of the loading rule when the blocked-time approach is used.     

DPP: An agent-based approach for distributed process planning

A changing shop floor environment characterized by larger variety of products in smaller batch sizes requires creating an intelligent and dynamic process planning system that is responsive and adaptive to the rapid adjustment of production capacity and functionality. In response to the requirement, this research proposes a new methodology of distributed process planning (DPP). The primary focus of this paper is on the architecture of the new process planning approach, using multi-agent negotiation and cooperation. The secondary focus is on the other supporting technologies such as machining feature-based planning and function block-based control. Different from traditional methods, the proposed approach uses two-level decision-making—supervisory planning and operation planning. The former focuses on product data analysis, machine selection, and machining sequence planning, while the latter considers the detailed working steps of the machining operations inside of each process plan and is accomplished by intelligent NC controllers. By the nature of decentralization, the DPP shows promise of improving system performance within the continually changing shop floor environment.     

Dynamic planning model for determining cutting parameters using neural networks in feature-based process planning

Although feature-based computer-aided process planning plays a vital role in automating and integrating design and manufacturing for efficient production, its off-line properties prohibit the shop floor controllers from rapidly coping with unexpected production errors. The objective of the paper is to suggest a neural network-based dynamic planning model, by which the shop floor controllers determine cutting parameters in real-time based on shop floor status. At off-line is the dynamic planning model constructed as a neural network form, and then embedded into each removal feature. The dynamic planning model will be executed by the shop floor controllers to determine the cutting parameters. A prototype system is constructed to validate whether the dynamic planning model is capable of determining dynamically and efficiently the cutting parameters for a particular set of shop operating factors. Owing to the dynamic planning model, the shop floor controller will increase flexibility and robustness by rapidly and adaptively determining the cutting parameters in unexpected errors occurring.     

A Petri net model for integrated process and job shop production planning

In this paper a new Petri net class (Chameleon systems) for modeling and analyzing joined process planning and job shop production planning is proposed based on the multi-level Petri net model introduced by Valk (1995). Chameleon system is a High-level Petri net extended with time that consists of a High-level Petri net modeling the workshop which is called system net and ordinary Petri net modeling the jobs and setups which act as tokens of the system net and therefore are called token nets. Process plans of the jobs are partial orders of operation groups and alternatives. The uncertainty of operation duration is captured by a new time model. The advantages of this new modeling approach are the following: (i) a modular construction of the joined process and job shop planning is allowed, (ii) classical known Petri net analysis methods can be applied and (iii) the analysis can be done at different levels of abstraction.     

Web-based Visual Decision Support System (WVDSS) for letter shop

Decision Support Systems (DSS) is one of the key tools to transform data to decisions. In this paper, a successful implementation of a web based DSS in a direct mail (letter shop) is explained. Utilizing continuous improvement framework, we provide how the Web-based Visual Decision Support System (WVDSS) helped the organization to successfully turn around the scheduling and capacity planning function along with extending the success to customer service, and warehouse operations. The outcome of implementing the WVDSS were (1) attainment of 100% scheduled delivery date, (2) improved efficiencies across the value stream, and (3) real-time notification of shipment to the customer service representatives. The unique contribution of this work is the original implementation of a web based decision support system in a high variety and low volume letter shop environment spanning transactional and manufacturing value streams.     

An integrated real time MRP and group technology system

The approach taken in this paper is twofold. First manufacturing environment is simplified for the purposes of planning and control without losing any of the essential characteristics. Second, a simple GT model is applied to the shop floor area and real time MRP is applied to the assembly area. The aim of this study is to develop and compare with a simulation of similar proposal except that jobshop is used in the shop floor area instead. The variable factors in both models were the set up time to operation time ratio and the intensity of the loading on the machines. In the highly loaded situations, the GT model faired better than the job shop model. However, for low loaded situations the performances of the two models were similar.     

用SuperMap Object开发基于GIS的连锁店配送信息系统

支撑整个连锁店运营过程的是基于GIS的连锁店配送信息系统。在明确了业务流程的基础上,研究了基于GIS的连锁店配送信息系统的功能、GIS平台、空间数据模型、道路数据模型和最优路径分析,探讨了以Super Map Objects作为GIS平台,采用Microsoft Visual Basic作为开发工具实现基于GIS的连锁店配送信息系统的方案。     

Dynamic Drum-Buffer-Rope approach for production planning and control in capacitated flow-shop manufacturing systems

Drum-Buffer-Rope-based production planning and control (PPC) approaches provide production managers with effective tools to manage production disruptions and improve operational performance. The corner stone of these approaches is the proper selection of time-buffers which are considered as exogenously defined constant. However, the majority of real-world manufacturing systems are characterized by the dynamic change of demand and by stochastic production times. This fact calls for a dynamic approach in supporting the decision making on time-buffer policies. To this end, we study a capacitated, single-product, three-operation, flow-shop manufacturing system. We propose a dynamic time-buffer control mechanism for short/medium-term PPC with adaptive response to demand changes and robustness to sudden disturbances in both internal and external shop environment. By integrating the control mechanism into the flow-shop system, we develop a system dynamics model to support the decision-making on time-buffer policies. Using the model, we study the effect of policies on shop performance by means of analysis of variance. Extensive numerical investigation reveals the insensitivity of time-buffer policies to key factors related to demand, demand due date and operational characteristics such as protective capacity and production times.     

Multi-period part selection and loading problems in flexible manufacturing systems

We consider multi-period part selection and loading problems in flexible manufacturing systems with the objective of minimizing subcontracting costs. The part selection problem is to select sets of part types and to determine their quantities to be produced during the upcoming planning horizon while satisfying due dates of all orders for the parts, and the loading problem involves allocation of operations and required tools to machines. Production demands should be satisfied for periods through subcontracting if production demands cannot be satisfied by the system due to machine capacity or tool magazine capacity constraints. For the part selection and loading problems, we develop three iterative algorithms, called the forward algorithm, the backward algorithm and the capacity approximation algorithm, that solve the part selection and loading problems iteratively for each period. To compare the three algorithms, a series of computational experiments is done on randomly generated test problems.     

Scheduling optimization of a stochastic flexible job-shop system with time-varying machine failure rate

Due to environmental circumstances encountered in manufacturing processes, operating machines need to be maintained preventively, so as to ensure satisfactory operating condition. This paper investigates a scheduling problem in a flexible job-shop system with maintenance considerations where each operation can be processed by a machine out of a set of capable machines, and so, jobs may have alternative routes. Machine failure rates are assumed to be time-varying. This is a real assumption comes from a fact in realistic environments, where failure rate of a machine is variable when environmental situations like shop temperature, shop light, shop humidity or even worker skill change significantly. Moreover, in order to more close the addressed problem into the situations encountered in real world, the processing times and due dates are considered to be stochastic parameters. A mixed integer linear programming (MILP) model is constructed for addressed problem with the objective of number of tardy jobs and a minimum total availability constraint. Then a simulation-optimization framework based on a simulated annealing (SA) optimizer and Monte Carlo (MC) simulator is presented to solve the problem.     

机器故障及产品废品条件下Job Shop的周期定额计划方法

本文分析了在机器故障及产品废品条件下job shop生产的周期定额计划方法,给出 了生产计划中不同作业的作业定额满足job shop外部需求的必要条件,同时给出了job shop 在一个生产计划期内完成满足此条件的作业定额时,对各个工作中心的能力需求．当job sh op中物料搬运系统的能力充裕,物料搬运对生产提前期的影响可以忽略的情形下,本文提出 了估计job shop的生产提前期的近似方法．与仿真实验结果的对比表明,估计结果有较高精 度,从而可作为job shop的性能评价及其生产管理的依据．     

The performance of workload rules for order acceptance in batch chemical manufacturing

We investigate the performance of workload rules used to support customer order acceptance decisions in the hierarchical production control structure of a batch chemical plant. Customer order acceptance decisions need to be made at a point in time when no detailed information is available about the actual shop floor status during execution of the order. These decisions need therefore be based on aggregate models of the shop floor, which predict the feasibility of completing the customer order in time. In practice, workload rules are commonly used to estimate the availability of sufficient capacity to complete a set of orders in a given planning period. Actual observations in a batch chemical manufacturing plant show that the set of orders accepted needs to be reconsidered later, because the schedule turns out to be infeasible. Analysis of the planning processes used at the plant shows that workload rules can yields reliable results, however at the expense of a rather low capacity utilization. In practice this is often unacceptable. Since, solving a detailed scheduling problem is not feasible at this stage, this creates a dilemma that only can be solved if we can find more detailed aggregate models than workload rules can provide.     

Comparative study of the applicability of fuzzy multi-objective linear programming models through cost-effective analysis for mold manufacturing

The mold-manufacturing process consists of prototype design, production, assembly, and testing. As products tend to vary, have short due dates, and life cycles, are highly precise and must be responsiveness to customers, production system planning is complex and the relationship between outsourcing capability and in-house capacity is crucial to mold-manufacturing. Differentiation of core operations vs. non-core operations in internal vs. external environments and time control are essential for mold manufacturing when planning production systems. To analyze the cost-effectiveness of capacity planning and its relationship to suppliers, this work applies a novel fuzzy multi-objective linear programming model. Considered factors are order quantity allocation, due dates, manufacturing quantity, capacity, defect rates, back-log, and the purchasing discount. The applicability of three fuzzy theories is assessed using total costs, punishment costs, and crashing costs. Implementation results demonstrate the potentials for cost-effective capacity planning and outsourcing, and identify the applicability of these fuzzy theories to a specific mold-manufacturing case.     

RFID-enabled automation in support of factory integration

In recognition of the need for more effective and efficient factory system integration solutions, this paper improves the method proposed in our previous work, focusing on new mechanisms to bridge the gap between shop floor automation and factory information systems. As one of the mainstream identification technologies, radio frequency identification (RFID) technology makes possible all products uniquely identifiable across factories. By taking advantage of data capacity stored in an RFID tag, critical manufacturing information on a product can be locally stored with the product. RFID technology provides a means for a product to rapidly retrieve its needed information as it advances through shop floors. A corresponding framework to enable the instant delivery of pertinent data and information on a uniquely identifiable job/product at point-of-need across factories is proposed. A small-scale manufacturing application is presented to show how the proposed approach can be implemented on the shop floor.     

Designing function blocks for distributed process planning and adaptive control

The objective of this research is to develop methodologies and a framework for distributed process planning and adaptive control using function blocks. Facilitated by a real-time monitoring system, the proposed methodologies can be applied to integrate with functions of dynamic scheduling in a distributed environment. A function block-enabled process planning approach is proposed to handle dynamic changes during process plan generation and execution. This paper focuses mainly on distributed process planning, particularly on the development of a function block designer that can encapsulate generic process plans into function blocks for runtime execution. As function blocks can sense environmental changes on a shop floor, it is expected that a so-generated process plan can adapt itself to the shop floor environment with dynamically optimized solutions for plan execution and process monitoring.     

基于TSAPO的柔性作业车间计划和调度

为使多目标柔性作业车间计划与调度的制定更适合实际生产的动态变化,提出增加动态反馈的闭环柔性作业车间计划模型及二阶式蚁群粒子群混合优化算法TSAPO。通过增加动态监视功能,及时更新和反馈实际生产数据。利用对优化目标的二阶段分解,设计带有反馈机制的调度算法。实验结果证明,该算法在求解多目标柔性作业车间调度问题中具有较好的优化效果。     

基于资源约束的CAPP系统

在分析目前的专用型 ＣＡＰＰ和通用型 ＣＡＰＰ的优缺点的基础上，建立了基于资源约束的 ＣＡＰＰ模型，该模型可在用户所建的资源子系统约束下，进行工艺评价，同时在车间环境下动态生成工艺规划和加工参数，能适应目前制造业的柔性化趋势。     

Distributed adaptive control of production scheduling and machine capacity

This paper considers modeling and simulation of a unified control system that uses a continuous control-theoretic approach for distributed production scheduling at the shop floor and machine capacity control at the CNC level. Specifically, a distributed production scheduling method is unified with a distributed machine capacity control to generate realistic schedules considering the available capacity of production resources. In this distributed control system, machine capacity is adaptively controlled based on current physical conditions of the production resources and changes in production demands at the shop-floor level as well. The proposed system considers a multi-attribute objective that consists of production rate and product quality, production cost, and mean-squared deviation of job completions about due dates. The results obtained from the computational experiments show that the proposed system can improve the system performance through fully utilizing machine capacity while reducing production costs, production delays, missed deliveries, and customer dissatisfaction.     

An architecture for a self-improving instructional planner for intelligent tutoring systems

Machine instructional planners use changing and uncertain data to incrementally configure plans and control the execution and dynamic refinement of these plans. Current instructional planners cannot adequately plan, replan, and monitor the delivery of instruction. This is due in part to the fact that current instructional planners are incapable of planning in a global context, developing competing plans in parallel, monitoring their planning behavior, and dynamically adapting their control behavior. In response to these and other deficiencies of instructional planners a generic system architecture based on the blackboard model was implemented. This self-improving instructional planner (SUP) dynamically creates instructional plans, requests execution of these plans, replans, and improves its planning behavior based on a student's responses to tutoring. Global planning was facilitated by explicitly representing decisions about past, current, and future plans on a global data structure called the plan blackboard. Planning in multiple worlds is facilitated by labeling plan decisions by the context in which they were generated. Plan monitoring was implemented as a set of monitoring knowledge sources. The flexible control capability for instructional planner was adapted from the blackboard architecture BB1. The explicit control structure of SUP enabled complex and flexible planning behavior while maintaining a simple planning architecture.     

A Decision Support System for a small scale industry in a developing country

This paper describes the design and development of a Decision Support System (DSS) for a small scale manufacturing company in a developing country. The DSS provides a solution for an integrated problem of planning, shopfloor scheduling, and control. The DSS system accepts the demand data with priorities, then uses the information from the imbedded data base system regarding product structure, production process, available resources with the status, and inventory. This in turn is used by the imbedded models to solve planning and scheduling problems, and provide solutions containing job order release, shopfloor scheduling and material requirement report. What if analysis feature provided by DSS allows the managers to incorporate their knowledge and experience to improve the quality of the solution by evaluating different scenarios presented by the manager. The laboratory equipment manufactured by the company is made to order. Product information regarding twenty-two high demand items is incorporated into the data base system, and the scheduling problem is solved for these items. The jobshop is a mixed shop problem containing a flow shop, an open shop, and a flow shop. A heuristic method used for solving this problem is presented.