Design of a feature-based order acceptance and scheduling module in an ERP system

Customer-oriented manufacturing competes on timely responses to customer requirements, and precise scheduling control for delivery. This challenge demands engineering design and production planning to be fully integrated via advanced enterprise resource planning (ERP) systems. This paper proposes a generic feature association method and a detailed framework that can unify product and process models in order to satisfy customer orders with small batch sizes and high variations. A conceptual solution is introduced by integrating two traditionally separate feature domains: design configuration features and manufacturing process features. To achieve the proposed method, a customer feature class is suggested for the characterization of customers’ profiles related to the manufacturer. An instantiated customer feature object functionally tracks each customer’s selection of product configurations related to its requirements, specific orders, and production schedules with dynamic associations to the live manufacturing capacity. With the new associative integration method, a preliminary order acceptance system (OAS) prototype system has been implemented within an ERP order management system and its conceptual structure model is demonstrated within a multi-facet feature framework.     

The order fulfillment planning problem considering multi-site order allocation and single-site shop floor scheduling

The order fulfillment planning process in the thin film transistor–liquid crystal display panel industry is analyzed in this study. A two-phase order fulfillment planning structure is proposed, including the multi-site order allocation among module factories and single-site shop floor scheduling in each factory. In the first phase, the order allocation problem is solved using a mathematical programming model considering practical characteristics, including product structures, customer preferences, alternative bill-of-material, and production constraints. In the second phase, a constraint-based simulation scheduling algorithm is developed to address the scheduling problem in each module factory for determining the ideal order release time. Since production planning and scheduling are dealt with different time scales, the major challenge for the integration lies in the large problem size of the optimization model and becomes intractable. Most of the time bucket-based planning methods in the past literature simplify their scheduling models, but in this paper the detailed shop floor operations and processing behaviors are considered, such as changeover time, processing sequence of orders, and machine characteristics. Finally, a practical case in Taiwan will be employed to testify the feasibility of the proposed order fulfillment planning process; meanwhile, through the analysis of experiments, the adaptability and comparison of different planning approaches in an environment of various market demands are discussed.     

Greedy randomized adaptive search for dynamic flexible job-shop scheduling

Dynamic flexible job shop scheduling problem is studied under the events such as new order arrivals, changes in due dates, machine breakdowns, order cancellations, and appearance of urgent orders. This paper presents a constructive algorithm which can solve FJSP and DFJSP with machine capacity constraints and sequence-dependent setup times, and employs greedy randomized adaptive search procedure (GRASP). Besides, Order Review Release (ORR) mechanism and order acceptance/rejection decisions are also incorporated into the proposed method in order to adjust capacity execution considering customer due date requirements. The lexicographic method is utilized to assess the objectives: schedule instability, makespan, mean tardiness and mean flow time. A group of experiments is also carried out in order to verify the suitability of the GRASP in solving the flexible job shop scheduling problem. Benchmark problems are formed for different problem scales with dynamic events. The event-driven rescheduling strategy is also compared with periodical rescheduling strategy. Results of the extensive computational experiment presents that proposed approach is very effective and can provide reasonable schedules under event-driven and periodic scheduling scenarios.     

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.     

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.     

Capacity planning in job-shop environment

Presented here is a computerized capacity planning system for the IBM Microcomputer family. The system maintains the profile of the job shop in a data base along with data pertinent to various products that can be manufactured in the shop. Projected orders for the planning period are input to the system with their associated quantities and delivery dates. The system uses the forward and backward loading rules in generating capacity loading scenarios. User selects the best course of action which may satisfy delivery dates subject to the limitations of the work centers. Efficiency figures are provided to aid the user in his/her decision.     

An operation scheme for make-to-order job-shop production systems

The purpose of this paper is to solve multi-job order release problem whose objective is to minimize the sum of mean shop floor flow times (SFFTs) of orders subject to due-date constraint. A heuristic algorithm is proposed for the problem by decomposing the load profile of the shop. The load profile is divided into two parts. One is for the planning orders (new orders and unreleased orders) and the other is for the current orders being processed. The advantage of decomposing the load profile is to reduce computational complexity in the estimation step of order completion time. A job dispatching rule is also suggested to synchronize part flows in the shop floor. The experimental results show that work-in-process (WIP) in both bottleneck stations and assembly station, the SFFT of orders, and the percentage of tardy orders are significantly reduced.     

A new decision making structure for managing arriving orders in MTO environments

In order to improve the quality of decision about orders incoming to make to order (MTO) company, an effective evaluation approach is essential. So, in this paper a comprehensive decision making structure is presented for acceptance or rejection of incoming orders. The aim of the proposed structure is to manage the arriving orders so that the MTO system just proceeds to produce those arriving orders which are feasible and profitable for the system. The proposed structure composed of three phases. At the first phase, arriving orders are prioritized into high and low priority orders, considering characteristics of order and customer and utilizing technique for order performance by similarity to ideal solution (TOPSIS). At the second phase, rough-cut capacity was calculated for each order regarding priority level and so, acceptance or rejection decision is taken based on it. Finally, at the third phase, the previous phase accepted orders are evaluated based on their due dates and material arrival times and final decisions for orders are made. At the end, the effectiveness of the proposed structure is demonstrated through a case study.     

有限产能下多部件设备视情维修策略与多订单批量生产联合优化研究

针对有限产能下,面对多个不同要求订单时的订单选取和多部件设备维护决策问题,提出了基于客户满意度的多部件设备视情维修策略和多订单批量生产的联合优化模型。首先,基于比例风险模型描述出各部件的具体劣化趋势,并以虚拟年龄表示各部件的健康状态;其次,根据单次批量生产结束后的实时监测结果和维修阈值,确定不同的维修方式;再次,以利润最大化为优化目标,结合有限产能批量问题模型和客户满意度模型,建立视情维修策略和多订单批量生产联合优化模型;最后,利用模拟退火算法和粒子群算法就行模型求解,并通过算例验证了模型的有效性,使模型更具有现实意义,并对模型相应参数进行了灵敏度分析。     

A methodology for joint optimization for maintenance planning, process quality and production scheduling

Performance of a manufacturing system depends significantly on the shop floor performance. Traditionally, shop floor operational policies concerning maintenance scheduling, quality control and production scheduling have been considered and optimized independently. However, these three aspects of operations planning do have an interaction effect on each other and hence need to be considered jointly for improving the system performance. In this paper, a model is developed for joint optimization of these three aspects in a manufacturing system. First, a model has been developed for integrating maintenance scheduling and process quality control policy decisions. It provided an optimal preventive maintenance interval and control chart parameters that minimize expected cost per unit time. Subsequently, the optimal preventive maintenance interval is integrated with the production schedule in order to determine the optimal batch sequence that will minimize penalty-cost incurred due to schedule delay. An example is presented to illustrate the proposed model. It also compares the system performance employing the proposed integrated approach with that obtained by considering maintenance, quality and production scheduling independently. Substantial economic benefits are seen in the joint optimization.     

Integrated production and delivery with single machine and multiple vehicles

This paper considers a class of multi-objective production–distribution scheduling problem with a single machine and multiple vehicles. The objective is to minimize the vehicle delivery cost and the total customer waiting time. It is assumed that the manufacturer’s production department has a single machine to process orders. The distribution department has multiple vehicles to deliver multiple orders to multiple customers after the orders have been processed. Since each delivery involves multiple customers, it involves a vehicle routing problem. Most previous research work attempts at tackling this problem focus on single-objective optimization system. This paper builds a multi-objective mathematical model for the problem. Through deep analysis, this paper proposes that for each non-dominated solution in the Pareto solution set, the orders in the same delivery batch are processed contiguously and their processing order is immaterial. Thus we can view the orders in the same delivery batch as a block. The blocks should be processed in ascending order of the values of their average workload. All the analysis results are embedded into a non-dominated genetic algorithm with the elite strategy (PD-NSGA-II). The performance of the algorithm is tested through random data. It is shown that the proposed algorithm can offer high-quality solutions in reasonable time.     

A project scheduling approach to production and material requirement planning in Manufacturing-to-Order environments

Long- and medium-term production planning are tools to match production orders with resource capacity and that can also be used as a baseline for material procurement. The lack of a detailed schedule for the manufacturing operations, however, may cause difficulties in providing a proper material requirements planning and may affect the feasibility of the production plan itself. This paper proposes an approach, based on production process knowledge, to extract scheduling information from an aggregate production plan in order to support material procurement. The proposed approach is applied to an industrial case involving machining center production.     

Pheromone-based coordination for manufacturing system control

A pheromone-based coordination approach, which comes from the collective behavior of ant colonies for food foraging, is applied to control manufacturing system in this paper, aiming at handling dynamic changes and disturbances. The pheromone quantum of manufacturing cell is calculated inversely proportional to the cost, which can guarantee a minimal cost to process the orders. This approach has the capacity for optimization model to automatically find efficient routing paths for processing orders and to reduce communication overhead which exists in contract net protocol in shop floor control system. A prototype system is developed, and experiments confirm that pheromone-based coordination approach has excellent control performance and adaptability to disturbances in shop floor.     

考虑转包的平行机供应链排序

研究一类考虑转包的供应链排序问题, 即工厂从客户处接受一批订单, 这些订单既可以由工厂完成, 也可以通过支付一定费用进行转包. 工厂需要确定被转包的订单集并安排未被转包订单的生产和运输. 针对工厂为平行机生产环境的情况, 以交货期限内完成所有订单的转包成本、生产成本与运输成本之和最小化为目标, 构建了问题的数学模型, 并设计了启发式算法. 最后通过数值实验结果表明了算法的有效性.

     

Real-time scheduling for hybrid flowshop in ubiquitous manufacturing environment

This paper discusses the implementation of RFID technologies, which enable the shop floor visibility and reduce uncertainties in the real-time scheduling for hybrid flowshop (HFS) production. In the real-time HFS environment, the arriving of new jobs is dynamic, while the processes in work stages are not continuous. The decision makers in shop floor level and stage level have different objectives. Therefore, classical off-line HFS scheduling approaches cannot be used under these situations. In this research, two major measures are taken to deal with these specific real-time features. Firstly, a ubiquitous manufacturing (UM) environment is created by deploying advanced wireless devices into value-adding points for the collection and synchronization of real-time shop floor data. Secondly, a multi-period hierarchical scheduling (MPHS) mechanism is developed to divide the planning time horizon into multiple shorter periods. The shop floor manager and stage managers can hierarchically make decisions for their own objectives. Finally, the proposed MPHS mechanism is illustrated by a numerical case study.     

An architecture for the vertical integration of tooling considerations from design to process planning

To meet the competitive demands of modern manufacturing, it is necessary to reduce design times and enrich decision making by integrating process planning into the design activity using Concurrent Engineering principles. Although this is traditionally done through the interaction between designers and process planners, it is perhaps more desirable for a CAD system to have the functionality necessary to automatically advise the designer of the shop floor implications of design decisions. Cutting tool selection is an essential thread linking feature-based design of machined parts to process planning. Thus, the implementation of tooling considerations into design is an important requirement for an integrated CAD/CAPP system. This paper defines an architecture to enable the vertical integration of tooling considerations from early design to process planning and scheduling. The architecture is based on a five-level tool selection procedure which is mapped to a time-phased aggregate, management and detailed process planning framework. This paper draws on literature and the results of an industrial survey to identify the tooling methods suitable for integration within a CAD system and categorises them into the five levels of tool selection. The functions are then placed on a time-dependent framework that covers the progression of a product from design to process planning. The new functionality is being implemented as an object-oriented application called VITool, which is being developed so that it can be fully integrated within an existing CAD system.     

Simulation-based planning and control: From shop floor to top floor

This paper illustrates how simulation-based shop-floor planning and control can be extended to enterprise-level activities (top floor). First, the general planning and control concept are discussed, followed by an overview of simulation-based shop-floor planning and control. Analogies between the shop floor and top floor are discussed in terms of the components required to construct simulation-based planning and control systems. Analogies are developed for resource models, coordination models, physical entities, and simulation models. Differences between the shop floor and top floor are also discussed in order to identify new challenges faced for top-floor planning and control. A major difference between the top floor and the shop floor is the way a simulation model is constructed for use in planning, depending on whether time synchronization among member simulations becomes an issue or not. Another difference is in the distributed communication/computing platform. This work uses a distributed computing platform using Web services technology to integrate heterogeneous simulations and systems in a distributed top-floor control environment. The research results reveal that simulation-based planning and control is extensible to the top-floor environment’s evolving new research challenges.     

Workload vs scheduling policies in a dual-resource constrained job shop

The purpose of this paper is to report on research conducted to examine the effectiveness of different scheduling policies in a dual-constrained job shop under various workload conditions. The standard assumption in most job shop scheduling research has been that a 90% utilization of the shop is achieved. However, since shop utilization levels vary widely, it was hypothesized that scheduling policies that are optimum under one load condition might not be as effective under other load conditions. The model for this simulation experiment represented a job shop constrained by both labor and machines. The shop contains four machine centers with random routing of jobs through the shop. Shop workload was defined at three levels: 70, 85 and 99% utilization. Four machine scheduling rules and three labor assignment rules were tested for each of the shop workload levels, with mean job flow time as. the performance criterion. The results of the 3 × 4 × 3 factorial experiment showed that the advantage of the SPT (shortest processing time) machine scheduling rule over other rules is diminished dramatically when shop utilization is reduced from 99 to 85% or below. This same observation holds for other rules considered. The LNQ (longest queue length) labor assignment rule outperformed other rules at the 99% utilization level, but yielded no significant difference in performance at the 85% and below workload levels.     

Setting thresholds for periodic order release

In job shop manufacturing environments, controlling the release of work orders can significantly improve system performance, especially when a few bottleneck resources limit shop throughput. A periodic order release policy measures the remaining workload of the bottlenecks and releases just enough work orders to bring the workload to specified threshold values. The thresholds should limit the work-in-process inventory but not delay order completion by starving the bottlenecks. In this paper we consider the problem of setting thresholds that minimize the expected cost of work-in-process inventory and order tardiness. For the single-bottleneck system, we propose a stochastic optimization approach that uses smoothed perturbation analysis to estimate the objective function gradient. Our computational experiments compare different methods that set good threshold values and show that periodic order release improves system performance.     

Job shop scheduling under resource constraints and varying order priorities

This paper describes the development of a job shop scheduling system, which is based upon dynamic scheduling rules incorporating customer importance and/or order priority under constrained resources. An important part of the system is the development of a forecasting method for determining the estimated completion time for a job by estimating the future work load on the shop. The system has been developed from the analysis of the maintenance operations of a large petrochemical plant. The use of the computer programs which have been developed will be illustrated.