Modified drum–buffer–rope scheduling mechanism for a non-identical parallel machine flow shop with processing-time variation

The drum–buffer–rope (DBR) is a scheduling mechanism under the Theory of Constraints (TOC) philosophy. In DBR, ‘drum’ is a production schedule on the capacity-constrained resources (CCRs), which controls the speed of production for the whole system; ‘rope’ is a mechanism to release the required material to the CCRs; and ‘buffer’ is used to protect the CCRs from starvation due to statistical fluctuations. For a non-identical parallel machine flow-shop environment, estimating an efficient rope and time buffer for DBR implementation is not an easy task because of the complexity of non-identical parallel machine loading. This paper proposes a new scheduling method, which is called the modified DBR (MOD-DBR). It applies a backward finite capacity scheduling technique, including machine loadings and detail scheduling, instead of the rope mechanism in DBR. The scheduling performances of MOD-DBR are evaluated under variable processing time situations. The experimental results indicate that the MOD-DBR without a time buffer outperformed the DBR with a considerable level of buffer on the average flow time, while they have the same performance on tardiness, constraint resource utilization, and throughput.     

Capacitated lot sizing with alternative routings and overtime decisions

Material requirements planning (MRP) is a basic tool for performing detailed material planning function in the manufacture of component parts and their assembly into finished items. MRP's managerial objective is to provide ‘the right part at the right time’ to meet the schedules for completed products. However satisfying end customer demands faster with lower inventories implies smarter scheduling which must simultaneously reflect actual capacity conditions. Therefore, the need is to schedule both capacity and materials simultaneously. Since MRP does not consider the availability of capacity resources to schedule production, consequently the schedules so developed are usually capacity infeasible. This paper proposes a three-step procedure to develop capacity feasible material and production schedules in a finite capacity environment. In the first step, an LP model produces capacity feasible but lot size relaxed planned order releases for all end products and assembly components which are then fed into a MRP processor, where a bill of material (BOM) explosion process generates material plans. Finally, these material plans are introduced to another LP model which assures that capacity feasibility is again restored. The mathematical models developed consider restrictions on lot sizes as well as alternative production routings and overtime decisions. A numerical example also is provided and some future research directions are outlined.     

Available-to-promise-based flexible order allocation in ATO supply chains

This paper presents a new available-to-promise (ATP) based order allocation model, which is different from traditional order fulfilment systems in that it considers customer priority in an assemble-to-order (ATO) supply chain environment before the order is fulfilled. First, in terms of customer priority, a pre-allocated model is proposed in order to obtain reserving production capacity ATP and reserving components ATP for each customer class. Then, two order fulfilment models are formulated: the batch order fulfilment model for processing all orders during an order reception period and the real-time order fulfilment model for making order fulfilment decisions when an order arrives immediately. In case that the available production capacity and components are not enough, the ATP searching rules are developed along four dimensions (time dimension, customer demand priority level dimension, product dimension and selling area dimension). Finally, the proposed models are illustrated through an electronic product manufacturing case. The results of the case study show that the performance of the proposed order fulfilment system is better than that of the traditional one.     

Formulation and solution of the drum-buffer-rope constraint scheduling problem ( DBRCSP)

This paper seeks to advance the current understanding of constraint scheduling in several ways. First, it describes the need for constraint scheduling in DBR systems. It then formally presents the production scheduling problem DBR attempts to solve and relates that formulation to prior research. Finally, it evaluates the quality of solutions produced by the solution algorithm incorporated by the Goldratt Institute (and now maintained by the TOC Center's Goal Systems Group) in their production software on a set of benchmark problems involving multiple constraints. The results show that generally good results can be obtained as long as the constraints are scheduled in the best sequence.     

Self-adaptive dynamic scheduling of virtual production systems

To enhance the agility of virtual production systems (VPSs) under today's dynamic and changing manufacturing environment, a self-adaptive dynamic scheduling method based on event-driven is proposed for VPSs in this paper. This method is composed of the mechanisms and algorithm of self-adaptive dynamic scheduling. In the mechanisms, the dynamic events faced by VPSs are determined through users’ inputs or supervisory controllers’ detections, the local effects made on the schedule are analysed according to the dynamic events, and the self-adaptive measures and rules are specified correspondingly. To implement the dynamic scheduling of VPSs under the guidance of self-adaptive rules, a modified heuristic rescheduling algorithm is proposed for affected operations. A case study illustrates that the proposed method can well accomplish the dynamic scheduling of VPSs in a self-adaptive manner.     

Adaptive production scheduling of virtual production systems using object-oriented Petri nets with changeable structure

Virtual Production Systems (VPSs) are logically constructed by organizing production resources belonging to one or more physical manufacturing systems. VPSs can enhance the agility of manufacturing systems. However, an effective scheduling approach is required to cope with disturbance and changes to these systems. An adaptive production scheduling method is proposed. Object-oriented Petri nets with changeable structure (OPNs-CS) formulate the scheduling problem of VPSs. To resolve resource constraints in a VPS, the OPNs-CS is modified by introducing limited token available time and by revising the enabling and firing rules. The artificial intelligent heuristic search (A*) algorithm is modified and applied to generate the optimal or near optimal schedule. When a VPS encounters any disturbance, an estimate of the effects of the disturbance can be estimated by simulation on the OPNs-CS model. If the scheduling target (completion time) is not affected, rescheduling is not required. Whenever there is a change to the VPS, the TOPNs-CS model is updated to refresh VPS schedule. A case study is presented to demonstrate the procedures for applying the proposed scheduling approach. The given case study shows that the proposed approach is capable of scheduling a VPS dynamically in response to disturbances and changes are involved.     

A hierarchical system for effective coordination of available-to-promise logic mechanisms

This paper aims to provide a combinatory approach towards addressing the advanced available-to-promise (ATP) problem, consisting of three deterministic optimisation models that operate on both sides of the Customer Order Decoupling Point. The proposed approach is based on long-term aggregate capacity reservation for periods when increased volatility is expected, while still obtaining production plans that meet the predefined and agreed customer service levels. The three optimisation models together guide a system that helps manufacturers to optimally decide on ATP quantity and due date quoting on the basis of available manufacturing resources. To support this system, a prototype software module was designed and implemented in Java that loosely integrates with the popular Open Source ERP system Compiere2's databases and uses the Linear Programming solver QS-Opt to solve the models developed in this research. The system response times as evidenced in the experiments described in this paper are quite acceptable for real-world operations. The proposed solution of the ATP problem is of great value for all competitive and proactive organisations that need a practical tool to support, in the best possible way and in an almost real-time fashion, their decision on whether to accept or decline an incoming customer order request. It is our belief that an integration of the proposed models into existing ERP systems will enhance their limited ATP functionality and provide management with a powerful decision support tool.     

Applying the TOC thinking process in manufacturing: A case study

A case study is presented to illustrate the application of the Theory of Constraints thinking process logic tools in a manufacturing environment. The study firm performs design activities related to meeting future product requirements while concurrently meeting existing production schedules for the current design of the product. Current approaches to managing the firm's limited productive capacity do not allow for both design and production activities to occur simultaneously while meeting the customer's current product delivery schedule. Thus, despite their desire to satisfy their customer's future design requirements, management uses the majority of its production capacity to meet its customer's current product delivery schedule. This case study demonstrates how a team of employees used thinking process logic diagrams to document reality, identify a core conflict and problem, develop proposed changes to address the core problem, and create several detailed action plans to implement changes within the study organization. Initially, scenarios associated with some undesirable effects are used to understand how prevailing policies and behaviours result in less than desired production line performance. Then, a current reality tree is constructed to link the core problem or system constraint with the previously identified undesirable effects. Next, two major injections are developed to address the core problem in managing the production line as logically documented in a future reality tree. Finally, three transition trees are presented to guide the implementation of change at the study organization.     

原油处理过程短期生产计划优化

在原油处理过程短期生产计划的递阶求解方法中,原油处理短期生产计划问题分为上下两层,上层根据市场需求产生一个目标炼油计划;在此基础上,下层得到一个详细生产计划以实现目标炼油计划。研究了在上层目标炼油计划已知的情况下,下层详细生产计划的求解问题。为该问题建立了基于离散时间表示的混合整数线性规划模型,分析了问题的特点并将其进行转化,给出了基于启发式的求解方法,在保证目标炼油计划实现的前提下,对原油转运过程中油品切换及不同油品的罐底混合进行了优化,取得了一定的成果。用一个工业实例验证了启发式规则的可行性和有效性。     

A scheduling system for the steelmaking-continuous casting process. A case study from the steel-making industry

This paper describes the models, algorithms and implementation results of a computerised scheduling system for the steelmaking-continuous casting process of a steel plant in Austria. The basis for the scheduling task is a preliminary production schedule for the continuous casters (sequence of charges that must be consecutively cast and their allocation to the continuous casters). The scheduling task can be structured as four sub-problems: (1) scheduling the continuous casters. (2) Allocation of the charges to the parallel facilities at the upstream stages (converter and refining facilities). (3) Sequencing the charges at the converters and refining facilities. (4) Exact timing of all operations. The heuristic algorithm consists of three planning levels: (1) scheduling the continuous casters, considering the capacity restrictions at the upstream stages and the limited availability of hot metal. (2) Scheduling of the converter and refining facilities according to priorities, performing allocation and sequencing. (3) Improving the schedule by means of an LP model. The system visualises the schedules as Gantt charts. Extensive numerical tests with real-life data and more than two years of experience with the implementation demonstrate that the system produces reasonable schedules and is accepted by the planners.     

Application of genetic algorithms with dominant genes in a distributed scheduling problem in flexible manufacturing systems

Multi-factory production networks have increased in recent years. With the factories located in different geographic areas, companies can benefit from various advantages, such as closeness to their customers, and can respond faster to market changes. Products (jobs) in the network can usually be produced in more than one factory. However, each factory has its operations efficiency, capacity, and utilization level. Allocation of jobs inappropriately in a factory will produce high cost, long lead time, overloading or idling resources, etc. This makes distributed scheduling more complicated than classical production scheduling problems because it has to determine how to allocate the jobs into suitable factories, and simultaneously determine the production scheduling in each factory as well. The problem is even more complicated when alternative production routing is allowed in the factories. This paper proposed a genetic algorithm with dominant genes to deal with distributed scheduling problems, especially in a flexible manufacturing system (FMS) environment. The idea of dominant genes is to identify and record the critical genes in the chromosome and to enhance the performance of genetic search. To testify and benchmark the optimization reliability, the proposed algorithm has been compared with other approaches on several distributed scheduling problems. These comparisons demonstrate the importance of distributed scheduling and indicate the optimization reliability of the proposed algorithm.     

A maintenance driven scheduling cockpit for integrated production and maintenance operation schedule

The production and maintenance functions have objectives that are often in contrast and it is essential for management to ensure that their activities are carried out synergistically, to ensure the maximum efficiency of the production plant as well as the minimization of management costs. The current evolution of ICT technologies and maintenance strategies in the industrial field is making possible a greater integration between production and maintenance. This work addresses this challenge by combining the knowledge of the data collected from physical assets for predictive maintenance management with the possibility of dynamic simulate the future behaviour of the manufacturing system through a digital twin for optimal management of maintenance interventions. The paper, indeed, presents a supporting digital cockpit for production and maintenance integrated scheduling. The tool proposes an innovative approach to manage health data from machines being in any production system and provides support to compare the information about their remaining useful life (RUL) with the respective production schedule. The maintenance driven scheduling cockpit (MDSC) offers, indeed, a supporting decision tool for the maintenance strategy to be implemented that can help production and maintenance managers in the optimal scheduling of preventive maintenance interventions based on RUL estimation. The simulation is performed by varying the production schedule with the maintenance tasks involvement; opportune decisions are taken evaluating the total costs related to the simulated strategy and the impact on the production schedule.The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-021-00380-z     

The study of an Easy-to-Use DBR and BM system

The concept and advantages of the Drum–Buffer–Rope (DBR) scheduling and buffer management (BM) system are now widely accepted and recognized by the industrial communities. Therefore, there are several types of commercial DBR and BM business solution software such as OPT21?, DISASTER?, Visual DBR?, Synchrono?, Drummer?, etc., on the market. However, prior to implementation of any of these, factories must first gather a complete data set for their perspective throughput nets. This means that the users will be required to enter and maintain a complicated database and the degree of difficulty of the entire software system implementation process will also increase. Furthermore, it is also unrealistic to maintain the accuracy of these dynamic data in the fast-paced and competitive business environment. Therefore, in this article, we have proposed the Easy-to-Use DBR and BM system concept. The term ‘Easy-to-Use’ refers to the fact that users will be required to enter and maintain a minimal set of fundamental data to satisfy the factories’ operation needs. The system framework mentioned in our article contains the full and complete function of the DBR and BM system but with very limited data maintenance by the users. Therefore, with less procedural complexity, this system can produce a higher range of operational application and can attract higher interest from the users. The concept of simplified throughput net design proposed in our article cannot only be used as a reference when factories develop their own information systems, but can also provide a new design model and algorithm for the system software developers as well. Since the database maintenance is significantly simplified, the factories can quickly adapt to any changes in the dynamic, rapid-changing, and highly competitive production environment. In the article, we first describe the concept of the Easy-to-Use DBR and BM system and the steps to simplify the information system by using a simple throughput net. We then explain the system framework and design methodology. At the end, we have used a prototype system to demonstrate and to verify the applicability and efficiency of the concept and framework mentioned in our article.     

Reactive scheduling in a make-to-order flexible job shop with re-entrant process and assembly: a mathematical programming approach

A mixed-integer linear programming model is presented for the scheduling of flexible job shops, a production mode characteristic of make-to-order industries. Re-entrant process (multiple visits to the same machine group) and a final assembly stage are simultaneously considered in the model. The formulation uses a continuous time representation and optimises an objective function that is a weighted sum of order earliness, order tardiness and in-process inventory. An algorithm for predictive-reactive scheduling is derived from the proposed model to deal with the arrival of new orders. This is illustrated with a realistic example based on data from the mould making industry. Different reactive scheduling scenarios, ranging from unchanged schedule to full re-scheduling, are optimally generated for order insertion in a predictive schedule. Since choosing the most suitable scenario requires balancing criteria of scheduling efficiency and stability, measures of schedule changes were computed for each re-scheduling solution. The short computational times obtained are promising regarding future application of this approach in the manufacturing environment studied.     

Comparison of DBR with CONWIP in an unbalanced production line with three stations

The recently developed alternatives to traditional production planning and control systems such as material requirement planning (MRP) and Kanban are the drum–buffer–rope (DBR) and CONWIP (CONstant Work In Process) systems. Each system is best described as a combination push (like an MRP)/pull (like a Kanban) logistical procedure. Materials are pulled into the shop via the appropriate logic, and once released, materials are then pushed to subsequent workcentres. The performance of the DBR and CONWIP control policies are analysed and compared in a three-stage unbalanced tandem production line. Using a continuous Markov process model, steady-state probability distributions for the systems are derived, and then the performance measures of the systems can be evaluated. To compare the two systems, an optimization model for each system is proposed. From sensitivity analyses for the optimization models, the proposed models are validated, the differences of the two systems are investigated, and it is found that DBR is better than CONWIP under the proposed performance measures.     

Service-level performance of MRP,kanban, CONWIP and DBR due to parameter stability and environmental robustness

Decisions regarding production planning and control strategy (PPCS) choices can be classified as strategic, whereas parametrization issues are of a tactical nature. However, readjustment is often skipped either as a result of a lack of planning expertise or because it would require extended planning. For this reason, robustness, which is defined as PPCS behaviour within dynamic environments, is investigated. To achieve a greater understanding of the sensitivity on parameter changes in a production system, PPCS stability is examined. An eM-Plant based simulation model is presented that discusses the service-level performance of material requirement planning (MRP), kanban, constant work in process (CONWIP) and drum–buffer–rope (DBR) in a flow-shop with attention to the work in process (WIP). Although the service-level performance of CONWIP exceeds that of the other systems, CONWIP struggles to maintain its advantage under dynamic conditions. The paper seeks to support industrial practitioneers both in their choice of a specific PPCS and to parametrize the PPCS successfully.     

Deep Reinforcement Learning-Based Job Shop Scheduling ofSmartManufacturing

Industry 4.0 production environments and smart manufacturing systems integrate both the physical and decision-making aspects of manufacturing operations into autonomous and decentralized systems. One of the key aspects of these systems is a production planning, specifically, Scheduling operations on the machines. To cope with this problem, this paper proposed a Deep Reinforcement Learning with an Actor-Critic algorithm (DRLAC). We model the Job-Shop Scheduling Problem (JSSP) as a Markov Decision Process (MDP), represent the state of a JSSP as simple Graph Isomorphism Networks (GIN) to extract nodes features during scheduling, and derive the policy of optimal scheduling which guides the included node features to the best next action of schedule. In addition, we adopt the Actor-Critic (AC) network’s training algorithm-based reinforcement learning for achieving the optimal policy of the scheduling. To prove the proposed model’s effectiveness, first, we will present a case study that illustrated a conflict between two job scheduling, secondly, we will apply the proposed model to a known benchmark dataset and compare the results with the traditional scheduling methods and trending approaches. The numerical results indicate that the proposed model can be adaptive with real-time production scheduling, where the average percentage deviation (APD) of our model achieved values between 0.009 and 0.21 compared with heuristic methods and values between 0.014 and 0.18 compared with other trending approaches.     

物料需求计划不稳定性的模拟研究

介绍物料需求计划不稳定性的基本概念和模拟研究方法。在不确定性需求的流动式计划环境下,研究冻结参数和计划算法在不同生产条件下对物料需求计划不稳定性的影响。通过设计模拟实验和大量模拟计算及统计分析表现：费用结构、预测模式、冻结比例、计划周期和计划算法对物料需求计划不稳定性有较大影响,且交互作用显著。研究结果对减小物料需求计划的不稳定性有一定指导意义。     

Scheduling at an auction logistics centre with physical internet

Auction logistics centre (ALC) performs transshipment operation on auction products from their inbound-from-supplier transporters to their outbound-to-client transporters with goods trading functions. Major third-party trading service providers have solved technological problems of dealing with millions of simultaneous biddings. But logistics that fulfils the massive and lumpy auction demands in the centre is still challengeable. The lack of process visibility and synchronised schedule has made the congestion on material flow, especially for the trolley loading and auction trading stages. Space resource is wasted and auction products deteriorate as holding time increases. This paper aims to provide a first demonstration of scheduling for auctions of perishable goods using Physical Internet (PI). PI-enabled scheduling is vital to facilitate the decision-making process while ensuring required throughput time with large trading volumes. A PI-ALC is created to automate the flow of information and enable the flexible implementation of scheduling. Following the hybrid flowshop classification, a timely operation scheduling model is developed. A heuristic-based solution approach is proposed to minimise either makespan or value loss using a set of dispatching rules. Simulation experiments show that the dispatching–picking mechanisms have statistically significant interaction impacts on both performance criteria. Decision-makers should strike a balance between minimising makespan and value loss based upon the growth in the frozen buffer size. Finally, the sensitivity analyses justify that schedulers can flexibly select dispatching rules under various demand patterns and operation time windows, as well as system configurations and trolley sizes.     

Applying Job Shop Scheduling to SMEs Manufacturing Platform to Revitalize B2B Relationship

A small and medium enterprises (SMEs) manufacturing platform aims to perform as a significant revenue to SMEs and vendors by providing scheduling and monitoring capabilities. The optimal job shop scheduling is generated by utilizing the scheduling system of the platform, and a minimum production time, i.e., makespan decides whether the scheduling is optimal or not. This scheduling result allows manufacturers to achieve high productivity, energy savings, and customer satisfaction. Manufacturing in Industry 4.0 requires dynamic, uncertain, complex production environments, and customer-centered services. This paper proposes a novel method for solving the difficulties of the SMEs manufacturing by applying and implementing the job shop scheduling system on a SMEs manufacturing platform. The primary purpose of the SMEs manufacturing platform is to improve the B2B relationship between manufacturing companies and vendors. The platform also serves qualified and satisfactory production opportunities for buyers and producers by meeting two key factors: early delivery date and fulfillment of processing as many orders as possible. The genetic algorithm (GA)-based scheduling method results indicated that the proposed platform enables SME manufacturers to obtain optimized schedules by solving the job shop scheduling problem (JSSP) by comparing with the real-world data from a textile weaving factory in South Korea. The proposed platform will provide producers with an optimal production schedule, introduce new producers to buyers, and eventually foster relationships and mutual economic interests.