An integrated scheduler for manufacturing planning and control (INSCH)

The most basic problem in a manufacturing process is to create valid scheduling system which determines the sequence of jobs to be processed at each of the series of machine centers. An integrated scheduler (INSCH) is developed for small job shop manufacturing systems while considering high machine utilization, low work-in-process, and reduced job lateness.

As an and to understand the interaction of live jobs with the shop, sequence scheduler is developed to complement production scheduler such as Gantt bar chart. INSCH can achieve better performance than simple static models as shown in an example. It is desirable for INSCH to be applied to small job shop manufacturing companies using micro personal computer with relevant modifications discussed.     

Service perspective based production control system for smart job shop under industry 4.0

Industry 4.0 describes a smart job shop as follows: it can meet individual customer requirements even if the requirements are changed at the last minute; its production control system (PCS) can rapidly respond to unexpected disruptions in production, and smart workpieces in the smart job shop can communicate with workstations to tell them what to do next. Present PCSs issue production instruction (PI) to workstation in a relatively long period such as a day, a week, even a month. And the PI is usually at process level, which means it is not sufficient to maintain smooth production flow at the operational level. Therefore, the existing PCSs cannot meet the requirements of Industry 4.0. On account of this, this article proposes a smart workpiece enabled production instruction service system for smart job shop under Industry 4.0. The PI service system in smart job shop consists of three parts such as PI sets generation, PI sets execution and PI sets update. In PI sets generation, the PI is viewed as a service requirement from the smart workpiece for the workstation, and then a PI service model is established to integrate machining actions with different kinds of manufacturing resources, processing place and processing time. Based on that, a method of converting the Gantt chart to PI sets is presented. In PI sets execution, a PI service unit is proposed for real-time issuing PIs to the radio-frequency identification (RFID) tags of smart workpieces. In PI sets update, the update of PI sets including unexecuted processes PI sets and current processes PI sets is discussed in detail. Finally, a small-scale smart job shop is taken as an example to illustrate the feasibility of the PI service system.     

Multi-objective simulation optimization for uncertain resource assignment and job sequence in automated flexible job shop

In this study a multi-objective problem considering uncertainty and flexibility of job sequence in an automated flexible job shop (AFJS) is considered using manufacturing simulation. The AFJS production system is considered as a complex problem due to automatic elements requiring planning and optimization. Several solution approaches are proposed lately in different categories of meta-heuristics, combinatorial optimization and mathematically originated methods. This paper provides the metamodel using simulation optimization approach based on multi-objective efficiency. The proposed metamodel includes different general techniques and swarm intelligent technique to reach the optimum solution of uncertain resource assignment and job sequences in an AFJS. In order to show the efficiency and productivity of the proposed approach, various experimental scenarios are considered. Results show the optimal resources assignment and optimal job sequence which cause efficiency and productivity maximization. The makespan, number of late jobs, total flow time and total weighted flow time minimization have been resulted in an automated flexible job shop too.     

Holonic job shop scheduling using a multiagent system

Manufacturing job shop scheduling is a notoriously difficult problem that lends itself to various approaches - from optimal algorithms to suboptimal heuristics. We combined popular heuristic job shop-scheduling approaches with emerging AI techniques to create a dynamic and responsive scheduler. We fashioned our job shop scheduler's architecture around recent holonic manufacturing systems architectures and implemented our system using multiagent systems. Our scheduling approach is based on evolutionary algorithms but differs from common approaches by evolving the scheduler rather than the schedule. A holonic, multiagent systems approach to manufacturing job shop scheduling evolves the schedule creation rules rather than the schedule itself. The authors test their approach using a benchmark agent-based scheduling problem and compare performance results with other heuristic-scheduling approaches.     

Flow shop scheduling with multiple operations and time lags

A scheduling system is proposed and developed for a special type of flow shop. In this flow shop there is one machine at each stage. A job may require multiple operations at each stage. The first operation of a job on stage j cannot start until the last operation of the job on stage j - 1 has finished. Pre-emption of the operations of a job is not allowed. The flow shop that the authors consider has another feature, namely time lags between the multiple operations of a job. To move from one operation of a job to another requires a finite amount of time. This time lag is independent of the sequence and need not be the same for all operations or jobs. During a time lag of a job, operations of other jobs may be processed. This problem originates from a flexible manufacturing system scheduling problem where, between operations of a job on the same workstation, refixturing of the parts has to take place in a load/unload station, accompanied by (manual) transportation activities. In this paper a scheduling system is proposed in which the inherent structure of this flow shop is used in the formulation of lowerbounds on the makespan. A number of lowerbounds are developed and discussed. The use of these bounds makes it possible to generate a schedule that minimizes makespan or to construct approximate solutions. Finally, some heuristic procedures for this type of flow shop are proposed and compared with some well-known heuristic scheduling rules for job shop/flow shop scheduling.An earlier version of this paper was presented at the First International Conference on Industrial Engineering and Production Management 1993, 2–4 June 1993, Mons, Belgium.     

A heuristic algorithm for the distributed and flexible job-shop scheduling problem

The distributed manufacturing takes place in a multi-factory environment including several factories, which may be geographically distributed in different locations, or in a multi-cell environment including several independent manufacturing cells located in the same plant. Each factory/cell is capable of manufacturing a variety of product types. An important issue in dealing with the production in this decentralized manner is the scheduling of manufacturing operations of products (jobs) in the distributed manufacturing system. In this paper, we study the distributed and flexible job-shop scheduling problem (DFJSP) which involves the scheduling of jobs (products) in a distributed manufacturing environment, under the assumption that the shop floor of each factory/cell is configured as a flexible job shop. A fast heuristic algorithm based on a constructive procedure is developed to obtain good quality schedules very quickly. The algorithm is tested on benchmark instances from the literature in order to evaluate its performance. Computational results show that, despite its simplicity, the proposed heuristic is computationally efficient and promising for practical problems.     

A Case Study on Integrated Production Planning and Scheduling in a Three-Stage Manufacturing System

This paper presents an integrated optimization model of production planning and scheduling for a three-stage manufacturing system, which is composed of a forward chain of three kinds of workshops: a job shop, a parallel flow shop consisting of parallel production lines, and a single machine shop. As the products at the second stage are assembled from the parts produced in its upstream workshop, a complicated production process is involved. On the basis of the analysis of the batch production, a dynamic batch splitting and amalgamating algorithm is proposed. Then, a heuristic algorithm based on a genetic algorithm (known as the integrated optimization algorithm) is proposed for solving the problem. Note to Practitioners-This paper presents a method for integrated production planning and scheduling in a three-stage manufacturing system consisting of a forward chain of three kinds of workshops, which is common in such enterprises as producers of automobiles and household electric appliances, as in the case of an autobody plant usually with the stamping workshop, the welding and assembling workshop, and the painting workshop. Herein, the production planning and scheduling problems are simultaneously addressed in the way that a feasible production plan can be obtained and the inventory reduced. A batch splitting and amalgamating algorithm is proposed for balancing the production time of the production lines. And a case study of the integrated planning and scheduling problem in a real autobody plant verifies the effectiveness of our method     

半导体制造中的车间层控制

由于半导体制造业的高收益性激烈竞争，它的生产线复杂，特别是车间层的复杂控制问题，已成为研究的热点，半导体产生产线不同于Ｊｏｂ－ｓｈｏｐ和Ｆｌｏｗ－ｓｈｏｐ的第三种类型生产线，它的加工路径长，在回流（Ｎｏｎ－ａｃｙｃｌｉｃ）且随机性大，此我，设备特性各异，因此，如何有效地管理，使之满意市场竞争的需要，是对研究者和工业 巨大挑战，本文从设备特性，控制问题内容和解决途径的角度，对该领域近几年的主要研究成     

制造执行系统的生产管理控制研究

车间调度是制造执行系统生产管理控制的核心功能,因此制造执行系统必须实现车间的优化调度。文章分析了制造执行系统的生产管理控制方法,提出了制造执行系统的车间调度方法。一个已在某航天制造企业使用的制造执行系统软件证实了制造执行系统的生产管理控制方法的正确性。     

Computational complexity of determining resource loops inre-entrant flow lines

This paper presents a comparison study of the computational complexity of the general job shop protocol and the more structured flow line protocol in a flexible manufacturing system. It is shown that the representative problem of finding resource invariants is NP-complete in the case of the job shop, while in the flow line case it admits a closed form solution. The importance of correctly selecting part flow and job routing protocols in flexible manufacturing systems to reduce complexity is thereby conclusively demonstrated     

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.     

Research on flexible job shop scheduling under finite transportation conditions for digital twin workshop

Flexible job shop scheduling is one of the most effective methods for solving multiple varieties and small batch production problems in discrete manufacturing enterprises. However, limitations of actual transportation conditions in the flexible job shop scheduling problem (FJSP) are neglected, which limits its application in actual production. In this paper, the constraint influence imposed by finite transportation conditions in the FJSP is addressed. The coupling relationship between transportation and processing stages is analyzed, and a finite transportation conditions model is established. Then, a three-layer encoding with redundancy and decoding with correction is designed to improve the genetic algorithm and solve the FJSP model. Furthermore, an entity-JavaScript Object Notation (JSON) method is proposed for transmission between scheduling services and Digital Twin (DT) virtual equipment to apply the scheduling results to the DT system. The results confirm that the proposed finite transportation conditions have a significant impact on scheduling under different scales of scheduling problems and transportation times.     

Real-world production scheduling for the food industry: An integrated approach

The present paper offers an integrated approach to real-world production scheduling for the food processing industries. A manufacturing execution system is very appropriate to monitor and control the activities on the shop floor. Therefore, a specialized scheduler, which is the focus of this paper, has been developed to run at the core of such a system. The scheduler builds on the very general Resource Constrained Project Scheduling Problem with Generalized Precedence Relations. Each local decision step (e.g. choosing a route in the plant layout) is modeled as a separate module interconnected in a feedback loop. The quality of the generated schedules will guide the overall search process to continuously improve the decisions at an intermediate level by using local search strategies. Besides optimization methods, data mining techniques are applied to historical data in order to feed the scheduling process with realistic background knowledge on key performance indicators, such as processing times, setup times, breakdowns, etc. The approach leads to substantial speed and quality improvements of the scheduling process compared to the manual practice common in production companies. Moreover, our modular approach allows for further extending or improving modules separately, without interfering with other modules.     

A decentralized model for flow shop production with flexible transportation system

The recent advances in technology sectors often clash with traditional organizational paradigms which can limit or make difficult an efficient implementation in the real world. In this paper we show how it is possible to exploit the advantages of innovative technologies in manufacturing when these are supported by new and efficient methods for production management. More in details, we face a flow shop scheduling problem in a shoe manufacturing system in which overtaking of jobs is allowed thanks to an innovative transportation system. Overtaking means that a job can be put in waiting state and another job can surpass it, allowing the change of the scheduling sequence. Preemption is not allowed. The objective function of the problem is the minimization of the maximum lateness. We propose a decentralized model, based on multi-agent system theory, to represent the production cells of the plant and to include the potentiality offered by overtaking of jobs at decisional level. The adoption of a decentralized approach increases the system flexibility since each machine is able to solve its local scheduling problem. Adding or removing machines to the plant will not imply a change in the scheduling algorithms. The outcomes of this work are reached firstly through a formulation of the problem with three flow shop scheduling models, secondly through a comparison of the models with respect to different performance indicators. The results highlight as the decentralized approach is able to reach comparable performances with the centralized one for a relevant number of instances. Moreover sensitivity analysis shows as in the decentralized model the computational time required to solve bigger instances increases less quickly than in the case of centralized ones. Finally, simulations of the decentralized approach clarify as the correlation of the local solution procedure is effected by the number of machines of the flow shop and the coordination mechanism is effected by the number of the jobs to be scheduled.     

An integrated job shop scheduling and assembly sequence planning approach for discrete manufacturing

This paper proposes an integrated job shop scheduling and assembly sequence planning (IJSSASP) approach for discrete manufacturing, enabling the part processing sequence and assembly sequence to be optimized simultaneously. The optimization objectives are to minimize the total production completion time and the total inventory time of parts during production. The interaction effects between the job shop schedule and the assembly sequence plan in discrete manufacturing are analyzed, and the mathematical models including the objective functions and the constraints are established for IJSSASP. Based on the above, a non-dominated sorting genetic algorithm-II (NSGA-Ⅱ) with a hybrid chromosome coding mechanism is applied to solve the IJSSASP problem. Through the case studies and comparison tests for different scale problems, the proposed IJSSASP approach is verified to be able to improve the production efficiency and save the manufacturing cost of the discrete manufacturing enterprise more effectively.     

Synchronized manufacturing as in OPT: from practice to theory

Synchronized manufacturing as in the OPT system (Optimized Production Technology, by Creative Output Inc.) is a relatively new concept for production management.

This paper analyses the nine OPT rules, the OPT concepts and OPT philosophy in order to establish an analytical, theoretical basis for the OPT system. The analysis is based on systems theory concepts, mathematical programming theory and techniques, as well as queuing theory, the Pareto rule, and the Japanese production experience.

It is shown that the OPT rules reflect substantial integration of well established MS/OR techniques with the whole OPT philosophy. The paper analyzes the OPT classification of VAT plants and the Drum-Buffer-Rope concept used in OPT.

Our analysis makes a clear distinction between BIG OPT (the management system) and its subsystem SMALL OPT (the scheduler). The paper suggests that while the BIG OPT concepts may be applied in all types of processes, job shop processes and very complex assembly lines are the most suitable ones for the SMALL OPT scheduling system. The paper then shows the management by constraints methodology as a natural enhancement of the OPT concepts.     

Development of a simulation-based decision support system for controlling stochastic flexible job shop manufacturing systems

This paper describes a simulation-based decision support system (DSS) to production control of a stochastic flexible job shop (SFJS) manufacturing system. The controller design approach is built around the theory of supervisory control based on discrete-event simulation with an event–condition–action (ECA) real-time rule-based system. The proposed controller constitutes the framework of an adaptive controller supporting the co-ordination and co-operation relations by integrating a real-time simulator and a rule-based DSS. For implementing SFJS controller, the proposed DSS receives online results from simulator and identifies opportunities for incremental improvement of performance criteria within real-time simulation data exchange (SDX). A bilateral method for multi-performance criteria optimization combines a gradient based method and the DSS to control dynamic state variables of SFJS concurrently. The model is validated by some benchmark test problems.     

A genetic algorithm for permutation flow shop scheduling under make to stock production system

The permutation flow shop scheduling is a well-known combinatorial optimization problem that arises in many manufacturing systems. Over the last few decades, permutation flow shop problems have widely been studied and solved as a static problem. However, in many practical systems, permutation flow shop problems are not really static, but rather dynamic, where the challenge is to schedule n different products that must be produced on a permutation shop floor in a cyclical pattern. In this paper, we have considered a make-to-stock production system, where three related issues must be considered: the length of a production cycle, the batch size of each product, and the order of the products in each cycle. To deal with these tasks, we have proposed a genetic algorithm based lot scheduling approach with an objective of minimizing the sum of the setup and holding costs. The proposed algorithm has been tested using scenarios from a real-world sanitaryware production system, and the experimental results illustrates that the proposed algorithm can obtain better results in comparison to traditional reactive approaches.