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Introduction for Integrated Process Planning and Scheduling

  • Xinyu LiEmail author
  • Liang Gao
Chapter
  • 38 Downloads
Part of the Engineering Applications of Computational Methods book series (EACM, volume 2)

Abstract

Process planning and scheduling are two of the most important subsystems in manufacturing systems. In the traditional approach, process planning and scheduling were carried out sequentially. The researchers did not pay much attention to the integration of them. This approach has become an obstacle to improve the productivity and responsiveness of manufacturing systems. However, in fact, the Integrated Process Planning and Scheduling (IPPS) can greatly enhance the productivity of the manufacturing system. Therefore, IPPS has attracted more and more researchers and engineers. IPPS is one of the most complicated NP-complete combinational optimization problems.

Keywords

Integration Process planning Scheduling 

1.1 Process Planning

Process planning determines the processing method of products, so it is one of the most important tasks in production preparation and the basis of all production activities. Both industry and academia have done a lot of work on the research of process planning. There are many definitions of process planning, which are summarized as follows [1]:
  1. (1)

    Process planning is a bridge between product design and manufacturing, transforming product design data into manufacturing information [2]. In other words, process planning is an important activity that connects the design function with the manufacturing function, and it specifies the processing strategies and steps of product parts [3].

     
  2. (2)

    Process planning is a complex process involving many tasks, which requires the process designer to have a deep knowledge of product design and manufacturing. Ramesh [4] believes that these tasks include part coding, feature recognition, mapping between machining methods and features, internal and external sequencing, clamping planning, middleware modeling, machining equipment tools and corresponding parameter selection, process optimization, cost assessment, tolerance analysis, test plan, path planning, CNC program, etc.

     
  3. (3)

    Process planning is the activity of converting raw materials into the detailed operation of final parts or preparing detailed documents for the process of parts processing and assembly [5].

     
  4. (4)

    Process planning systematically identifies detailed manufacturing processes to meet design specifications within available resources and capabilities [6].

     

The above definitions describe process planning from different engineering technical perspectives. These definitions can be summarized as: process planning is an activity that connects product design and manufacturing, combines manufacturing process knowledge with the specific design under the limitation of workshop or factory manufacturing resources, and prepares specific operation instructions.

Traditionally, process planning has been done manually and empirically. The following problems include: shortage of experienced personnel, low efficiency of designated process route, inconsistency of process route caused by differences in experience and judgment of process personnel, slow reaction to the actual manufacturing environment, etc. In order to alleviate these problems, Computer-Aided Process Planning (CAPP) emerged in the mid-1960s [7, 8].

CAPP uses computer-aided parts processing process formulation, in order to determine the raw materials into the design drawings required parts. It is a process in which the computer automatically completes the formulation of part processing technology and outputs part process route and process content by inputting the geometric information (shape, size, etc.) and process information (material, heat treatment, batch, etc.) of the processed parts to the computer [9]. Research on CAPP began in the 1960s. CIRP established the first CAPP working group at the annual conference in 1966, which marked the beginning of CAPP research work. In 1969, Norway introduced the world’s first CAPP system—AUTOPROS [10], and in 1973 it officially launched the commercial AUTOPROS system. A milestone in the development history of CAPP is the CAPP system, which was introduced in 1976 by CAM-I (Computer-Aided Manufacturing-International), a U.S. International organization for Computer-Aided Manufacturing, namely CAM-I’s Automated Process Planning system. China began to study the CAPP system in the early 1980s. In 1983, Tongji University developed the first CAPP system in China—TOJICAP [11]. After years of research, CAPP has made great progress, and Zhang [12] summarized 187 CAPP systems. However, due to the complexity of process planning and the particularity of product and manufacturing environment, CAPP is difficult to be applied and generalized. In the field of manufacturing automation, CAPP is the last part to be developed. Even today, when CAD, CAE, CAM, MRPII, ERP, MES, and even e-commerce are very mature and widely used, some key problems of CAPP have not been well solved and become the key bottleneck of the manufacturing industry [13].

Since its birth, CAPP has been a research hotspot and difficulty in the field of advanced manufacturing technology. Since the 1960s, CAPP has made great progress in both the technical level and practical application. Its main research focuses on the following aspects:
  1. (1)

    Integration of CAPP with other systems [14]

     
Information integration is one of the development directions of advanced manufacturing technology and one of the technical means to shorten the product development cycle and respond to the market quickly. Therefore, the integration of CAPP and other systems is one of the hot topics of researchers. As a CAPP system connecting design and manufacturing, it should not only integrate with CAD and CAM, but also realize the integration of Production Planning and Scheduling (PPS) system, ERP (Enterprise Resource Planning) system, PDM (Product Data Management), CATD (Computer-Aided Tolerance Design), and other systems.
  1. (2)

    Optimization and selection of process route

     

The traditional process planning system produces a single and fixed process route for a part without considering the dynamic information of the workshop, which greatly reduces the feasibility and flexibility of the process route in actual production. In order to adapt to the workshop environment, the process planning system must produce a large number of flexible process routes for each part, and optimize and select them according to the production requirements. Therefore, the optimization and selection of process route is one of the main research directions of the process planning system.

The optimization and selection of process route is an NP-complete problem, and it is difficult to realize the optimization and selection of process route only by using traditional gradient descent method, graph theory method, and simulation method [15]. In order to solve this problem, a lot of research scholars introduce the method of artificial intelligence to study and solve the process route optimization problem. The main research methods include: multi-agent system [16], genetic algorithm [17, 18, 19, 20], genetic programming [21], tabu search [22, 23], simulated annealing [24], particle swarm optimization [25], ant colony algorithm [26, 27], psychological clone algorithm [28], immune algorithm [29], neural network method [30], and etc.

1.2 Shop Scheduling

1.2.1 Problem Statement

Shop scheduling problem can be generally described as: n jobs are processed on m machines. A job contains k processes, each of which can be processed on several machines and must be processed in some feasible process sequence. Each machine can process several processes of the job, and the set of processes can be different on different machines. The goal of shop scheduling is to reasonably arrange the jobs to each machine, reasonably arrange the processing order and starting time of the jobs, so as to satisfy the constraint conditions and optimize some performance indicators [31]. In general, scheduling problems can be represented by “n/m/A/B”, where n is the number of jobs, m is the number of machines, A represents the morphological type of jobs flowing through the machines, and B represents the type of performance indicators. There are many classification methods for scheduling problems [32, 33]. Common types of jobs flowing through the machines include [34]:
  1. (1)

    Single Machine Scheduling Problem (SMP)

     
SMP is the most basic one of shop scheduling problems, which is characterized by the jobs to be processed on only one process. The processing system has only one machine tool, and all the jobs are to be processed on the machine tool.
  1. (2)

    Parallel Machine Scheduling Problem (PMP)

     
The processing system has a set of machine tools with the same functions. The jobs are to be processed on one process, and one can select the machine to process it.
  1. (3)

    Job shop Scheduling Problem (JSP)

     
The processing system has a set of machine tools with different functions. Given the processing order of the jobs to be processed, the process route of all jobs is different. It is required to determine the processing order of the jobs on each machine and the starting time of each job with satisfying the constraints of the processing order.
  1. (4)

    Flow Shop Scheduling Problem (FSP)

     
The processing system has a group of machine tools with different functions. Given the processing order of the jobs to be processed, the process route of all jobs is the same. It is required to determine the processing order of the jobs on each machine and the starting time of the job with satisfying the constraints of the processing order.
  1. (5)

    Open Shop Scheduling Problem (OSP)

     

The processing system has a group of machine tools with different functions. Given the processing process of the jobs to be processed, the processing order of each process is arbitrary. It is required to determine the processing order of the jobs on each machine and the starting time of the job with satisfying the constraints of the processing order.

Performance indicators B have various forms, which can be roughly divided into the following categories: (1) Performance indicators based on the processing completion time, such as Cmax (maximum completion time), Fmax (maximum flow time), etc. (2) Performance indicators based on the delivery date, such as Lmax (maximum delayed completion time), Tmax (maximum delayed time), etc.; (3) Multi-objective comprehensive performance indicators, such as the maximum completion time and the maximum delay in the completion, etc.

1.2.2 Problem Properties

The objects and objectives of shop scheduling determine that this problem has the following characteristics [35]:
  1. (1)

    Multi-objective

     
In different enterprises or different production environments, the optimization objectives of shop scheduling are different. For example, the general requirement is the shortest production cycle. In some environments, in order to deliver goods as quickly as possible, it is necessary to guarantee the delivery date of some products. In order to reduce costs, it also needs to consider the utilization of production equipment and reduce work-in-process inventory. In the actual production process, not only one objective is considered, but multiple objectives need to be considered simultaneously. As each objective may conflict with each other, comprehensive consideration must be taken into account in the formulation of shop scheduling.
  1. (2)

    Multi-constraint

     
The research object and objective of shop scheduling determine that the problem is a multi-constraint one. In the scheduling process, the constraints of the job itself need to be taken into account including: process route constraints and process constraints on the processing machine. Resource constraints also need to be considered. The results are feasible only if they meet the resource constraints of the shop. Meanwhile, shop scheduling needs to be constrained by the constraints such as operators, transport vehicles, knives, and other auxiliary production tools.
  1. (3)

    Dynamic randomness

     
The processing environment of the manufacturing system is constantly changing, and a variety of random events will be encountered, such as the variable processing time, machine tool failure, shortage of raw materials, emergency order insertion, etc. Thus the shop scheduling process is a dynamic random process.
  1. (4)

    Discretization

     
General manufacturing system is a typical discrete system, which is a discrete optimization problem. The start time of jobs, arrival of task, addition and failure of equipment, change of order and so on are discrete events. It is possible to study shop scheduling problem by mathematical programming, discrete system modeling and simulation, sorting theory and other methods.
  1. (5)

    Computational complexity

     

Shop scheduling is a combinatorial optimization problem constrained by several equations and inequalities, which has been proved to be an NP-complete problem. With the increase of the scheduling scale, the number of feasible solutions to the problem increases exponentially.

The characteristics of the shop scheduling problem determine that the problem is a very complex problem. This is why over the years the research on this problem has attracted a large number of researchers from different fields. A number of solutions methods are proposed, aiming to meet the needs of practical application. However, these achievements cannot fully meet the needs of practical application, so we have to conduct a more comprehensive study on the nature of the problem and the relevant solution methods, so as to propose more effective theories, methods, and technologies to meet the practical application needs of enterprises.

1.2.3 Literature Review

The research of shop scheduling problem is basically synchronous with the development of operational research. As early as 1954, Johnson proposed an effective optimization algorithm to solve n/2/F/Cmax and some special n/3/F/Cmax problems, which kicked off the research on scheduling problems [36]. In the 1960s, researchers tended to design definite shaping methods with polynomial time complexity in order to find the optimal solution to the shop scheduling problem. These methods include integer programming [37], dynamic programming, branch and bound method [38] and backtracking algorithm [39], etc. But these algorithms can solve a limited number of examples, the experiments show that even today’s mainframe computers cannot solve them in an acceptable time. In the 1970s, researchers conducted an in-depth study on the computational complexity of scheduling problems, proving that the vast majority of scheduling problems are NP-complete problems [40, 41]. Instead of seeking the optimal solutions to the problems by exact algorithms, researchers seek the satisfactory solution of the problems by approximate algorithms in an acceptable time. Therefore, a heuristic method is proposed to solve the problem. Panwalkar [42] summarized 113 scheduling rules and divided them into three categories: simple rules, compound rules, and heuristic rules. Since the 1980s, with the intersection of computer technology, life science, and engineering science, the meta-heuristic algorithms developed by imitating the mechanism of natural phenomena have been applied to solve scheduling problems and has shown the potential of solving large-scale scheduling problems, including genetic algorithm, tabu search, constraint satisfaction algorithm, particle swarm optimization algorithm, ant colony algorithm, etc. The researchers improve the above algorithms all the time and also propose some novel algorithms. The occurrence and development of these algorithms have greatly promoted the development of the scheduling problems.

1.3 Integrated Process Planning and Shop Scheduling

The research on the integration of process planning and shop scheduling began in the mid-1980s [43, 44, 45]. Chryssolouris and Chan [46, 47] first put forward the concept of process planning and workshop scheduling integration in the mid-1980s. Beckendorff [48] then USES alternative process paths to add flexibility to the system. Khoshnevis [49] introduced the idea of dynamic feedback into the integration of process planning and workshop scheduling. The integrated model proposed by Zhang [50] and Larsen [51] not only inherits the idea of alternative process route and dynamic feedback, but also embodies the idea of hierarchical planning to a certain extent. In recent years, domestic and foreign researchers have conducted a lot of researches on the integration of process planning and workshop scheduling, and put forward various integration models and research methods to enrich the research on the integration of process planning and workshop scheduling [43, 52].

At present, in view of the integration of process planning and shop scheduling, the researchers have put forward some integration models, which can be roughly summarized into the following three categories [53]: non-linear process planning, closed-loop process planning, and distributed process planning. Their common characteristic is to make use of the integration of process planning and shop scheduling, and give full play to the flexibility of the process planning system through some improvement of the process planning system, so as to improve the flexibility of the whole integrated system.
  1. (1)

    Non-linear process planning

     

Non-linear Process Planning (NLPP) model is based on static manufacturing environment. It aims to generate all possible process routes before each part entering into the shop. According to the optimization objective of process planning, each optional technology is given a certain priority. And then according to specific resources and shop status, the shop scheduling system chooses the optimal process route. Most of the existing literature on the integration of process planning and shop scheduling have adopted the idea of this integration model. The advantage of this model is that all possible process routes are generated, which expands the optimization space of shop scheduling and is conducive to finding the optimal process route. The disadvantage is that all possible process routes are generated, which increases the storage space of the system. The optimization search for all process routes increases the calculation time, which may cause that satisfactory solutions cannot be found in an acceptable time. Its specific forms are as follows: Non-linear Process Planning (NPP), Multi-process Planning (MPP), and Alternative Process Planning (APP). The above forms are not different in nature only in description.

Non-linear Process Planning: when making a craft route, all of the process routes of the processed parts are generated. All of them are stored in the database, according to the premise of optimization, the selection of process route after evaluation sequence determines the priority. The process route with the highest priority is first selected to see if it is suitable for the current resource status. If not, select another route with the second priority, and so on until a satisfactory process route is found. Literature [54] proposed an integrated model of non-linear process planning in FMS. Lee [55] proposed a non-linear process planning model based on genetic algorithm, which can greatly reduce scheduling time and product delay. Literature [54] proposed the integration method of FMS-oriented flexible process planning and production scheduling based on a unified resource database under the idea of concurrent engineering. Literature [56] proposed a non-linear process based on Petri net, which has been widely used in the flexible production scheduling system. Jablonski [57] introduced the concept of flexible integrated system (the system includes three subsystems, namely, feature recognition system, static process planning system, and dynamic resource allocation system), and introduced some applications of the system.

Multi-process planning [58]: it can be generally expressed by tree structure, which is called process route tree. Nodes in the tree represent the processing process, edges represent the sequence relationship between processing processes, a path from root to leaf represents a process route scheme, and a path from any non-root to leaf represents a subprocess route scheme. The job processing process is equivalent to the search process of process route tree. Literature [59, 60] uses a coevolution algorithm to realize multi-process route decision and integration of process planning and production scheduling. Literature [60] proposed a scheduling system supporting multi-process routes based on a negotiation mechanism. Literature [61] designed a system based on characteristics of process planning, the product shape design of the machine tool machine manufacturing characteristics, manufacturing resource capability information, and process knowledge rules, design process. And according to the machine load information for each process route allocation, the rationality of various scheduling schemes was evaluated. Literature [62, 63] summarized the mixed integer programming model of the shop scheduling problem based on the multi-process route, and proposed two solutions. Literature [64] proposed an integrated mechanism of process planning and shop scheduling based on multi-process routes.

Optional process planning: the basic idea is to produce a variety of optional process routes on the basis of the various constraints of the part process route, so as to improve the flexibility of process planning and provide convenience for production scheduling. By analyzing the objective of production scheduling integration, Literature [65] pointed out that these problems can be solved by reducing the optional factors of the optional process route, optimizing and linearizing the optional process planning decision-making process. Literature [66] proposed an optimization algorithm to solve the shop scheduling problem based on alternative process routes under the JIT production environment. Literature [67] used the branch and bound method to solve the integration problem of process planning and shop scheduling based on alternative process plans. Literature [68] proposed two meta-heuristic algorithms (genetic algorithm and tabu search algorithm) to solve the integration problem of process planning and shop scheduling based on alternative process planning. References [69, 70], respectively, adopted simulated annealing and improved genetic algorithm to solve this problem.

NLPP is the most basic model for the integration of process planning and shop scheduling. Because the integration idea of this model is simple and easy to be operated, the existing research on the integration model mainly focuses on this model.
  1. (2)

    Closed-loop process planning

     

Closed-Loop Process Planning (CLPP) model generates process route according to the shop resource information feedback from the scheduling system, so CLPP can better consider the state of shop resources and the generated process route is feasible compared with the current production environment. Real-time state data is the key of CLPP, and dynamic process planning is carried out according to real-time feedback information. Its specific forms are as follows: closed-loop Process Planning, Dynamic Process Planning, and On-Line Process Planning (OLPP).

Closed-loop process planning: it is a dynamic process planning system for the shop [71], which generates real-time optimized process routes through the dynamic feedback of shop resource information. The shop scheduling system feeds back the currently available equipment status of the shop to the draft process line, so that it can be changed and adjusted in time, and thus improve the feasibility of the process plan. Process design integrates operation planning and production scheduling system together to form a closed loop for automatic adjustment of process route. This kind of dynamic system can obviously improve the real-time performance, guidance, and operability of the process planning system. Literature [72, 73] proposed a dynamic optimization mechanism for the integration of process planning and shop scheduling in a batch manufacturing environment.

Dynamic process planning: it is based on the real-time dynamic scheduling information feedback from the shop scheduling system to generate the process route. At this time, the generated process route better considers the real-time situation of workshop resources, and thus to improve the feasibility of the process route. Literature [74] studied the dynamic CAPP system and put forward the network architecture, functional modules, and flowchart of the dynamic CAPP system. The system can make timely response and feedback to the changing workshop working environment and adjust the process route in time, so as to provide correct guidance to the production practice. Literature [75] studied an integrated model of the dynamic process planning system, and realized the dynamic selection of processing resources based on BP neural network, so as to produce a process route meeting the production conditions of the shop. Literature [76] divided the dynamic process planning into two stages according to functions: static planning stage and dynamic planning stage, and focused on the static planning stage. Literature [71] pointed out the problems existing in the traditional process planning system. To solve these problems, dynamic process planning system is introduced. The process route generated by the dynamic process planning system can better adapt to the actual situation of the shop and the needs of scheduling. Literature [77] combined the function of process planning and shop scheduling, and used a priority allocation method combined with parallel allocation algorithm, which used time window plan to control the allocation quantity of each stage. The dynamic process planning system mentioned in literature [78] can not only make the initial process route, but also make the process route according to the feedback of the integrated system. The process module, decision-making algorithm, and control strategy of the system were studied. Literature [79] proposed and constructed a multitask process route optimization decision-making model of the noncooperative game.

On-line process planning: it is integrated by considering the specific situation of the shop. This helps the process line to adapt to the real-time production situation. The most important part of the scheme is the real-time information of the workshop and the dynamic feedback of scheduling. Literature [74] discussed the importance of process planning and shop scheduling system integration, and proposed a method to make on-line process planning specific, and carried out a simulation on it, and achieved good results. Literature [80] proposed scheduling system based on the selected machine tool, the complete scheduling simulation to determine the process route at the same time, considering workshop equipment utilization rate, fault, and processing cost of the specific situation of the on-line process route design, according to the purchase cost, use cost of the machine tool, process factors such as the number and alternative machine, every working procedure to calculate the coefficient of scheduling and process planning system.

CLPP makes use of the feedback mechanism in the integration principle, which can better realize the integration of process planning and shop scheduling. However, since the existing CLPP only provides the interface of information and function, the depth of the coupling of information and function is not enough. How to improve the coupling depth of CLPP information and function is a problem to be solved. Moreover, CLPP needs to collect real-time information about workshop resources. How to represent, transmit, and process real-time information of workshop resources is also a problem to be solved.
  1. (3)

    Distributed process planning [81]

     

Distributed Process Planning (DPP) can be expressed in the following forms: Distributed Process Planning, Concurrent Process Planning, and Collaborative Process Planning.

Distributed Process Planning: it is also known as just-in-time Process Planning (JTPP) [45]. In this model, process planning and scheduling are completed synchronously. The process planning and scheduling are divided into two stages: the first stage is the preliminary planning stage. In this stage, features and characteristics and the relationship between the parts are mainly analyzed. And according to the features of parts information, the initial processing method is determined, as well as the processing of resources (such as raw materials, processing equipment, etc.) are preliminary estimated. The second stage is the detailed planning stage. The main task is to match the information of shop processing equipment and production task, and generate a complete process route and scheduling plan. Literature [82] systematically elaborated the conceptual model of parallel distributed integration, and adopted the multilevel distributed hierarchical structure different from the traditional process planning system structure, so as to better realize the integration of them and effectively solve the existing production problems. The hierarchical planning idea and the integrated model proposed in literature [83] were the specific forms of distributed process planning. Literature [45] made a more specific description of timely process planning, and gave the model frame diagram of timely process planning. Literature [84] briefly mentioned the model of distributed process planning and pointed out that it was a decentralized integration method among multilevel functional modules. Literature [85] proposed a two-level hierarchical model to integrate process planning and workshop scheduling. A distributed process planning method was proposed in literature [86], and Multi-Agent System (MAS) was adopted to construct the framework of the proposed method.

Parallel process planning: it uses the idea of concurrent engineering to divide process design and production scheduling into several levels. The parts related to process design and production scheduling are integrated, respectively, at each level, so as to realize the purpose of mutual cooperation and common decision-making. Literature [87] is the process planning based on concurrent engineering. Based on concurrent engineering theory, the integration of process planning system with process segmentation design and production scheduling system based on an extended time rescheduling strategy is proposed, and the effectiveness and feasibility of the system are proved by examples. Literature [88] proposed and verified the parallel integration model of process planning and production scheduling in the distributed virtual manufacturing environment. Literature [89] proposed the framework of parallel integrated process planning system based on Holon, which divided the process planning into three stages: preliminary design stage, decision-making stage, and detailed design stage of process planning. It integrated CAD, process planning, and production scheduling system with concurrent engineering thought. An Integrated Process Planning/Production Scheduling system (IP3S) based on parallel engineering was proposed in literature [90]. In Literature [91], an integrated process planning and workshop scheduling system was designed and applied to the Holonic manufacturing system.

Collaborative process planning: the resource condition of workshop is taken into consideration as well as the process planning, so that both process planning and scheduling plan can be carried out cooperatively. Literature [92] proposed an integrated model of collaborative process planning and workshop scheduling system, including three modules: shop resource evaluation module, scheduling evaluation module, and process planning evaluation module. And then the three modules are integrated by using the collaborative mechanism. A collaborative process planning system framework based on a real-time monitoring system was proposed in Literature [93]. And the design and use of functional modules in the integrated system were introduced in detail.

The basic idea of DPP is hierarchical planning. This model considers the integration of the process planning system and the shop scheduling system in the early stage, and the process planning and scheduling plan are always carried out in parallel. Both sides reflect the interaction, coordination and cooperation on the whole integrate the decision-making process. However, its ability to optimize process route and schedule is not enough, so this model can be integrated with other models to improve its overall optimization.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature and Science Press, Beijing 2020

Authors and Affiliations

  1. 1.School of Mechanical Science and Engineering, HUSTState Key Laboratory of Digital Manufacturing and Equipment TechnologyWuhanChina
  2. 2.School of Mechanical Science and Engineering, HUSTState Key Laboratory of Digital Manufacturing and Equipment TechnologyWuhanChina

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