Introduction for Integrated Process Planning and Scheduling

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


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.


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)


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)


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)


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.


  1. 1.
    Xu HZ, Li DP (2008) Review of process planning research with perspectives. Manufac Auto 30(3):1–7MathSciNetGoogle Scholar
  2. 2.
    Mahmood F (1998) Computer aided process planning for Wire Electrical Discharge Machining (WEDM), [PhD Thesis]. University of PittsburghGoogle Scholar
  3. 3.
    Pande SS, Walvekar MG (1989) PC-CAPP- A computer assisted process planning system for prismatic components. Comput Aided Eng J, 133–138Google Scholar
  4. 4.
    Ramesh MM (2002) Feature based methods for machining process planning of automotive power-train components, [PhD Thesis]. University of MichiganGoogle Scholar
  5. 5.
    Chang TC, Wysk RA (1985) An introduction to automated process planning systems. Prentice Hall, New JerseyGoogle Scholar
  6. 6.
    Deb S, Ghosh K, Paul S (2006) A neural network based methodology for machining operations selection in computer aided process planning for rotationally symmetrical parts. J Intell Manuf 17:557–569CrossRefGoogle Scholar
  7. 7.
    Scheck DE (1966) Feasibility of automated process planning, [PhD Thesis]. Purdue UniversityGoogle Scholar
  8. 8.
    Berra PB, Barash MM (1968) Investigation of automated planning and optimization of metal working processes. Report 14, Purdue Laboratory Fir Applied Industrial ControlGoogle Scholar
  9. 9.
    Yang YT (2006) Study on the key technoligies of cooperative CAPP system supporting bilinggual languages, [PhD Thesis]. Nanjing, Nanjing University of Aeronautics and Astronautics, 2006.4Google Scholar
  10. 10.
    Du P, Huang NK (1990) Principle of computer aided process design. Beihang University Press, BeijingGoogle Scholar
  11. 11.
    Wang XK (1999) Computer aided manufacturing. Tsinghua University Press, BeijingGoogle Scholar
  12. 12.
    Zhang H, Alting L (1994) Computerized manufacturing process planning systems. Chapman & HallGoogle Scholar
  13. 13.
    Shao XY, Cai LG (2004) Modern CAPP technology and application. Machinery Industry Press, BeijingGoogle Scholar
  14. 14.
    Wu FJ, Wang GC (2001) Research and development of CAPP. J North China Ins Technol 22(6):426–429Google Scholar
  15. 15.
    Wang ZB, Wang NS, Chen YL (2004) Process route optimization based on genetic algorithm. J Tsinghua Uni 44(7):988–992Google Scholar
  16. 16.
    Zhang W, Xie S (2007) Agent technology for collaborative process planning: a review. Int J Adv Manuf Technol 32:315–325CrossRefGoogle Scholar
  17. 17.
    Li WD, Ong SK, Nee AYC (2002) Hybrid genetic algorithm and simulated annealing approach for the optimization of process plans for prismatic parts. Int J Prod Res 40(8):1899–1922zbMATHCrossRefGoogle Scholar
  18. 18.
    Li L, Fuh JYH, Zhang YF, Nee AYC (2005) Application genetic algorithm to computer-aided process planning in distributed manufacturing environments. Robot Comput Int Manufac 21:568–578CrossRefGoogle Scholar
  19. 19.
    Kingsly D, Singh J, Jebaraj C (2005) Feature-based design for process planning of machining processes with optimization using genetic algorithms. Int J Prod Res 43(18):3855–3887CrossRefGoogle Scholar
  20. 20.
    Zhang F, Zhang YF, Nee AYC (1997) Using genetic algorithm in process planning for job shop machining. IEEE Trans Evol Comput 1(4):278–289Google Scholar
  21. 21.
    Li XY, Shao XY, Gao L (2008) Optimization of flexible process planning by genetic programming. Int J Adv Manuf Technol 38:143–153CrossRefGoogle Scholar
  22. 22.
    Li WD, Ong SK, Nee AYC (2004) Optimization of process plans using a constraint-based tabu search approach. Int J Prod Res 42(10):1955–1985zbMATHCrossRefGoogle Scholar
  23. 23.
    Veeramani D, Stinnes AH, Sanghi D (1999) Application of tabu search to process plan optimization for four-axis CNC turning centre. Int J Prod Res 37(16):3803–3822zbMATHCrossRefGoogle Scholar
  24. 24.
    Ma GH, Zhang YF, Nee AYC (2000) A simulated annealing based optimization algorithm for process planning. Int J Prod Res 38(12):2671–2687CrossRefGoogle Scholar
  25. 25.
    Guo YW, Mileham AR, Owen GW, Li WD (2006) Operation sequencing optimization using a particle swarm optimization approach. Proc IMechE Part B: J Eng Manufac 220:1945–1958CrossRefGoogle Scholar
  26. 26.
    Tiwari MK, Dashora Y, Kumar S, Shankar R (2006) Ant colony optimization to select the best process plan in an automated manufacturing environment. Proc IMechE Part B: J Eng Manufac 220:1457–1472CrossRefGoogle Scholar
  27. 27.
    Krishna AG, Rao KM (2006) Optimization of operations sequence in CAPP using an ant colony algorithm. Int J Adv Manuf Technol 29:159–164CrossRefGoogle Scholar
  28. 28.
    Dashora Y, Tiwari MK, Karunakaran KP (2008) A psycho-clonal-algorithm-based approach to the solve operation sequencing problem in a CAPP environment. Int J Prod Res 21(5):510–525Google Scholar
  29. 29.
    Chan FTS, Swarnkar R, Tiwari MK (2005) Fuzzy goal-programming model with an Artificial Immune System (AIS) approach for a machine tool selection and operation allocation problem in a flexible manufacturing system. Int J Prod Res 43(19):4147–4163zbMATHCrossRefGoogle Scholar
  30. 30.
    Ming XG, Mak KL (2000) A hybrid hopfield network-genetic algorithm approach to optimal process plan selection. Int J Prod Res 38(8):1823–1839zbMATHCrossRefGoogle Scholar
  31. 31.
    Zhang CY (2006) Research on the theory and application of job shop scheduling based on natural algorithm, [PhD Thesis]. Wuhan, School of Mechanical Science & Engineering of HUST, 2006.12Google Scholar
  32. 32.
    Pinedo M (2000) Scheduling: theory, algorithms, and systems (2nd Edition). Prentice-Hall, IncGoogle Scholar
  33. 33.
    Graves SC (1981) A review of production scheduling. Oper Res 29(4):646–675MathSciNetzbMATHCrossRefGoogle Scholar
  34. 34.
    Jacek B, Klaus HE, Erwin P, Gunter S, Jan W (2007) Handbook on scheduling: from theory to applications. Springer-VerlagGoogle Scholar
  35. 35.
    Pan QK (2003) Research on multiobjective shop scheduling in manufacturing system, [PhD Thesis]. Nanjing, School of mechanical and electrical engineering, Nanjing University of Aeronautics and Astronautics, 2003.3Google Scholar
  36. 36.
    Johnson SM (1954) Optimal two and three-stage production scheduling with set-up times included. Naval Research Logistics Quarterly 1:64–68CrossRefGoogle Scholar
  37. 37.
    Manner AS (1960) On the job-shop scheduling problem. Oper Res 8:219–223MathSciNetCrossRefGoogle Scholar
  38. 38.
    Lomnicki Z (1965) A branch and bound algorithm for the exact solution of the three machine scheduling problem. Electr Eng 19(2):87–101Google Scholar
  39. 39.
    Golomb SW, Baumert LD (1965) Backtrack programming. J ACM 12:516–524MathSciNetzbMATHCrossRefGoogle Scholar
  40. 40.
    Garey MR, Graham RL, Johnson DS (1978) Performance guarantees for scheduling algorithms. Oper Res 26:3–21MathSciNetzbMATHCrossRefGoogle Scholar
  41. 41.
    Gonzalez T, Sahni S (1978) flow shop and job shop schedules: complexity and approximation. Oper Res 26:36–52zbMATHCrossRefGoogle Scholar
  42. 42.
    Panwalkar SS, Iskander W (1977) A survey of scheduling rules. Oper Res 25(1):45–61MathSciNetzbMATHCrossRefGoogle Scholar
  43. 43.
    Tan W, Khoshnevis B (2000) Integration of process planning and scheduling—a review. J Intell Manuf 11:51–63CrossRefGoogle Scholar
  44. 44.
    Kumar M, Rajotia S (2005) Integration of process planning and scheduling in a job shop environment. Int J Adv Manuf Technol 28(1–2):109–116Google Scholar
  45. 45.
    Wu DZ, Yan JQ, Jin H (1999) Research status and progress of CAPP and PPC integration. J Shanghai Jiaotong Uni 33(7):912–916Google Scholar
  46. 46.
    Chryssolouris G, Chan S, Cobb W (1984) Decision making on the factory floor: an integrated approach to process planning and scheduling. Robot Comput Int Manufac 1(3–4):315–319CrossRefGoogle Scholar
  47. 47.
    Chryssolouris G, Chan S (1985) An integrated approach to process planning and scheduling. Annals CIRP 34(1):413–417CrossRefGoogle Scholar
  48. 48.
    Beckendorff U, Kreutzfeldt J, Ullmann W (1991) Reactive workshop scheduling based on alternative routings. In: Proceedings of a conference on factory automation and information management. Florida: CRC Press Inc, 875–885Google Scholar
  49. 49.
    Khoshnevis B, Chen QM (1989) Integration of process planning and scheduling function. In: IIE integrated systems conference & society for integrated manufacturing conference proceedings. Atlanta: Industrial Engineering & Management Press, 415–420Google Scholar
  50. 50.
    Zhang HC (1993) IPPM-A prototype to integrated process planning and job shop scheduling functions. Annals CIRP 42(1):513–517CrossRefGoogle Scholar
  51. 51.
    Larsen NE (1993) Methods for integration of process planning and production planning. Int J Comput Integr Manuf 6(1–2):152–162CrossRefGoogle Scholar
  52. 52.
    Wang L, Shen W, Hao Q (2006) An overview of distributed process planning and its integration with scheduling. Int J Comput Appl Technol 26(1–2):3–14CrossRefGoogle Scholar
  53. 53.
    Deng C, Li PG, Luo B (1997) Research on the integration of job planning and process design. J Huazhong Uni Sci Technol 25(3):16–17Google Scholar
  54. 54.
    Zhang ZY, Tang CT, Zhang JM (2002) Application of genetic algorithm in flexible capp and production scheduling integration. Comput Int Manufac Sys 8(8):621–624Google Scholar
  55. 55.
    Lee H, Kim S (2001) Integration of process planning and scheduling using simulation based genetic algorithms. Int J Adv Manuf Technol 18:586–590CrossRefGoogle Scholar
  56. 56.
    Li JL, Wang ZY (2003) Flexible process planning based on Petri net. J Yanshan Uni 27(1):71–73Google Scholar
  57. 57.
    Jablonski S, Reinwald B, Ruf T (1990) Integration of process planning and job shop scheduling for dynamic and adaptive manufacturing control. IEEE, 444–450Google Scholar
  58. 58.
    Sun RL, Xiong YL, Du RS (2002) Evaluation of process planning flexibility and its application in production scheduling. Comp Int Manufac Sys 8(8):612–615Google Scholar
  59. 59.
    Yang YG, Zhang Y, Wang NS (2005) Research on multi-process route decision based on coevolutionary algorithm. Mec Sci Technol 24(8):921–925Google Scholar
  60. 60.
    Kim KH, Song JY, Wang KH (1997) A negotiation based scheduling for items with flexible process plans. Comput Ind Eng 33(3–4):785–788Google Scholar
  61. 61.
    Yang YN, Parsaei HR, Leep HR (2001) A prototype of a feature-based multiple-alternative process planning system with scheduling verification. Comput Ind Eng 39:109–124CrossRefGoogle Scholar
  62. 62.
    Kim K, Egbelu J (1998) A mathematical model for job shop scheduling with multiple process plan consideration per job. Produc Plan Cont 9(3):250–259CrossRefGoogle Scholar
  63. 63.
    Kim K, Egbelu P (1999) Scheduling in a production environment with multiple process plans per job. Int J Prod Res 37(12):2725–2753zbMATHCrossRefGoogle Scholar
  64. 64.
    Jain A, Jain P, Singh I (2006) An integrated scheme for process planning and scheduling in FMS. Int J Adv Manuf Technol 30:1111–1118CrossRefGoogle Scholar
  65. 65.
    Lan GH, Wang LY (2001) An alternative process planning decision process integrating CAD/CAPP/CAM/CAE with production scheduling. Mod Manufac Eng 10:24–25Google Scholar
  66. 66.
    Thomalla C (2001) Job shop scheduling with alternative process plans. Int J Prod Econ 74:125–134CrossRefGoogle Scholar
  67. 67.
    Gan P, Lee K (2002) Scheduling of flexible sequenced process plans in a mould manufacturing shop. Int J Adv Manuf Technol 20:214–222CrossRefGoogle Scholar
  68. 68.
    Kis T (2003) Job shop scheduling with processing alternatives. Eur J Oper Res 151:307–332MathSciNetzbMATHCrossRefGoogle Scholar
  69. 69.
    Li WD, McMahon C (2007) A simulated annealing—based optimization approach for integrated process planning and scheduling. Int J Comput Integr Manuf 20(1):80–95CrossRefGoogle Scholar
  70. 70.
    Shao XY, Li XY, Gao L, Zhang CY (2009) Integration of process planning and scheduling-a modified genetic algorithm-based approach. Comput Oper Res 36(6):2082–2096zbMATHCrossRefGoogle Scholar
  71. 71.
    Wang ZB, Chen YL, Wang NS (2004) Research on dynamic process planning system considering decision about machines. In: Proceeding of the 5th world congress on intelligent control and automation, June 15–19, Hangzhou, and P.R. China:2758–2762Google Scholar
  72. 72.
    Wang J, Zhang YF, Nee AYC (2002) Integrating process planning and scheduling with an intelligent facilitator. In: Proceeding of the 10th international manufacturing conference in China (IMCC2002) Xiamen, China, OctoberGoogle Scholar
  73. 73.
    Zhang Y, Saravanan A, Fuh J (2003) Integration of process planning and scheduling by exploring the flexibility of process planning. Int J Prod Res 41(3):611–628zbMATHCrossRefGoogle Scholar
  74. 74.
    Shen B, Tao RH (2004) Research on dynamic CAPP system integrating process planning and production scheduling. Combin Mac Tool Auto Mac Technol 5:45–48Google Scholar
  75. 75.
    Wang ZB, Wang NS, Chen YL (2005) Research on dynamic CAPP system and processing resource decision method. CAD Network WorldGoogle Scholar
  76. 76.
    Usher JM, Fernandes KJ (1996) Dynamic process planning-the static phase. J Mater Process Technol 61:53–58CrossRefGoogle Scholar
  77. 77.
    Khoshnevis B, Chen Q (1990) Integration of process planning and scheduling functions. J Intell Manuf 1:165–176CrossRefGoogle Scholar
  78. 78.
    Seethaler RJ, Yellowley I (2000) Process control and dynamic process planning. Int J Mach Tools Manuf 40:239–257CrossRefGoogle Scholar
  79. 79.
    Lin Ye (2006) Research on process route optimization method for multi-manufacturing tasks. Chinese Mechanic Eng 17(9):911–918Google Scholar
  80. 80.
    Baker RP, Maropoulos PG (2000) An architecture for the vertical integration of tooling considerations from design to process planning. Rob Comp Int Manufac 6:121–131CrossRefGoogle Scholar
  81. 81.
    Wang LH, Shen WM (2007) Process planning and scheduling for distributed manufacturing. Springer-VerlagGoogle Scholar
  82. 82.
    Wu DZ, Yan JQ, Wang LY (1996) Research on parallel distributed integration of CAPP and PPC. J Shanghai Jiaotong Uni 12(30):1–6Google Scholar
  83. 83.
    Tang DB, Li DB, Sun Y (1997) Research on integration of CAPP and job shop plan. Chinese Mechanic Eng 8(6):15–17Google Scholar
  84. 84.
    Li JL, Wang ZY, Wang J (2002) Formal logic expression of flexible process and its database structure. Manufact Auto 24:25–28Google Scholar
  85. 85.
    Brandimarte P, Calderini M (1995) A hierarchical bi-criterion approach to integrated process plan selection and job shop scheduling. Int J Prod Res 33(1):161–181zbMATHCrossRefGoogle Scholar
  86. 86.
    Wang L, Shen W (2003) DPP: an agent based approach for distributed process planning. J Intell Manuf 14:429–439CrossRefGoogle Scholar
  87. 87.
    Hua GR, Zhao LX, Zhou XH (2005) Research on integration of capp and production scheduling based on concurrent engineering. Manufacturing Automation 27(3):45–47Google Scholar
  88. 88.
    Wu SH, Fuh JYH, Nee AYC (2002) Concurrent process planning and scheduling in distributed virtual manufacturing. IIE Trans 34:77–89Google Scholar
  89. 89.
    Zhang J, Gao L, Chan FTS (2003) A holonic architecture of the concurrent integrated process planning system. J Mater Process Technol 139:267–272CrossRefGoogle Scholar
  90. 90.
    Sadeh N, Hildum D, Laliberty T, McANulty J, Kjenstad D, Tseng A (1998) A blackboard architecture for integrating process planning and production scheduling. Concur Eng Res Appl 6(2):88–100CrossRefGoogle Scholar
  91. 91.
    Sugimura N, Shrestha R, Tanimizu Y, Iwamura K (2006) A study on integrated process planning and scheduling system for holonic manufacturing. Process planning and scheduling for distributed manufacturing, 311–334. SpringerGoogle Scholar
  92. 92.
    Kempenaers J, Pinte J, Detand J (1996) A collaborative process planning and scheduling system. Adv Eng Softw 25:3–8CrossRefGoogle Scholar
  93. 93.
    Wang LH, Song YJ, Shen WM (2005) Development of a function block designer for collaborative process planning. In: Proceeding of CSCWD2005, 24–26. Coventry, UKGoogle Scholar
  94. 94.
    Saygin C, Kilic SE (1999) Integrating flexible process plans with scheduling in flexible manufacturing systems. Int J Adv Manuf Technol 15:268–280CrossRefGoogle Scholar
  95. 95.
    Kim Y, Park K, Ko J (2003) A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling. Comput Oper Res 30:1151–1171MathSciNetzbMATHCrossRefGoogle Scholar

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

Personalised recommendations