Introduction for Integrated Process Planning and Scheduling
- 38 Downloads
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.
KeywordsIntegration Process planning Scheduling
1.1 Process Planning
Process planning is a bridge between product design and manufacturing, transforming product design data into manufacturing information . 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 .
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  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.
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 .
Process planning systematically identifies detailed manufacturing processes to meet design specifications within available resources and capabilities .
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 . 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 , 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 . After years of research, CAPP has made great progress, and Zhang  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 .
Integration of CAPP with other systems 
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 . 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 , genetic algorithm [17, 18, 19, 20], genetic programming , tabu search [22, 23], simulated annealing , particle swarm optimization , ant colony algorithm [26, 27], psychological clone algorithm , immune algorithm , neural network method , and etc.
1.2 Shop Scheduling
1.2.1 Problem Statement
Single Machine Scheduling Problem (SMP)
Parallel Machine Scheduling Problem (PMP)
Job shop Scheduling Problem (JSP)
Flow Shop Scheduling Problem (FSP)
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
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 . 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 , dynamic programming, branch and bound method  and backtracking algorithm , 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  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  then USES alternative process paths to add flexibility to the system. Khoshnevis  introduced the idea of dynamic feedback into the integration of process planning and workshop scheduling. The integrated model proposed by Zhang  and Larsen  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].
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  proposed an integrated model of non-linear process planning in FMS. Lee  proposed a non-linear process planning model based on genetic algorithm, which can greatly reduce scheduling time and product delay. Literature  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  proposed a non-linear process based on Petri net, which has been widely used in the flexible production scheduling system. Jablonski  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 : 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  proposed a scheduling system supporting multi-process routes based on a negotiation mechanism. Literature  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  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  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  proposed an optimization algorithm to solve the shop scheduling problem based on alternative process routes under the JIT production environment. Literature  used the branch and bound method to solve the integration problem of process planning and shop scheduling based on alternative process plans. Literature  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.
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 , 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  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  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  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  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  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  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  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  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  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.
Distributed process planning 
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) . 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  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  were the specific forms of distributed process planning. Literature  made a more specific description of timely process planning, and gave the model frame diagram of timely process planning. Literature  briefly mentioned the model of distributed process planning and pointed out that it was a decentralized integration method among multilevel functional modules. Literature  proposed a two-level hierarchical model to integrate process planning and workshop scheduling. A distributed process planning method was proposed in literature , 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  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  proposed and verified the parallel integration model of process planning and production scheduling in the distributed virtual manufacturing environment. Literature  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 . In Literature , 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  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 . 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.
- 2.Mahmood F (1998) Computer aided process planning for Wire Electrical Discharge Machining (WEDM), [PhD Thesis]. University of PittsburghGoogle Scholar
- 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.Ramesh MM (2002) Feature based methods for machining process planning of automotive power-train components, [PhD Thesis]. University of MichiganGoogle Scholar
- 5.Chang TC, Wysk RA (1985) An introduction to automated process planning systems. Prentice Hall, New JerseyGoogle Scholar
- 7.Scheck DE (1966) Feasibility of automated process planning, [PhD Thesis]. Purdue UniversityGoogle Scholar
- 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.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.Du P, Huang NK (1990) Principle of computer aided process design. Beihang University Press, BeijingGoogle Scholar
- 11.Wang XK (1999) Computer aided manufacturing. Tsinghua University Press, BeijingGoogle Scholar
- 12.Zhang H, Alting L (1994) Computerized manufacturing process planning systems. Chapman & HallGoogle Scholar
- 13.Shao XY, Cai LG (2004) Modern CAPP technology and application. Machinery Industry Press, BeijingGoogle Scholar
- 14.Wu FJ, Wang GC (2001) Research and development of CAPP. J North China Ins Technol 22(6):426–429Google Scholar
- 15.Wang ZB, Wang NS, Chen YL (2004) Process route optimization based on genetic algorithm. J Tsinghua Uni 44(7):988–992Google Scholar
- 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
- 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
- 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.Pinedo M (2000) Scheduling: theory, algorithms, and systems (2nd Edition). Prentice-Hall, IncGoogle Scholar
- 34.Jacek B, Klaus HE, Erwin P, Gunter S, Jan W (2007) Handbook on scheduling: from theory to applications. Springer-VerlagGoogle Scholar
- 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
- 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
- 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.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
- 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.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
- 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.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
- 56.Li JL, Wang ZY (2003) Flexible process planning based on Petri net. J Yanshan Uni 27(1):71–73Google Scholar
- 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.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.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.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
- 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
- 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.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
- 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.Wang ZB, Wang NS, Chen YL (2005) Research on dynamic CAPP system and processing resource decision method. CAD Network WorldGoogle Scholar
- 79.Lin Ye (2006) Research on process route optimization method for multi-manufacturing tasks. Chinese Mechanic Eng 17(9):911–918Google Scholar
- 81.Wang LH, Shen WM (2007) Process planning and scheduling for distributed manufacturing. Springer-VerlagGoogle Scholar
- 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.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.Li JL, Wang ZY, Wang J (2002) Formal logic expression of flexible process and its database structure. Manufact Auto 24:25–28Google Scholar
- 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.Wu SH, Fuh JYH, Nee AYC (2002) Concurrent process planning and scheduling in distributed virtual manufacturing. IIE Trans 34:77–89Google Scholar
- 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
- 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