An effective hybrid algorithm for integrated process planning and scheduling

In this chapter, a new Hybrid Algorithm (HA)-based approach has been developed to facilitate the integration and optimization of these two systems. To improve the optimization performance of the approach, the efficient genetic representation, operator, and local search strategy have been developed. Experimental studies have been used to test the performance of the proposed approach and make the comparisons between this approach and some previous works. The results show that the research on Integrated Process Planning and Scheduling (IPPS) is necessary and the proposed approach is a promising and very effective method on the research of IPPS.

At present, green manufacturing plays a vital role in the manufacturing industry. In a sense, Integrated Process Planning and Scheduling (IPPS) itself is a complex NP-Complete problem. However, multi-objective problems in green model are more difficult to address. Consequently, solving IPPS problem under green manufacturing environment is a challenging job. The Green Multi-Objective Integrated Process Planning and Scheduling (GMOIPPS) problem is studied in this paper. A mathematical model for GMOIPPS including efficiency objective and energy consumption objective is established. An effective two-stage optimization method is adopted to deal with GMOIPPS problem. The basic NSGA-II algorithm is employed to optimize the flexible process planning stage and provide the near-optimal process plans for job shop scheduling stage dynamically. An improved NSGA-II algorithm (INSGA-II) with N5 neighborhood structure is designed to find the non-dominated scheduling plans in job shop scheduling stage. Three instances with different scales are constructed to verify the validity of the proposed model and optimization method. The experimental results show that the proposed two-stage optimization method can effectively solve the GMOIPPS problem.

Traditionally, process planning and scheduling for parts were carried out in a sequential way, where scheduling was done after process plans had been generated. Considering the fact that the two functions are usually complementary, it is necessary to integrate them more tightly so that performance of a manufacturing system can be improved greatly. In this paper, we present a review of the reported research in Integrated Process Planning and Scheduling (IPPS), discuss the extent of applicability of various approaches and suggest some future research trends.

With the emergence of the concept of green manufacturing, more manufacturers have attached importance to energy consumption indicators. The process planning and shop scheduling procedures involved in manufacturing processes can both independently achieve energy savings, however independent optimization approaches limit the optimization space. In order to achieve a better optimization effect, the optimization of energy savings for integrated process planning and scheduling (IPPS) was studied in this paper. A mathematical model for multi-objective optimization of IPPS was established to minimize the total energy consumption, makespan, and peak power of the job shop. A hierarchical multi-strategy genetic algorithm based on non-dominated sorting (NSHMSGA) was proposed to solve the problem. This algorithm was based on the non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) framework, in which an improved hierarchical coding method is used, containing a variety of genetic operators with different strategies, and in which a population degradation mechanism based on crowding distance is adopted. The results from the case study in this paper showed that the proposed method reduced the energy consumption by approximately 15% for two different scheduling schemes with the same makespan. The computational results for NSHMSGA and NSGA-Ⅱ approaches were evaluated quantitatively in the case study. The C-metric values for NSHMSGA and NSGA-Ⅱ were 0.78 and 0, the spacing metric values were 0.4724 and 0.5775, and the maximum spread values were 1.6404 and 1.3351, respectively. The evaluation indexes showed that the NSHMSGA approach could obtain a better non-dominated solution set than the NSGA-Ⅱ approach in order to solve the multi-objective IPPS problem proposed in this paper.

Integrated process planning and scheduling (IPPS) is important for modern manufacturing companies to achieve manufacturing efficiency and improve resource utilization. Meanwhile, multiple objectives need to be considered in the realistic decision-making process for manufacturing systems. Based on the above realistic manufacturing system requirements, it becomes increasingly important to develop effective methods to deal with multi-objective IPPS problems. Therefore, an improved NSGA-II (INSGA-II) algorithm is proposed in this research, which uses the fast non-dominated ranking method for multiple optimization objectives as an assignment scheme for fitness. A multi-layer integrated coding method is adopted to address the characteristics of the integrated optimization model, which involves many optimization parameters and interactions. Elite and mutation strategies are employed during the evolutionary process to enhance population diversity and the quality of solutions. An external archive is also used to store and update the Pareto solution. The experimental results on the Kim test set demonstrate the effectiveness of the proposed INSGA-II algorithm.

A multi-population co-evolutionary algorithm for green integrated process planning and scheduling considering logistics system

Integrated process planning and scheduling is a significant research focus in recent years, which could improve the performance of manufacturing system. In real manufacturing environment, multi-objectives should be taken into consideration simultaneously during the machining process. Meanwhile, the processing time for each job is often imprecise in many real applications. Therefore, multi-objective integrated process planning and scheduling (IPPS) problem with fuzzy processing time is addressed in this paper. The processing time is described as triangular fuzzy number. A multi-objective genetic algorithm (MOGA) is designed to search for the Pareto solutions of multi-objective IPPS problem with fuzzy processing time. An instance has been designed to test the performance of proposed algorithm. The experiment result shows that the proposed MOGA could obtain satisfactory Pareto solutions for the multi-objective IPPS problem with fuzzy processing time.

Traditionally, process planning and scheduling for parts are carried out in a sequential way, where scheduling is done after process plans have been generated. Considering the two functions are usually complementary, there is a great potential to integrate them more tightly so that greater performance and higher productivity of a manufacturing system can be achieved. In this paper, a genetic algorithm-based approach has been developed to facilitate the integration and optimization of these two functions. To improve the optimized performance of the genetic algorithm-based approach, efficient genetic representations and operator schemes have been developed. Experiment studies have been conducted. The experimental results show that the proposed approach is a promising and very effective method for the integration of process planning and scheduling.

Process planning and scheduling are important manufacturing planning activities which deal with resource utilization and time span of manufacturing operations. The process plans and the schedules generated in the planning phase shall be modified in the execution phase due to the disturbances in the manufacturing systems. This paper deals with a multi-agent architecture of an integrated and dynamic system for process planning and scheduling for multi jobs. A negotiation protocol is discussed, in this paper, to generate the process plans and the schedules of the manufacturing resources and the individual jobs, dynamically and incrementally, based on the alternative manufacturing processes. The alternative manufacturing processes are presented by the process plan networks discussed in the previous paper, and the suitable process plans and schedules are searched and generated to cope with both the dynamic status and the disturbances of the manufacturing systems. We initiatively combine the heuristic search algorithms of the process plan networks with the negotiation protocols, in order to generate suitable process plans and schedules in the dynamic manufacturing environment. A simulation software has been developed to carry out case studies, aimed at verifying the performance of the proposed multi-agent architecture.

The traditional manufacturing system research literature generally assumed that there was only one feasible process plan for each job. This implied that there was no flexibility considered in the process plan. But, in the modern manufacturing system, most jobs may have a large number of flexible process plans. So, flexible process plans selection in a manufacturing environment has become a crucial problem. In this chapter, a new method using an evolutionary algorithm, called Genetic Programming (GP), is presented to optimize flexible process planning. The flexible process plans and the mathematical model of flexible process planning have been described, and a network representation is adopted to describe the flexibility of process plans. To satisfy GP, it is very important to convert the network to a tree. The efficient genetic representations and operator schemes also have been considered. Case studies have been used to test the algorithm, and the comparison has been made for this approach and Genetic Algorithm (GA), which is another popular evolutionary approach to indicate the adaptability and superiority of the GP-based approach. The experimental results show that the proposed method is promising and very effective in the optimization research of flexible process planning.

An Integrated Process Planning and Scheduling problem solved from an adaptive multi-objective perspective

SUMMARY Process planning and inventory control are important manufacturing functions; proper execution of these functions can result in large cost savings. Process planning is typically responsible for identification and sequencing of required manufacturing processes. Inventory control then uses the result of process planning activities to determine economic production quantities. Today, this sequential decision process is commonly used in industry. However, although easy to implement, it cannot guarantee that an overall minimum cost plan will be generated. An integrated approach to process planning and inventory control functions should presumably yield better results. Such an integrated approach is presented in this paper. The problem is formulated as a constrained optimization model with sequence-dependent set-up and processing costs. Dynamic programming is used to solve the model.

Process planning is an important bridge between design and machining in a manufacturing system. However, this process in Vietnam is mostly done manually. At that time, process planning needs quite a lot of effort and time of the engineer. In modern manufacturing, CAD/CAM/CNC integrated technology has developed so much. However, the development of computer-aided process planning (CAPP) is limited and has not caught with the rapid development of CAD/CAM technology. This article presents the methodology for developing and building computer-aided process planning systems for prismatic parts. In this system, the entire feature recognition and some basic modules of the process planning, such as equipment selection as well as operation sequences, are carried out automatically based on diversity database suitable for practical production. The system automatically generates a process planning instruction sheet directly from the solid 3D model in the SolidWorks environment. Testing of the system shows that process planning preparation time is reduced by up to 10 times compared to the manual method while ensuring technical requirements are met.

Process planning was traditionally considered as manufacturing preparation that provides manufacturing methods and operation instructions. The process planning has become an important part in computer integrated manufacturing environment. To increase flexibility in a production life cycle, process planning has to play a significant role by dealing with dynamic activities and time-dependent problems from product design to shop floor manufacturing. The approaches to process planning actually refers to the approaches to the design of computer aided process planning systems. Generally there are two conventional approaches, the variant approach and the generative approach. The nature of process planning can be generally described as knowledge-intensive. The knowledge involved is mostly subjective, nondeterministic, nonheuristic, and difficult to represent. Process planning is time-dependent and dynamic. The planning functions being capable of dealing with dynamic changes in manufacturing processes and manufacturing requirements have far-fetching importance in modern manufacturing environment.

A unique method for the integration of process planning and scheduling in a batch-manufacturing environment is reported. This integration is essential for the optimum use of production resources and for the generation of realistic process plans that can be readily executed with little modification. The integration problem is modelled at two levels: process planning and scheduling, which are linked by an intelligent facilitator. The process-planning module employs an optimization approach in which the whole plan solution space in terms of available machines, tools, tool accessibility and precedence constraints is first generated and a search algorithm is then used to find the optimal plan. For a given set of jobs, the scheduling module takes the optimal plans for each job and generates a schedule based on a given criterion, as well as the performance parameters (machine utilization and number of tardy jobs). An unsatisfied performance parameter is fed back to the facilitator, which then identifies a particular job and issues a change to its process plan solution space. The iteration of process Planning -scheduling-solution space modification continues until a schedule is satisfactory or until no further improvement can be made. The uniqueness of this approach is characterized by the flexibility of the process-planning strategy and the intelligent facilitator, which makes full use of the plan solution space intuitively to reach a satisfactory schedule. (It may not be the optimal, though.) The integrated system was implemented in the manufacturing of prismatic parts. The testing results show that the developed integration method can achieve satisfactory process plans and a schedule in an effective and efficient manner.