Dunbing Tang

Product lifecycle management for automotive development focusing on supplier integration

The past years have seen growing investments in the area of product lifecycle management (PLM) by the automotive sector. Due to its complex development cycle, the automotive OEM has begun to adopt the supplier integration into its product development process. To respond to this new trend, the PLM system needs to evolve to support the collaboration and partnership management between the automotive OEM and associated suppliers. Regarding the depth of collaboration, the integration of supplier into OEM process chain has been defined in two ways, quasi-supplier integration and full supplier integration. To enable the success of supplier integration, one of the PLM tasks is to control the collaboration between the automotive OEM and its suppliers, through deciding on an appropriate supplier integration way. Meanwhile, aiming at reduction of the expenditure for partnership management and coordination, the automotive OEM tends to have direct connections with limited number of capable and effective suppliers, called system suppliers. Other suppliers, called sub-suppliers no longer directly communicate with the automotive OEM, but instead with a system supplier who works closer with the automotive OEM and deals with the task of sub-supplier management and coordination. To keep up with these tasks above, a PLM framework is established in a broader perspective in this paper, enabling supplier integration and partnership management in the automotive development process all along the life cycle. Finally, an automotive case study is presented to illustrate the PLM implementation procedure focusing on supplier integration.

A Web-based Product Service System for aerospace maintenance, repair and overhaul services

Manufacturing enterprises around the world have made significant efforts to provide high value added services in addition to their traditional product development and manufacturing business. A product service system (PSS) is presented in this paper which aims to better integrate product development with maintenance and service operations. This project focuses on the maintenance, repair and overhaul (MRO) services in the aerospace industry. A MRO service model is proposed for the development of the proposed PSS. An ontology-based knowledge representation model is developed for the reuse of knowledge unambiguously in MRO services. An initial attempt has been made to demonstrate the role of PSS in the aerospace industry as a decision support tool for MRO services. Product lifecycle management (PLM) environment and Web-based technologies have been developed to enable the methodology to provide services and support in the aerospace manufacturing and flight operations business.

Energy-efficient dynamic scheduling for a flexible flow shop using an improved particle swarm optimization

Manufacturing companies are facing the emergent challenges to meet the demand of sustainable manufacturing.Energy-efficient dynamic scheduling is a NP-hard problem presented in manufacturing systems.A novel particle swarm optimization algorithm based on Hill function is presented to minimize makespan and energy consumption.The relationship between makespan and energy consumption is conflicting.The results show that the proposed algorithm outperforms the behavior of state of the art algorithms. Due to increasing energy requirements and associated environmental impacts, nowadays manufacturing companies are facing the emergent challenges to meet the demand of sustainable manufacturing. Most existing research on reducing energy consumption in production scheduling problems has focused on static scheduling models. However, there exist many unexpected disruptions like new job arrivals and machine breakdown in a real-world production scheduling. In this paper, it is proposed an approach to address the dynamic scheduling problem reducing energy consumption and makespan for a flexible flow shop scheduling. Since the problem is strongly NP-hard, a novel algorithm based on an improved particle swarm optimization is adopted to search for the Pareto optimal solution in dynamic flexible flow shop scheduling problems. Finally, numerical experiments are carried out to evaluate the performance and efficiency of the proposed approach.

Energy-aware integrated process planning and scheduling for job shops:

Process planning that is based on environmental consciousness and energy-efficient scheduling currently plays a critical role in sustainable manufacturing processes. Despite their interrelationship, these two topics have often been considered to be independent of each other. It therefore would be beneficial to integrate process planning and scheduling for an integrated energy-efficient optimisation of product design and manufacturing in a sustainable manufacturing system. This article proposes an energy-aware mathematical model for job shops that integrates process planning and scheduling. First, a mixed integrated programming model with performance indicators such as energy consumption and scheduling makespan is established to describe a multi-objective optimisation problem. Because the problem is strongly non-deterministic polynomial-time hard (NP-hard), a modified genetic algorithm is adopted to explore the optimal solution (Pareto solution) between energy consumption and makespan. Finally, case studies of energy-aware integrated process planning and scheduling are performed, and the proposed algorithm is compared with other methods. The approach is shown to generate interesting results and can be used to improve the energy efficiency of sustainable manufacturing processes at the process planning and scheduling levels.

Optimisation of product configuration in consideration of customer satisfaction and low carbon

Product configuration is one of the key technologies in the environment of mass customisation, and it has been emphasised and concerned by much research. However, previous studies mostly focus on the cost or the customer utility, but ignore the environmental concern which becomes an important design criterion due to the rising awareness of environmental protection. Moreover, various preferences of customers are also not considered. In this paper, we develop a new bi-objective optimisation model with simultaneous consideration of customer satisfaction and the environmental impact in product configuration. Two objectives of our model are the customer satisfaction index (CSI) and greenhouse gas (GHG) emissions of products. The CSI is presented for the evaluation of customer satisfaction and the GHG emission model is developed to assess the environmental impact of the product. Essential constraints, such as selection, cost and compatibility, are also considered in the model. In addition, a two-phase approach is proposed to solve the optimisation model. Finally, the effectiveness of the proposed method is demonstrated through a case study.

Dynamic shop floor re-scheduling approach inspired by a neuroendocrine regulation mechanism

With the development of the market globalisation trend and increasing customer orientation, many uncertainties have entered into the manufacturing context. To create an agile response to the emergence of and change in conditions, this article presents a dynamic shop floor re-scheduling approach inspired by a neuroendocrine regulation mechanism. The dynamic re-scheduling function is the result of cooperation among several autonomous bio-inspired manufacturing cells with computing power and optimisation capabilities. The dynamic re-scheduling model is designed based on hormone regulation principles to agilely respond to the frequent occurrence of unexpected disturbances at the shop floor level. The cooperation mechanisms of the dynamic re-scheduling model are described in detail, and a test bed is set up to simulate and verify the dynamic re-scheduling approach. The results verify that the proposed method is able to improve the performances and enhance the stability of a manufacturing system.

Topology face–based change propagation analysis in aircraft-assembly tooling design

Assembly tooling design is implemented according to the constraints of geometric information and technical requirements of aircraft. Changes in aircraft parts will cause changes in assembly tooling parts. Using topology faces as basic processing units and focusing on the constraint relationships between topology faces, this article presents a method for change propagation analysis from aircraft to assembly tooling, which assists designers to detect changes efficiently and assess the change influence qualitatively in a uniform way. First, change vector representing the change state of the original parts’ topology faces and relation matrix recording the relationships between the topology faces are introduced. Then, based on the change vectors and relation matrices, an algorithm is developed to obtain the overall geometrical change propagations from aircraft parts to assembly tooling parts. Meanwhile, a concept of change influence index is presented to evaluate the change qualitatively. Finally, a computer-aided system is developed and the proposed change propagation analysis method is demonstrated by applying it to an aircraft-assembly tooling.

Workload-based change propagation analysis in engineering design

As engineering change is a significant activity in industry and uses a lot of engineering design capacity, the management of engineering changes has become a crucial discipline. Some methods and tools to support dealing with changes have been provided. From the perspective of the workload, our work extends the method of change management through incorporation of requirement-driven change propagation. The primary result of this work is the provision of a design support to find the optimal solution of design change by examining the workload of each change propagation path. In this article, two types of change relationships are illustrated: relationships between change requirements and components and relationships between components. A matrix-based approach and two types of change propagation pattern (AND and OR) are implemented to generate change propagation paths. The concept of workload transformation is applied to calculate the workload of each change component, and then the total workload of each change propagation path is calculated to achieve the optimal solution of design change. Based on the analysis results, the schemes of design change with high workload can be avoided and those with low workload can be implemented efficiently. To demonstrate how the developed method can be applied, a blowing and suction machine is taken into consideration. Finally, the method is initially discussed and evaluated.

Energy efficiency, robustness, and makespan optimality in job-shop scheduling problems

Abstract Many real-world problems are known as planning and scheduling problems, where resources must be allocated so as to optimize overall performance objectives. The traditional scheduling models consider performance indicators such as processing time, cost, and quality as optimization objectives. However, most of them do not take into account energy consumption and robustness. We focus our attention in a job-shop scheduling problem where machines can work at different speeds. It represents an extension of the classical job-shop scheduling problem, where each operation has to be executed by one machine and this machine can work at different speeds. The main goal of the paper is focused on the analysis of three important objectives (energy efficiency, robustness, and makespan) and the relationship among them. We present some analytical formulas to estimate the ratio/relationship between these parameters. It can be observed that there exists a clear relationship between robustness and energy efficiency and a clear trade-off between robustness/energy efficiency and makespan. It represents an advance in the state of the art of production scheduling, so obtaining energy-efficient solutions also supposes obtaining robust solutions, and vice versa.

A hormone regulation–based approach for distributed and on-line scheduling of machines and automated guided vehicles:

With the continuous innovation of technology, automated guided vehicles are playing an increasingly important role on manufacturing systems. Both the scheduling of operations on machines as well as the scheduling of automated guided vehicles are essential factors contributing to the efficiency of the overall manufacturing systems. In this article, a hormone regulation–based approach for on-line scheduling of machines and automated guided vehicles within a distributed system is proposed. In a real-time environment, the proposed approach assigns emergent tasks and generates feasible schedules implementing a task allocation approach based on hormonal regulation mechanism. This approach is tested on two scheduling problems in literatures. The results from the evaluation show that the proposed approach improves the scheduling quality compared with state-of-the-art on-line and off-line approaches.