Ming J. Zuo

Optimal design of series-parallel systems considering maintenance and salvage value

A reliability based design (RBD) model is developed for a series-parallel system with deteriorating components in order to minimize the life cycle cost of the system. The effects of fixed asset depreciation, preventive maintenance and minimal repair are incorporated in the model. We also propose equations to model the effects of preventive maintenance on the systems failure rate and the salvage value as functions of time. Genetic algorithms (GAs) are used to perform constrained optimization of the system cost function subject to both active and non-active constraints. The results are useful for engineering economists, reliability engineers, and system designers.

Reliability-Based Design of Systems Considering Preventive Maintenance and Minimal Repair

A reliability-based design (RBD) of a mixed series–parallel system with deteriorative components for minimal life cycle cost is presented in this paper. Two formulations are presented based on the type of preventive maintenance modeling. These formulations incorporate the effects of preventive maintenance and minimal repairs to adjust the system failure rate. Genetic Algorithms are used to obtain an optimal system design. The economic life of the designed system is also evaluated. The results have practical applications in the area of computer integrated manufacturing where a system must perform below a given failure rate.

Temporized coloured Petri nets with changeable structure (CPN-CS) for performance modelling of dynamic production systems

One of the difficulties in modelling dynamic production systems is the representation of changes to the system along time. Coloured Petri nets with changeable structure (CPN-CS) are capable of modelling systems subject to change. However, this is restricted to functionality modelling of the system. In this paper, we extend CPN-CSs into temporized CPN-CS (TCPN-CS). The extension is carried out by associating relevant modelling elements with time parameters and modifying the mechanism for structure change of the CPN-CS model. TCPN-CS makes it possible to model a dynamic production system considering both functionality and performance. A case study is provided to show that TCPN-CS is effective for modelling dynamic production systems.

Adaptive production scheduling of virtual production systems using object-oriented Petri nets with changeable structure

Virtual Production Systems (VPSs) are logically constructed by organizing production resources belonging to one or more physical manufacturing systems. VPSs can enhance the agility of manufacturing systems. However, an effective scheduling approach is required to cope with disturbance and changes to these systems. An adaptive production scheduling method is proposed. Object-oriented Petri nets with changeable structure (OPNs-CS) formulate the scheduling problem of VPSs. To resolve resource constraints in a VPS, the OPNs-CS is modified by introducing limited token available time and by revising the enabling and firing rules. The artificial intelligent heuristic search (A*) algorithm is modified and applied to generate the optimal or near optimal schedule. When a VPS encounters any disturbance, an estimate of the effects of the disturbance can be estimated by simulation on the OPNs-CS model. If the scheduling target (completion time) is not affected, rescheduling is not required. Whenever there is a change to the VPS, the TOPNs-CS model is updated to refresh VPS schedule. A case study is presented to demonstrate the procedures for applying the proposed scheduling approach. The given case study shows that the proposed approach is capable of scheduling a VPS dynamically in response to disturbances and changes are involved.

Colored Petri Nets with changeable structures (CPN-CS) and their applications in modeling one-of-a-kind production (OKP) systems

In this paper, colored Petri Nets with changeable structures (CPN-CS) are defined. The mechanisms used for carrying out the structure changes include change-by-modification (CBM) and change-by-composition (CBC). CBM is for modification of the structure of the current CPN model while CBC is for the composition of another CPN into the current CPN. A case study is provided to show that CPN-CS is effective for modeling one-of-a-kind production (OKP) system that is characterized by product design uncertainty and changes in resource availability.

Stochastic object-oriented Petri nets (SOPNs) for reliability modeling of manufacturing systems

We propose stochastic object-oriented Petri nets (SOPNs) by associating the OPN of an object with stochastic transitions and introducing stochastic places. SOPNs of production resources are constructed to model their reliability. The SOPN model of a case production system is built to illustrate the relationship between the systems performances and the failures of individual production resources.

Solving the combined part sequencing and tool replacement problem for an automated machining center: a tabu search approach

Abstract This paper addresses a joint part sequencing and tool replacement problem on an automated machining center. The objective is to minimize the expected production cost subject to available spare tools. In the literature, it has been shown that the sequencing problems with sequence-dependent setups are equivalent to the “travelling salesman problem” (TSP) and thus are NP-complete. The problem under consideration is further complicated by the position-dependent cost components associated with tool replacement decisions. To provide an efficient planning tool for shop floor decision making, a tabu search approach is proposed. The application of the proposed approach is demonstrated using an example problem. Our computational experience shows that good solutions can be found within a relatively short search time. The impact of tool spare level on the performance of the machining center is also examined. Finally, the effects of tabu-list size and path diversification are discussed.

A framework for adaptive control of virtual production systems

Agile manufacturing (AM) requires manufacturing systems to be agile to react quickly to sudden and unpredictable changes in customer demand for their products and services. To meet with this requirement, a paradigm in which several virtual production systems (VPSs) are formed by organizing resources belonging to one or more manufacturing systems according to each individual customer ordered products is proposed in the paper. Since this paradigm combines the two resource structures, that is, functional and product-oriented ones, it can ensure a manufacturing system to be both flexible and easier to be controlled. Furthermore the structure of adaptive controllers of VPSs is presented based on the principle of adaptive control, and the four adaptive mechanisms are specified. The adaptive control can make a VPS to rapidly respond to disturbances and changes encountered during the production of a specific product. The coordination is set up among adaptive controllers of each individual VPSs to resolve conflicts for a shared resource and to optimize manufacturing systems globally.

On-line tool adjustment with adaptive tool wear function identification

This paper proposes a systematic approach for on-line tool management, including tool wear process monitoring, average tool wear function identification, and optimal tool adjustment/replacement decision making. The proposed method is ideal when some prior information is available regarding the upcoming tool wear process, although the prior information may be inaccurate or the working condition of the upcoming process may change.

System design with deteriorative components for minimal life cycle costs

As modern systems are becoming more complex and automated, an important measure of their effectiveness is their reliability. For a system to perform below a maximum allowable failure rate, a reliability based design (RBD) of the system is imperative. An RBD of a mixed series-parallel system with deteriorative components for minimal life cycle costs is presented. This formulation incorporates the effects of preventive maintenance (PM) and minimal repair into the system failure rate and uses a five step methodology to obtain optimal system configuration, PM intervals and system economic life. Genetic algorithms are used to optimize the average annual costs of the system. The results presented are useful for reliability and maintenance engineers.