published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

Effective process scheduling is very important to the modern manufacturing production. This research addresses a classical scheduling problem — the job shop scheduling problem from the standpoint of both static and dynamic environment. In this study, the job shop scheduling problem (JSSP) is investigated in three aspects: (1) static JSSP that operates under a static scheduling environment with known information about the jobs and machines without unexpected events; (2) semi-dynamic JSSP which is developed based on static JSSP but violating the non-operation disruption assumption due to the presence of uncertainties occurring in the dynamic scheduling process; (3) dynamic online JSSP that operates under a dynamic operating environment in which jobs continuously arrive that are accompanied by unpredictable disruptions, such as machine failures. In the thesis, these three types of JSSP are solved by artificial immune systems (AIS) based algorithms. For static JSSP, a hybrid algorithm is proposed based on clonal selection theory and immune network theory of AIS, and particle swarm optimization (PSO). The clonal selection theory establishes the framework of the hybrid algorithm, while the immune network theory is applied to increase the diversity of antibody set which represents the solution candidates. The proposed framework involves the processes of selection, cloning, hypermutation, memory, and receptor editing. The PSO is designed to optimize the hypermutation process of the antibodies to accelerate the search procedure. This hybrid algorithm is tested with benchmark problems of different sizes and is compared with other methods. The results demonstrate the efficiency of the proposed algorithm, the effectiveness of PSO, and the contribution of long-lasting memory which is one of the key features of AIS. The semi-dynamic JSSP is handled by the rescheduling process. An extended deterministic dendritic cell algorithm (dDCA) is proposed to control the rescheduling process under considerations of the stability and efficiency of the scheduling system. The main role of the extended dDCA is to quantify the negative effect generated from the unexpected disturbances and to determine the best time to trigger the rescheduling process. This algorithm is tested on static benchmark problems with the existence of different kinds of disruptions. The experimental results demonstrate its capability of timely triggering the rescheduling process. The dynamic online JSSP is modeled as a multi-objective optimization problem. In this case, the immune network theory of AIS is hybridized with priority dispatching rules (PDRs) to establish the idiotypic network model for dispatching rules. This idiotypic network model drives the dispatching rule selection process under a dynamic scheduling environment. Based on the job shop situations represented by the antigens, the dispatching rules that perform best under specific conditions are selected as the antibodies of the idiotypic network model. Finally, the thesis proposes a generic framework of JSSP that combines the three different aspects studied in this research with corresponding scheduling strategies. The scheduling framework for a job shop system consists of four collaborating modules and is designed to solve various scheduling situations efficiently under a dynamic operating environment. / published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

In recent years, lots of research effort was spent on the integration of process planning and job-shop scheduling. Various integrated process planning and scheduling (IPPS) models and solution approaches have been proposed. The previous and existing research approaches are able to demonstrate the feasibility of implementing IPPS. However, most of them assumed that setup time is negligible or only part of the processing time. For machined parts, the setup for each operation includes workpiece loading and unloading, tool change, etc. For setup that depends only on the operation to be processed (sequence-independent), it is applicable to adopt the assumption of not considering setup in IPPS. For setup that depends on both the operation to be processed and the immediately preceding operation (sequence-dependent), it is an oversimplification to adopt such assumption. In such cases, the setup time varies with the sequence of the operations. The process plans and schedules constructed under such assumption are not realistic or not even feasible. In actual practice, therefore, the setup time should be separated from the process time in performing the IPPS functions. In this thesis, a new approach is proposed for IPPS problems with setup time consideration for machined parts. Inseparable and sequence-dependent setup requirements are added into the IPPS problems. The setup times are separated from the process times and they vary with the sequence of the operations. IPPS is regarded as NP-hard problem. With the separated consideration of setup times, it becomes even more complicated. An Ant Colony Optimization (ACO) approach is proposed to handle this complicated problem. The system is constructed under a multi-agent system (MAS). AND/OR graph is used to record the set of feasible production procedures and sequences. The ACO algorithm computes results by an autocatalytic process with the objective to minimize the makespan. Software agents called “artificial ants” traverse through the feasible routes in the graph and finally construct a schedule. A setup time parameter is added into the algorithm to influence the ants to select the process with less setup time. The approach is able to construct a feasible solution with less setup time. Experimental studies have been performed to evaluate the performance of MAS-ACO approach in solving IPPS problems with separated consideration of setup times. The experimental results show that the MAS-ACO approach can effectively handle the problem. / published_or_final_version / Industrial and Manufacturing Systems Engineering / Master / Master of Philosophy

RFID (Radio Frequency Identification) technology has been widely used in manufacturing companies to support their production decision-makings such as planning and scheduling. Significant benefits have been obtained like real-time data collection, advanced production planning and scheduling (APS), as well as efficient material tracing & tracking. However, these companies are dazed when facing vast amount of RFID data, which could be further processed to obtain some invaluable knowledge for advanced decision-makings. This thesis proposes a holistic RFID-enabled solution for manufacturing companies which are facing typical challenges like paper-based data collection, inefficient planning and scheduling, ineffective work-in-progress (WIP) items visibility and traceability, as well as unsynchronized decision-making procedures. This solution includes several aspects. Firstly, RFID devices are systematically deployed in manufacturing sites (e.g. warehouse and shopfloors) to create an RFID-enabled ubiquitous production environment, where typical resources are converted into smart manufacturing objects (SMOs) which are able to sense and interact with each other. Thus, production logics could be carried out adaptively. Secondly, a real-time production planning and scheduling model is worked out for suiting the RFID-enabled ubiquitous manufacturing environment. This model uses several key concepts like hybrid flow shop scheduling (HFS), real-time job pool, and hierarchical decision-making principle to integrate production planning and scheduling level interactively. A real-time Kanban is proposed to coordinate these two levels. Thus, production decisions achieve a real-time fashion. Thirdly, in order to make full use of the RFID-captured real-time shopfloor production data, a data mining approach is introduced to excavate invaluable information and knowledge for APS decision-makings. Standard operation times (SOTs) and decision rules are mined for this purpose. Fourthly, an RFID-enabled real-time APS model is proposed for production decision-making. The resulting APS model is based on a hierarchical production decision-making principle to formulate planning and scheduling levels. An RFID-event driven mechanism is adopted to integrate these two levels for collaborative decision-making with the data mining approach. An RFID-enabled real-time advanced production planning and scheduling shell (RAPShell) is developed by using the concepts and models proposed in this thesis. Some cutting-edge technologies are implemented within RAPShell such as service-oriented architecture (SOA), Software as a Service (SaaS), and XML-based (re)configuration. A case study from a real-life automotive manufacturer is presented for demonstrating how RAPShell is able to facilitate the production activities and decision-making procedures. Benefits from quantitative and qualitative aspects in this case are summarized and discussed. Some innovative contributions are significant. Firstly, an affordable and systematic RFID deployment scheme is proposed to create an RFID-enabled ubiquitous manufacturing environment. Secondly, an entire data mining approach is worked out for discovering the invaluable information and knowledge from vast amount of RFID production data. Thirdly, an APS model using RFID-event driven and data mining technique is proposed to achieve ultimate APS within the ubiquitous manufacturing. Finally, insights and lessons learnt from this research and implementations are generated as managerial implications which could be referred by both academics and practitioners when contemplating the RFID-enabled solution. / published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

Process planning and scheduling are two important manufacturing planning functions which are closely related to each other. Usually, process planning and scheduling have to be performed sequentially, whereby the process plans are the input for scheduling. Many investigations have shown that the separate conduction of the two functions is much likely to ruin the effectiveness and feasibility of the process plans and schedules, and it is also difficult to cater for the occurrence of uncertainties in the dynamic manufacturing environment. The purpose of integrated process planning and scheduling (IPPS) is to perform the two functions concurrently. IPPS is a typical combinatorial optimization problem which belongs to the category of NP-hard problems. Research on IPPS has intensified in recent years. Researchers have reported various IPPS systems and solution approaches which are able to generate good solutions for specific IPPS problems. However, there is in general an absence of theoretical models for the IPPS problem representation, and research on the theoretical aspects of the IPPS is limited. The objective of this research is to establish a metaheuristic-based solution approach for the IPPS problem in flexible jobshop type of manufacturing systems. To begin with, a graph-based modeling approach for formulating the IPPS problem domain is proposed. This approach defines a way to use a category of AND/OR graphs to construct IPPS models. The graph-based IPPS model can be formulated using mathematical programming tools including polynomial mixed integer programming (PMIP) and mixed integer linear programming (MILP). The analytical mathematical programming approaches can be used to solve simple IPPS instances but they are not capable for large-scale IPPS problems. This research proposes a new IPPS modelling approach to incorporate metaheuristics in the solution strategy. Actually, the solution strategy comprises the metaheuristics and a mapping function. The metaheuristic is responsible for generating the operation sequences; a mapping function is then used to assign the operations to appropriate time slots on a schedule. General studies of applying constructive and improvement metaheuristics to solve the IPPS problem are conducted in this research. The ant colony optimization (ACO) is applied as a representative constructive metaheuristic, and a nonstandard genetic algorithm approach object-coding genetic algorithm (OCGA) is implemented as an improvement metaheuristic. The OCGA contains dedicated genetic operations to support the object-based genetic representation, and three particular mechanisms for population evolution. The metaheuristic-based solution approaches are implemented in a multi-agent system (MAS) platform. The hybrid MAS and metaheuristics based IPPS solution methodology is able to carry out dynamic rescheduling to cope with occurrence of uncertainties in practical manufacturing environments. Experiments have been carried out to test the IPPS solution approach proposed in this thesis. It is shown that both metaheuristics, ACO and OCGA, are having good performance in terms of solution quality and computational efficiency. In particular, due to the special genetic operations and population evolutionary mechanisms, the OCGA shows great advantages in experiments on benchmark problems. Finally, it is shown that the hybrid approach of MSA and metaheuristics is able to support real-time rescheduling in dynamic manufacturing systems. / published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / Mathematics / Master / Master of Philosophy

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