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In the capital goods industry, companies produce plant and machinery that is used to produce consumer products or commodities such as electricity or gas. Typical products produced in these companies include steam turbines, large boilers and oil rigs. Scheduling of these products is difficult due to the complexity of the product structure, which involves many levels of assembly and long complex routings of many operations which are operated in multiple machines. There are also many scheduling constraints such as machine capacity as well as operation and assembly precedence relationships. Products manufactured in the capital goods industry are usually highly customised in order to meet specific customer requirements. Delivery performance is a particularly important aspect of customer service and it is common for contracts to include severe penalties for late deliveries. Holding costs are incurred if items are completed before the due date. Effective planning and inventory control are important to ensure that products are delivered on time and that inventory costs are minimised. Capital goods companies also give priority to resource utilisation to ensure production efficiency. In practice there are tradeoffs between achieving on time delivery, minimising inventory costs whilst simultaneously maximising resource utilisation. Most production scheduling research has focused on job-shops or flow-shops which ignored assembly relationships. There is a limited literature that has focused on assembly production. However, production scheduling in capital goods industry is a combination of component manufacturing (using jobbing, batch and flow processes), assembly and construction. Some components have complex operations and routings. The product structures for major products are usually complex and deep. A practical scheduling tool not only needs to solve some extremely large scheduling problems, but also needs to solve these problems within a realistic time. Multiple objectives are usually encountered in production scheduling in the capital goods industry. Most literature has focused on minimisation of total flow time, or makespan and earliness and tardiness of jobs. In the capital goods industry, inventory costs, delivery performance and machine utilisation are crucial competitive. This research develops a scheduling tool that can successfully optimise these criteria simultaneously within a realistic time. ii The aim of this research was firstly to develop the Enhanced Single-Objective Genetic Algorithm Scheduling Tool (ESOGAST) to make it suitable for solving very large production scheduling problems in capital goods industry within a realistic time. This tool aimed to minimise the combination of earliness and lateness penalties caused by early or late completion of items. The tool was compared with previous approaches in literature and was proved superior in terms of the solution quality and the computational time. Secondly, this research developed a Multi-Objective Genetic Algorithm Scheduling Tool (MOGAST) that was based upon the development of ESOGAST but was able to solve scheduling problems with multiple objectives. The objectives of this tool were to optimise delivery performance, minimise inventory costs, and maximise resource utilisation simultaneously. Thirdly, this research developed an Artificial Immune System Scheduling Tool (AISST) that achieved the same objective of the ESOGAST. The performances of both tools were compared and analysed. Results showed that AISST performs better than ESOGAST on relatively small scheduling problems, but the computation time required by the AISST was several times longer. However ESOGAST performed better than the AISST for larger problems. Optimum configurations were identified in a series of experiments that conducted for each tool. The most efficient configuration was also successfully applied for each tool to solve the full size problem and all three tools achieved satisfactory results.

The single-stage simultaneous lot sizing and scheduling problem was the object of study in this work. The problem aims to minimize the total inventory and backlog costs associated with producing N products on M parallel machines over T time-period. This thesis presented an extensive literature review on the differences found when modelling and implementing lot sizing and scheduling systems. We observed several studies used metaheuristics successfully to address large instances of these problems achieving near, and sometimes, optimal results. However, little has been found to demonstrate the capabilities of Evolutionary Algorithms in this field. A description of the methodology used to s problem : is given in terms of a Hybrid Evolutionary Algorithm which represents the problem using two novel ideas, i.e., sets of products are modelled as alleles of the solution encoded, and the use of a decoder process performing a Linear Programming, guides the solution towards overall optimality.

The lot sizing and scheduling problem in a Flexible Flow Line (FFL) has extensive real-world applications in many industries. An FFL consists of several production stages in series with parallel machines at each stage. The decisions to be taken are the determination of production quantities (lots), machine assignments and production sequences (schedules) on each machine at each stage in an FFL. Lot sizing and scheduling problems are closely interrelated. Solving them separately and then coordinating their interdependencies is often ineffective. However due to their complexity, there is a lack of mathematical modelling and solution procedures in the literature to combine and jointly solve them. Up to now most research has been focused on combining lotsizing and scheduling for the single machine configuration, and research on other configurations like FFL is sparse. This thesis presents several mathematical models with practical assumptions and appropriate algorithms, along with experimental test problems, for simultaneously lotsizing and scheduling in FFL. This problem, called the ‘General Lot sizing and Scheduling Problem in a Flexible Flow Line’ (GLSP-FFL). The objective is to satisfy varying demand over a finite planning horizon with minimal inventory, backorder and production setup costs. The problem is complex as any product can be processed on any machine, but these have different processing rates and sequence-dependent setup times & costs. As a result, even finding a feasible solution of large problems in reasonable time is impossible. Therefore the heuristic solution procedure named Adaptive Simulated Annealing (ASA), with four well-designed initial solutions, is designed to solve GLSP-FFL. A further original contribution of this study is to design linear mixed-integer programming (MILP) formulations for this problem, incorporating all necessary features of setup carryovers, setup overlapping, non-triangular setup while allowing multiple lot production per periods, lot splitting and sequencing through ATSP-adaption based on a variety of subtour elimination.

Workflow systems have been widely recognised as a successful way of modelling business processes. However, workflows can only present how processes are performed and do not support possible improvements to business processes. This issue of workflow optimisation has received a lot of attention in the literature, but the support of temporal constraints in this area has received significantly less. Furthermore, the issues that come from the enterprise, such as actors performing tasks, resources that these tasks utilise, etc. have not been taken into account.

The current competitive scenario forces firms to improve their manufacturing and service systems in order to cope with permanent market changes which drives them to seek means of cutting costs without sacrificing the quality and innovation of their products and services. For many manufacturers the number of base products and options in their mix has grown enormously, forcing production managers to be prepared to produce a widening range of different product configurations in lower volumes. Customers, on the other hand, are expecting greater responsiveness in fulfilling their needs, greater flexibility and permanent cost reductions. To cope with this, firms need to change the way they manage their operations. Traditional planning and control practices need to be revised or abandoned and new approaches adopted so firms can achieve the required levels of performance. The research presented in this thesis is devoted to study the current trends in Shop Floor Control systems. The shop floor is the place where the real things happen, and its activities involve the factory resources that most impact on time and costs, and are responsible for adding value to materials and services provided by each industrial firm. The success of any industrial supply chain management system is ultimately based on the performance of its shop floor. The research carried out involved an extensive literature review on SFC theory and practice, and recognised the gap between operations research (OR) methods and operations management (OM) approaches. Whereas OR is concerned with finding the optimal solution to mathematically-based scheduling problems and OM seeks to get the best performance from production resources at a point in time so that a firm's goals are realized. Two industrial surveys were undertaken with the aim to identify current SFC practices within the batch manufacturing sector. The findings revealed a gap between what is studied and researched in the SFC field and its actual practice. There is a significant dissociation between what is being offered by the academic world and the needs of the industrial practices, being the software houses (providers) somewhere in the middle trying to introduce new tools or improvement on the existing ones (which in turn continue to offer a limited range of valuable features that can actually be used by SFC practitioners). To reinforce the findings from the literature review and the industrial surveying process, a series of case studies and industrial visits were carried out. The conclusions drawn from this stage show an industrial practice based on traditional approaches to SFC system, mainly manual and informal systems with fewer or none assistance from computer-based systems. The dynamics of the industrial environments normally found in industry are not compatible with available SFC applications. It was demonstrated that most of SFC managers are "eager to find simple solutions in a complex world". The research proposed a new reference model to SFC system. The reference model is based on the industrial experience and in the most proved models to SFC found in the literature. The reference model adopts a systemic approach to the SFC problem by promoting its integration and synchronization with the firm's supply chain management system. Another important feature of the proposed reference model was its dependence upon human-based systems, whereas computer-based applications are proposed as tools (forming a hybrid system). The simplicity and its practical orientation proved to be an important advantage of the reference model. Several orientations and guidance is provided when the reference model are to be applied into a practical situation

In this thesis, we propose a bundled solution to address the limitations of peer-to- peer (P2P) workflow management systems such as the architectural weakness and the lack to support exception handling at instance level. The aim is to conceptualise, formalise, implement and validate this bundled solution. After an in-depth analysis of the above mentioned problems, a novel decentralized P2P workflow architecture (P-FLOW) has been developed. Then, an innovative mechanism is proposed to handle the instance level exceptions in P-FLOW. This mechanism ensures the handling of exceptions in a coordinated manner and separates the exception logic from the business logic. Furthermore, we proposed a novel feasible and flexible intelligent problem solving approach which facilitates more common understanding and exception handling experience sharing in P2P WFMSs. In our approach, a case- based reasoning (CBR) based exception handling mechanism with integrated human involvement is used. The CBR enhances the exception handling capabilities by capturing and collecting experience in handling exceptions and storing them in the form of cases. Then, these cases will be retrieved and reused for new similar situations. This mechanism allows the workflow participants to handle exceptions which need to be managed in similar way, but may occur in different instances. In our work, the workflow participants describe the exceptions in flexible way using natural language and store them as cases in case repository for future use. Then natural language processing techniques such as sentence similarity measures are used to retrieve the relevant information within CBR methodology. The feasibility of the main concepts presented in this thesis has been demonstrated using a prototype implementation. Further work has been discussed to apply the proposed P-FLOW in inter-organizational workflow scenario and potential system enhancement using agent technology.

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