Quantitative modelling of reverse logistics networks and product recovery have been the focus of many research activities in the past few decades. Interest to these models are mostly due to the complexity of reverse logistics networks that necessitates further analysis with the help of mathematical models. In comparison to the traditional forward logistics networks, reverse logistics networks have to deal with the quality of returns issues as well as a high degree of uncertainty in return flow. Additionally, a variety of recovery routes, such as reuse, repair, remanufacturing and recycling, exist. The decision making for utilising these routes requires the quality of returns and uncertainty of return flow to be considered. In this research, integrated forward and reverse logistics networks with repair, remanufacturing and disposal routes are considered. Returns are assumed to be classified based on their quality in ordinal quality levels and quality thresholds are used to split the returned products into repairable, remanufacturable and disposable returns. Fuzzy numbers are used to model the uncertainty in demand and return quantities of different quality levels. Setup costs, non-stationary demand and return quantities, and different lead times have been considered. To facilitate decision making in such networks, a two phase optimisation model is proposed. Given quality thresholds as parameters, the decision variables including the quantities of products being sent to repair, disassembly and disposal, components to be procured and products to be repaired, disassembled or produced for each time period within the time horizon are determined using a fuzzy optimisation model. A sensitivity analysis of the fuzzy optimisation model is carried out on the network parameters including quantity of returned products, unit repair an disassembly costs and procurement, production, disassembly and repair setup costs. A fuzzy controller is proposed to determine quality thresholds based on some ratios of the reverse logistics network parameters including repair to new unit cost, disassembly to new unit cost, repair to disassembly setup, disassembly to procurement setup and return to demand ratios. Fuzzy controller’s sensitivity is also examined in relation to parameters such as average repair and disassembly costs, repair, disassembly, production and procurement setup costs and return to demand ratio. Finally, a genetic fuzzy method is developed to tune the fuzzy controller and improve its rule base. The rule base obtained and the results of sensitivity analyses are utilised to gain better managerial insights into these reverse logistics networks.

The objective of this dissertation is to introduce a unified framework for modeling and simulating discrete event logistics systems (DELS) by using a formal language, the System Modeling Language (SysML), for conceptual modeling and a corresponding methodology for translating the conceptual model into a simulation model. There are three parts in this research: plant modeling, control modeling, and simulation generation. Part 1:Plant Modeling of Discrete Event Logistics Systems. Contemporary DELS are complex and challenging to design. One challenge is to describe the system in a formal language. We propose a unified framework for modeling DELS using SysML. A SysML subset for plant modeling is identified in this research. We show that any system can be described by using the proposed subset if the system can be modeled using finite state machines or finite state automata. Furthermore, the system modeled by the proposed subset can avoid the state explosion problem, i.e., the number of the system states grows exponentially when the number of the components increases. We also compare this approach to other existing modeling languages. Part 2:Control Modeling of Discrete Event Logistics Systems. The development of contemporary manufacturing control systems is an extremely complex process. One approach for modeling control systems uses activity diagrams from SysML, providing a standard object-oriented graphical notation and enhancing reusability. However, SysML activity diagrams do not directly support the kind of analysis needed to verify the control model, such as might be available with a Petri net (PN) model. We show that a control model represented by UML/SysML activity diagrams can be transformed into an equivalent PN, so the analysis capability of PN can be used and the results applied back in the activity diagram model. We define a formal mathematical notation for activity diagrams, show the mapping rules between PN and activity diagrams, and propose a formal transformation algorithm. Part 3:Discrete Event Simulation Generation. The challenge of cost-effectively creating discrete event simulation models is well-known. One approach to alleviate this issue is to describe a system using a descriptive modeling language and then transform the system model to a simulation model. Some researchers have tried to realize this idea using a transformation script. However, most of the transformation approaches depend on a domain specific language, so extending the domain specific language may require modifying the transformation script. We propose a transformation approach from SysML to a simulation language. We show that a transformation script can be independent of the associated domain specific language if the domain specific language is implemented as domain libraries using a proposed SysML subset. In this case, both the domain library and the system model can be transformed to a target simulation language. We demonstrate a proof-of-concept example using AnyLogic as the target simulation language.