Mathematical Optimization of the Tactical Allocation of Machining Resources in Aerospace Industry

In the aerospace industry, efficient management of machining capacity is crucial to meet the required service levels to customers (which includes, measures of quality and production lead-times) and to maintain control of the tied-up working capital. We introduce a new multi-item, multi-level capacitated planning model with a medium-to-long term planning horizon. The model can be used by most companies having functional workshops where costly and/or time- and resource demanding preparations (or qualifications) are required each time a product needs to be (re)allocated to a machining resource. Our goal is to identify possible product routings through the factory which minimizes the maximum excess resource loading above a given loading threshold, while incurring as low qualification costs as possible. In Paper I (Bi-objective optimization of the tactical allocation of jobtypes to machines), we propose a new bi-objective mathematical optimization model for the Tactical Resource Allocation Problem (TRAP). We highlight some of the mathematical properties of the TRAP which are utilized to enhance the solution process. Another contribution is a modified version of the bi-directional $\epsilon$ -constraint method especially tailored for our problem. We perform numerical tests on industrial test cases generated for our class of problem which indicates computational superiority of our method over conventional solution approaches. In Paper II (Robust optimization of a bi-objective tactical resource allocation problem with uncertain qualification costs), we address the uncertainty in the coefficients of one of the objective functions considered in the bi-objective TRAP. We propose a new bi-objective robust efficiency concept and highlight its benefits over existing robust efficiency concepts. We also suggest a solution approach for identifying all the relevant robust efficient (RE) solutions. Our proposed approach is significantly faster than an existing approach for robust bi-objective optimization problems

Mathematical Multi-Objective Optimization of the Tactical Allocation of Machining Resources in Functional Workshops

In the aerospace industry, efficient management of machining capacity is crucial to meet the required service levels to customers and to maintain control of the tied-up working capital. We introduce new multi-item, multi-level capacitated resource allocation models with a medium--to--long--term planning horizon. The model refers to functional workshops where costly and/or time- and resource-demanding preparations (or qualifications) are required each time a product needs to be (re)allocated to a machining resource. Our goal is to identify possible product routings through the factory which minimize the maximum excess resource loading above a given loading threshold while incurring as low qualification costs as possible and minimizing the inventory.In Paper I, we propose a new bi-objective mixed-integer (linear) optimization model for the Tactical Resource Allocation Problem (TRAP). We highlight some of the mathematical properties of the TRAP which are utilized to enhance the solution process. In Paper II, we address the uncertainty in the coefficients of one of the objective functions considered in the bi-objective TRAP. We propose a new bi-objective robust efficiency concept and highlight its benefits over existing robust efficiency concepts. In Paper III, we extend the TRAP with an inventory of semi-finished as well as finished parts, resulting in a tri-objective mixed-integer (linear) programming model. We create a criterion space partitioning approach that enables solving sub-problems simultaneously. In Paper IV, using our knowledge from our previous work we embarked upon a task to generalize our findings to develop an approach for any discrete tri-objective optimization problem. The focus is on identifying a representative set of non-dominated points with a pre-defined desired coverage gap

Multi-agent path planning for mobile robots with discrete-step locomotion

The \u201cswing-and-dock\u201d (SaD) model for realizing displacements has been invented for and is used by the mobile robotic fixtures developed in the SwarmItFix European project. This form of locomotion can be a valuable capability for material handling agents, and fixturing agents enabling simultaneous handling in a non-linear fashion and increasing manufacturing flexibility. The thesis focuses on the design of SaD path planning algorithms for the motion of the agent

Best matching processes in distributed systems

The growing complexity and dynamic behavior of modern manufacturing and service industries along with competitive and globalized markets have gradually transformed traditional centralized systems into distributed networks of e- (electronic) Systems. Emerging examples include e-Factories, virtual enterprises, smart farms, automated warehouses, and intelligent transportation systems. These (and similar) distributed systems, regardless of context and application, have a property in common: They all involve certain types of interactions (collaborative, competitive, or both) among their distributed individuals—from clusters of passive sensors and machines to complex networks of computers, intelligent robots, humans, and enterprises. Having this common property, such systems may encounter common challenges in terms of suboptimal interactions and thus poor performance, caused by potential mismatch between individuals. For example, mismatched subassembly parts, vehicles—routes, suppliers—retailers, employees—departments, and products—automated guided vehicles—storage locations may lead to low-quality products, congested roads, unstable supply networks, conflicts, and low service level, respectively. This research refers to this problem as best matching, and investigates it as a major design principle of CCT, the Collaborative Control Theory. The original contribution of this research is to elaborate on the fundamentals of best matching in distributed and collaborative systems, by providing general frameworks for (1) Systematic analysis, inclusive taxonomy, analogical and structural comparison between different matching processes; (2) Specification and formulation of problems, and development of algorithms and protocols for best matching; (3) Validation of the models, algorithms, and protocols through extensive numerical experiments and case studies. The first goal is addressed by investigating matching problems in distributed production, manufacturing, supply, and service systems based on a recently developed reference model, the PRISM Taxonomy of Best Matching. Following the second goal, the identified problems are then formulated as mixed-integer programs. Due to the computational complexity of matching problems, various optimization algorithms are developed for solving different problem instances, including modified genetic algorithms, tabu search, and neighbourhood search heuristics. The dynamic and collaborative/competitive behaviors of matching processes in distributed settings are also formulated and examined through various collaboration, best matching, and task administration protocols. In line with the third goal, four case studies are conducted on various manufacturing, supply, and service systems to highlight the impact of best matching on their operational performance, including service level, utilization, stability, and cost-effectiveness, and validate the computational merits of the developed solution methodologies

Multi-criteria Decision Analysis Applied to a Potential U.S. Commercial Spent Nuclear Fuel Allocation Queue Strategy

Although work has been done on a wide variety of fields in multi-criteria decision analysis, no literature was found that has specifically studied the development of a spent nuclear fuel (SNF) allocation queue strategy to maximize value to decision maker (DM) based on multiple objectives (allocation queue will be mostly used in this document as a shortened version of this, but allocation queue and allocation queue refer to this same thing). In this document, the DM is the person or persons who ultimately decide what allocation queue is selected. Previous work by Petersen [1] researched optimizing the order in which SNF is removed from nuclear reactor sites with the goal of reducing the number of years after all reactors on a site shut down by when all fuel is cleared from a site. This research proposal seeks to build on those methods to optimize the allocation queue by employing multiple criteria, because the development of allocation strategies for clearing nuclear reactor sites is expected to depend upon several other factors in addition to minimizing the number of Shutdown Reactor Years (SRY). Shutdown reactor years are the cumulative number of years that reactor sites have SNF remaining on-site after they are shut down summed over the entire reactor fleet.In this dissertation, a new model has been developed with the ability to consider a multiple number of DM’s preferences when developing an optimal allocation queue (in terms of maximizing value to the DM). Unlike traditional multi-objective evaluations where potential allocation strategies are developed manually, and the results compared after analyzing each scenario separately, the model was developed to search for optimum allocation strategies based on DM’s preferences. A Chebyshev integer programming method was developed for this application and the results herein provided show that the new model, denoted as the Tractable Validation Model for Value (TVMV), performs as intended.Additionally, major assumptions that affect the TVMV were explored to investigate the implications of different system assumptions. These parameters include the year in which acceptance from reactor sites begins, the maximum fleet-wide acceptance rate per year, the maximum number of canisters that can be accepted from operating or shutdown reactors in each year, and the assumed storage and transportation cask thermal limits

Mixed Integer Programming Approaches to Planning and Scheduling in Electronics Supply Chains

This paper discusses mixed integer programming (MIP) approaches to planning and scheduling in electronics supply chains. First, the short-term detailed scheduling of wafer fabrication in semiconductor manufacturing and detailed scheduling of printed wiring boards assembly in surface mount technology lines are discussed. Then, the medium-term aggregate production planning in a production/assembly facility of consumer electronics supply chain is described, and finally coordinated aggregate planning and scheduling of manufacturing and supply of parts and production of finished products is presented. The decision variables are defined and MIP modelling frameworks provided. The two decision-making approaches are discussed and compared: integrated (simultaneous) approach, in which all required decisions are made simultaneously using a complex, large monolithic MIP model; and hierarchical (sequential) approach, in which the required decisions are made successively, using hierarchies of simpler and smaller-size MIP models. The paper highlights also the research on stochastic MIP applications to planning and scheduling in electronics supply chains with disrupted material and information flows due to natural or man-made disasters

An Evaluative Study of Operation Grouping Policies in an FMS

The increased use of flexible manufacturing systems to provide customers with diversified products efficiently has created a significant set of operational challenges for managers. This technology poses a number of decision problems that need to be solved by researchers and practitioners. In the literature, there have been a number of attempts to solve design and operational problems. Special attention has been given to machine loading problems, which involve the assignment of job operations and allocation of tools and resources to optimize specific measures of productivity. Most existing studies focus on modeling the problem and developing heuristics in order to optimize certain performance metrics rather than on understanding the problem and the interaction between the different factors in the system. The objective of this paper is to study the machine loading problem. More specifically, we compare operation aggregation and disaggregation policies in a random flexible manufacturing system (EMS) and analyze its interaction with other factors such as routing flexibility, sequencing flexibility, machine load, buffer capacity, and alternative processing-time ratio. For this purpose, a simulation study is conducted and the results are analyzed by statistical methods. The analysis of results highlights the important factors and their levels that could yield near-optimal system performance

The investigation of the effect of scheduling rules on FMS performance

The application of Flexible Manufacturing Systems (FMSs) has an effect in competitiveness, not only of individual companies but of those countries whose manufactured exports play a significant part in their economy (Hartley, 1984). However, the increasing use of FM Ss to effectively provide customers with diversified products has created a significant set of operational challenges for managers (Mahmoodi et al., 1999). In more recent years therefore, there has been a concentration of effort on FMS scheduling without which the benefits of an FMS cannot be realized. The objective of the reported research is to investigate and extend the contribution which can be made to the FMS scheduling problem through the implementation of computer-based experiments that consider real-time situations. [Continues.

Parallel Manipulators

In recent years, parallel kinematics mechanisms have attracted a lot of attention from the academic and industrial communities due to potential applications not only as robot manipulators but also as machine tools. Generally, the criteria used to compare the performance of traditional serial robots and parallel robots are the workspace, the ratio between the payload and the robot mass, accuracy, and dynamic behaviour. In addition to the reduced coupling effect between joints, parallel robots bring the benefits of much higher payload-robot mass ratios, superior accuracy and greater stiffness; qualities which lead to better dynamic performance. The main drawback with parallel robots is the relatively small workspace. A great deal of research on parallel robots has been carried out worldwide, and a large number of parallel mechanism systems have been built for various applications, such as remote handling, machine tools, medical robots, simulators, micro-robots, and humanoid robots. This book opens a window to exceptional research and development work on parallel mechanisms contributed by authors from around the world. Through this window the reader can get a good view of current parallel robot research and applications

Online Optimisation of Casualty Processing in Major Incident Response

Recent emergency response operations to Mass Casualty Incidents (MCIs) have been criticised for a lack of coordination, implying that there is clear potential for response operations to be improved and for corresponding benefits in terms of the health and well-being of those affected by such incidents. In this thesis, the use of mathematical modelling, and in particular optimisation, is considered as a means with which to help improve the coordination of MCI response. Upon reviewing the nature of decision making in MCIs and other disaster response operations in practice, this work demonstrates through an in-depth review of the available academic literature that an important problem has yet to be modelled and solved using an optimisation methodology. This thesis involves the development of such a model, identifying an appropriate task scheduling formulation of the decision problem and a number of objective functions corresponding to the goals of the MCI response decision makers. Efficient solution methodologies are developed to allow for solutions to the model, and therefore to the MCI response operation, to be found in a timely manner. Following on from the development of the optimisation model, the dynamic and uncertain nature of the MCI response environment is considered in detail. Highlighting the lack of relevant research considering this important aspect of the problem, the optimisation model is extended to allow for its use in real-time. In order to allow for the utility of the model to be thoroughly examined, a complementary simulation is developed and an interface allowing for its communication with the optimisation model specified. Extensive computational experiments are reported, demonstrating both the danger of developing and applying optimisation models under a set of unrealistic assumptions, and the potential for the model developed in this work to deliver improvements in MCI response operations