A hierarchical heuristic approach for machine loading problems in a partially grouped environment

The loading problem in a Flexible Manufacturing System (FMS) lies in the allocation of operations and associated cutting tools to machines for a given set of parts subject to capacity constraints. This dissertation proposes a hierarchical approach to the machine loading problem when the workload and tool magazine capacity of each machine are restrained. This hierarchical approach reduces the maximum workload of the machines by partially grouping them. This research deals with situations where different groups of machines performing the same operation require different processing times and this problem is formulated as an integer linear problem. This work proposes a solution that is comprised of two phases. In the first phase (Phase I), demand is divided into batches and then operations are allocated to groups of machines by using a heuristic constrained by the workload and tool magazine capacity of each group. The processing time of the operation is different for each machine group, which is composed of the same identical machines; however, these machines can perform different sets of operations if tooled differently. Each machine and each group of machines has a limited time for completing an operation. Operations are allocated to groups based on their respective workload limits. In the second phase (Phase II), demand is divided into batches again and operations are assigned to machines based on their workload and tool magazine capacity defined by Longest Processing Time (LPT) and Multifit algorithms. In Phase II, like Phase I, partial grouping is more effective in balancing the workload than total grouping. In partial grouping, each machine is tooled differently, but they can assist one another in processing each individual operation. Phase I demonstrates the efficiency of allocating operations to each group. Phase II demonstrates the efficiency of allocating operations to each machine within each group. This two-phase solution enhances routing flexibility with the same or a smaller number of machines through partial grouping rather than through total grouping. This partial grouping provides a balanced solution for problems involving a large number of machines. Performance of the suggested loading heuristics is tested by means of randomly generated tests

Reference architecture for configuration, planning and control of 21st century manufacturing systems.

Today's dynamic marketplace requires flexible manufacturing systems capable of cost-effective high variety - low volume production in frequently changing product demand and mix. Several new paradigms, e.g. holonic, fractal, biological and responsive manufacturing, have recently been proposed and studied in the academic literature. These 'next generation of manufacturing systems' have been especially designed to meet the requirements of an unstable and unpredictable marketplace. However, very little in-depth research of the configuration, planning and control methodologies of these new concepts has been conducted. This research aims to improve the comprehension and implementation of these 21st century manufacturing systems by developing an integrated reference architecture from the combination of their distinctive features that would enable manufacturing enterprises to handle successfully the configuration/reconfiguration, planning and control activities under the conditions of uncertainty and continuous change.In the course of the research, a detailed investigation into the fractal, biological and responsive manufacturing systems is conducted in order to identify the strengths and weaknesses of each concept. The common and distinctive features of the paradigms are then used to merge them to create an integrated reference architecture. The fractal configuration, biological scheduling and 'resource element' representation of resource capabilities and product processing requirements are selected as the major elements of the new system. A detailed study of fractal layout design resulted in seven distinctive methods for structuring and managing fractal cellular systems. A design methodology that supports three types of dynamic scheduling is developed for biological manufacturing systems. Resource elements are used with fractal layouts and biological scheduling to enhance performance and to enable an integration of the concepts. The proposed reference architecture is modelled and evaluated using object-oriented programming, computer simulation and heuristic algorithms. The research results indicate that the performance of systems that employ biological scheduling and fractal layouts can be improved by using the concept of resource elements to utilise any hidden capabilities of resources and to achieve an optimal distribution of resources on the shop floor

Multiple objective decision support framework for configuring, loading and reconfiguring manufacturing cells

The potential advantages of Cellular Manufacturing Systems (CMS) are very well known in industry. However it is also shown that their performance is very sensitive to changing production requirements. The detrimental effects of changing production requirements on the performance of CMS can be alleviated by "implementing better manufacturing cell designs", "employing effective part loading strategies" and "reconfiguration". This thesis proposes a decision support framework that provides solution strategies for manufacturing cell design, cell loading and reconfiguration problems. There are three main modules in the proposed framework, named as cell formation, loading and reconfiguration. Each module can handle multiple objectives and integrates several planning and design functions, by considering the capabilities of manufacturing resources. Reconfiguration decisions are made explicitly in the proposed framework by answering the questions "when to reconfigure?" and "how to reconfigure?”. In order to answer these questions, the modules of the proposed framework are interconnected. The cell formation module creates the initial set of cells. The loading module makes the 'part to cell assignment' and the scheduling in each production period. The reconfiguration module regenerates manufacturing cells, if the loading module can not find a satisfactory solution. The cell formation module solves the part-machine cell formation problem by simultaneously considering multiple objectives and constraints. Overlapping machine capabilities and generic part process plans are taken into account in the model formulation. A new approach for the evaluation of machine capacities is also presented. Results of the comparative study show that the proposed cell formation method gives better results than several other cell-formation procedures. The manufacturing cells are formed with improved capacity utilisation levels and reduced extra machine requirements. The method is also more likely to produce independent manufacturing cells with higher flexibility. The loading module solves the 'part to cell assignment' and 'cell scheduling' problems simultaneously for cellular manufacturing applications. Alternative parts to cell and machine assignments are considered by making use of generic part process plans in the model formulation. A parametric simulation model is developed to determine cell schedules for a given part assignment scenario. The proposed loading system can assess performance of the CMS in each production period. Therefore a decision can be made about its reconfiguration. It is also shown that the efficiency of CMSs facing changing production requirements can be improved and/or sustained by using the proposed loading strategy. The reconfiguration module takes the existing cell configuration as the current solution and generates a new solution from it, to enhance its performance. The model is objective driven and considers multiple objectives and constraints within a goal programming framework. The virtual cell concept is applied as the reconfiguration strategy. In the virtual cell approach the physical locations of machines are not changed, only cell memberships of machines are updated after reconfiguration. The results of the test studies showed that it is possible to improve the performance of CMS by reconfiguring it using virtual cells. The cell formation, loading and reconfiguration problems issues discussed in this thesis are combinatorially complex multiple objective optimisation problems. Additionally simulation is used to evaluate several of the objective functions used in the modelling of loading and reconfiguration problems. Classical optimisation algorithms have various limitations in solving such problems. Therefore Tabu Search (TS) based multiple objective optimisation algorithms are developed. The proposed TS algorithms are general-purpose and can also be used to solve other multiple objective optimisation problems. The results obtained from several test problems show the proposed TS algorithms to be very effective in solving multiple objective optimisation problems. More than 500/0 improvement in solution quality is obtained in some test problems

A new integrated system for loading and scheduling in cellular manufacturing

In this paper, a multiple objective optimization technique, based on simulation, and a taboo search algorithm are proposed for loading and scheduling Cellular Manufacturing systems (CM). Machine independent capability units known as Resource Elements (REs) are used to define the processing capabilities of machines and the processing requirements of parts. The loading problem is formally represented by a Goal Programming formulation with the objectives of system performance measures that are obtained by a simulation-based scheduling system. The problem is solved by a taboo search based algorithm. The proposed integrated system is implemented in C/C++ programming language and simulation. A practical case study is also explained and reported in detail