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

Weighted superposition attraction-repulsion (WSAR) algorithm for truss optimization with multiple frequency constraints

Structural optimization of truss structures under multiple frequency constraints is a highly nonlinear and complex optimization problem with non-convex solution space. The optimization method used to solve mentioned problem is expected to provide a very good balance between solution accuracy and computational cost. In this work, the Weighted Superposition Attraction-Repulsion (WSAR) algorithm, which is a recent swarm intelligence based metaheuristic algorithm, is proposed for effective solution of truss optimization problems with multiple natural frequency constraints. The effectiveness and robustness of the WSAR algorithm is studied by solving several planar/space truss structures optimization problems. The optimization results reveal that the successfulness and effectiveness of WSAR in solving truss optimization problems under multiple frequency constraints where WSAR is able to generate the best results in terms of optimized weight and standard deviation compared to the other state-of-the-art metaheuristic algorithms

An Interactive data-driven (dynamic) multiple attribute decision making model via interval type-2 fuzzy functions

A new multiple attribute decision making (MADM) model was proposed in this paper in order to cope with the temporal performance of alternatives during different time periods. Although dynamic MADM problems are enjoying a more visible position in the literature, majority of the applications deal with combining past and present data by means of aggregation operators. There is a research gap in developing data-driven methodologies to capture the patterns and trends in the historical data. In parallel with the fact that style of decision making evolving from intuition-based to data-driven, the present study proposes a new interval type-2 fuzzy (IT2F) functions model in order to predict current performance of alternatives based on the historical decision matrices. As the availability of accurate historical data with desired quality cannot always be obtained and the data usually involves imprecision and uncertainty, predictions regarding the performance of alternatives are modeled as IT2F sets. These estimated outputs are transformed into interpretable forms by utilizing the vocabulary matching procedures. Then the interactive procedures are employed to allow decision makers to modify the predicted decision matrix based on their perceptions and subjective judgments. Finally, ranking of alternatives are performed based on past and current performance scores

Optimal design of truss structures using weighted superposition attraction algorithm

In this paper, a recently developed swarm based metaheuristic algorithm called weighted superposition attraction (WSA) is implemented for sizing optimization of truss structures first time in literature. The WSA algorithm based on superposition and attracted movement of agents that are observable in many natural systems. The efficiency and robustness of the WSA are investigated by solving five classic 2D and 3D truss-weight minimization problems with fixed-geometry and up to 200 elements. Optimization results demonstrated that WSA is able to generate the best results in terms of optimized weight, standard deviation and number of structural analyses in comparison to all other compared state-of-the-art metaheuristic algorithms

Multiple objective crashworthiness optimization of circular tubes with functionally graded thickness via artificial neural networks and genetic algorithms

The objective of this paper is to develop a multiple objective optimization procedure for crashworthiness optimization of circular tubes having functionally graded thickness. The proposed optimization approach is based on finite element analyses for construction of sample design space and verification; artificial neural networks for predicting objective functions values (peak crash force and specific energy absorption) for design parameters; and genetic algorithms for generating design parameters alternatives and determining optimal combination of them. The proposed approach seaminglesly integrates artificial neural networks and genetic algorithms. Artificial neural network acts as an objective function evaluator within the multiple objective genetic algorithms. We have shown that the proposed approach is able to generate Pareto optimal designs which are in a very good agreement with the finite element results

Crashworthiness optimization of circular tubes with functionally-graded thickness

Purpose - The purpose of this paper is to develop a new multi-objective optimization procedure for crashworthiness optimization of thin-walled structures especially circular tubes with functionally graded thickness