Concurrent assembly planning with genetic algorithms

This work investigates the application of genetic algorithm (GA)-based search techniques to concurrent assembly planning, where product design and assembly process planning are performed in parallel, and the evaluation of a design configuration is influenced by the performance of its related assembly process. Several types of GAs and an exhaustive combinatorial approach are compared, in terms of reliability and speed in locating the global optimum. The different algorithms are tested first on a set of artificially generated assembly planning problems, which are intended to represent a broad spectrum of combinatorial complexity; then an industrial case study is presented. Test problems indicate that GAs are slightly less reliable than the combinatorial approach in finding the global, but are capable of identifying solutions which are very close to the global optimum with consistency, soon outperforming the combinatorial approach in terms of execution times, as the problem complexity grows. For an industrial case study of low combinatorial complexity, such as the one chosen in this work, GAs and combinatorial approach perform almost equivalently, both in terms of reliability and speed. In summary, GAs seem a suitable choice for those planning applications where response time is an important factor, and results which are close enough to the global optimum are still considered acceptable such as in concurrent assembly planning, where response time is a key factor when assessing the validity of a product design configuration in terms of the performance of its assembly plan.     

Cam shape optimisation by genetic algorithm

This article overviews a genetic algorithm based computer-aided approach for preliminary design and shape optimisation of cam profiles for cam operated mechanisms. The primary objective of the work was to create a complete systematic approach for preliminary cam shape design including cam shape design automation and true cam shape optimisation with respect to the simulated computer models of cam mechanisms. Typically, shape optimisation of a cam cross-section is a multiobjective optimisation problem of two-dimensional geometric shape in a heavily constrained environment. In order to illustrate the genetic algorithm based cam shape optimisation approach, a cam shape design example is described, in which a cam shape designed by genetic algorithm is compared with its more conventionally designed counterpart.     

A review of the current applications of genetic algorithms in assembly line balancing

Most of the problems involving the design and plan of manufacturing systems are combinatorial and NP-hard. A well-known manufacturing optimization problem is the assembly line balancing problem (ALBP). Due to the complexity of the problem, in recent years, a growing number of researchers have employed genetic algorithms. In this article, a survey has been conducted from the recent published literature on assembly line balancing including genetic algorithms. In particular, we have summarized the main specifications of the problems studied, the genetic algorithms suggested and the objective functions used in evaluating the performance of the genetic algorithms. Moreover, future research directions have been identified and are suggested.     

A genetic algorithm based approach to the mixed-model assembly line balancing problem of type II

Mixed-model assembly lines allow for the simultaneous assembly of a set of similar models of a product, which may be launched in the assembly line in any order and mix. As current markets are characterized by a growing trend for higher product variability, mixed-model assembly lines are preferred over the traditional single-model assembly lines.

This paper presents a mathematical programming model and an iterative genetic algorithm-based procedure for the mixed-model assembly line balancing problem (MALBP) with parallel workstations, in which the goal is to maximise the production rate of the line for a pre-determined number of operators.

The addressed problem accounts for some relevant issues that reflect the operating conditions of real-world assembly lines, like zoning constraints and workload balancing and also allows the decision maker to control the generation of parallel workstations.     

A genetic algorithm for combined topology and shape optimisations

A method to find optimal topology and shape of structures is presented. With the first the optimal distribution of an assigned mass is found using an approach based on homogenisation theory, that seeks in which elements of a meshed domain it is present mass; with the second the discontinuous boundaries are smoothed. The problem of the optimal topology search has an ON/OFF nature and has suggested the employment of genetic algorithms. Thus in this paper a genetic algorithm has been developed, which uses as design variables, in the topology optimisation, the relative densities (with respect to effective material density) 0 or 1 of each element of the structure and, in the shape one, the coordinates of the keypoints of changeable boundaries constituted by curves. In both the steps the aim is that to find the variable sets producing the maximum stiffness of the structure, respecting an upper limit on the employed mass. The structural evaluations are carried out with a FEM commercial code, linked to the algorithm. Some applications have been performed and results compared with solutions reported in literature.     

An enhanced genetic algorithm for automated assembly planning

Automated assembly planning reduces manufacturing manpower requirements and helps simplify product assembly planning, by clearly defining input data, and input data format, needed to complete an assembly plan. In addition, automation provides the computational power needed to find optimal or near-optimal assembly plans, even for complex mechanical products. As a result, modern manufacturing systems use, to an ever greater extent, automated assembly planning rather than technician-scheduled assembly planning. Thus, many current research reports describe efforts to develop more efficient automated assembly planning algorithms. Genetic algorithms show particular promise for automated assembly planning. As a result, several recent research reports present assembly planners based upon traditional genetic algorithms. Although prior genetic assembly planners find improved assembly plans with some success, they also tend to converge prematurely at local-optimal solutions. Thus, we present an assembly planner, based upon an enhanced genetic algorithm, that demonstrates improved searching characteristics over an assembly planner based upon a traditional genetic algorithm. In particular, our planner finds optimal or near-optimal solutions more reliably and more quickly than an assembly planner that uses a traditional genetic algorithm.     

A hybrid genetic algorithm for multi-objective product plan selection problem with ASP and ALB

Facing current environment full of a variety of small quantity customized requests, enterprises must provide diversified products for speedy and effective responses to customers’ requests. Among multiple plans of product, both assembly sequence planning (ASP) and assembly line balance (ALB) must be taken into consideration for the selection of optimal product plan because assembly sequence and assembly line balance have significant impact on production efficiency. Considering different setup times among different assembly tasks, this issue is an NP-hard problem which cannot be easily solved by general method. In this study the multi-objective optimization mathematical model for the selection of product plan integrating ASP and ALB has been established. Introduced cases will be solved by the established model connecting to database statistics. The results show that the proposed Guided-modified weighted Pareto-based multi-objective genetic algorithm (G-WPMOGA) can effectively solve this difficult problem. The results of comparison among three different kinds of hybrid algorithms show that in terms of the issues of ASP and ALB for multiple plans, G-WPMOGA shows better problem-solving capability for four-objective optimization.     

A hierarchical approach on assembly sequence planning and optimal sequences analyzing

Assembly sequence planning (ASP) is a critical technology that bridges product design and realization. Deriving and fulfilling of the assembly precedence relations (APRs) are the essential points in assembly sequences reasoning. In this paper, focusing on APRs reasoning, ASP, and optimizing, a hierarchical ASP approach is proposed and its key technologies are studied systematically. APR inferring and the optimal sequences searching algorithms are designed and realized in an integrated software prototype system. The system can find out the geometric APRs correctly and completely based on the assembly CAD model. Combined with the process APRs, the geometric and engineering feasible assembly sequences can be inferred out automatically. Furthermore, an algorithm is designed by which optimal assembly sequences can be calculated out from the immense geometric and engineering feasible assembly sequences. The case study demonstrates that the approach and its algorithms may provide significant assistance in finding the optimal ASP and improving product assembling.     

A meta-heuristic algorithm for the fuzzy assembly line balancing type-E problem

Consideration is given to a single-model assembly line balancing problem with fuzzy task processing times. The problem referred to herein as f-SALBP-E consists of finding a combination of the number of workstations and the cycle time as well as a respective line balance such that the efficiency of the line is maximized. f-SALBP-E is an extension of the classical SALBP-E under fuzziness. First, a formulation of the problem is given with the tasks processing times presented by triangular fuzzy membership functions. Then, since the problem is known to be NP-hard, a meta-heuristic based on a Genetic Algorithm (GA) is developed for its solution. The performance of the proposed solution approach is studied and discussed over multiple benchmarks test problems taken from the open literature. The results demonstrate very satisfactory performance for the developed approach in terms of both solution time and quality.     

A multi-objective genetic algorithm for mixed-model assembly line rebalancing

When demand structure or production technology changes, a mixed-model assembly line (MAL) may have to be reconfigured to improve its efficiency in the new production environment. In this paper, we address the rebalancing problem for a MAL with seasonal demands. The rebalancing problem concerns how to reassign assembly tasks and operators to candidate stations under the constraint of a given cycle time. The objectives are to minimize the number of stations, workload variation at each station for different models, and rebalancing cost. A multi-objective genetic algorithm (moGA) is proposed to solve this problem. The genetic algorithm (GA) uses a partial representation technique, where only a part of the decision information about a candidate solution is expressed in the chromosome and the rest is computed optimally. A non-dominated ranking method is used to evaluate the fitness of each chromosome. A local search procedure is developed to enhance the search ability of moGA. The performance of moGA is tested on 23 reprehensive problems and the obtained results are compared with those by other authors.     

Fuzzy assembly line balancing using genetic algorithms

In this paper, we implement genetic algorithms to synthesis fuzzy assembly line balancing problem which is well-known as a NP-hard problem. The genetic operators concerned with the feasibility of chromosomes will be discussed, and its performance will be shown with a numerical example.     

A Tabu-enhanced genetic algorithm approach for assembly process planning

Over the past decade, much work has been done to optimize assembly process plans to improve productivity. Among them, genetic algorithms (GAs) are one of the most widely used techniques. Basically, GAs are optimization methodologies based on a direct analogy to Darwinian natural selection and genetics in biological systems. They can deal with complex product assembly planning. However, during the process, the neighborhood may converge too fast and limit the search to a local optimum prematurely. In a similar domain, Tabu search (TS) constitutes a meta-procedure that organizes and directs the operation of a search process. It is able to systematically impose and release constraints so as to permit the exploration of otherwise forbidden regions in a search space. This study attempts to combine the strengths of GAs and TS to realize a hybrid approach for optimal assembly process planning. More robust search behavior can possibly be obtained by incorporating the Tabus intensification and diversification strategies into GAs. The hybrid approach also takes into account assembly guidelines and assembly constraints in the derivation of near optimal assembly process plans. A case study on a cordless telephone assembly is used to demonstrate the approach. Results show that the assembly process plans obtained are superior to those derived by GA alone. The details of the hybrid approach and the case study are presented.     

A glass furnace operation system using fuzzy modelling and genetic algorithms for performance optimisation

An architecture for the operation of a recuperative-type glass furnace is introduced in this paper. It is based on a hierarchical scheme, with two main parts: process optimisation and process modelling. Process optimisation is carried out by an expert controller, and uses genetic algorithms to solve a multiobjective optimisation problem. Process modelling is performed by a learning system, based on a fuzzy learning-by-examples algorithm. Results of real and simulated experiments with the glass manufacturing process are presented.     

An effective asexual genetic algorithm for solving the job shop scheduling problem

By using the notion of elite pool, this paper presents an effective asexual genetic algorithm for solving the job shop scheduling problem. Based on mutation operations, the algorithm selectively picks the solution with the highest quality from the pool and after its modification, it can replace the solution with the lowest quality with such a modified solution. The elite pool is initially filled with a number of non-delay schedules, and then, in each iteration, the best solution of the elite pool is removed and mutated in a biased fashion through running a limited tabu search procedure. A decision strategy which balances exploitation versus exploration determines (i) whether any intermediate solution along the run of tabu search should join the elite pool, and (ii) whether upon joining a new solution to the pool, the worst solution should leave the pool. The genetic algorithm procedure is repeated until either a time limit is reached or the elite pool becomes empty. The results of extensive computational experiments on the benchmark instances indicate that the success of the procedure significantly depends on the employed mechanism of updating the elite pool. In these experiments, the optimal value of the well-known 10 × 10 instance, ft10, is obtained in 0.06 s. Moreover, for larger problems, solutions with the precision of less than one percent from the best known solutions are achieved within several seconds.     

A fuzzy genetic algorithm for automatic orthogonal graph drawing

This paper reflects results of research related to developing a new methodology for automatic graph drawing based on applying genetic algorithms. The methodology has permitted the elaboration of a hybrid technique that combines the most popular, classical, topology-shape-metric approach to orthogonal drawings on the grid and a genetic algorithm that is directed, in its evolutionary process, at multicriteria decision making in a fuzzy environment. In the traditional use of the topology-shape-metric approach, a single fixed planar embedding is obtained in the planarization step. Thereafter this embedding is subjected to the orthogonalization and compaction steps. However, this sequence does not guarantee that the fixed planar embedding will generate a final drawing of a good quality. Moreover, every topology-shape-metric step is classified as a NP-hard problem, and choices as well as heuristics used in previous stages have a direct impact on subsequent ones. Taking this into account, the developed hybrid technique generates a greater number of planar embeddings by varying the order of edges’ insertion when forming the planar embeddings in planarization step. Thus, the problem is formulated as a permutation-based combinatorial optimization problem. The genetic algorithm is applied at the planarization step of the topology-shape-metric. This allows one to generate the population with the corresponding number of planar embeddings. Each planar embedding obtained in the planarization step is submitted to the orthogonalization and compaction. Their results serve for applying the procedures of multicriteria decision making in a fuzzy environment. Thus, in the evolutionary process, the genetic algorithm is able to select individuals, which provide more harmonious solutions (relatively of the solutions obtained with traditional applying the topology-shape-metric approach) from the point of view of the aesthetic criteria that are usually utilized at the three steps of automatic graph drawing. This is convincingly demonstrated by experimental results given in the paper.     

A hybrid genetic algorithm to optimize simple distillation column sequences

Based on the principles of Genetic Algorithms (GAs), a hybrid genetic algorithm used to optimize simple distillation column sequences was established. A new data structure, a novel arithmetic crossover operator and a dynamic mutation operator were proposed. Together with the feasibility test of distillation columns, they are capable to obtain the optimum simple column sequence at one time without the limitation of the number of mixture components, ideal or non-ideal mixtures and sloppy or sharp splits. Compared with conventional algorithms, this hybrid genetic algorithm avoids solving complicated nonlinear equations and demands less derivative information and computation time. Result comparison between this genetic algorithm and Underwood method and Doherty method shows that this hybrid genetic algorithm is reliable.     

Sequencing optimisation for makespan improvement at wet-etch tools

The complex nature of wet-etch tools and their peculiar scheduling constraints pose a relevant challenge for the development and implementation of makespan optimisation strategies, especially when rigid scheduling rules have to be considered. In this paper, an optimisation model is developed for sequencing of wafer batches outside a wet-etch tool and scheduling of tool-internal handler moves. The scheduling algorithm is inspired by the control logics governing wet-etch tools operating in a real semiconductor manufacturing plant and proves effective in generating efficient and detailed schedules in short computational times. The mathematical formulation developed for the scheduling problem is based on generic and realistic assumptions for both the job flow and the material handling system. The sequencing module combines an exact optimisation approach, based on an efficient permutation concept, and a heuristics optimisation approach, based on genetic algorithms. The results obtained show that significant makespan reductions can be obtained by means of a mere sequencing optimisation. Using this optimisation strategy, variations to the scheduling logics, that are generally more difficult and expensive to implement, are avoided. A sensitivity analysis on genetic algorithm operators is also conducted and considerations on the best performing selection, cross-over and mutation operators are presented.     

Designing feedforward command shapers with multi-objective genetic optimisation for vibration control of a single-link flexible manipulator

This paper presents investigations into the design of a command-shaping technique using multi-objective genetic optimisation process for vibration control of a single-link flexible manipulator. Conventional design of a command shaper requires a priori knowledge of natural frequencies and associated damping ratios of the system, which may not be available for complex flexible systems. Moreover, command shaping in principle causes delay in system's response while it reduces system vibration and in this manner the amount of vibration reduction and the rise time conflict one another. Furthermore, system performance objectives, such as, reduced overshoot, rise time, settling time, and end-point vibration are found in conflict with one another due to the construction and mode of operation of a flexible manipulator. Conventional methods can hardly provide a solution, for a designer-oriented formulation, satisfying several objectives and associated goals as demanded by a practical application due to the competing nature of those objectives. In such cases, multi-objective optimisation can provide a wide range of solutions, which trade-off these conflicting objectives so as to satisfy associated goals. A multi-modal command shaper consists of impulses of different amplitudes at different time locations, which are convolved with one another and then with the desired reference and then used as reference (for closed loop) or applied to system (for open loop) with the view to reduce vibration of the system, mainly at dominant modes. Multi-objective optimisation technique is used to determine a set of solutions for the amplitudes and corresponding time locations of impulses of a multi-modal command shaper. The effectiveness of the proposed technique is assessed both in the time domain and the frequency domain. Moreover, a comparative assessment of the performance of the technique with the system response with unshaped bang–bang input is presented.     

The optimisation of block layout and aisle structure by a genetic algorithm

The design of a manufacturing layout is incomplete without consideration of aisle structure for material handling. This paper presents a method to solve the layout and aisle structure problems simultaneously by a slicing floorplan. In this representation, the slicing lines are utilised as the aisles for a material handling system. The method decomposes the problem into two stages. The first stage minimises the material handling cost with aisle distance, and the second stage optimises the aisles in the aisle structure. A representation of slicing floorplan is introduced for the optimisation by genetic algorithms (GAs). The corresponding operators of the GA are also developed. Computational tests demonstrate the goodness of the method. A comparison study of the GA and the random search (RS) for the problem was performed. It showed that the GA has a much higher efficiency than a RS, though further study is still needed to improve the efficiency of the GA.