Search

Green and sustainable manufacturing is an inevitable development trend of manufacturing in the future, wherein low-energy consumption is regarded as a significant component. As an emerging manufacturing service system, cloud manufacturing (CMfg) is characterized by wide distribution, large quantity and complicated calling method for manufacturing services. Therefore, the energy consumption in the execution of composition service should be considered. However, few works related to energy consumption are focused on service composition at present. In response, this paper proposes an energy-aware service composition and optimal selection (EA-SCOS) model to ensure high-quality and low-energy consumption during the tasks. The mathematical model of energy consumption based on quality of service (QoS) is established, wherein the evaluation methods for manufacturing energy consumption and logistics energy consumption are described in detail. In order to solve the EA-SCOS problem effectively, a state-of-the-art algorithm which has been successfully applied in other fields, named grey wolf optimizer (GWO), is introduced. In addition, two key improvements for GWO are proposed to ensure the accuracy of solution. Finally, the effectiveness and feasibility of improved GWO (IGWO) are verified by a comparative study with GWO, genetic algorithm (GA) and max–min ant system (MMAS). Simulation results show that the proposed model is valid for reducing service energy consumption. Moreover, the improved strategies have obvious, positive effect on the accuracy of solutions and the performance of IGWO for addressing EA-SCOS problem is obviously better than the other three algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a new method based on evolutionary algorithms—Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Differential Evolution (DE)—for the sensitivity-based conceptual design and the tolerance allocation for mechanical assemblies. The approach I of this paper moves the nominal values of non-critical dimensions to a less sensitive portion, and the approach II deals with the allocation of cost-based optimal tolerances. An improved optimization model that considers three objective functions (minimization of the deviation of critical dimensions, manufacturing cost, and the quality loss), eight constraints, and six variables is proposed. To show the effectiveness of the proposed methods, the stepped bar assembly is considered as a numerical example. The results obtained from NSGA-II and DE are compared and analyzed. The results show that the proposed methods are much effective, cost, and time saving than the ones considered in literature. [ABSTRACT FROM AUTHOR]

The objective of this paper is to propose and evaluate heuristic search algorithms for a two-machine flowshop problem with multiple jobs requiring lot streaming that minimizes makespan. A job here implies many identical items. Lot streaming creates sublots to move the completed portion of a production lot to second machine. The three heuristic search algorithms evaluated in this paper are Baker’s approach (Baker), genetic algorithm (GA) and simulated annealing (SA) algorithm. To create neighborhoods for SA, three perturbation schemes, viz., pair-wise exchange, insertion and random insertion are used, and the performance of these on the final schedule is also compared. A wide variety of data sets is randomly generated for comparative evaluation. The parameters for GA and SA are obtained after conducting sensitivity analysis. The genetic algorithm is found to perform well for lot streaming in the two-machine flowshop scheduling. [ABSTRACT FROM AUTHOR]

In this paper, we consider the machining condition optimization models presented in earlier studies. Finding the optimal combination of machining conditions within the constraints is a difficult task. Hence, in earlier studies standard optimization methods are used. The non-linear nature of the objective function, and the constraints that need to be satisfied makes it difficult to use the standard optimization methods for the solution. In this paper, we present a real coded genetic algorithm (RCGA), to find the optimal combination of machining conditions. We present various issues related to real coded genetic algorithm such as solution representation, crossover operators, and repair algorithm in detail. We also present the results obtained for these models using real coded genetic algorithm and discuss the advantages of using real coded genetic algorithm for these problems. From the results obtained, we conclude that real coded genetic algorithm is reliable and accurate for solving the machining condition optimization models. [ABSTRACT FROM AUTHOR]

In order to maximize an availability of machine and utilization of space, the parallel machines scheduling problem with space limit is frequently discussed in the industrial field. In this paper, we consider the parallel machine scheduling problem in which n jobs having different release times, due dates, and space limits are to be scheduled on m parallel machines. The objective function is to minimize the weighted sum of earliness and tardiness. To solve this problem, a heuristic is developed which is divided into three modules hierarchically: job selection, machine selection and job sequencing, and solution improvement. To illustrate its effectiveness, a proposed heuristic is compared with genetic algorithm (GA), hybrid genetic algorithm (HGA), and tabu search (TS), which are well-known meta-heuristics in a large number of randomly generated test problems based on the field situation. Also, we determine the job selection rule that is suitable to the problem situation considered in this paper and show the effectiveness of our heuristic method. [ABSTRACT FROM AUTHOR]

In this paper, the optimization of cutting parameters for constrained machining operations is reported. Modified genetic algorithm (MGA) is an evolutionary computation technique that has been proposed in this paper to solve the machining problem. Additional constraints have been incorporated to the multipass turning model. The optimization of drilling and facing parameters have also been carried out. To demonstrate the procedure and performance of the approach, an illustrative example is discussed. The results of the proposed algorithm are compared with other traditional and non-traditional techniques such as Newton’s method, hill climbing and ants colony technique. [ABSTRACT FROM AUTHOR]

Localisation of workpieces with large sculptured surfaces can be difficult. Previously, such pieces were measured by the probe of the machine and adjusted manually. This process is arduous and time-consuming and an even allowance cannot be ensured for every measured point. Searching for the optimal location of surface matching by computer can realise computer aided localisation (CAL) and thus enhance the precision of localisation, saving a lot of time and greatly accelerating machining cycles while reducing machining cost. In this paper, a method is proposed for a localisation problem that consists of rough surface matching and accuracy surface matching. Rough matching determines the variable range of the follow-up algorithms. Accuracy matching acquires the optimal location. Then fast localisation and a clamp can be implemented. A hybrid global optimisation method is adopted to cope with the accuracy matching. It shares the advantages of both genetic algorithms (GAs) and simplex algorithms. The method presented in this paper has been used in the 5-axis machining of both large Kaplan and Francis hydro turbine blades. It shows that the method is easy to realise and the algorithm is steady and robust. [ABSTRACT FROM AUTHOR]

Considering alternative machines for operations, forbidden intervals during which machines cannot be available and a job’s batch size greater than one in the real manufacturing environment, this paper studies the batch splitting scheduling problem on alternative machines with forbidden intervals, based on the objective to minimize the makespan. A scheduling model is established, taking before-arrival set-up, processing, and transfer time into account. And a new hybrid parallel algorithm, based on differential evolution and genetic algorithm, is brought forward to solve both the batch splitting problem and the batch scheduling problem by assuming a common number of sub-batches in advance. A solution consists of the actual optimum number of sub-batches for each job, the optimum batch size for each sub-batch, and the optimum sequence of operations for these sub-batches. Experiments on the performance of the proposed algorithm under different common numbers of sub-batches are carried out. The results of simulations indicate that the algorithm is feasible and efficient. [ABSTRACT FROM AUTHOR]

This paper focuses on an extension of the Economic Lot Scheduling Problem, which schedules productions of products on multiple identical machines. The objective is to minimize the total average production and inventory costs per unit time for all products. We develop a genetic algorithm under the Common Cycle policy and compare it with an existing heuristic under the same policy. Computational results show that our genetic algorithm outperforms the existing heuristic and its running time does not increase much even for high utilization problems, while the latter requires substantial time to solve most of the high utilization problems. In addition, a genetic algorithm under the Extended Basic Period and Power-of-Two policy is proposed. This new heuristic performs much better, especially when the number of machines is small and the machine utilization is not very high. [ABSTRACT FROM AUTHOR]

In this paper, hybrid algorithms are developed for the multisource location-allocation problem in continuous space. Three hybrid algorithms are proposed to solve this problem that combine elements of several traditional metaheuristics (genetic algorithm and variable neighborhood search) and local searches to find near-optimal solutions. Many problems from the literature have been solved with these algorithms and the obtained results confirm the robustness of the proposed hybrid algorithms. Moreover, the results show that in comparison to the best methods in literature (GA and VNS), these algorithms provide some better solutions. [ABSTRACT FROM AUTHOR]

Flow shop scheduling problems have gained wide attention both in practical and academic fields. In this paper, we consider a multi-objective no-wait flow shop scheduling problem by minimizing the weighted mean completion time and weighted mean tardiness simultaneously. Since a flow shop scheduling problem has been proved to be NP-hard in a strong sense, an effective immune algorithm (IA) is proposed for searching locally the Pareto-optimal frontier for the given problem. To validate the performance of the proposed algorithm in terms of solution quality and diversity level, various test problems are carried out and the efficiency of the proposed algorithm, based on some comparison metrics, is compared with a prominent multi-objective genetic algorithm, i.e., strength Pareto evolutionary algorithm II (SPEA-II). The computational results show that the proposed IA outperforms the above genetic algorithm, especially for large problems. [ABSTRACT FROM AUTHOR]

This research is motivated by the scheduling problem found in the burn-in operation of semiconductor final testing, where jobs are associated with release times, processing times, and sizes. The burn-in ovens are modeled as batch-processing machines which can process a batch of several jobs as long as the total sizes of the jobs do not exceed the machine capacity, and the processing time of a batch is equal to the longest time among all the jobs in the batch. Moreover, this paper attempts to schedule jobs on a single batch-processing machine to minimize makespan. A joint GA+DP algorithm is proposed involving two stages: (1) the formation of job sequence by genetic algorithm operators, and (2) the formation of batches by a dynamic programming algorithm. Computational experiments are given to examine the performance of the proposed algorithm. The experimental results indicate that the joint GA+DP approach has well improved on all instances with respect to solution quality and runtime. [ABSTRACT FROM AUTHOR]

Open job shop scheduling is a kind of job shop scheduling in which operations can be performed in any order. In this paper an attempt is made to develop a heuristic for the open job shop scheduling problem using genetic algorithm to minimize makespan. Genetic algorithm operators are suitably modified to maintain feasibility. The results are statistically compared and found to be significantly better than the earlier reported results. [ABSTRACT FROM AUTHOR]

This paper studies dynamic selection of sequencing rules for a class-based unit-load automated storage and retrieval system (AS/RS). A multi-pass and genetic algorithm (MPGA) simulation system is developed and it divides storage and retrieval requests or dual commands into a series of blocks, and then conquers each block to find the most promising combination of sequencing rules. These rules are first come first served (FCFS), shortest total-travel time (STT) and shortest due time (SDT), and they can be chosen dynamically in any decision points in the system. An experiment shows that our approach with dynamic rules is much better than those approaches with any single rule used from the beginning to the end in the whole system. The results of this study provide a better way to control and manage the operation of AS/RS. [ABSTRACT FROM AUTHOR]

This paper considers an interesting topic of recipe qualification during each fabrication step of semiconductor manufacturing. In particular, this type of stochastic optimization scheme within a discrete-event simulation process is discussed and a new optimization method, with the capability to anchor robust recipes for batch processing, is proposed, implemented and evaluated. A particular real-coded genetic algorithm (GA) suitable for resolving continuous optimization problems in noisy environments is developed. Four test functions appearing in the literature are used as a test-bed to assess the proposed genetic algorithm via a variety of experimental studies, showing that the new method can produce much more accurate estimates of the true optimum points than the other two optimization procedures, the Nelder-Mead (NM) simplex search procedure and a well-known variant (RS+S9) of Nelder-Mead suitable for stochastic optimization. As such, the new method could serve as a useful tool for process recipe optimization in noisy semiconductor manufacturing environments. Finally, the chemical mechanical planarization (CMP) process, a turnkey process during semiconductor fabrication, is simulated from batch to batch based on the real-data equipment model and the presented algorithm is employed to seek the optimal recipe profile while processing each batch of wafers sequentially through the CMP tool. [ABSTRACT FROM AUTHOR]

The selection of optimal machining parameters plays an important part in computer-aided manufacturing. The optimisation of machining parameters is still the subject of many studies. Genetic algorithm (GA) and simulated annealing (SA) have been applied to many difficult combinatorial optimisation problems with certain strengths and weaknesses. In this paper, genetic simulated annealing (GSA), which is a hybrid of GA and SA, is used to determine optimal machining parameters for milling operations. For comparison, basic GA is also chosen as another optimisation method. An application example that has previously been solved using geometric programming (GP) method is presented. The results indicate that GSA is more efficient than GA and GP in the application of optimisation. [ABSTRACT FROM AUTHOR]

A multi-objective evolutionary search algorithm using a travelling salesman algorithm and genetic algorithm for flow-shop scheduling is proposed in this paper. The initial sequence is obtained by solving the TSP. The initial population of the genetic algorithm is created with the help of a neighbourhood creation scheme known as a random insertion perturbation scheme, which uses the sequence obtained from TSP. The proposed algorithm uses a weighted sum of multiple objectives as a fitness function. The weights are randomly generated for each generation to enable a multi-directional search. The performance measures considered include minimising makespan, mean flow time and machine idle time. The performance of the proposed algorithm is demonstrated by applying it to benchmark problems available in the OR-Library. [ABSTRACT FROM AUTHOR]

Genetic algorithms (GAs) have been widely applied for many non-polynomial hard optimisation problems, such as flow shop and job shop scheduling. It is well known that the efficiency and effectiveness of a GA is highly depend on its control parameters, but setting suitable parameters often involves tedious trial and error. Currently, setting optimal parameters is still a substantial problem and is one of the most important and promising areas for GAs. In this paper, the determination of optimal GA control parameters with limited computational effort and simulation replication constraints, namely, population size, crossover and mutation probabilities, is firstly formulated as a stochastic optimisation problem. Then, the ordinal optimisation (OO) and the optimal computing budget allocation (OCBA) are applied to select the optimal GA control parameters, thereby providing a reasonable performance evaluation for hard flow shop scheduling problems. The effectiveness of the methodology is demonstrated by simulation results based on benchmarks. [ABSTRACT FROM AUTHOR]

This paper proposes a modified discrete firefly algorithm (MDFA) to solve the problem of assembly sequence planning. Firstly, to improve the performance of the firefly algorithm (FA), we proposed a MDFA by endowing the fireflies with the capability of changeable visual range. The computing case shows the proposed algorithm is more effective and robust than standard FA, genetic algorithm and particle swarm optimization algorithm. Secondly, a method of how to set parameters for FA and MDFA is proposed. This method is practical in the application of FA to solve discrete problem. Thirdly, to make the sequences more closer to real industrial requirements, a so called process precedence relations (PPR) evaluation function is presented, which not only considering the interference between parts, assembly tools and clamps, but also regarding the assembly order between parts and their reference parts. Finally, the evolution performance of the MDFA is investigated, and the performance of the proposed approach to solve ASP is verified through two cases study. [ABSTRACT FROM AUTHOR]

The paper presents an algorithm for integrated scheduling, dispatching, and conflict-free routing of jobs and AGVs in FMS environment using a hybrid genetic algorithm. The algorithm generates an integrated schedule and detail routing paths while optimizing makespan, AGV travel time, and penalty cost due to jobs tardiness and delay as a result of conflict avoidance. The multi-objective fitness function use adaptive weight approach to assign weights to each objective for every generation based on objective improvement performance. Fuzzy expert system is used to control genetic operators using the overall population performance improvements of the last two previous generations. Computational experiments was conducted on the developed algorithm coded in Matlab to test the effectiveness of the algorithm. Integrated scheduling of jobs in FMS which are in synchrony with AGV dispatching, scheduling, and routing proved to ensure the feasibility and effectiveness of all the solutions of the integrated constituent elements. [ABSTRACT FROM AUTHOR]

There are two main assumptions in multiperiodic inventory control problems. The first is the continuous review, where, depending on the inventory level, orders can happen at any time, and the other is the periodic review, where orders can only happen at the beginning of each period. In this paper, these assumptions are relaxed, and the periods between two replenishments are assumed independent and identically distributed random variables. Furthermore, the decision variables are assumed integer-type and that there are two kinds of space and budget constraints. The incremental discounts to purchase products are considered, and a combination of backorder and lost sales are taken into account for the shortages. The model of this problem is shown to be a mixed integer-nonlinear programming type, and in order to solve it, both genetic algorithm and simulated annealing approaches are employed. At the end, two numerical examples are given to demonstrate the applicability of the proposed methodologies in which genetic algorithm method performs better than simulated annealing in terms of objective function values. [ABSTRACT FROM AUTHOR]

Economic design of multivariate exponentially weighted moving average (MEWMA) control charts for monitoring the process mean vector involves determining economically the optimum values of the three control parameters: the sample size, the sampling interval between successive samples, and the control limits or the critical region of the chart. In the economic-statistical design, constraints (including the requirements of type I error probability and power) are added such that the statistical property of the chart is satisfied. In this paper, using the multivariate Taguchi loss approach, the Lorenzen–Vance (Technometrics 28:3-10, ) cost function of implementing the control chart is extended to include intangible external costs along with the in-control average run length (ARL0) and out-of-control average run length (ARL1) as statistical constraints. A Markov chain model is then developed to estimate the ARLs and a genetic algorithm whose parameters are optimally obtained by design of experiments is used to solve the model and estimate the optimum values of the control chart parameters. A numerical example and a sensitivity analysis are provided to illustrate the solution procedure and to investigate the effects of cost parameters on the optimal designs. The results show that the proposed economic-statistical design of the chart has better statistical properties in comparison to the economic design while the difference between the costs is negligible. [ABSTRACT FROM AUTHOR]

This paper considers group scheduling problem in hybrid flexible flow shop with sequence-dependent setup times to minimize makespan. Group scheduling problem consists of two levels, namely scheduling of groups and jobs within each group. In order to solve problems with this context, two new metaheuristics based on simulated annealing (SA) and genetic algorithm (GA) are developed. A design procedure is developed to specify and adjust significant parameters for SA- and GA-based metaheuristics. The proposed procedure is based on the response surface methodology and two types of objective function are considered to develop multiple-objective decision making model. For comparing metaheuristics, makespan and elapsed time to obtain it are considered as two response variables representing effectiveness and efficiency of algorithms. Based on obtained results in the aspect of makespan, GA-based metaheuristic is recommended for solving group scheduling problems in hybrid flexible flow shop in all sizes and for elapsed time SA-based metaheuristic has better results. [ABSTRACT FROM AUTHOR]

The objective of this paper is to determine a schedule for parallel flow line with bicriteria objective of minimizing the total tardiness and earliness of jobs. An enhancement to its basic greedy randomized adaptive search procedure (GRASP) is used in conjunction with genetic algorithm (GA) and particle swarm optimization (PSO). The feasible solution of GRASP construction phase is used as initial population for both GA and PSO. A number of problems are solved, by varying the number of jobs, lines, and machines, using the hybrid PSO, hybrid GA, PSO, and GA-based methods and the results are compared. [ABSTRACT FROM AUTHOR]

This paper presents optimization procedures based on evolutionary algorithms such as the elitist non-dominated sorting genetic algorithm (NSGA-II) and differential evolution (DE) for solving the trajectory planning problem of intelligent robot manipulators with the prevalence of fixed, moving, and oscillating obstacles. The aim is the minimization of a combined objective function, with the constraints being actuator constraints, joint limits, and the obstacle avoidance constraint by considering dynamic equations of motion. Trajectories are defined by B-spline functions. This is a non-linear constrained optimization problem with six objective functions, 31 constraints, and 42 variables. The combined objective function is a weighted balance of transfer time, the mean average of actuator efforts and power, penalty for collision-free motion, singularity avoidance, joint jerks, and joint accelerations. The obstacles are present in the workspace of the robot. The distance between potentially colliding parts is expressed as obstacle avoidance. Further, the motion is represented using translational and rotational matrices. The proposed optimization techniques are explained by applying them to an industrial robot (PUMA 560 robot). Also, the results obtained from NSGA-II and DE are compared and analyzed. This is the first research work which considers all the decision criteria for the trajectory planning of industrial robots with obstacle avoidance. A comprehensive user-friendly general-purpose software package has been developed using VC++ to obtain the optimal solutions using the proposed DE algorithm. [ABSTRACT FROM AUTHOR]

Quality and performance of the products requiring higher precision and involving moving-parts mainly depends on the surface finish and dimensional accuracy. Generally, finishing operations use fine abrasive particles in different forms along with some carrier or binding medium. Finishing operations are crucial, expensive, uncontrollable, and a labor-intensive phase in the overall production, and contribute significantly to the total production time and cost. As surface finish requirement increases, the cost of finishing operations increases exponentially. Though progress has been made in automating the finishing operations to reduce the production time to some extent, it increases the initial investment and operating costs significantly. Quality, cost, time, and efficiency of finishing operations can be improved significantly by choosing the optimum values of the process parameters. This paper presents the details of process parameters optimization of two advanced fine-finishing processes namely abrasive flow machining (AFM) and magnetic abrasive finishing (MAF), which are capable of giving nano-level surface finish, using real-coded genetic algorithms (GA). It also describes the development of a surface roughness model that was developed to form the objective function for the optimization of AFM process. [ABSTRACT FROM AUTHOR]

This paper presents an optimal design method to optimize cost of three-phase submersible motors. The optimally designed motor is compared with an industrial motor having the same ratings. The motor design procedure consists of a system of non-linear equations, which imposes induction motor characteristics, motor performance, magnetic stresses, and thermal limits. The genetic algorithm (GA) is used for cost optimization, and a software algorithm has been developed. As a result of the realized optimization, besides the improvements on the motor cost, motor torque improvements have also been acquired. The 2-D finite element method (FEM) is then used to confirm the validity of the optimal design. Computer simulation results are given to show the effectiveness of the proposed design process that can achieve a good prediction of the motor performance. Through the studies accomplished, it has been observed that submersible induction motors’ torques and efficiencies improve, their length reduces, and hence some material savings are obtained. [ABSTRACT FROM AUTHOR]

In printed circuit board (PCB) assembly, the efficiency of the component placement process is dependent on two interrelated issues: the sequence of component placement, that is, the component sequencing problem, and the assignment of component types to feeders of the placement machine, that is, the feeder arrangement problem. In cases where some components with the same type are assigned to more than one feeder, the component retrieval problem should also be considered. Due to their inseparable relationship, a hybrid genetic algorithm is adopted to solve these three problems simultaneously for a type of PCB placement machines called the sequential pick-and-place (PAP) machine in this paper. The objective is to minimise the total distance travelled by the placement head for assembling all components on a PCB. Besides, the algorithm is compared with the methods proposed by other researchers in order to examine its effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

In the past three decades many studies have been carried out on cellular manufacturing. The main problem in the development of cellular manufacturing is that of machine cell formation. In this paper a new metaheuristic called a memetic algorithm (MA) is introduced to solve the machine cell formation problem in group technology. The objective functions considered in this work are (a) minimization of total number of moves and (b) minimization of cell load variation and the constraints considered are minimum number of machines in each cell as two and each machine should be assigned in one cell only. Effort has been made to develop an algorithm that is more reliable than conventional methods and some non-traditional optimization techniques like the genetic algorithm (GA) and the tabu search algorithm (TS) for solving machine cell formation problem. In the memetic algorithm approach local optimization is applied to each newly generated offspring at the end of genetic algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a real-time segmentation rescheduling (RSR) approach using genetic algorithms to handle the production planning and scheduling problem in dynamic apparel manufacturing environment is proposed. Experiments based on the actual production data were conducted to validate the performance of the RSR approach. The experimental results indicated that the makespan and the influence caused by the change of schedule could be minimised. [ABSTRACT FROM AUTHOR]

Conventional tolerance analysis is tedious and time consuming, which makes engineers resist doing it. Complex assembly problems are generally beyond the capabilities of most design and manufacturing engineers. In this paper, genetic algorithm, a kind of non-traditional optimization technique is used as the basic foundation for optimal tolerance allocation to help design and manufacturing engineers to overcome the shortcomings in the conventional tolerance stack analysis and allocation system. [ABSTRACT FROM AUTHOR]

The occurrence of compound faults in real-time conditions leads to the early failure of components. However, identifying compound faults in a rotor system is more complex because extracting the fault information from the vibration signals is challenging. Hence, it is inevitable to devise a reliable strategy for predicting compound faults in a rotor system to ensure its life. This work proposes a novel feature selection method using the firefly algorithm (FA) to identify compound gear-bearing faults. The statistical features are extracted from the time domain vibration signals. The firefly algorithm is employed to select the features that have the most pertinent information about the faults. The classification potential of the selected features is tested with an optimized feed forward fully connected neural network (FFFCNN) architecture. Further, the performance of the proposed approach is compared with the genetic algorithm, relief-based feature selection techniques and without the feature selection approach. The FA-based feature selection combined with the FFFCNN achieves a prediction accuracy of 94.86% in identifying the compound gear-bearing faults. [ABSTRACT FROM AUTHOR]

In a multi-plant collaborative manufacturing system, the manufacturing operations and assembly operations for producing a product can be distributed at different plants at various locations. The components are assembled with assembly operations performed in a multi-plant assembly sequence. In this research, a multi-plant assembly sequence planning model is presented by integrating (1) assembly sequence planning, and (2) plant assignment. In assembly sequence planning, the components and assembly operations are sequenced according to the assembly constraints and assembly cost objectives. In plant assignment, the components and assembly operations are assigned to the suitable plants to achieve multi-plant cost objectives. The feasible assembly sequences are generated using the developed graph-based models of assembly precedence graphs and matrices. A genetic algorithm (GA) method is presented to evaluate the multi-plant assembly sequences with an objective of minimizing the total of assembly operational costs and multi-plant costs. The main contribution lies in the new model for multi-plant assembly sequence planning and the new GA encoding scheme for simultaneous assembly sequence planning and plant assignment. Example products are tested and discussed. The test results show that the presented method is feasible and efficient for solving the multi-plant assembly sequence planning problem. [ABSTRACT FROM AUTHOR]

A Taguchi-based genetic algorithm (TBGA) is proposed as an improved genetic algorithm to solve the job-shop scheduling problems (JSP). The TBGA combines the powerful global exploration capabilities of conventional genetic algorithm (GA) with the Taguchi method that exploits optimal offspring. The latter method is used as a new crossover and is incorporated in the crossover operation of a GA. The reasoning ability of the Taguchi-based crossover can systematically select the better genes to achieve crossover and, consequently, enhance the GA. Furthermore, mutation is designed to have the neighbor search technique of performing the fine-tuning on the positions of jobs for the JSP. Therefore, the proposed TBGA approach possesses the merits of global exploration and robustness. The proposed TBGA approach is effectively applied to solve the famous Fisher-Thompson and Lawrence benchmarks of the JSP. In these studied problems, there are numerous local optima so that these studied problems are challenging enough for evaluating the performances of any proposed evolutionary approaches. The computational experiments show that the proposed TBGA approach can obtain both better and more robust results than those evolutionary methods reported recently. [ABSTRACT FROM AUTHOR]

The main objective of this research is to identify ways to simplify the STL meshes, while simultaneously maintaining the accuracy of the STL file. It demonstrates a technique to simplify large and complex STL triangular meshes and to optimise STL shape for rapid prototyping and manufacture. STL mesh re-triangulation can be separated into two processes, i.e., mesh deletion and mesh rearrangement. In the mesh deletion process, two kinds of weighted-value ratio algorithms can be chosen for determining the suitable value of STL mesh deletion. A genetic algorithm that incorporated fitness functions was used for the optimisation of the rearranged STL meshes. Two case studies of STL mesh reconstruction, which examine the effects of the algorithms, are presented. These studies show that optimisation of the shape of the STL file reduces its size, resulting in a reduction of the fabrication time and improvement of the rapid prototyping process. [ABSTRACT FROM AUTHOR]

Genetic algorithm has been applied to the problem of determining optimal joint solution of sheet panel assembly in the early design stage. The optimum sought is that for which an assembly of the components shows least variability, as measured by one or more relevant parameters (e.g., a key dimension) for a fixed set of tolerances. A skeleton representation of sheet panel assembly is proposed in order to provide basic support for the algorithm. Genetic coding and operators for the joint selection problem are developed with examination of the algorithm performance. The aim of the method is demonstrated by an example of a multiple parts assembly. [ABSTRACT FROM AUTHOR]

In an advanced manufacturing system, accurate assessment of tool life estimation is very essential for optimising the cutting performance in turning operation. Estimation of tool life generally requires considerable time and material and hence it is a relatively expensive procedure. In this present work, back-propagation feed forward artificial neural network (ANN) has been used for tool life prediction. Speed, feed, depth of cut and flank wear were taken as input parameters and tool life as an output parameter. Twenty-five patterns were used for training the network. Recently there have been significant research efforts to apply evolutionary computational techniques for determining the network weights. Hence an evolutionary technique named particle swarm optimisation has been used instead of the back-propagation algorithm and it is proved that the experimental results matched well with the values predicted by both artificial neural network with back-propagation and the proposed method. It is found that the computational time is greatly reduced by this method. [ABSTRACT FROM AUTHOR]

This work examines the possibility of using genetic algorithms for the optimization of the loads transmitted in mechanisms. The variables of design are the relative positions of the various connections, considered in a comparative manner. The minimization of the loads transmitted in the connections is achieved by optimizing the respective positions of those lead to less expensive solutions for bearings and sections of beams. The examples show that using this stochastic method is an efficient way to minimize loads in mechanisms. [ABSTRACT FROM AUTHOR]

This work examines the possibility of using genetic algorithms and some neural networks to optimise mechanisms. A detailed example shows that using this stochastic method along with neural networks is very efficient. We can thus speak of a metamodel for optimisation in the context of integrated design . [ABSTRACT FROM AUTHOR]