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This paper proposes a 3D nesting algorithm which combines the flower pollination algorithm (FPA) with the oriented bounding box (OBB) collision detection to solve the 3D packing of irregular shaped objects for powder-based additive manufacturing. In the powder-based 3D printing process, stacking multiple models most compactly in the build volume is an important task because no support structure is needed. Given a fixed printing area, the post-nesting build height directly impacts the printing cost and efficiency. To reduce the printing cost, 3D models must be packed as closely as possible and the build height must be minimized. This has been found to be a combinatorial optimization and an NP-hard problem. We propose to use the most recent and effective meta-heuristic optimization algorithm FPA, combined with the collision detection of printed objects set up as the optimization constraint using the OBB tree, to find the near-optimal 3D nesting solution. In FPA optimization, a significant amount of time is spent on collision detection. This paper uses the safety clearance distance to adaptively reduce the OBB tree subdivision, hence significantly reducing the computation of collision detection. As a result, the proposed method finds the model positions and rotations of the global solution, and generates the near-optimal solution in reasonable time. Finally, our method is compared with state-of-the-art commercial softwares. The results show that the proposed method produces lower build height with better efficiency. For real-world complex engine parts (30 different STL models and a total of 192,018 triangles), the computation time is only 160 s, and the build height is 12.4% and 13% better than the results from Netfabb and Magics, respectively. [ABSTRACT FROM AUTHOR]

The bi-objective hybrid flow shop problem with sequence-dependent setup times and limited buffers is mentioned in this paper. In this environment, there are limited buffer spaces between any two successive stages; thus, maybe there is not enough room for queues of jobs that are waiting in the system for their next operations. This problem is shown to be NP-hard in the strong sense. Up to now, some heuristic and metaheuristic approaches are proposed to minimize makespan or total tardiness of jobs. This paper presents several methods for optimization which consider two objectives simultaneously. The resolution of several specific instances from the open literature with the adaptations of non-dominated sorting genetic algorithm and sub-population genetic algorithm suggest that the proposed algorithms are effective and useful methods for solving this problem. [ABSTRACT FROM AUTHOR]

A mixed-model assembly line (MMAL) is a type of a production line where a variety of products is assembled on it. A mixed-model assembly line problem involves not only solving the traditional problems of the assembly line design (i.e., determining the cycle time, the number and sequence of stations, and the balancing problem) but also determining the sequence of products in assembly line. The product sequencing has a high effect on the mixed-model assembly line efficiency. In this paper, we consider sequencing problem with a variable launching interval between products on the assembly line. A mathematical model is presented, which is capable to solve the small-sized ones of these problems. The considered problem involves two optimization problems (the sequencing problem and launching interval problem). Since this problem is strongly NP-hard, a hybrid metaheuristic algorithm based on the simulated annealing approach and a heuristic approach is developed. The heuristic approach (launching interval between products algorithm) is presented to solve the launching interval problem for each sequence. Numerical experiments are used to evaluate the performance and effectiveness of the proposed algorithm. Variable launching interval consideration in MMAL problem causes the higher complexity of this problem. However, this assumption improves the considered goals for this problem. Not only a power algorithm for MMAL is presented in this paper but also the effect of this assumption is discussed. Numerical experiments are used to evaluate the performance and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Setup planning and operation sequencing are two interrelated problems in computer-aided process planning. However, they are addressed by most traditional methods separately, in which setup planning focuses on precision assurance, while operation sequencing is devoted to cost reduction. One drawback of these methods is that they cannot balance the requirements between precision and cost. In this paper, a constrained optimization approach is proposed to address the setup planning and operation sequencing problems in a combined way. With an objective to minimize the manufacturing cost, the optimization model includes various constraints, especially the precision constraints on setup error, tool error, and stacking error. A modified particle swarm optimization algorithm is developed to search for the optimal solution. The case study shows that the proposed method has relatively more flexibility in guaranteeing the precision and decreasing the cost. [ABSTRACT FROM AUTHOR]

This paper presents a multi-objective greedy randomized adaptive search procedure (GRASP)-based heuristic for solving the permutation flowshop scheduling problem in order to minimize two and three objectives simultaneously: (1) makespan and maximum tardiness; (2) makespan, maximum tardiness, and total flowtime. GRASP is a competitive metaheuristic for solving combinatorial optimization problems. We have customized the basic concepts of GRASP algorithm to solve a multi-objective problem and a new algorithm named multi-objective GRASP algorithm is proposed. In order to find a variety of non-dominated solutions, the heuristic blends two typical approaches used in multi-objective optimization: scalarizing functions and Pareto dominance. For instances involving two machines, the heuristic is compared with a bi-objective branch-and-bound algorithm proposed in the literature. For instances involving up to 80 jobs and 20 machines, the non-dominated solutions obtained by the heuristic are compared with solutions obtained by multi-objective genetic algorithms from the literature. Computational results indicate that GRASP is a promising approach for multi-objective optimization. [ABSTRACT FROM AUTHOR]

This paper focuses on multi-criteria assembly sequence planning (ASP) known as a large-scale, time-consuming combinatorial problem. Although the ASP problem has been tackled via a variety of optimization techniques, these techniques are often inefficient when applied to larger-scale problems. Genetic algorithm (GA) is the most widely known type of evolutionary computation method, incorporating biological concepts into analytical studies of systems. In this research, an approach is proposed to optimize multi-criteria ASP based on GA. A precedence matrix is proposed to determine feasible assembly sequences that satisfy precedence constraints. A numerical example is presented to demonstrate the performance of the proposed algorithm. The results of comparison in the provided experiment show that the developed algorithm is an efficient approach to solve the ASP problem and can be suitably applied to any kind of ASP with large numbers of components and multi-objective functions. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid evolutionary algorithm with marriage of genetic algorithm (GA) and extremal optimization (EO) for solving a class of production scheduling problems in manufacturing. The scheduling problem, which is derived from hot rolling production in steel industry, is characterized by two major requirements: (i) selecting a subset of orders from manufacturing orders to be processed; (ii) determining the optimal production sequence under multiple constraints, such as sequence-dependant transition costs, non-execution penalties, earliness/tardiness (E/T) penalties, etc. A combinatorial optimization model is proposed to formulate it mathematically. For its NP-hard complexity, an effective hybrid evolutionary algorithm is developed to solve the scheduling problem through combining the population-based search capacity of GA and the fine-grained local search efficacy of EO. The experimental results with production scale data demonstrate that the proposed hybrid evolutionary algorithm can provide superior performances in scheduling quality and computation efficiency. [ABSTRACT FROM AUTHOR]

The no-wait flow shop scheduling that requires jobs to be processed without interruption between consecutive machines is a typical NP-hard combinatorial optimization problem, and represents an important area in production scheduling. This paper proposes an effective hybrid algorithm based on particle swarm optimization (PSO) for no-wait flow shop scheduling with the criterion to minimize the maximum completion time (makespan). In the algorithm, a novel encoding scheme based on random key representation is developed, and an efficient population initialization, an effective local search based on the Nawaz-Enscore-Ham (NEH) heuristic, as well as a local search based on simulated annealing (SA) with an adaptive meta-Lamarckian learning strategy are proposed and incorporated into PSO. Simulation results based on well-known benchmarks and comparisons with some existing algorithms demonstrate the effectiveness of the proposed hybrid algorithm. [ABSTRACT FROM AUTHOR]

This paper models the binocular vision system focused on 3D reconstruction and describes an improved genetic algorithm (GA) for estimating camera system parameters. The two-camera system model that takes into account camera radial distortion includes a total of 24 parameters. The proposed improved GA is used to solve this nonlinear optimization problem with high dimension. In our improved GA, the adaptive control of camera parameter search interval and the catastrophe strategy with elitist preservation are employed. The experimental results indicate that our improved GA is effective to solve the multi-peak function optimization problem and the 3D reconstruction accuracy of the binocular vision system is promising. [ABSTRACT FROM AUTHOR]

Scheduling problems are difficult combinatorial problems because of the extremely large search space of possible solutions and the large number of local optima that arise. A multi-objective genetic algorithm is presented as an intelligent algorithm for scheduling of the mixed-model assembly line in this paper. The Pareto ranking method and distance-dispersed approach are employed to evaluate the fitness of the individuals. The computational results show that the proposed multi-objective genetic algorithm is quite effective. [ABSTRACT FROM AUTHOR]

In this paper, optimization procedures based on the genetic algorithm, tabu search, ant colony algorithm and particle swarm optimization Algorithm were developed for the optimization of machining parameters for milling operation. This paper describes development and utilization of an optimization system, which determines optimum machining parameters for milling operations. An objective function based on maximum profit in milling operation has been used. An example has been presented at the end of the paper to give a clear picture from the application of the system and its efficiency. The results are compared and analysed using the method of feasible directions and handbook recommendations. [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]

Cell formation (CF) is a key step in group technology (GT). This combinatorial optimization problem is NP-complete. So, meta-heuristic algorithms have been extensively adopted to efficiently solve the CF problem. Particle swarm optimization (PSO) is a modern evolutionary computation technique based on a population mechanism. Since Kennedy and Eberhart invented the PSO, the challenge has been to employ the algorithm to different problem areas other than those that the inventors originally focused on. This paper investigates the first applications of this emerging novel optimization algorithm into the CF problem, and a newly developed PSO-based optimization algorithm for it is elaborated. Forming manufacturing cells lead to process each part family within a machine group with reduction intracellular travel of parts and setup time. A maximum number of machines in a cell and the maximum number of cells are imposed. Some published results in various problem sizes have been used as benchmarks to assess the proposed algorithm. Overall, the advantages of the proposed PSO are that it is rapidly converging towards an optimum, there are fewer parameters to adjust, it is simple to compute, it is easy to implement, it is free from the complex computation, and it is very efficient to use in CF with a wide variety of machine/part matrices. [ABSTRACT FROM AUTHOR]

In so many combinatorial optimization problems, job shop scheduling problems have earned a reputation for being difficult to solve. Genetic algorithm has demonstrated considerable success in providing efficient solutions to many nonpolynomial-hard optimization problems. In the field of job shop scheduling, genetic algorithm has been intensively researched, and nine methods were proposed to encode a chromosome to represent a solution. In this paper, we proposed a novel genetic chromosome-encoding approach; in this encoding method, the operation of crossover and mutation was done in three-dimensional coded space. Some big benchmark problems were tried with the proposed three-dimensional encoding genetic algorithm for validation and the results are encouraging. [ABSTRACT FROM AUTHOR]

This paper presents the development of a parameter optimization system that integrates mold flow analysis, the Taguchi method, analysis of variance (ANOVA), back-propagation neural networks (BPNNs), genetic algorithms (GAs), and the Davidon–Fletcher–Powell (DFP) method to generate optimal process parameter settings for multiple-input single-output plastic injection molding. In the computer-aided engineering simulations, Moldex3D software was employed to determine the preliminary process parameter settings. For process parameter optimization, an L25 orthogonal array experiment was conducted to arrange the number of experimental runs. The injection time, velocity pressure switch position, packing pressure, and injection velocity were employed as process control parameters, with product weight as the target quality. The significant process parameters influencing the product weight and the signal to noise (S/N) ratio were determined using experimental data based on the ANOVA method. Experimental data from the Taguchi method were used to train and test the BPNNs. Then, the BPNN was combined with the DFP method and the GAs to determine the final optimal parameter settings. Three confirmation experiments were performed to verify the effectiveness of the proposed system. Experimental results show that the proposed system not only avoids shortcomings inherent in the commonly used Taguchi method but also produced significant quality and cost advantages. [ABSTRACT FROM AUTHOR]

The facility layout design problem is an extensively studied research problem and belongs to nonpolynomial hard (NP-hard) combinatorial optimization problem. Quadratic assignment problem (QAP) is one of the formulations that is investigated for facility layout design because of its wide applicability. Ant colony optimization (ACO), a biologically inspired heuristic has centered on solving the QAP by achieving approximation as good as possible. This paper presents a population-based hybrid ant system (PHAS), which is an extension of the hybrid ant system (HAS) in which the size of the ant colony has been fixed. The performance of the proposed ant algorithm for QAP is compared with the existing metaheuristic implementations such as tabu search, reactive tabu search, simulated annealing, genetic hybrid method, HAS, and max–min ant system. The experimental results show that the proposed PHAS perform significantly better than the other existing algorithms of QAP. [ABSTRACT FROM AUTHOR]

Camshaft grinding is more complex comparing with the ordinary cylindrical grinding. Since its quality is mostly influenced by more factors, how to select process parameters quickly and accurately becomes the key to improve its quality and processing efficiency. In this paper, a hybrid artificial neural network (ANN) and genetic algorithm (GA) model is proposed to optimize the process parameters. In this method, a BP neural network model is developed to map the complex nonlinear relationship between process parameters and processing requirements, and a GA is used in order to improve the accuracy and speed based on the ANN model. The results show that the hybrid ANN/GA model is an effective tool for the process parameters optimization in NC camshaft grinding. [ABSTRACT FROM AUTHOR]

Online monitoring and in-process control improves machining quality and efficiency in the drive towards intelligent machining. It is particularly significant in machining difficult-to-machine materials like super alloys. This paper attempts to develop a tool wear observer model for flank wear monitoring in machining nickel-based alloys. The model can be implemented in an online tool wear monitoring system which predicts the actual state of tool wear in real time by measuring the cutting force variations. The correlation between the cutting force components and the flank wear width has been established through experimental studies. It was used in an observer model, which uses control theory to reconstruct the flank wear development from the cutting force signal obtained through online measurements. The monitoring method can be implemented as an outer feedback control loop in an adaptive machining system. [ABSTRACT FROM AUTHOR]

This paper proposes a neurogenetic-based optimization scheme for predicting localized stable cutting parameters in inward turning operation. A set of cutting experiments are performed in inward orthogonal turning operation. The cutting forces, surface roughness, and critical chatter locations are predicted as a function of operating variables including tool overhang length. Radial basis function neural network is employed to develop the generalization models. Optimum cutting parameters are predicted from the model using binary-coded genetic algorithms. Results are illustrated with the data corresponding to four work materials, i.e., EN8 steel, EN24 steel, mild steel, and aluminum operated over a high speed steel tool. [ABSTRACT FROM AUTHOR]

Turn-milling is a relatively new process in manufacturing technology, where both the workpiece and the tool are given a rotary movement simultaneously. This paper presents an approach for optimization of cutting parameters at cylindrical workpieces leading to minimum surface roughness by using genetic algorithm in the tangential turn-milling process. During testing, the effects of the cutting parameters on the surface roughness were investigated. Additionally, by using genetic algorithms for each of the cutting parameters (depth of cut, workpiece speed, tool speed and feed rate) minimum surface roughness for the process of tangential turn-milling was determined according to the cutting parameters. [ABSTRACT FROM AUTHOR]

The genetic algorithm (GA) is a heuristics, and commonly used to solve combinational problems. It has been proven to have high effectiveness and efficiency in many application areas. However, a successful GA application requires adapting the algorithm to the characteristics of a problem. In the past decades, various articles on single machine earliness and tardiness (SET) problem using a heuristic approaching have been published. Yet, there are few research addressing the area of the SET problem with distinct due dates and ready times for jobs. In this paper, a designed GA, modified optimal timing procedure, which starts the searching with a feasible solution obtained by applying the EXP-ET rule developed by Ow and Morton is developed for the SET problem. Computational results in the provided experiment show that the designed GA improves the SET solution in both quality and efficiency. [ABSTRACT FROM AUTHOR]

The dynamic lot sizing plays an important role when the demand of an item varies with time. This paper addresses a dynamic lot sizing problem (DLSP) with capacity constraint and discount price structure. Although the well-known dynamic programming (DP) of Wagner-Within is capable of providing an optimal solution for single stage lot sizing problems, it suffers from its high computational complexity. This limits the use of DP in practical problems that are generally larger in size. Two meta-heuristics, genetic algorithm (GA) and Hopfield neural network (HNN) are designed for DLSP to get best trade-off between solution quality and computational time. The DP algorithm is modeled to derive the optimal solution to the experimental problem. The optimality of GA and HNN are tested by comparing the percentage deviation of GA and HNN results against the optimal solution derived using DP. [ABSTRACT FROM AUTHOR]

The aim of a cellular manufacturing system is to group parts that have similar processing needs into part families and machines that meet these needs into machine cells. This paper addresses the problem of grouping machines with the objective of minimizing the total cell load variation and the total intercellular moves. The parameters considered include demands for number of parts, routing sequences, processing time, machine capacities, and machine workload status. For grouping the machines, an ant colony system (ACS) approach is proposed. The computational procedure of the approach is explained with a numerical illustration. Large problems with up to 40 machines and 100 part types are tested and analyzed. The results of ACS are compared with the results obtained from a genetic algorithm (GA), and it is observed that its performance is better than that of GA. [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]

Developments in automation and the resulting complexity of the systems involved have made the reliability of machines an important issue. This is especially true in the process industry, which is characterised by expensive specialised equipment and stringent environmental considerations. Nowadays, with profit margins decreasing, the need for good maintenance planning and control is obvious. Determining the best cost-effective maintenance, though, is computationally difficult, when the parameters, viz., the mean time between failures (MTBF) and the mean time to repair (MTTR) of the critical components in the system can be perturbed. In this paper, the use of metaheuristic, genetic algorithms to create cost effective maintenance in a process plant is presented. [ABSTRACT FROM AUTHOR]

The economics of machining have been of interest to many researchers. Many researchers have dealt with the optimisation of machining parameters for turning operations with constant diameters only. All CNC machines produce finished components from bar stock. Finished profiles consist of straight turning, facing, taper and circular machining. This research concentrates on optimising the machining parameters for turning cylindrical stock into continuous finished profiles. Arriving at a finished profile from a cylindrical stock is done in two stages, rough machining and finish machining. Rough machining consists of multiple passes and finish machining consists of single-pass contouring after the stock is removed in rough machining. The machining parameters in multipass turning are depth of cut, cutting speed and feed. The machining performance is measured by the production cost. In this paper the optimal machining parameters for continuous profile machining are determined with respect to the minimum production cost, subject to a set of practical con-straints. The constraints considered in this problem are cutting force, power constraint and tool tip temperature. Due to high complexity of this machining optimisation problem, a simulated annealing (SA) and genetic algorithm (GA) are applied to resolve the problem. The results obtained from GA and SA are compared. [ABSTRACT FROM AUTHOR]

Build orientation is an important fabrication parameter in layer manufacturing (LM) since it affects the part fabrication accuracy, cost, and time. Despite its importance, orientation selection relies quite heavily on the experience and skill of the operator of the LM system, which does not guarantee optimality of the decision. In the present work, a decision support system that automates the orientation selection task is proposed. The proposed system utilizes genetic algorithms and multi-criteria optimization techniques for the definition of (near) optimum build orientation for parts fabricated with stereolithography. Build time, surface roughness, and post-processing time are considered as the main optimization criteria. [ABSTRACT FROM AUTHOR]

Assembly sequence planning is a typical of combinatorial optimization problem which is difficult to be tackled when the number of parts of assembly becomes large. To reduce the searching space of assembly sequence planning of complex products, assembly sequences merging based on assembly unit partitioning is suggested. Assembly unit partitioning is presented to decompose the complex products into a group of assembly units containing a reduced number of parts or components, and the assembly design constraints and the assembly process constraints are comprehensively taken into account. The global optimal assembly sequences can be acquired through three steps. Firstly, the assembly units and decision graph of assembly unit are generated utilizing fuzzy analytical hierarchy process approach. Secondly, the optimal or near-optimal subsequences of assembly units can be obtained with current efficient methods of assembly sequence planning. Thirdly, under the assembly interference of assembly relations (geometrical constraints) of the whole products and the assembly precedence concluded by subsequences of assembly units, the assembly sequence merging is implemented to generate the global assembly sequences, and the optimal sequence is obtained through assembly sequences evaluation. The assembly constraints considered at the two previous steps is represented by the evaluation function. The effectiveness of the method is verified by an illustrative example and the results show that the searching space of assembly sequence merging of complex products is reduced remarkably and the optimal assembly sequence of the whole produces is obtained. [ABSTRACT FROM AUTHOR]

Virtual machining, based-on the model of a machining system, aims to simulate, evaluate and optimize the actual machining process with high sense of reality. It provides digital off-line optimization tools for NC machining. Taking advantage of virtual machining used in machining process simulation, one can build the framework of optimization system on NC machining so that the processes of reliability verification, cutting parameter optimization and error compensation can be integrated into one system to improve machining processes comprehensively. The optimization is realized via modifying NC programs. Several key issues such as virtual machining, cutting parameters optimization, error prediction and compensation are also highlighted. Optimization systems based on virtual machining have been developed to demonstrate the effectiveness of off-line optimization for different purposes. The results show that the machining process is obviously improved. [ABSTRACT FROM AUTHOR]

In apparel manufacturing, it is difficult to achieve line balance because the production rate of each workstation is different. This difficulty is particularly prominent in the labour-intensive assembly process. The development of a line balancing technique using genetic algorithms is thus proposed for optimising the assignment of operators in an assembly line. The impact of a different level of skill inventory SIn on the assembly makespan is also investigated in order to find out the optimal number of task skills an operator should possess in the apparel assembly process. Experimental results will be discussed to demonstrate the performance of the proposed genetic optimisation approach. [ABSTRACT FROM AUTHOR]