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In this paper, we introduce a human-robot collaboration (HRC) mold assembly cell to cope with small-volume mold production and reduce the risk of musculoskeletal disorders (MSDs) on a human worker during manual mold assembly operation. Besides, the wide variety of types and weights of the mold components motivated us to design an HRC system that consists of two robots. Therefore, we propose two collaboration modes for HRC systems using two robots and develop a task-allocation model to demonstrate the application of these collaboration modes in the mold assembly. The task-allocation model assigns a task based on the task characteristics and capability of agents in the collaboration cell. First, we decompose the assembly operation into functional actions to analyze the characteristics of tasks. Then, we obtain the agent assignment preference based on task characteristics and capability of agents using the analytic network process. Finally, we apply the genetic algorithm in the final task allocation to minimize assembly time, use of a less capable agent, and ergonomic risk. This paper contributes to expanding the HRC system with two robots in the mold assembly to allow the execution of a greater diversity of tasks and improve the assembly time and MSD risk level for the human worker.

Currently, the largest percentage of the employed workforce in the manufacturing industry is involved in the assembly process, making ergonomics a key factor when dealing with assembly-related problems. During these processes, repetitive tasks and heavy component handling are frequent for workers, who may result overloaded from an energetic point of view, thus affecting several aspects not only relating to the human factor but also to potentially reduced productivity. Different organizational strategies and technological solutions could be adopted to overcome these drawbacks. For these purposes, the present paper proposes a genetic algorithm for solving the typical problem of assembly line balancing, taking into account job rotation and human–robot collaboration for enhancing ergonomics of workers. The objectives of the problem are related to both economic aspects and human factor: (i) the cost for implementing the assembly line is minimized, evaluated on the basis of the number of workers and differentiated by skill levels and on equipment installed on workstations, including collaborative robots, and (ii) the energy load variance among workers is also minimized, so as to smooth their energy expenditure in performing the assigned assembly operations, calculated according to their movements, physiological characteristics, job rotations and degree of collaboration with robots. The paper finally presents and discusses the application of the developed tool to an industrial assembly case.

In this paper, a cold extrusion process was developed to manufacture the clutch outer gear hub. To reduce the forming load and improve the filling effect of internal spline fillet, this paper established the finite element (FE) model of the cold extrusion process and analyzed the influences of main process variables on the forming load and fillet clearance. Then, the second-order response models with four main process parameters as the optimization variables and forming load and fillet clearance as the optimization objectives were established by using the response surface method (RSM). Furthermore, the meta-heuristic approaches of non-dominated sorting genetic algorithm (NSGA-II) and multi-objective genetic algorithm (MOGA) were applied to optimize the models iteratively and a set of optimal process parameters was obtained. Finally, the finite element simulation and experiment confirmation results show that with the optimal parameters, the forming load can be effectively reduced and the filling effect of internal spline fillets is significantly improved. Thus, it is proved that the optimization method is feasible and that a clutch outer gear hub can be produced by cold extrusion process.

Industrial manipulators should be able to execute difficult tasks in the minimum cycle time in order to increase performance in a robotic work cell. This paper is focused on determining the near optimum route of a manipulator’s end-effector which is requested to reach a predefined set of demand points in a robotic work cell. Two subproblems are related with this goal: the motion planning problem and the task scheduling problem. A new approach is presented in this paper for simultaneously planning collision-free motion and scheduling time near optimum route along the demand points. A combination of a geometrical approach and an adaptive neuro-fuzzy system is employed to consider the multiple manipulator’s configurations, while a special genetic algorithm is designed to solve the derived optimization problem. The experiments show that the proposed method has the capacity to determine both the near optimum manipulator configurations and the near optimum sequence of demand points.

The selection of printing parameters for 3D printing can dramatically affect the dynamic performance of components such as polymer spur gears. In this paper, the performance of 3D printed gears has been optimised using a machine learning process. A genetic algorithm (GA)–based artificial neural network (ANN) multi-parameter regression model was created. There were four print parameters considered in 3D printing process, i.e. printing temperature, printing speed, printing bed temperature and infill percentage. The parameter setting was generated by the Sobol sequence. Moreover, sensitivity analysis was carried out in this paper, and leave-one cross validation was applied to the genetic algorithm-based ANN which showed a relatively accurate performance in predictions and performance optimisation of 3D printed gears. Wear performance of 3D printed gears increased by 3 times after optimised parameter setting was applied during their manufacture.

Energy-efficient manufacturing is playing an important role in addressing worldwide problems like air pollution, climate change, and energy crisis. Despite of the close relationship between process planning and scheduling, existing research efforts often consider energy efficiency-related issues by treating them separately and sequentially. This may cause various problems such as unbalanced resource allocation and infeasible production solutions, thus limiting their potential application. To advance the current state of the art in this area, this paper investigates energy-efficient integration of process planning and scheduling. Specifically, a mixed-integer linear programming model is firstly developed to formulate this problem mathematically. Subsequently, to solve more realistic large-sized problems, a heuristic-based two-stage approach is proposed. Stage 1 and Stage 2 aim at minimizing the total tardiness and reducing the total energy consumption, respectively. A realistic case study shows that the proposed two-stage approach works more efficiently and effectively than a modified genetic algorithm from existing literature. Through instructive modification of the solution space, the proposed approach can effectively reduce the total tardiness and total energy consumption; meanwhile the machining cost of individual process plans can also be kept at low level throughout the searching process. Results show that the proposed approach can allocate the machining resources in a more reasonable manner. As such, this paper may be a valuable supplement to existing efforts aiming at developing energy-efficient manufacturing techniques.

This paper introduces a new hybrid optimization algorithm (HWOANM) based on the Nelder-Mead local search algorithm (NM) and whale optimization algorithm (WOA). The aim of hybridization is to accelerate global convergence speed of the whale algorithm for solving manufacturing optimization problems. The main objective of our study on hybridization is to accelerate the global convergence rate of the whale algorithm to solve production optimization problems. This paper is the first research study of both the whale algorithm and HWOANM for the optimization of processing parameters in manufacturing processes. The HWOANM is evaluated using the well-known benchmark problems such as cantilever beam problem, welded beam problem, and three-bar truss problem. Finally, a grinding manufacturing optimization problem is solved to investigate the performance of the HWOANM. The results of the HWOANM for both the design and manufacturing problems solved in this paper are compared with other optimization algorithms presented in the literature such as the ant colony algorithm, genetic algorithm, scatter search algorithm, differential evolution algorithm, particle swarm optimization algorithm, simulated annealing algorithm, artificial bee colony algorithm, improved differential evolution algorithm, harmony search algorithm, hybrid particle swarm algorithm, teaching-learning-based optimization algorithm, cuckoo search algorithm, grasshopper optimization algorithm, salp swarm optimization algorithm, mine blast algorithm, gravitational search algorithm, ant lion optimizer, multi-verse optimizer, whale optimization algorithm, and the Harris hawks optimization algorithm. The results show that the HWOANM provides better exploration and exploitation properties, and can be considered as a promising new algorithm for optimizing both design and manufacturing optimization problems.

With the development of the information technology and logistics industry, industrial production models are more likely to be innovated than ever before. Therefore, there is a tendency for a large number of manufacturing enterprises to start outsourcing their manufacturing activities to more professional subcontractors so they could pay more attention to their core business. Cloud manufacturing (CMfg), as a supplement to cloud computing and big data, is also a new network manufacturing mode that is service-oriented. This mode makes it even more complex and impractical to organize and optimize manufacturing resources. Considering this problem, this paper proposes a manufacturing resource selection strategy based on an improved distributed genetic algorithm (DGA) for manufacturing resource combinatorial optimization (MRCO) in CMfg. We divided the DGA into several sections and distributed and optimized the process, which not only guaranteed algorithm speed but also expanded the search range and improved the accuracy. A case study, a performance comparison between a simple genetic algorithm (SGA) and a working procedure priority-based algorithm (WPPBA) is presented later in this paper. Experimental results showed that the proposed method is preferable and a more effective choice for searching for the optimal solution.

This paper presents, evaluates, and validates a genetic algorithm procedure with parallel-populations for the obtaining of minimum time trajectories for robot manipulators. The aim of the algorithm is to construct smooth joint trajectories for robot manipulators using cubic polynomial functions, where the sequence of the robot configurations is already given. Three different types of constraints are considered in this work: (1) Kinematics: these include the limits of joint velocities, accelerations, and jerk. (2) Dynamic: which include limits of torque, power, and energy. (3) Payload constraints. A complete statistical analysis using ANOVA test is introduced in order to evaluate the efficiency of the proposed algorithm. In addition, a comparison analysis between the results of the proposed algorithm and other different techniques found in the literature is described in the experimental section of this paper.

This paper addresses the problem of scheduling n jobs on a single machine and on m identical parallel machines to minimize the completion time variance of jobs. This problem of scheduling jobs on parallel machines is motivated by a case study in an automobile ancillary unit. First, a heuristic to solve the single-machine scheduling problem is proposed. The parallel-machine scheduling problem is solved in two phases: job-allocation phase and job-sequencing phase. Two heuristics are proposed in the job-allocation phase, whereas in the job-scheduling phase, the single-machine scheduling approach is used. In this paper, both versions of parallel-machine scheduling problem (restricted and unrestricted) are considered. A good upper bound is obtained using a genetic algorithm, to evaluate the performance of the proposed heuristics for the parallel-machine scheduling problem. An extensive computation evaluation of the proposed heuristics is presented for both single-machine scheduling problem and the parallel-machine scheduling problem (especially considering the case study), along with the comparison of performances with the existing heuristics in the literature.

In this paper, a hybrid co-evolutionary genetic algorithm (HCGA) has been presented for determining the optimal moving paths of several nanoparticles in a complex environment. In the proposed approach, an artificial potential field (APF) has been used to determine the feasible initial paths for moving the nanoparticles. The proposed APF prepares a potential map of the environment using the initial positions of the nanoparticles and positions of the obstacles and surface roughness. The cost function used in this paper includes the area under the critical force-time diagram, surface roughness as well as path smoothness. The performed investigations indicate the importance of each of these parameters in determining the optimal paths for displacing the nanoparticles. Also, the dynamic application of crossover and mutation operators has been used to avoid premature convergence. Using the information of the potential map, two new operators have been introduced to improve the feasible and infeasible paths. Furthermore, a novel co-evolutionary mechanism for solving the multiple nanoparticle path planning problems has been presented. The proposed co-evolutionary mechanism is able to determine the best destination for each particle, optimal sequence of moves for several particles, and also the optimal path for moving each particle. Finally, the performance of the proposed HCGA has been compared with the conventional genetic algorithm (CGA) and the ant colony optimization algorithm.

This paper focuses on optimisation of process parameters of the turning operation, using artificial intelligence techniques such as support vector regression (SVR) and artificial neural networks (ANN) integrated with genetic algorithm (GA). The model is trained using the turning parameters as the input and corresponding surface roughness, tool wear and power required as the output. Data, obtained from conducting experiments is analysed using support vector machine (SVM) and artificial neural network. SVM, a nonlinear model, is learned by linear learning machine by mapping into high-dimensional kernel-induced feature space. The genetic algorithm is integrated with these to find the optimum from the response surface generated. The results are compared with those obtained by integrating GA with traditional models like response surface methodology (RSM) and regression analysis (RA). This paper illustrates the impact that techniques based on artificial intelligence have on optimising processes.

In today’s competitive world, each company which is involved in production activities should try to adjust its production scheduling with customers’ order. This reality was one of the principals of emerging just-in-time philosophy which tries to fulfill each customer’s order in exact size of his demand and at the earliest time which is preferred by him. This is the main reason why those industries which produce high variety of goods have been trying to change their production policy from batch production of a single type to batch production mixed of different types which is called minimal part set. With this motivation, cyclic policy which also attempts to reduce work-in-process inventory arouse in production systems. Although hybrid flow shop is one of the most important and notable problems in sequencing and scheduling, there has not yet been any research that considers use of cyclic policy in hybrid flow shop. This problem is which this paper tries to peruse. Moreover, we try to reduce the gap between theoretical and practical problems by considering unrelated parallel machines, eligibility, and limited buffer constraints. In this paper, a mixed integer linear programming model is proposed at first. Because of the NP-hard nature of the problem, some heuristics and meta-heuristics are proposed for solving the problem and are compared then. At last, all the results show that a simulated annealing method using some embedded heuristics in it is an effective approach for solving this problem.

This paper proposes a multi-objective mathematical formulation and a hybrid approach to solve buffer sizing and machine allocation problems simultaneously in unreliable production and assembly lines. This paper unlike prior researches assumes that time-dependent parameters of production systems are generally distributed (e.g., uniform, normal, gamma, etc.) and not only deterministic or exponential. This paper proposes a multi-objective mixed binary integer non-linear mathematical model to solve the problem of buffer sizing and machine allocation. The proposed mathematical model is capable of purchasing new machines (candidate) and also selling old machines (current available). In other words, this model compares the candidate machines to current available machines in each station based on different aspects and is capable to replace the current machines with candidate machines or to sell some of the current machines without replacement. To solve the mentioned problem, a new formulation for dealing with multi-objectiveness of the problem is proposed. This formulation generates a series of non-dominated solutions, and also, it is capable of generating a non-dominated solution between two adjacent non-dominated solutions determined by decision maker. A hybrid genetic algorithm (HGA) with a new dynamic mutation probability is proposed to solve the model. Since the proposed mathematical model and the proposed solution method are novel, the proposed HGA is compared to simple genetic algorithm and non-dominated sorting genetic algorithm (NSGA-II). The computational results indicate the effectiveness of the proposed HGA.

This paper proposes a nonlinear controlling model in three-level supply chain. The proposed model consists of a manufacturer, a warehouse and two retailers. As nonlinear multi level programming problems are much more difficult to solve, the proposed model was converted to two nonlinear bilevel programming problems in order to make the model easier both to solve and to describe. The first model consists of warehouse's objective function at its first level and the manufacturer at the second level. In the second model, retailers are the leader and warehouse is the follower. Bilevel programming has been investigated to be NP-hard problem. Numerous algorithms have been developed so far for solving bilevel programming problem; however, algorithm proposed in this paper is easier than other algorithms for solving this type of problems. In this paper, we proposed a modified randomly iterated search and statistical competency approach in genetic algorithm to provide precise and reliable optimal solutions for manufacturer, warehouse and retailers' value in situations with no empirical observations and we presented confidence interval as solution.

Selection of a building direction is an important step for rapid prototyping regardless of the specific processes used to create the part. It involves the consideration of multi-factors that have influences on surface quality, build efficiency and support structure, etc. Contemporary approaches did not consider the global directional space to search for the optimal building direction. In this paper, we use a multi-sphere model for multi-criteria optimization of building direction in rapid prototyping. Each sphere represents the global directional space for one optimization criterion, and is obtained by uniformly discretizing the surface of a unit sphere. Optimization is then conducted over each discretized spherical surface for each criterion. Two objectives, theoretical volume deviation (TVD) and part height are simultaneously optimized using genetic algorithm. TVD is computed to evaluate the volumetric error along each building direction in a general way. The Pareto front is computed as well in order to study the competing effect from these two criteria. At the end of the paper, examples are presented to show the effectiveness of the method.

Material selection plays an important role in product design. A design demands specific material properties in order to fulfill its functional requirements and to minimize its detrimental impact on the environment. Hence, much effort has been devoted to the recyclability of a product at the end of its life during product design. It is the responsibility of the designers to incorporate recyclability considerations during product design to ensure that a certain level of recyclability is attained at the end of a product’s life as imposed by legislation. The complex relationships between different selection parameters complicate the material selection process. Designers are faced with the challenge of selecting suitable materials that could satisfy cost and functional and environmental performance. Therefore, solving material selection in respect of satisfying product recyclability is an area worthy of investigation. This paper attempts to propose an approach that could facilitate designers to select recyclable materials as well as reducing the complexities in decision making. In this paper, the use of a genetic algorithm is proposed to optimize the multi-objective problem in the selection of recyclable materials. A case study on the automobile’s side mirror is used to demonstrate this approach.

In many industrialized countries, manufacturing industries pay stratified electricity charges depending on the time of day (i.e., peak-load, mid-load, and off-peak-load). In contrast, the emerging smart grid concept may demand that industries pay real-time hourly electricity costs so as to use energy most efficiently. This paper deals with the production and energy efficiency of the unrelated parallel machine scheduling problem. This method allows the decision maker to seek a compromise solution using the weighted sum objective of production scheduling and electricity usage. Reliability models are used to consider the energy cost aspect of the problem. This paper aims to optimize the weighted sum of two criteria: the minimization of the makespan of production and the minimization of time-dependent electricity costs. We suggest a hybrid genetic algorithm with our blank job insertion algorithm and demonstrate its performance in simulation experiments.

The fixture determines the workpiece position in a machining process; therefore, an increasing amount of attention has been given to fixture layout design. While machining, the workpiece position is affected by two major sources: (a) the locator displacement and (b) the force–deformation of the workpiece–fixture system. In the beginning of this paper, a geometric model considering the shape of a locator is developed to analyze the location performance, followed by the presentation of a simplified solving method and a location layout performance index. Second, to complete the force–deformation analysis, a finite element method-based force–deformation model is built and accelerated by a new method with a lower computer memory cost. Based on these two models, multiple objects of fixture layout optimization problems are proposed, and a multi-objective genetic algorithm-based optimization method is constructed. Finally, testing examples are approved to examine the validity of the method represented in this paper. These methods can provide a more accurate prediction of the locating performance in more widely used cases, and they have faster calculating speeds with lower computer memory costs.

As a material-additive process, rapid prototyping (RP) has shown its capability in creating complex geometries that traditional material-removal processes cannot accomplish. However, its layer manufacturing nature still subjects itself to undesired staircase effects. It has been shown that staircase effects have relationship with the building orientation in RP processes. In order to minimize staircase effects, the building orientation has to be properly selected prior to the implementation of RP processes. This paper presents a method to select the optimal building direction in RP processes that leads to the minimized volumetric error. In order to explore the global directional space, a unit sphere is uniformly discretized first to represent the potential directions in a 3-dimensional (3-D) space. Following that, each facet comprising the STL geometric model is mapped onto the discretized unit sphere as a great circle individually, which represents the optimal directions for that facet. In order to find the globally optimal solution, both an exhaustive search and a genetic algorithm (GA)-based searching strategy are presented to identify the globally optimal direction for building the 3-D geometry. At the end of the paper, examples are presented to show the effectiveness of the method.

This paper develops a new mathematical model and proposes two meta-heuristics for solving a two-machine flowshop scheduling problem that minimizes bi-objectives, namely the total idle time and the mean deviation from a common due data. In this paper, we assume the arrival time of jobs is dynamic, in which each job has a time window and can arrive in its time window randomly. We also assume the learning effect on the processing times considering as a position-dependent effect. Since the problem is an NP-hard one, we present a multiobjective genetic algorithm (MOGA) and a multiobjective simulated annealing (MOSA) algorithm to solve the given problems. The computational results confirm that the proposed MOGA has a better solution in comparison with the proposed MOSA, especially in large-sized problems.

No-wait job shop scheduling problems refer to the set of problems in which a number of jobs are available for processing on a number of machines in a job shop context with the added constraint that there should be no waiting time between consecutive operations of the jobs. In this paper, a two-machine, no-wait job shop problem with separable setup times and a single-server constraint is considered. The considered performance measure is the makespan. This problem is strongly NP-hard. A mathematical model of the problem is developed and a number of propositions are proven for the special cases. Moreover, a genetic algorithm is proposed in this paper to find the optimal (or near-optimal) solutions. In order to evaluate the developed algorithm, a number of small instances are solved to optimality using the developed mathematical model. The proposed algorithm is able to find the optimal solution of all of these cases. For larger instances, the developed algorithm has been compared with the 2-opt algorithm as well as a proposed lower bound. Computational results show the efficiency of the proposed algorithm in generating good quality solutions compared to the developed lower bounds and 2-opt algorithm.

The aim of this paper is to study multi-objective flexible job shop scheduling problem (MOFJSP). Flexible job shop scheduling problem is a modified version of job shop scheduling problem (JSP) in which an operation is allowed to be processed by any machine from a given set of capable machines. The objectives that are considered in this study are makespan, critical machine work load, and total work load of machines. In the literature of the MOFJSP, since this problem is known as an NP-hard problem, most of the studies have developed metaheuristic algorithms to solve it. Most of them have integrated their objective functions and used an integrated single-objective metaheuristic algorithm though. In this study, two new version of multi-objective evolutionary algorithms including non-dominated sorting genetic algorithm and non-dominated ranking genetic algorithm are adapted for MOFJSP. These algorithms use new multi-objective Pareto-based modules instead of multi-criteria concepts to guide their process. Another contribution of this paper is introducing of famous metrics of the multi-objective evaluation to literature of the MOFJSP. A new measure is also proposed. Finally, through using numerous test problems, calculating a number of measures, performing different statistical tests, and plotting different types of figures, it is shown that proposed algorithms are at least as good as literature’s algorithm.

This paper presents a multiobjective formulation of the buffer allocation problem in unreliable production lines. Majority of the solution methods for buffer allocation problems assume that the process times, time between failures, and repair times are deterministic or exponentially distributed. This paper relaxes these restrictions by proposing a simulation-based methodology which can consider general function distributions for all parameters of production lines. Factorial design has been used to build a meta-model for estimating production rate based on a detailed, discrete event simulation model. We use genetic algorithm combined to line search method to solve the multiobjective model and determining the optimal (or near optimal) size of each buffer storage.

This paper considers the problem of no-wait flow shop scheduling, in which a number of jobs are available for processing on a number of machines in a flow shop context with the added constraint that there should be no waiting time between consecutive operations of a job. Each operation has a separable setup time, meaning that the setup time of an operation is independent on the previous operations; and the machine can be prepared for a specific operation and remain idle before the operation actually starts. The considered objective function in this paper is the makespan. The problem is proven to be NP-hard. In this paper, two frameworks based on genetic algorithm and particle swarm optimization are developed to deal with the problem. For the case of no-wait flow shop problem without setup times, the developed algorithms are applied to a large number of benchmark problems from the literature. Computational results confirm that the proposed algorithms outperform other methods by improving many of the best-known solutions for the test problems. For the problems with setup time, the algorithms are compared against the famous 2-Opt algorithm. Such comparison reveals the efficiency of the proposed method in solving the problem when separable setup times are considered.

This paper studies a job shop scheduling problem with due dates and deadlines in the presence of tardiness and earliness penalties. Due dates are desired completion dates of jobs given by the customer, while deadlines are determined by the manufacturer based on customer due dates. Due dates can be violated at the cost of tardiness, whereas deadlines must be met and cannot be violated. The aforementioned scheduling problem, which is NP-hard, can be formulated with the objective function of minimizing the sum of weighted earliness and weighted tardiness of jobs subject to due dates and deadlines. In order to solve this problem, an enhanced genetic algorithm (EGA) is introduced in this paper. EGA utilizes an operation-based scheme to represent schedules as chromosomes. After the initial population of chromosomes is randomly generated, each chromosome is processed through a three-stage decoder, which first reduces tardiness based on due dates, second ensures deadlines are not violated, and finally reduces earliness based on due dates. After the population size is reached, EGA continues with selection, crossover, and mutation. The proposed algorithm is tested on 180 job shop scheduling problems of varying sizes and its performance is discussed.

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.

Transportation is a key issue in supply chain management and is a major concern for a company. This paper considers a joint-location inventory problem involving a set of suppliers producing different products and a set of retailers where some retailers are treated as distribution centers (DCs). The problem is to determine which retailers to be assigned as DCs, which retailers to receive direct shipments, how much of the retailer’s demand to allocate to the DCs, and how much of the DC’s demand is to be met by different suppliers. The problem is formulated as a mixed integer model and it has been solved through an adaptive differential evolution algorithm known as modified J. Adaptive Differential Evolution. The solutions obtained are compared with that of simple genetic algorithm. This paper also shows that the proposed model is robust in nature and offers near-optimal results for different distributions. The sum of the cost of establishing some retailers as DCs and the total transportation cost incurred in shipping products from the suppliers to the retailers via DCs(for some retailers) or directly (for the other retailers) is also compared with the total transportation cost incurred when all the products are shipped directly from the suppliers to the retailers.

A widespread supposition on mixed-model assembly line-balancing problems assigns a task, which is shared between two or more models to a single station. Bukchin and Rabinowitch (European Journal of Operational Research, 174:492–508, 2006) relaxed the restriction for mixed-model straight-line assembly line problems and allowed tasks common to multiple models to be assigned to different stations, called task duplication. In this paper, considering the same relaxation but for mixed-model U-shaped assembly lines, a novel genetic algorithm (GA) approach for solving large-scale problems is developed. Although superiorities of U-shaped assembly lines over straight lines have been discussed in several articles, this paper makes the advantage more tangible by providing a quantitative example. This paper also presents a novel two-stage genetic algorithm which is fittingly devised for solving the new proposed model. In order to evaluate the effectiveness of the GA, one small-scale and one medium-scale problem are solved using both the proposed GA and Lingo 8.0 software, and the obtained outcomes are compared. The computational results indicate that the GA is capable of providing high-quality solutions for small- and medium-scale problems in negligible central processing unit (CPU) times. It is worth mentioning that, for large-scale problems, such as Kim and Arcus test problems, no analogous results for those obtained by our proposed GA exist. To conclude, it can be said that the proposed GA performs well and is able to solve large-scale problems within acceptable CPU times.

Nowadays, the importance of timely delivery, which is based on the just in time concept, has caused a number of criteria related to scheduling problems to be taken into consideration. One of the most important of these criteria is maximum earliness to control final costs and number of tardy jobs in an attempt to win customer satisfaction. In this paper, the strongly NP-hard problem of the single machine scheduling with two criteria, i.e., maximum earliness and number of tardy jobs, has been considered. For this purpose, artificial immune system which is inspired by the immunology theory in biology has been used. This algorithm is applied to different instances of small to large sizes and the obtained results is compared with those obtained from a heuristic method and a genetic algorithm reported in the literature. Computational results show a significant preference for the algorithm proposed in this paper.

This paper addresses a dynamic capacitated production planning problem in steel enterprise employing a closed-loop supply chain strategy, in which the remanufacturing process is applied. Particularly, the remanufacturing problem considered in this paper is obviously different from the typical lot-sizing problems, within which all demands are met by production or remanufacturing without backlogs; the production, inventory, and remanufacturing levels all have limits; both the production and remanufacturing setup cost functions are arbitrary and time-varying; the objective is to minimize the total cost. Firstly, the closed-loop supply chain with remanufacturing model is formulated. Then, the genetic algorithm heuristic approaches are proposed to solve the NP-hard problem. Finally, a computational experiment is presented which can solve the 200 size problem efficiently. Furthermore, the comparisons against the branch and bound method show the effectiveness and efficiency of the proposed approach for solving the large size remanufacturing problem.

This paper presents a genetic algorithm (GA) for identifying virtual models of machine tool drives with minimal intervention to the production machine. The proposed solution builds on the “rapid identification” concept reported earlier in literature, in which a short series of motion data is captured from the Computer Numerical Control (CNC) and used for closed-loop transfer function identification subject to stability constraints. Compared to the Lagrange multipliers solution that was used in the original implementation of rapid identification, the proposed GA is both significantly faster and better suited for industrial use. The novel contributions in the paper include the reduction of the search space from eight unknown variables down to three, which also enables stability constraints to be incorporated in a natural manner, and the re-formulation of the objective function, which streamlines the convergence of the GA by two to three orders of magnitude. Following initial verification in simulations, the GA has been used to identify the closed-loop dynamics of a stand-alone ball screw drive and the x- and y-axes of a five-axis machining center. In experimental results, it is shown that drive models constructed using the GA can be successfully used to predict the tracking and contouring errors for different part programs in a virtual process planning environment.

International audience; The complexity of a multi-stage machining process makes it very difficult to assess process plans, from a geometrical point of view. Today, repeated trial runs or very simple simulations are the methods applied to assess a process plan prior to production release. Due to the cost and time involved in trial runs and the inaccuracy of current simulations, an alternative means should be investigated. This could involve developing accurate 3D simulation methods. This paper proposes a detailed mathematical formulation of tolerance analysis based on searching for the worst case. The performance of two current optimization methods for worst case identification (genetic algorithm and sequential quadratic programming) will be explored through two 3D examples. This paper builds on the Model of Manufactured Part (the MMP) previously proposed by the authors for modeling the different geometrical deviation impacts on the part produced (error stack-up) in a multi-stage machining process. The model cumulates the impacts of various sources of manufacturing errors hence enabling tolerance analysis. Previously, the same authors also presented a generic formulation for tolerance analysis based on searching for the worst case using the MMP.

The idea of Milk Run has been used in the context of logistic and supply chain problems in order to manage the transportation of materials. In this paper, we propose a new Milk Run method, as a mixed integer approach, to manage supply chain problems. Since the resulted problem formulation is NP-Hard, we use some meta-heuristic and compare the results with the optimal solutions of the proposed Milk Run method. The mathematical modeling of this paper is purposely customized for a special case of an auto industry. We implement the mathematical formulation and the meta-heuristic using some actual data and compare the results with the current strategy. The preliminary results indicate that the proposed method could provide a practical tool to significantly reduce the cost of logistic.

In this paper, a real-world test problem is presented and made available for the use of evolutionary multi-objective community. The generation of manipulator trajectories by considering multiple objectives and obstacle avoidance is a non-trivial optimisation problem. In this paper two multi-objective evolutionary algorithms viz., elitist non-dominated sorting genetic algorithm (NSGA-II) and multi-objective differential evolution (MODE) algorithm are proposed to address this problem. Multiple criteria are optimised to two simultaneous objectives. Simulations results are presented for industrial robots with two degrees of freedom (Cartesian robot (PP) with two prismatic joints) and six degrees of freedom (PUMA 560 robot), by considering two objectives optimisation. Two methods (normalized weighting objective functions and average fuzzy membership function) are used to select the best optimal solution from Pareto optimal fronts. Two multi-objective performance measures namely solution spread measure and ratio of non-dominated individuals are used to evaluate the strength of Pareto optimal fronts. Two more multi-objective performance measures namely optimiser overhead and algorithm effort are used to find computational effort of NSGA-II and MODE algorithms. The Pareto optimal fronts and results obtained from various techniques are compared and analysed.

This paper addresses an n-job, single-machine scheduling problem with an objective to minimize flow-time variance, a non-regular performance measure. A spreadsheet-based genetic algorithm (GA) approach is presented to solve the problem. A domain-independent general-purpose GA is used, which is an add-in to the spreadsheet software. The paper demonstrates an adaptation of the proprietary GA software to the problem of variance minimization. To test the performance of the proposed approach, test problems are taken from already published literature. The proposed approach is found to perform better than the previous GA, simulated annealing (SA) and Tabu search (TS) approaches for variance minimization in that it produces an optimal solution for all the test problems. Empirical analysis was carried out to study the effect of GA parameters, namely, the crossover rate, mutation rate, and population size.

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.

Automated inspection of surface mount PCB boards is a requirement to assure quality and to reduce manufacturing scrap costs and rework. This paper investigates methodologies for locating and identifying multiple objects in images used for surface mount device inspection. One of the main challenges for surface mount device inspection is component placement inspection. Component placement errors such as missing, misaligned or incorrectly rotated components are a major cause of defects and need to be detected before and after the solder reflow process. This paper focuses on automated object-recognition techniques for locating multiple objects using grey-model fitting for producing a generalised template for a set of components. The work uses the normalised cross correlation (NCC) template-matching approach and examines a method for constraining the search space to reduce computational calculations. The search for template positions has been performed exhaustively and by using a genetic algorithm. Experimental results using a typical PCB image are reported.

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.

This paper considers a single batch machine dynamic scheduling problem, which is readily found in the burn-in operation of semiconductor manufacturing. The batch machine can process several jobs as a batch simultaneously, within the capacity limit of the machine, and the processing time is represented by the longest processing time among all jobs in a batch. For a single batch machine problem with arbitrary job release time, we proposed an improved algorithm (merge-split procedure) to refine the solution obtained by the LPT-BFF heuristic, and two versions of a hybrid genetic algorithm (GA) are introduced in this paper. Each version of the hybrid GA diversifies job sequences using the GA operators in stage 1, forms batches in stage 2, and finally sequence the batches in stage 3. The difference is that merge-split procedures are involved in the second version of the hybrid GA. Computational experiments showed that the hybrid GA would obtain satisfactory average solution quality and the merge-split procedures would be good at reinforcing the solution consistency of the hybrid GA.

Here, an attempt is made to present a state-of-the-art review of papers on facility layout problems. This paper aims to deal with the current and future trends of research on facility layout problems based on previous research including formulations, solution methodologies and development of various software packages. New developments of various techniques provide a perspective of the future research in facility layout problems. A trend toward multi-objective approaches, developing facility layout software using meta-heuristics such as simulated annealing (SA), genetic algorithm (GA) and concurrent engineering to facility layout is observed.

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.

Dispatching rules have been successfully applied to job sequencing and scheduling in large-scale manufacturing systems such as wafer fabrication plants, automatic guided vehicle systems, etc. Because they can be easily communicated and implemented, and because they can be speedily applied, dispatching rules are also one of the most prevalent approaches in this field. However, naysayers often criticize the sluggish performance levels of traditional dispatching rules. Furthermore, in many large-scale factories, scheduling systems have been installed and operational for more than 5 years with “satisfactory” results, but managers still believe that more beneficial modifications are possible. Specifically, better scheduling methods, dispatching rules, test environments, and reporting tools are needed. Over the years, a few new solutions have been proposed to address these issues. For instance, most traditional dispatching rules are based on historical data. With the emergence of data mining and online analytic processing, dispatching rules can now take predictive information into account. Further, rather than concentrating on a single performance measure, some dispatching rules are designed to optimize multiple objectives at the same time. Moreover, the content of a dispatching rule can be optimized for a largescale manufacturing system. In light of advanced computing systems, dispatching rules continue to be one of the most promising technologies for practical applications. This special issue focuses on innovative but practical dispatching rules rather than complex algorithms. This type of dispatching rule will continue to drive the mainstream of practical applications in factories for the foreseeable future. This special issue is intended to provide the details of advanced dispatching rule development and applications of those rules to job sequencing and scheduling in large-scale manufacturing systems. We are very grateful for the positive responses we have received from the authors who submitted papers and the marvelous help provided by a number of referees in the paper reviewing process. After a strict review, 25 papers were finally accepted for publication in this special issue. Zhang et al. used a genetic algorithm (GA) to optimize a set of dispatching rules for scheduling a job shop. Bayesian networks were also utilized to model the distribution of high-quality solutions in the population and to produce each new generation of individuals. In addition, some selected individuals were further improved by a special local search. One advantage of their method is that it can be readily applied in various dynamic scheduling environments which must be investigated with simulation. Lu and Romanowski considered a dynamic job shop problem in which job shops are disrupted by unforeseen events such as job arrivals and machine breakdowns. They used multi-contextual functions (MCFs) to describe the unique characteristics of a dynamic job shop at a specific time and examined 11 basic dispatching rules and 33 composite rules made with MCFs that describe machine idle time and job waiting time. The experimental data showed that schedules made by the composite rules outperformed schedules made by conventional rules. Lin et al. integrated an ant colony optimization (ACO) algorithm with a number of new ideas (heuristic initial solution, machine reselection step, and local search procedure) and T. Chen (*) Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung, Taiwan e-mail: tolychen@ms37.hinet.net

The present paper deals with the formation of an optimal sequence of flow shop scheduling (FSS) for efficient operation. The primary concern of FSS is to obtain the optimal sequence, which minimises the idle time, tardiness, makespan, etc. Among these, the criteria of minimising the makespan plays a vital part. Thus, in this paper, the sequencing of the FSS for minimising the makespan is addressed. An effective hybrid has been formed with the metaheuristics, namely an ant system and a genetic algorithm (GA). A number of illustrative examples with different combinations of machines and jobs have been solved using the proposed hybrid method.

An important problem that faces design engineers is how to assign tolerance limits. In practical applications, tolerances are most often assigned as an informal compromise between functionality, quality and manufacturing cost. Frequently, the compromise is obtained iteratively by trial and error. A more scientific approach is often desirable for better performance. In this paper, a genetic algorithm (GA) is used for the design of tolerances of machine elements to obtain the global optimal solution. The objective is to design the optimum tolerances of the individual components to achieve the required assembly tolerance, zero percentage rejection of the components and minimum cost of manufacturing. The proposed procedure using GA is described in this paper for two tolerance design optimization problems: gear train and overrunning clutch assemblies. Results are compared with conventional techniques and the performances are analyzed.

The work presented in this paper describes a novel approach to the computation of the point-to-point motion of multi-degree-of-freedom parallel mechanisms. The common technique for solving for the inverse kinematics of parallel mechanisms is through intense computations and complex vector transformations. One obvious drawback of this method is the fact that the solution method varies with the type of mechanism being used. In this paper, a generalised alternative approach is presented, which avoids the problems encountered in the available techniques, for solving for the inverse kinematics of parallel manipulator systems. The proposed approach uses a fuzzy system as a general black box model to solve for the joint angles of the desired pose when the position and orientation of the parallel manipulator end-effector are given in advance. Since optimal membership functions are difficult to achieve using the experience of system designers, genetic algorithms are used instead and applied to the fuzzy rules set to optimise the search procedure. To verify the proposed scheme, a three degree-of-freedom parallel manipulator is analysed and the simulation results are very encouraging.

The optimisation fitting problems between two 3D parts with complicated profiles are the key focus in the problem of "body-in-white" (BIW) assembly for cars, which causes many assembly fitting issues. The paper present a novel algorithm based on a genetic algorithm (GA) to solve such assembly problems and treats it as a two-space curves-fitting problem. The analytical model of the global optimal fitting position of two space curves is discussed, and the mathematical optimal functions for 2D and 3D curves are set up separately. The global research procedure based on the genetic algorithm is described in detail, and some improved ways for GA in constrained optimisation are also discussed in the paper.

Given the great inconvenience caused by the randomness of the fault to the maintenance work, it is necessary to perform on-site and efficient disassembly planning for the faulty parts and present them in combination with virtual reality (VR) technology to achieve rapid repair. As a promising method in solving dynamistic and stochastic problems, deep reinforcement learning (DRL) is adopted in this paper for the solution of adaptive disassembly sequence planning (DSP) in the VR maintenance training system, in which sequences can be generated dynamically based on user inputs. Disassembly Petri net is established to describe and model the disassembly process, and then the DSP problem is defined as a Markov decision process (MDP) that can be solved by the deep Q-network (DQN). For handling the temporal credit assignment with sparse rewards, the long-term return in DQN is replaced with the fitness function of the genetic algorithm (GA). Meanwhile, the update method of gradient descent in DQN is adopted to speed up the iteration of the population in GA. A case study has been conducted to prove that the proposed method can provide better solutions for DSP problems in terms of VR maintenance training.

In today’s highly competitive environment, an effective supplier selection process is very important to the success of any manufacturing organization. A number of models and techniques have been developed to deal with supplier selection and evaluation methods. Traditional supplier selection methods are often based on the quoted initial price, which ignores the significant direct and indirect costs associated with quality, usage, maintenance, and service cost. This paper will look at the reliability-based total cost of ownership (RBTCO) approach which accounts both direct and indirect costs, as applied to the supplier selection process. The mathematical formulation of RBTCO for supplier selection problem fits into the nonlinear integer programming problem, which belongs to the NP-hard category. In this paper, a recently developed meta-heuristic optimization algorithm, cuckoo search (CS) hybridized with well-known genetic algorithm called HCSGA is proposed to solve the supplier selection problem. By embedding the genetic operators in standard CS, the balance between the exploration and exploitation ability further improved and more search space are observed during the algorithms’ performance. The computational test results show that the proposed hybrid algorithm significantly improves the original cuckoo search algorithm for small and larger sized problem instances.

In this paper the machine layout problem with a linear single-row flow path in automated manufacturing systems is investigated. Traditional machine layout approaches can produce an inappropriate layout design because they often do not consider flow path characteristics, such as flow path configuration and feasible flow path direction. This paper investigates the effects of such flow path characteristics on machine layouts. There are six flow-line analysis methods reported in the literature. Of these, the two best methods (FLA-5 for bidirectional flow lines and FLA-6 for unidirectional flow lines) are considered here. Two heuristic search algorithms, one combining FLA-5 and a genetic algorithm and other combining FLA-6 and a genetic algorithm, are proposed. A wide variety of data sets are used to test the proposed algorithms.