Assembly Line Scheduling Research Articles

Dynamic Scheduling of Assembly Line Using Neural Networks for SARIA Industrial Complex-Sudan

In this paper, neural network was developed to improve the dynamic scheduling of SARIA industry complex. This work implemented into two modules: modeling system by time calculation, the main purpose of this modeling is to calculate the total manufacturing times of the products. The second module is the neural network model architecture, constructed to hold a real-time optimization schedule to solve dynamic scheduling problems. Analytical model was built, included collection and manipulation of data, time calculations and the neural networks model was formulated. Several training tests were carried out, then the dynamic scheduling formulated. To assess the validity of the system and to investigate the efficiency and robustness of the system, the results were compared with those obtained from SARIA. The results reveal that the total time of products demand is easy calculated, and the system agile to scheduling any change occur in the demand, also the proposed system reduces 4 shift days for one demand. So the developed neural network leads to minimize the total costs.

Balancing and scheduling of assembly line with multi-type collaborative robots

Human–robot collaboration (HRC) is a promising production mode that is in line with the vision of human-centered Industry 4.0. HRC contributes to the improvement of productivity as well as the reduction of workers’ ergonomic risk. In this study, we present one of the first attempts to address the assembly line balancing problem with multi-type collaborative robots (cobots), which allows human and robots to perform tasks in parallel or in collaboration. A mixed-integer programming (MIP) model is formulated to minimize the cycle time and a tight lower bound is proposed. We further propose a multi-objective model and an extended model to expand the scope of the study. Due to the complexity, an adaptive neighborhood simulated annealing algorithm (ANSA) is developed with the designed neighborhood operators and structures. Furthermore, an adaptive mechanism is applied to the ANSA to dynamically update the weights of the neighborhood structures based on historical information. Extensive computational experiments and a real case study are conducted to verify the superiority of ANSA. We further compare the application of diverse collaboration modes, i.e., sequential, simultaneous and supportive modes. The results also indicate that a suitable type of robot can improve productivity and the utilization rate of robots.

A surrogate-assisted heuristic approach for the joint optimization of resource allocation and scheduling of an aircraft final assembly line

In an aircraft final assembly line, there exists flexibility in resource allocation in the sense that processing time of a task can be reduced by allocating more workers to the task. As task processing times change, assembly task scheduling needs to be adjusted accordingly, which in turn has an impact on resource allocation. This paper studies the joint optimization problem of resource allocation and task scheduling. Both resource requirement and starting time of each task need to be determined such that the total resource investment cost is minimized. We propose a surrogate-assisted heuristic approach to solve the problem by decomposing it into two phases. In the first phase, a multi-start iterative search algorithm is developed to perform a dedicated exploration on resource allocation. A surrogate model trained on numerical data is applied to evaluate the resource allocation strategy. In the second phase, a hybrid genetic algorithm embedded with a novel local search procedure is designed for task scheduling. Meanwhile, a fine search for resource allocation can also be achieved. Computational experiments demonstrate the superiority of our proposed approach and that the surrogate plays a significant role in terms of improving solution quality. A realistic case study provides valuable managerial insights for real production.

A new MIP approach for balancing and scheduling of mixed model assembly lines with alternative precedence relations

ABSTRACT In this paper, a new mixed integer programming (MIP) formulation is developed for balancing and scheduling of mixed model assembly lines with disjunctive precedence constraints among assembly tasks. To represent alternative precedence relations, AND/OR assembly graph was adopted. In case of alternative precedence relations, for each product multiple assembly plans exist, which can be represented by a set of alternative precedence subgraphs and only one of such subgraphs should be selected for each product. As the number of subgraphs exponentially increases with the number of disjunctive relations among the tasks, the computational complexity of simultaneous balancing and scheduling along with the assembly subgraph selection increases with the number of alternative precedence relations. Unlike the other MIP approaches known from the literature, the new model does not need the alternative assembly subgraphs to be to explicitly enumerated as input data and then used for indexing the variables. Instead, a new disjunctive precedence selection and task assignment variable and new constraints are introduced to optimally choose one relation for each subset of alternative precedence relations. The optimal solutions for computational examples of balancing and scheduling problems illustrate a superior performance of the new modelling approach.

A dynamic material distribution scheduling of automotive assembly line considering material-handling errors

PurposeIn a kitting supply system, the occurrence of material-handling errors is unavoidable and will cause serious production losses to an assembly line. To minimize production losses, this paper aims to present a dynamic scheduling problem of automotive assembly line considering material-handling mistakes by integrating abnormal disturbance into the material distribution problem of mixed-model assembly lines (MMALs).Design/methodology/approachA multi-phase dynamic scheduling (MPDS) algorithm is proposed based on the characteristics and properties of the dynamic scheduling problem. In the first phase, the static material distribution scheduling problem is decomposed into three optimization sub-problems, and the dynamic programming algorithm is used to jointly optimize the sub-problems to obtain the optimal initial scheduling plan. In the second phase, a two-stage rescheduling algorithm incorporating removing rules and adding rules was designed according to the status update mechanism of material demand and multi-load AGVs.FindingsThrough comparative experiments with the periodic distribution strategy (PD) and the direct insertion method (DI), the superiority of the proposed dynamic scheduling strategy and algorithm is verified.Originality/valueTo the best of the authors’ knowledge, this study is the first to consider the impact of material-handling errors on the material distribution scheduling problem when using a kitting strategy. By designing an MPDS algorithm, this paper aims to maximize the absorption of the disturbance caused by material-handling errors and reduce the production losses of the assembly line as well as the total cost of the material transportation.

Scheduling for Assembly Line with Human–Robot Collaboration

In the scheduling of assembly lines with human-robot collaboration, variations in workload caused by differences in the available working hours of workers and robots must be minimized. A scheduling method that considers buffers shared by automated guided vehicles and cooperative assembly by multiple workers is proposed herein. In particular, cooperative work requires an assembly schedule that minimizes the make span and satisfies the delivery date, while accounting for the possibility of work partitioning, the number of workers, as well as their available time slots and skills. Hence, it is difficult to obtain an exact optimal solution within a reasonable computation time using existing methods such as mathematical programming. Heuristic or metaheuristic approaches are effective for solving this problem. However, these approaches are not suitable for cooperative assembly by multiple workers. Therefore, a genetic algorithm supported by dispatching rules with four genes is proposed. Computational experiments are conducted based on multiple worker skills. The results showed that when the worker skills are the same, the genetic representation of the job name and part processing order is effective, whereas when the worker skills are different, the genetic representation of the cooperative process with the worker for each operation is effective.

A digital twin for 3D path planning of large-span curved-arm gantry robot

Gantry robots enjoy important applications in unmanned factories and intelligent manufacturing tasks. Aiming at the automated production of customized wooden furniture, this paper proposes a digital twin for three-dimensional (3D) path planning of a large-span curved-arm gantry robot. First, a digital twin platform is built to show the door panel loading and unloading process of the gantry robot. Then the 3D path planning problem and method are proposed based on the built platform. The core of the algorithm consists of a bilateral control method, a 3D mesh construction strategy, and multi-objective 3D path planning using assembly line scheduling. The bilateral control method is used to achieve the perception and control of the gantry robot in the physical space. The mesh construction strategy entails static point set construction, obstacle avoidance analysis, point set construction for leap obstacles, and convex plane construction analysis. Multi-objective 3D path planning is primarily based on the NavMesh algorithm, which leverages an assembly line scheduling model to complete the wooden door processing task. Experimental results show that the proposed method can ensure safety, improved production efficiency, and satisfactory real-time performance in controlling the real gantry robot.

Integrated consideration of assembly line scheduling and feeding: A new model and case study from the automotive industry

Line feeding policy decision models decide upon whether a warehouse part is supplied by line stocking, kitting or sequencing. Although the production schedule determines the point of use and the usage rates of parts at the assembly line, no line feeding model till date integrates the operational production schedule decision into the tactical line feeding decision. Therefore, we introduce a new mixed-integer linear programming formulation that models the interactions between both planning problems explicitly. The developed model is applied in a real-life case study of a mixed-model assembly line from a first-tier automotive supplier. The numerical results show that both the cost-optimal line feeding policy decision as well as the resulting costs depend on the production schedule with the inherent degree of grouping equal jobs and the product mix. Depending on the product mix, following a leveled scheduling approach instead of a grouping of equal jobs can increase line feeding costs by up to 18%. Thus, the scheduling decision has to become a crucial element of future line feeding models for mixed-model lines.

Dynamic Scheduling of Assembly Line Using Neural Networks for SARIA Industrial Complex - Sudan

In this paper, the neural network was developed to improve the dynamic scheduling of SARIA industry complex. This work implemented into two modules: modeling system by time calculation, the main purpose of this modeling is to calculate the total manufacturing times of the products. The second module is the neural network model architecture, constructed to hold a real-time optimization schedule to solve dynamic scheduling problems. The analytical model was built, including collection and manipulation of data, time calculations, and the neural networks model was formulated. Several training tests were carried out, then the dynamic scheduling was formulated. To assess the validity of the system and to investigate the efficiency and robustness of the system, the results were compared with those obtained from SARIA. The results reveal that the total time of products demand is easily calculated, and the system is agile to schedule any change that occurs in the demand, also the proposed system reduces 4 shift days for one demand. So the developed neural network leads to minimizing the total costs.

Dynamic Scheduling of Assembly Line Using Neural Networks for SARIA Industrial Complex - Sudan

In this paper, the neural network was developed to improve the dynamic scheduling of SARIA industry complex. This work implemented into two modules: modeling system by time calculation, the main purpose of this modeling is to calculate the total manufacturing times of the products. The second module is the neural network model architecture, constructed to hold a real-time optimization schedule to solve dynamic scheduling problems. The analytical model was built, including collection and manipulation of data, time calculations, and the neural networks model was formulated. Several training tests were carried out, then the dynamic scheduling was formulated. To assess the validity of the system and to investigate the efficiency and robustness of the system, the results were compared with those obtained from SARIA. The results reveal that the total time of products demand is easily calculated, and the system is agile to schedule any change that occurs in the demand, also the proposed system reduces 4 shift days for one demand. So the developed neural network leads to minimizing the total costs.

Anticipating technical car sequencing rules in the master production scheduling of mixed-model assembly lines

In this paper, we propose a new linear programming-based approach that enables the consideration of technical car sequencing rules in the master production scheduling of mixed-model assembly lines at a much higher level of detail than previous approaches. To this end, we investigate certain interdependencies of car sequencing rules, which have largely been neglected, both in practice and in the research literature. We illustrate the existence and impact of these interdependencies and show that they induce additional implicit constraints, which can be represented by linear inequalities and incorporated into linear optimization models for master production scheduling. In a numerical study, we evaluate the approach and show, that it can significantly reduce sequencing violations compared to existing approaches.

Research on optimization of assembly line based on product scheduling and just-in-time feeding of parts

PurposeReducing production auxiliary time is the key to improve the efficiency of the existing mixed-flow assembly line. This paper proposes a method combining improved genetic algorithm (GA) and Flexsim software. It also investigates mixed-flow assembly line scheduling and just-in-time (JIT) parts feeding scheme to reduce waste in production while taking the existing hill-drop mixed-flow assembly line as an example to verify the effectiveness of the method.Design/methodology/approachIn this research, a method is presented to optimize the efficiency of the present assembly line. The multi-objective mathematical model is established based on the objective function of the minimum production cycle and part consumption balance, and the solution model is developed using multi-objective GA to obtain the mixed flow scheduling scheme of the hill-drop planter. Furthermore, modeling and simulation with Flexsim software are investigated along with the contents of line inventory, parts transportation means, daily feeding times and time points.FindingsTheoretical analysis and simulation experiments are carried out in this paper while taking an example of a hill-drop planter mixed-flow assembly line. The results indicate that the method can effectively reduce the idle and overload of the assembly line, use the transportation resources rationally and decrease the accumulation of the line inventory.Originality/valueThe method of combining improved GA and Flexsim software was used here for the first time intuitively and efficiently to study the balance of existing production lines and JIT feeding of parts. Investigating the production scheduling scheme provides a reference for the enterprise production line accompanied by the quantity allocation of transportation tools, the inventory consumption of the spare parts along the line and the utilization rate of each station to reduce the auxiliary time and apply practically.

Production scheduling problem of automobile mixed model assembly line

Nowadays, the mode of production has changed from mass production to multi variety and small batch production, and mixed model assembly line has emerged. Mixed model assembly is widely used in automobile enterprises. This paper summarizes the theory and method of automobile mixed model assembly line scheduling, analyzes its characteristics and difficulties, summarizes the related optimization objectives, and introduces the related algorithms, so as to make the scheduling problem of mixed model assembly line more clear.

Balancing of assembly lines with collaborative robots

Motivated by recent developments to deploy collaborative robots in industrial production systems, we investigate the assembly line balancing problem with collaborative robots. The problem is characterized by the possibility that human and robots can simultaneously execute tasks at the same workpiece either in parallel or in collaboration. For this novel problem type, we present a mixed-integer programming formulation for balancing and scheduling of assembly lines with collaborative robots. The model decides on both the assignment of collaborative robots to stations and the distribution of workload to workers and robotic partners, aiming to minimize the cycle time. Given the high problem complexity, a hybrid genetic algorithm is presented as a solution procedure. Based on extensive computational experiments, the algorithm reveals promising results in both computational time and solution quality. Moreover, the results indicate that substantial productivity gains can be utilized by deploying collaborative robots in manual assembly lines. This holds especially true for a high average number of robots and tasks to be assigned to every station as well as a high portion of tasks that can be executed by the robot and in collaboration.

Balancing and cyclical scheduling of asynchronous mixed-model assembly lines with parallel stations

This paper considers two optimization problems commonly associated to mixed-model assembly lines: balancing task-station assignments and sequencing/scheduling different product models in a cyclical manner. Cyclical scheduling for this particular problem variant is challenging, and multiple approaches have been previously employed by different authors. This paper presents a new mixed-integer linear programming formulation to optimize the steady-state of these lines. Tests on a 36-instance benchmark demonstrated that the proposed model significantly outperformed the previous literature formulation. Furthermore, it is shown that common scheduling rules (often used in simulators) do not necessarily converge to optimal cyclical schedules even when the optimal launch order is used. Tests have also demonstrated that parallelism can allow a marginally increasing value for workstations: doubling (tripling) stations in a line with parallelism can often offer more than double (triple) the optimal throughput of lines without parallelism.

Multi-Level Planning and Scheduling for Parallel PCB Assembly Lines Using Hybrid Spider Monkey Optimization Approach

Printed circuit board (PCB) assembly lines are significant to produce a variety of electronic products. PCB manufacturing industries tend to move towards automated and complex manufacturing system due to an increase in the customer demand for more sophisticated products. PCB assembly process involves highly interrelated levels of planning and scheduling problems. Therefore, the current research investigates multi-level planning and scheduling of PCB assembly lines, which include line assignment to the PCB models, component allocations to machines, and component placement sequencing by machines on PCB boards. A mixed integer-programming model is developed to integrate the planning and scheduling problem of parallel PCB assembly lines to maximize the net profit. A hybrid spider monkey optimization (HSMO) algorithm is proposed to solve the multi-level planning and scheduling problem. The performance of the proposed HSMO algorithm is compared to artificial bee colonial (ABC), genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA) algorithms. Moreover, the proposed HSMO algorithm is validated against ABC, GA, PSO, and SA algorithms with the case problem taken from well-reputed PCB manufacturing industry in China. The computation experiments indicate that the proposed HSMO algorithm can achieve good near-optimal solutions when compared with the other-mentioned four algorithms based on performance and efficiency for benchmark problems and real case problem.

Scheduling of an aeronautical final assembly line: a case study

Within the aeronautical industry, there has been in the recent years a high effort towards digitalization. This has resulted in a deep transformation of the way of working all through the product lifecycle. This digitization has included the use of scheduling and line balancing support tools, which have been extremely important in the case of the industrialization of new products and existing products re-industrialization. In this case study we present the re-industrialization of a final assembly line from the line balancing point of view. It completes other previous studies that presented the theoretical framework for aeronautical final assembly line balancing.

Multi-objective optimal scheduling of reconfigurable assembly line for cloud manufacturing

We propose a multi-objective optimization scheduling model to improve the production efficiency of a reconfigurable assembly line. We aim to minimize the costs of assembly line reconstruction, achieve the production load equalization, and minimize the delayed workload using this model. However, the proposed multi-objective optimization model is significantly complex for conventional mathematical optimization methods. Thus, we present an efficient solution approach based on a distance sorting particle swarm optimization. Finally, a case study is conducted to illustrate the feasibility and efficiency of the proposed method. Experimental results indicate that our proposed approach can significantly improve the production efficiency (i.e. increased production load balance, minimized reconstruction cost, and minimized delayed workload).

Lagrangean relaxation approach to joint optimization for production planning and scheduling of synchronous assembly lines

This paper focuses on simultaneous optimisation of production planning and scheduling problem over a time period for synchronous assembly lines. Differing from traditional top-down approaches, a mixed integer programming model which jointly considers production planning and detailed scheduling constraints is formulated, and a Lagrangian relaxation method is developed for the proposed model, whereby the integrated problem is decomposed into planning, batch sequencing, tardiness and earliness sub-problems. The scheduling sub-problem is modelled as a time-dependent travelling salesman problem, which is solved using a dynasearch algorithm. A proposition of Lagrangian multipliers is established to accelerate the convergence speed of the proposed algorithm. The average direction strategy is employed to solve the Lagrangian dual problem. Test results demonstrate that the proposed model and algorithm are effective and efficient.

Cyclic scheduling of flexible mixed model assembly lines with parallel stations

In this paper, the problem of balancing and cyclic scheduling of flexible mixed model assembly lines with parallel stations is studied. To exploit the connection between balancing and cyclic scheduling problems for an efficient line management, they are considered simultaneously. A novel constraint programming model including problem specific symmetry breaking constraints is proposed to solve this problem. Experiments on extensive number of test instances with various sizes are also presented.