Assembly Line Problem Research Articles

A multipopulation grasshopper algorithm for multiobjective material distribution scheduling problem of assembly line with flexible line inventory

PurposeOwing to the finite nature of the boundary of the line (BOL), the conventional method, involving the strong matching of single-variety parts with storage locations at the periphery of the line, proves insufficient for mixed-model assembly lines (MMAL). Consequently, this paper aims to introduce a material distribution scheduling problem considering the shared storage area (MDSPSSA). To address the inherent trade-off requirement of achieving both just-in-time efficiency and energy savings, a mathematical model is developed with the bi-objectives of minimizing line-side inventory and energy consumption.Design/methodology/approachA nondominated and multipopulation multiobjective grasshopper optimization algorithm (NM-MOGOA) is proposed to address the medium-to-large-scale problem associated with MDSPSSA. This algorithm combines elements from the grasshopper optimization algorithm and the nondominated sorting genetic algorithm-II. The multipopulation and coevolutionary strategy, chaotic mapping and two further optimization operators are used to enhance the overall solution quality.FindingsFinally, the algorithm performance is evaluated by comparing NM-MOGOA with multi-objective grey wolf optimizer, multiobjective equilibrium optimizer and multi-objective atomic orbital search. The experimental findings substantiate the efficacy of NM-MOGOA, demonstrating its promise as a robust solution when confronted with the challenges posed by the MDSPSSA in MMALs.Originality/valueThe material distribution system devised in this paper takes into account the establishment of shared material storage areas between adjacent workstations. It permits the undifferentiated storage of various part types in fixed BOL areas. Concurrently, the innovative NM-MOGOA algorithm serves as the core of the system, supporting the formulation of scheduling plans.

Balancing two-sided multi-manned assembly line under time and space constraint

The multi-man concept is usually applied for assembly lines of complex large sized products incorporating large number of tasks with varying sized components. Most researchers consider multi-manned assembly line problem under the objective of minimizing number of stations resulting in workers walking around the two sides of the station. The aim of the present work is to balance a two-sided multi-manned assembly line using a genetic algorithm combined with a proposed assignment heuristic. The model is studied under the objective of minimizing the number of workers and mated stations simultaneously, with additional space constraint. This helps to minimize workers interference and time waste in workers movement. For the first time a dynamic crossover and mutation ratio technique is used in the assembly line balancing problem. Applying the proposed algorithm gives competitive results. The use of dynamic crossover and mutation proved to yield better results compared to the stationary values.

A genetic algorithm for balancing and sequencing of mixed-model two-sided assembly line with unpaced synchronous transfer

Research on the assembly line balancing problem has been continuously incorporated the characteristics of actual production lines without considering the mixed-model two-sided assembly line with unpaced synchronous transfer, which is common in body shops of cars or trucks. This study attempts to model a mixed-model two-sided assembly line balancing and sequencing problem with unpaced synchronous transfer, and designs a genetic algorithm to solve this problem. First, to describe the process whereby model sequence and its execution, station and its number, and the process time caused by task allocation lead to the variable output of an unpaced synchronous line, a model without an observation point restriction is proposed. Based on this, a mathematical model of a balancing and sequencing problem of a mixed-model two-sided assembly line with an unpaced synchronous transfer is established, in which the balancing problem is aimed at Type-II. Then, an improved genetic algorithm adapted to the complexity of the problem is proposed. The algorithm designs a combination and evaluation mechanism to ensure that the solution populations of the balancing and sequencing problems are sufficiently combined and evaluated in each iteration to improve efficiency. The proposed algorithm is illustrated using a small numerical example. Finally, the algorithm is experimentally investigated using a set of test cases and several comparison algorithms. The experimental results show that the combination and evaluation mechanism is suitable for these problems, especially for large-sized cases, and that the proposed algorithm is effective and has better performance than the comparison algorithms.

An aircraft final assembly line balancing problem considering resource constraints and parallel task scheduling

In this study, the balancing problem of an aircraft final assembly line (AFAL) is investigated. The decision is to assign all the assembly tasks to a given number of workstations, with the objectives to balance the workstation cycle time and to minimize the total resource investment cost. An integer linear programming model is developed to formulate the problem. A construction and improvement based two-stage heuristic approach is proposed to solve this problem. In the first stage, an initial task assignment is constructed based on rules that are evolved by a multi-dimensional archive of phenotypic elites algorithm. In the second stage, an improvement heuristic, consisting of several customized neighborhood operators, is developed to improve the initial task assignment. The proposed approach is compared with the existing methods and its variants. Computational experiments and a real case study reveal the superiority of our proposed 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.

A constraint programming approach to a real-world workforce scheduling problem for multi-manned assembly lines with sequence-dependent setup times

For over five decades, researchers have presented various assembly line problems. Recently, assembly lines with multiple workers at each workstation have become very common in the literature. These lines are often found in the manufacturing of large vehicles, where workers at a workstation may perform their assigned tasks at the same time. Most research on multi-manned assembly lines focuses on balancing tasks and workers among workstations and scheduling tasks for workers. This study, however, concentrates on assigning tasks to workers already assigned to a specific workstation, rather than balancing the entire line. The problem was identified through an industrial case study at a large vehicle manufacturing company. The study presents two methods, one using mixed integer linear programming and the other using constraint programming, to minimise the number of workers required on a multi-manned assembly line with sequence-dependent setup times. The results of the computational experiments indicate that the constraint programming method performs better than the mixed integer linear programming method on several modified benchmark instances from the literature. The constraint programming model is also tested on the real-world scenario of our industrial case study and leads to significant improvements in the productivity of the workstations.

Evaluation of redundant configurations in assembly lines with fractional tasks

Assembly lines with fractional tasks increase the throughput with a better workload balancing among the stations. The possibility to share tasks between two consecutive stations can also support the design of assembly lines with redundancies of the tasks. The redundancy of the tasks allows reacting to short failures or variations in processing time. This paper proposes a framework to support the design and control of redundancy assembly lines. The first problem concerns a modified mixed-integer linear programming model used for the fractional allocation assembly line problem to design redundancy assembly lines. Then, this research proposes control policies to manage the allocation of the tasks shared between two consecutive stations. Finally, the simulation evaluates the performance of the proposed methods to handle short failures and uncertain processing times. The numerical results show the potential improvements of the proposed methods with a limited number of redundancies. The main improvements concern the reduction of throughput time and work in process.

An exact method for job sequencing in a mixed-model assembly line considering feeding lines based on the Benders decomposition approach

ABSTRACT The study proposes an exact method based on the Benders decomposition approach to solve the sequencing problem of the mixed-model assembly line considering feeding lines. A mathematical model is presented and linearized that is capable of solving the small-size scales of the problem. Since the problem is well known as a strongly NP-hard problem, appropriate methods can improve the effectiveness of the outcomes of the model. Therefore, we introduce a Benders decomposition method, which can solve the proposed problem on the medium and large scales. Moreover, a proper accelerator is presented to enhance the performance of the proposed method and to reduce computational time. The outcomes show that the proposed method can provide solutions with a maximum of 4.47% deviation from the optimal for small-scale instances. The result indicates the success of this exact method in solving the problem with different dimensions. In addition, comparing results emphasized that the accelerator used can reduce iterations up to 24% to provide the final solution.

Modelling and Optimization of Energy Efficient Assembly Line Balancing Using Modified Moth Flame Optimizer

Energy utilization is a global issue due to the reduction of fossil resources and also negative environmental effect. The assembly process in the manufacturing sector needs to move to a new dimension by taking into account energy utilization when designing the assembly line. Recently, researchers studied assembly line balancing (ALB) by considering energy utilization. However, the current works were limited to robotic assembly line problem. This work has proposed a model of energy efficient ALB (EE-ALB) and optimize the problem using a new modified moth flame optimizer (MMFO). The MMFO introduces the best flame concept to guide the global search direction. The proposed MMFO is tested by using 34 cases from benchmark problems. The numerical experiment results showed that the proposed MMFO, in general, is able to optimize the EE-ALB problem better compared to five comparison algorithms within reasonable computational time. Statistical test indicated that the MMFO has a significant performance in 75% of the cases. The proposed model can be a guideline for manufacturer to set up a green assembly line in future.

A Parallel Deep Reinforcement Learning Framework for Controlling Industrial Assembly Lines

Decision-making in a complex, dynamic, interconnected, and data-intensive industrial environment can be improved with the assistance of machine-learning techniques. In this work, a complex instance of industrial assembly line control is formalized and a parallel deep reinforcement learning approach is presented. We consider an assembly line control problem in which a set of tasks (e.g., vehicle assembly tasks) needs to be planned and controlled during their execution, with the aim of optimizing given key performance criteria. Specifically, the aim will be that of planning the task in order to minimize the total time taken to execute all the tasks (also called cycle time). Tasks run on workstations in the assembly line. To run, tasks need specific resources. Therefore, the tackled problem is that of optimally mapping tasks and resources to workstations, and deciding the optimal execution times of the tasks. In doing so, several constraints need to be respected (e.g., precedence constraints among the tasks, constraints on needed resources to run tasks, deadlines, etc.). The proposed approach uses deep reinforcement learning to learn a tasks/resources mapping policy that is effective in minimizing the resulting cycle time. The proposed method allows us to explicitly take into account all the constraints, and, once training is complete, can be used in real time to dynamically control the execution of tasks. Another motivation for the proposed work is in the ability of the used method to also work in complex scenarios, and in the presence of uncertainties. As a matter of fact, the use of deep neural networks allows for learning the model of the assembly line problem, in contrast with, e.g., optimization-based techniques, which require explicitly writing all the equations of the model of the problem. In order to speed up the training phase, we adopt a learning scheme in which more agents are trained in parallel. Simulations show that the proposed method can provide effective real-time decision support to industrial operators for scheduling and rescheduling activities, achieving the goal of minimizing the total tasks’ execution time.

Mixed-model Assembly Line Problem with Consideration of Sequence and Conveyer Speed Simultaneously

Mixed-model Assembly Line Problem with Consideration of Sequence and Conveyer Speed Simultaneously

Time and space multi-manned assembly line balancing problem using genetic algorithm

Purpose: Time and Space assembly line balancing problem (TSALBP) is the problem of balancing the line taking the area required by the task and to store the tools into consideration. This area is important to be considered to minimize unplanned traveling distance by the workers and consequently unplanned time waste. Although TSALBP is a realistic problem that express the real-life situation, and it became more practical to consider multi-manned assembly line to get better space utilization, few literatures addressed the problem of time and space in simple assembly line and only one in multi-manned assembly line. In this paper the problem of balancing bi-objective time and space multi-manned assembly line is proposedDesign/methodology/approach: Hybrid genetic algorithm under time and space constraints besides assembly line conventional constraints is used to model this problem. The initial population is generated based on conventional assembly line heuristic added to random generations. The objective of this model is to minimize number of workers and number of stations.Findings: The results showed the effectiveness of the proposed model in solving multi-manned time and space assembly line problem. The proposed method gets better results in solving real-life Nissan problem compared to the literature. It is also found that there is a relationship between the variability of task time, maximum task time and cycle time on the solution of the problem. In some problem features it is more appropriate to solve the problem as simple assembly line than multi-manned assembly line.Originality/value: It is the first article to solve the problem of balancing multi-manned assembly line under time and area constraint using genetic algorithm. A relationship between the problem features and the solution is found according to it, the solution method (one sided or multi-manned) is defined.

Balancing and Simulation Algorithm For Manual Assembly Line Operations.(Dept.M)

This work consists of a model which is based on a computerized algorithm and consists of two main parts namely balancing and simulation routines. This model was used to determine the buffer in-process units required between work stations along manual assembly lines. Since the task durations and deviations vary from assembly of one product to another and frum one task to another for the same product; this model was developed to handle different cases of wide and narrow Work variability according tu the type or assembly work. The model is capable of banding two different assembly line problem cases. Firstly in case of an assembly line production without balancing, the balancing routine is invited first to produce a balancing solution for the problem and then the simulation routine is Invited to determine the number of in-process units needed for each individual work station. Secondly in case of balanced assembly line production, the simulation routine is invited to simulate the work performance in each work station along the line and to determine the number of buffer in-process units needed for each individual work station. An assembly line problem consisting of fourteen tasks was used to test the balancing and simulation capability of the model. The problem was prepared to produce a perfect balancing solution of three work stations. The resultant balanced problem data was then used to Investigate the relationship between task variance, buffer in-process units between work stations and confidence level.

Real-Time Scheduling of Mixed Model Assembly Line with Large Variety and Low Volume Based on Event-Triggered Simulated Annealing (ETSA)

In the contemporary industrial production, multiple resource constraints and uncertainty factors exist widely in the actual job shop. It is particularly important to make a reasonable scheduling scheme in workshop manufacturing. Traditional scheduling research focused on the one-time global optimization of production scheduling before the actual production. The dynamic scheduling problem of the workshop is getting more and more attention. This paper proposed a simulated annealing algorithm to solve the real-time scheduling problem of large variety and low-volume mixed model assembly line. This algorithm obtains three groups of optimal solutions and the optimal scheduling scheme of multiple products, with the shortest product completion time and the lowest cost. Finally, the feasibility and efficiency of the model are proved by the Matlab simulation.

Solving the Type-2 Assembly Line Balancing with Setups Using Logic-Based Benders Decomposition

We solve the type-2 assembly line balancing problem in the presence of sequence-dependent setup times, denoted SUALBP-2. The problem consists of a set of tasks of a product, requiring to be processed in different assembly stations. Each task has a definite processing and setup times. The magnitude of setup times for each task is dependent on the processing sequence within each station. Processing and setup times of tasks assigned to each station constitute the station time. The goal is to minimize the cycle time (the maximum station time) by optimally (i) assigning tasks to assembly stations and (ii) sequencing these tasks within each station. To solve this challenging optimization problem, we first improve upon an existing mixed-integer programming (MIP) model by our proposed lower and upper bounds. These enhancements reduce the MIP model’s (solved CPLEX) average optimality gap from 41.61% to 20.77% on extra-large instances of the problem. To further overcome the intractability of the MIP model, we develop an exact logic-based Benders decomposition (LBBD) algorithm. The LBBD algorithm effectively incorporates a novel two-phase solution approach, the lower and upper bounds, various preprocessing techniques, relaxations, and valid inequalities. Using existing benchmarks in the literature, we demonstrate that our LBBD algorithm finds integer feasible solutions for 100% of all 788 instances (64% for the MIP), verifies optimality for 47% of instances (37% for the MIP), and achieves an average optimality gap of 5.04% (7.72% for the MIP obtained over 64% solved small instances). The LBBD algorithm also significantly reduces the computational time required to solve these benchmarks. Summary of Contribution: Assembly line balancing plays a crucial role in productivity enhancement in manufacturing and service companies. A balanced assembly line ensures higher throughput rate and fairer distribution of workload among assembly stations (workers). Assembly line balancing, in its simplest form, is one of the most challenging combinatorial optimization problems. Its complexity is further intensified when the sequence of executing tasks assigned to each station influences the magnitude of the setup performed between any two successive tasks. In view of such complexity, most assembly line balancing problems have been solved by randomized search techniques that do not provide any guarantee on the quality of solutions found. The mission of this paper is to understand whether there is any special structure within the existing mathematical models in the literature and, if so, exploit them toward developing computationally efficient exact techniques that can provide guarantee on the quality of solutions. Indeed, we demonstrate that such a special mathematical structure exists and we thus develop the first decomposition technique in form of a logic-based Benders decomposition (LBBD) to efficiently solve the type-2 sequence-dependent assembly line balancing problem. Specifically, we show that our LBBD significantly reduces cycle time and the time required for decision making. Our LBBD generalizes the scope of exact techniques for decision-making beyond the assembly line problems and is extendable to many other shop scheduling problems that arrange their stations (machines) serially and there are sequence-dependent setup times among their tasks.

Solving Mixed-Model Assembly Lines Using a Hybrid of Ant Colony Optimization and Greedy Algorithm

The assembly line balancing problem deals with the assignment of tasks to work stations. Mixed-model assembly line problem is a type of assembly line balancing problem at which two or more models of the same product are assembled sequentially at the same line. To achieve optimality and efficiency of solving this problem, tasks at each work station have to be well balanced satisfying all constraints. This paper deals with the mixed-model assembly line balancing problem (MALBP) in which the objective is to minimize the cycle time for a given number of work stations. The problem is solved by using a hybrid of an ant colony optimization and a greedy algorithm (Ant-Greedy). MATLAB Software is used to perform the proposed method. Then, the proposed method is applied to a real case problem found in the literature for the assembly line of automatic changeover in the Electronic Industries Company in Iraq. The results of the proposed method are compared with the performance of the Merging Shortest and Longest Operation (MMSLO) method. The comparison shows that the Ant-Greedy optimization method is more efficient, where the efficiency increased from 93.53% for MMSLO method to 97.26% for the Ant-Greedy method.

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 and sequencing problem of mixed-model U-shaped robotic assembly line: Mathematical model and dragonfly algorithm based approach

Robotic assembly line outperforms labor assembly line in terms of efficiency and flexibility. This paper focuses on the mixed-model U-shaped robotic assembly line balancing and sequencing problem (MURALBSP). Different from most reported works, this paper includes the energy consideration. Two conflict objectives, i.e., energy consumption and makespan, are studied for energy saving and efficient production. To this end, a hybrid multi-objective dragonfly algorithm (HMODA) is proposed. First, the mathematical model of this bi-objective problem is formulated. Second, the basic dragonfly algorithm is improved to solve the problem. The specific encoding and decoding method is designed and the chaotic map is used to improve algorithm randomness. Besides, the solution update method is amended and multi-point crossover mechanism is employed. Finally, several multiple size benchmark problems are designed and comparisons are conducted, the sensitivity of decision variables is analyzed to provide managerial insights. The results suggest that HMODA is more efficient in solving the proposed problem than compared algorithms.

A Mathematical Model and a Simulated Annealing Algorithm for Balancing Multi-manned Assembly Line Problem with Sequence-Dependent Setup Time

Multi-manned assembly lines have been widely applied to the industrial production, especially for large-sized products such as cars, buses, and trucks, in which more than one operator in the same station simultaneously performs different tasks in parallel. This study deals with a multi-manned assembly line balancing problem by simultaneously considering the forward and backward sequence-dependent setup time (MALBPS). A mixed-integer programming is established to characterize the problem. Besides, a simulated annealing algorithm is also proposed to solve it. To validate the performance of the proposed approaches, a set of benchmark instances are tested and the lower bound of the proposed problem is also given. The results demonstrated that the proposed algorithm is quite effective to solve the problem.

Parallel U-shaped assembly line balancing with adaptive MOEA/D hybridized with BBO

ABSTRACTThe mixed-model line balancing problem of a parallel U-shaped assembly line (PUAL) is addressed in this paper. Four objectives (so-called many objectives) conflicting in nature were concurrently optimized to minimize the (i) task unrelatedness, (ii) different workloads between workstations, (iii) number of stations and (iv) number of workstations. The solution technique adopts the recently emerged and innovative concept of multi-objective evolutionary optimization based on decomposition (MOEA/D), which partitions the fundamental problem into several scalar optimization subproblems and then optimizes them simultaneously. To further strengthen the algorithm, the platform of the MOEA/D was hybridized with the biogeography-based optimization (BBO) algorithm and the adaptive mechanism was also embedded, namely AMOEA/D-BBO. The performances in Pareto sense of AMOEA/D-BBO were evaluated on various problem sizes in comparison with the MOEA/D-BBO, MOEA/D and BBO algorithms. The experimental results demonstrated that AMOEA/D-BBO outperforms the other algorithms in terms of the convergence rate and spread.