SALBPGen - A systematic data generator for (simple) assembly line balancing

Assembly line balancing is a well-known and extensively researched decision problem which arises when assembly line production systems are designed and operated. A large variety of real-world problem variations and elaborate solution methods were developed and presented in the academic literature in the past 60 years. Nevertheless, computational experiments examining and comparing the performance of solution procedures were mostly based on very limited data sets unsystematically collected from the literature and from some real-world cases. In particular, the precedence graphs used as the basis of former tests are limited in number and characteristics. As a consequence, former performance analyses suffer from a lack of systematics and statistical evidence. In this article, we propose SALPBGen, a new instance generator for the simple assembly line balancing problem (SALBP) which can be applied to any other assembly line balancing problem, too. It is able to systematically create instances with very diverse structures under full control of the experiment's designer. In particular, based on our analysis of real-world problems from automotive and related industries, typical substructures of the precedence graph like chains, bottlenecks and modules can be generated and combined as required based on a detailed analysis of graph structures and structure measures like the order strength. We also present a collection of new challenging benchmark data sets which are suited for comprehensive statistical tests in comparative studies of solution methods for SALBP and generalized problems as well. Researchers are invited to participate in a challenge to solve these new problem instances.manufacturing, benchmark data set, assembly line balancing, precedence graph, structure analysis, complexity measures

A Study of the Effects of Manufacturing Complexity on Product Quality in Mixed-Model Automotive Assembly

The objective of this research is to test the hypothesis that manufacturing complexity can reliably predict product quality in mixed-model automotive assembly. Originally, assembly lines were developed for cost efficient mass-production of standardized products. Today, in order to respond to diversified customer needs, companies have to allow for an individualization of their products, leading to the development of the Flexible Manufacturing Systems (FMS). Assembly line balancing problems (ALBP) consist of assigning the total workload for manufacturing a product to stations of an assembly line as typically applied in the automotive industry. Precedence relationships among tasks are required to conduct partly or fully automated Assembly Line Balancing. Efforts associated with manual precedence graph generation at a major automotive manufacturer have highlighted a potential relationship between manufacturing complexity (driven by product design, assembly process, and human factors) and product quality, a potential link that is usually ignored during Assembly Line Balancing and one that has received very little research focus so far. The methodology used in this research will potentially help develop a new set of constraints for an optimization model that can be used to minimize manufacturing complexity and maximize product quality, while satisfying the precedence constraints. This research aims to validate the hypothesis that the contribution of design variables, process variables, and human-factors can be represented by a complexity metric that can be used to predict their contribution on product quality. The research will also identify how classes of defect prevention methods can be incorporated in the predictive model to prevent defects in applications that exhibit high level of complexity. The manufacturing complexity model is applied to mechanical fastening processes which are accountable for the top 28% of defects found in automotive assembly, according to statistical analysis of historical data collected over the course of one year of vehicle production at a major automotive assembly plant. The predictive model is validated using mechanical fastening processes at an independent automotive assembly plant. This complexity-based predictive model will be the first of its kind that will take into account design, process, and human factors to define complexity and validate it using a real-world automotive manufacturing process. The model will have the potential to be utilized by design and process engineers to evaluate the effect of manufacturing complexity on product quality before implementing the process in a real-world assembly environment

Analysing and levelling manufacturing complexity in mixed-model assembly lines

In recent years, the automotive industry has witnessed a rapid increase in model variety and customization. New models, which are mainly being introduced in response to consumers demand, feature long lists of choices in terms of variants (engine model, comfort level, colour palette, etc.) and options (entertainment system, start/stop functionality, etc.). This high variability increases the complexity of factory processes and workstations and thus impacts directly upon the complexity of the manufacturing system as a whole. The shift from mass production to mass customized production is a trend that looks likely to continue in the foreseeable future, driven by automotive manufacturers' struggle to maintain market share in their traditional markets and seize market share in new, fast-growing markets. To cope with this intensified customization, automotive assembly platforms are designed to be capable of assembling a large range of relatively different models. That is they become mixed-model assembly lines. This implies that a high variety of tasks are to be performed at each workstation. As a consequence, the manufacturing complexity at these workstations increases. Mixed-model assembly lines are flow-line production systems that typically encounter the assembly line balancing problem (ALBP), a combinatorial optimization problem involving the optimal partitioning of assembly work among the workstations with a particular objective in mind. Subsequently, solving mixed-model assembly line balancing problems (MMALBPs) is much more complex than single-model cases, as workload must be smoothed for all workstations and all models in order to avoid overload or idle time. Despite the recent focus on manufacturing complexity and the extensive study of the ALBP, little research has explored how complexity can be applied to optimize line efficiency. Manufacturing complexity has been a key concern of many researchers and manufacturers in recent years, however, practical procedures to level complexity have not yet been considered and investigated when balancing the assembly lines. Analysing, measuring and monitoring complexity while creating line balancing solutions is a new and unexplored topic, especially when using real industry scenarios. In this dissertation, we propose an approach that can be used to monitor manufacturing complexity at each workstation while balancing the mixed-model assembly lines. The research carried out relies on an investigation of real MMAL's aiming to develop a deep analysis of complexity. The goal is to understand what and how complexity is generated, in order to cope and reduce the high complexity and its impacts in the line. During several visits and workshops carried out in collaboration with manufactures, we could observe that work load distribution is directly related with models variety, as tasks' time might differ from model to model. We first explored the existing scientific literature on the mixed-model assembly line balancing problem and manufacturing complexity in Chapter 2. Then, manufacturing complexity is investigated using two approaches: (1) an empirical analysis approach based on data collected in the Field and (2) a quantitative analysis approach measuring the level of uncertainty by means of entropy

Balancing and Sequencing of Mixed Model Assembly Lines

Assembly lines are cost efficient production systems that mass produce identical products. Due to customer demand, manufacturers use mixed model assembly lines to produce customized products that are not identical. To stay efficient, management decisions for the line such as number of workers and assembly task assignment to stations need to be optimized to increase throughput and decrease cost. In each station, the work to be done depends on the exact product configuration, and is not consistent across all products. In this dissertation, a mixed model line balancing integer program (IP) that considers parallel workers, zoning, task assignment, and ergonomic constraints with the objective of minimizing the number of workers is proposed. Upon observing the limitation of the IP, a Constraint Programming (CP) model that is based on CPLEX CP Optimizer is developed to solve larger assembly line balancing problems. Data from an automotive OEM are used to assess the performance of both the MIP and CP models. Using the OEM data, we show that the CP model outperforms the IP model for bigger problems. A sensitivity analysis is done to assess the cost of enforcing some of the constraint on the computation complexity and the amount of violations to these constraints once they are disabled. Results show that some of the constraints are helpful in reducing the computation time. Specifically, the assignment constraints in which decision variables are fixed or bounded result in a smaller search space. Finally, since the line balance for mixed model is based on task duration averages, we propose a mixed model sequencing model that minimize the number of overload situation that might occur due to variability in tasks times by providing an optimal production sequence. We consider the skip-policy to manage overload situations and allow interactions between stations via workers swimming. An IP model formulation is proposed and a GRASP solution heuristic is developed to solve the problem. Data from the literature are used to assess the performance of the developed heuristic and to show the benefit of swimming in reducing work overload situations

Development of a heuristic procedure for balancing mixed-model parallel assembly line type II

The single-model assembly line is not efficient for today’s competitive industry because to respond the customer’s expectation, companies need to produce mixedmodel products. On the other hand, using the mixed-model products increases the assembly complexity and makes it difficult to assign tasks to workstations because of the variety in model characteristics. As a result, the mixed-model products suffer from delays, limitations in the line workflow and longer lines. Parallel assembly lines as a production system in ALBPs which consists of a number of assembly lines in a parallel status, which by considering the cycle time of each line certain products are manufactured. This thesis takes advantages of the parallel assembly lines to produce mixed-model in order to assemble more than one model in each parallel assembly line and allocating tasks of models to workstations and balancing each parallel line to reduce the cycle times. To solve these problems, two heuristic algorithms were developed and coded in MATLAB®. The first one allocates each model to only one parallel assembly line and achieves the initial arrangement of tasks with the minimum number of workstations for each line. The second one called Tabu search Mixed-Model Parallel Assembly Line Balancing (TMMPALB), calculates final balancing tasks of different model in parallel lines with optimum cycle time for each line which tasks of each model can be allocated to more than one parallel assembly line through the TMMPALB. The main advantages of employing TS are using a flexible memory structure during the search process, and intensification and diversification strategies, which help to make a comprehensive search in the solution space. Fourteen data sets create 81 test problems that were solved to validate the performance of the TMMPALB. Each test problem consisted of the number of tasks, process time for each task (time unit), and the precedence relationship, minimum number of station and cycle time for each model. By considering that 80 out of the 81 test problems include three models and the remaining one has four models, 244 cycle times is made, which TMMPALB tries to minimize. The computational results showed that 205 cycle times out of the 244 cycle times have been improved. These results demonstrated that by arranging mixed-model through the parallel assembly lines with minimum number of workstations, the minimum cycle times are achieved in comparing with the single line

Development of Genetic Algorithm Procedure for Sequencing Problem in Mixed-Model Assembly Lines

One of the most important issues for manufacturing systems is to determine the optimal job sequence over the production period. Mixed model assembly line is a kind of manufacturing systems which is able to deal with variable market demand. In this research, an effective utilization of mixed-model assembly line is considered as problem statement through implementing different production strategies. The problem under study contains set of mixed-model assembly line where finding the optimal job sequence based on different production strategies is the objective of this research. Different production strategies have different objectives to be met, meanwhile the sequence of jobs can be varied based on different production strategies. The main contribution of the study was implementing four production strategies in mixed-model assembly line problems, so the company can take advantage of proposed production model in different situations to meet the challenges. The first production strategy aims to minimize the make span of assembly lines and release the products to the market as soon as possible. The second production strategies attempts to minimize the make-span, and also balancing the assembly lines. It helps to balance the workload among all assembly lines. Minimizing the variation of completion time is also considered as third production strategy. The last production strategy aims to provide ideal status for assembly lines by minimizing the make-span and variation of completion time, and balancing the assembly lines. Due to NP-hard nature of sequencing problem in mixed model assembly line, a genetic algorithm is applied to cope with problem complexity and obtain a near optimal solution in a reasonable amount of time. All data is taken from literature and the result obtained from genetic algorithm procedure for the first production strategy is compared to study mentioned in literature which represents an improvement of 5% in shortening the make-span for one set of product. For the rest of production strategies, simulated annealing algorithm is applied to check the well performance of proposed genetic algorithm through reaching the same solutions for each production strategy. In all production strategies both GA and SA reaches to the same job sequence and same value of objective functions. It confirms that the proposed genetic algorithm procedure is able to tackle the problem complexity and reach to optimal solutions in different production strategies

Multi-objective discrete particle swarm optimisation algorithm for integrated assembly sequence planning and assembly line balancing

In assembly optimisation, assembly sequence planning and assembly line balancing have been extensively studied because both activities are directly linked with assembly efficiency that influences the final assembly costs. Both activities are categorised as NP-hard and usually performed separately. Assembly sequence planning and assembly line balancing optimisation presents a good opportunity to be integrated, considering the benefits such as larger search space that leads to better solution quality, reduces error rate in planning and speeds up time-to-market for a product. In order to optimise an integrated assembly sequence planning and assembly line balancing, this work proposes a multi-objective discrete particle swarm optimisation algorithm that used discrete procedures to update its position and velocity in finding Pareto optimal solution. A computational experiment with 51 test problems at different difficulty levels was used to test the multi-objective discrete particle swarm optimisation performance compared with the existing algorithms. A statistical test of the algorithm performance indicates that the proposed multi-objective discrete particle swarm optimisation algorithm presents significant improvement in terms of the quality of the solution set towards the Pareto optimal set

Profit-oriented disassembly-line balancing

As product and material recovery has gained importance, disassembly volumes have increased, justifying construction of disassembly lines similar to assembly lines. Recent research on disassembly lines has focused on complete disassembly. Unlike assembly, the current industry practice involves partial disassembly with profit-maximization or cost-minimization objectives. Another difference between assembly and disassembly is that disassembly involves additional precedence relations among tasks due to processing alternatives or physical restrictions. In this study, we define and solve the profit-oriented partial disassembly-line balancing problem. We first characterize different types of precedence relations in disassembly and propose a new representation scheme that encompasses all these types. We then develop the first mixed integer programming formulation for the partial disassembly-line balancing problem, which simultaneously determines (1) the parts whose demand is to be fulfilled to generate revenue, (2) the tasks that will release the selected parts under task and station costs, (3) the number of stations that will be opened, (4) the cycle time, and (5) the balance of the disassembly line, i.e. the feasible assignment of selected tasks to stations such that various types of precedence relations are satisfied. We propose a lower and upper-bounding scheme based on linear programming relaxation of the formulation. Computational results show that our approach provides near optimal solutions for small problems and is capable of solving larger problems with up to 320 disassembly tasks in reasonable time

A Business Process Management System based on a General Optimium Criterion

Business Process Management Systems (BPMS) provide a broad range of facilities to manage operational business processes. These systems should provide support for the complete Business Process Management (BPM) life-cycle (16): (re)design, configuration, execution, control, and diagnosis of processes. BPMS can be seen as successors of Workflow Management (WFM) systems. However, already in the seventies people were working on office automation systems which are comparable with today’s WFM systems. Recently, WFM vendors started to position their systems as BPMS. Our paper’s goal is a proposal for a Tasks-to-Workstations Assignment Algorithm (TWAA) for assembly lines which is a special implementation of a stochastic descent technique, in the context of BPMS, especially at the control level. Both cases, single and mixed-model, are treated. For a family of product models having the same generic structure, the mixed-model assignment problem can be formulated through an equivalent single-model problem. A general optimum criterion is considered. As the assembly line balancing, this kind of optimisation problem leads to a graph partitioning problem meeting precedence and feasibility constraints. The proposed definition for the "neighbourhood" function involves an efficient way for treating the partition and precedence constraints. Moreover, the Stochastic Descent Technique (SDT) allows an implicit treatment of the feasibility constraint. The proposed algorithm converges with probability 1 to an optimal solution.BPMS, control assembly system, stochastic optimisation techniques, TWAA, SDT