Multi-objective Analysis for a Sequencing Planning of Mixed-model Assembly Line

Scheduling of multi-component products in a two-stage flexible flow shop

A sequencing problem to level part usage rates and work loads for a mixed-model assembly line with a bypass subline

In this paper, type II robotic mixed-model two-sided assembly line balancing (RMTALB-II) problem is considered. In recent years, robots have been widely used in assembly systems as called robotic assembly lines where a set of tasks have to be assigned to stations and each station needs to select one of the different robots to process the assigned tasks. Two-sided assembly lines are especially used to produce large-sized high-volume products, such as automobiles, trucks, and buses. In this type of production line, both left side and right side of the line are used in parallel. However, little attention has been paid to solve RMTALB problems. Moreover, according to our best knowledge, there is no published work in the literature on RMTALB. This paper presents a new mixed-integer programming model for RMTALB-II to minimize the cycle time for a given number of mated stations. Since RMTALB problems are in NP-hard class of combinatorial optimization problems, a simulated annealing (SA) algorithm as metaheuristic method is proposed to solve the problem. Some problems are solved by SA and performance of the SA is evaluated by comparing their results with the optimal solution. The computational results show that SA is more efficient.

<p>Hydrological models are crucial components in water and environmental resource management to provide simulations on streamflow, snow cover, and glacier mass balances. Effective model calibration is however challenging, especially if a multi-objective or multi-dataset calibration is necessary to generate realistic simulations of all flow components under consideration.</p><p>In this study, we explore the value of metaheuristics for multi-dataset calibration to simulate streamflow, snow cover and glacier mass balances using the HBV model in the glaciated catchment of the Rhonegletscher in Switzerland. We evaluate the performance of three metaheuristic calibration methods, i.e. Monte Carlo (MC), Simulated Annealing (SA) and Genetic Algorithms (GA), in regard to these three datasets. For all three methods, we compare the model performance using 100 best and 10 best optimized parameter sets.</p><p>Our results demonstrate that all three metaheuristic methods can generate realistic simulations of the snow cover, the glacier mass balance and the streamflow. The comparison of these three methods reveals that GA provides the most accurate simulations (with lowest confidence intervals) for all three datasets, for both 100 and 10 best simulations. However, when using all 100 simulations, GA yields also some worst solutions which are eliminated if only 10 best solutions are considered.</p><p>Based on our results we conclude that GA-based multi-dataset calibration provides more accurate and more precise simulation than MC or SA. This conclusion is fortified by a reduction of the parameter equifinality and an improvement of the Pareto frontier for GA in comparison to both other metaheuristic methods. This method should therefore lead to more reproducible and consistent hydrological simulations.</p>

Lean and agile manufacturing for work-in-process (WIP) control

Implementation of mixed-model U-shaped assembly lines (MMUL) is emerging and thriving in modern manufacturing systems owing to adaptation to changes in market demand and application of just-in-time production principles. In this study, the line balancing and model sequencing (MS) problems in MMUL are considered simultaneously, which results in the NP-hard mixed-model U-line balancing and sequencing (MMUL/BS) problem. A colonial competitive algorithm (CCA) is developed and modified to solve the MMUL/BS problem. The modified CCA (MCCA) improves performance of original CCA by introducing a third type of country, independent country, to the population of countries maintained by CCA. Implementation details of the proposed CCA and MCCA are elaborated using an illustrative example. Performance of the proposed algorithms is tested on a set of test-bed problems and compared with that of existing algorithms such as co-evolutionary algorithm, endosymbiotic evolutionary algorithm, simulated annealing, and genetic algorithm. Computational results and comparisons show that the proposed algorithms can improve the results obtained by existing algorithms developed for MMUL/BS.

SUMMARYIn this paper, an integrated mathematical model for the balancing and sequencing problems of a mixed-model assembly line (MMAL) is developed. The proposed model minimizes the total overload and idleness times. For the sake of reality, the impact of operator’s learning and fatigue issues on the optimization of the assembly line balancing and sequencing problems is considered. Furthermore, it is assumed that the Japanese mechanism is used in this assembly line to deal with the overload issue. With respect to the complexity level of the proposed model, a genetic algorithm is developed to solve the model. In order to set the parameters of the developed genetic algorithm, the well-known Taguchi method is used and the efficiency of this solution method is compared with the GAMS software using several test problems with different sizes. Finally, the sensitivity of the balancing and sequencing problems to the parameters such as station length, learning rate, and fatigue rate are analyzed and the impact of changing these parameters on the model is studied.

To solve the sequencing problem in mixed-model flexible assembly lines (MMFALs) with variable launching intervals, a mathematical model aiming to minimize the cost of utility and idle times is developed. To obtain high-quality sequences, an advanced scatter search (ASS) algorithm is proposed. A heuristic approach, i.e. launching intervals between products algorithm (LIBPA), is incorporated into the ASS algorithm to solve the launching interval problem for each sequence. Numerical experiments with different scales are conducted to compare the performance of ASS with genetic algorithm (GA). In addition, we compare the cost of variable launching intervals approach with fixed launching intervals approach. The results indicate that the ASS is efficient and effective, and considering variable launching intervals in mixed-model assembly lines (MMALs) sequencing problem can improve the performance of the line.

This research studies the cellular manufacturing system (CMS) controlled by kanban mechanism which defective items are produced in any production run of each product and rework is carried out to transform them into serviceable items. We consider and compare two different policies for rework where in the first policy rework is completed within the same production cycle and in the second policy rework done after N production cycles. Recently Aghajani et al. (2012) explain policy 2 and proposed a mixed-integer nonlinear programming (MINLP) model for this policy. In order to minimize total cost, MINLP model was developed for policy 1 to find simultaneously the optimal number of kanban, batch size, and number of batches. The cost function includes the cost of setup, holding, and transportation. Due to the high combinatorial structure of the problem, particle swarm optimization (PSO), and simulated annealing (SA) algorithms as meta-heuristic methods are proposed to solve the problem and numerical experiments are conducted to demonstrate the efficiency of the proposed algorithms. It is shown that both PSO and SA result are in a near optimal solution but the PSO algorithm gives a better performance than the SA method. Also, sensitivity analysis is carried out to study the effect of defective rate, holding cost, and setup cost variations on the total system cost is discussed the performance of these policies in different conditions.

The planning of assembly lines has always been a complex task, as a large number of factors that influence and disrupt production must be taken into account. Uncertainties in the early planning stages necessitate adjustments to the initial assumptions for the detailed assembly line plan throughout the planning project. Notably, the experience of assembly planners enables the integration of changing requirements into the planning process. This experience-based methodology is reaching its limits due to the increasing complexity of product variants and product portfolios. Newer data-driven approaches in assembly planning enable high productivity and the targeted use of synergies in assembly lines by utilising descriptive systematics that facilitate digital processing, thereby creating the basis for utilising digital planning assistance systems. For example, assembly precedence matrices (APMs) can structure and digitally represent the dependencies between assembly operations.If product variants with partially different assembly contents—whether in terms of the quantity of assembly operations or the specific activities within assembly operations—are processed within an assembly line, the individual consideration of these variants is no longer sufficient to optimise the system. To enable holistic optimisation, the synchronisation of APMs ensures consistency across variants. This is achieved through an expansion step, which integrates all relevant assembly operations into every APM within the product portfolio, followed by a transformation through row and column rearrangements. The synchronisation thus creates a daily basis for describing the assembly precedence relationships (APRs) of different product variants within the same product family.The scientific findings gained in this paper result from combining synchronised APMs of a mixed-model assembly line (MMAL) into a joint APM. The result includes defining operators for combining unified APM grids for a given product portfolio with different cell entries, taking into account application-related aspects. By combining the APMs of the product portfolio under consideration, APRs for the assembly procedure analysis can be described holistically as part of the assembly planning process. This enables analyses and optimisations for planning, such as assembly line balancing (ALB), the design of hybrid assembly structures or the dimensioning of assembly system networks.

A large number of studies have been conducted in the area of semiconductor final test scheduling (SFTS) problems. As a specific example of the simultaneous multiple resources scheduling problem, intelligent manufacturing planning and scheduling based on meta-heuristic methods, such as the genetic algorithm (GA), simulated annealing, and particle swarm optimization, have become common tools for finding satisfactory solutions within reasonable computational times in real settings. However, only a few studies have analyzed the effects of interdependent relations during group decision-making activities. Moreover, for complex and large problems, local constraints and objectives from each managerial entity and their contributions toward global objectives cannot be effectively represented in a single model. This paper proposes a novel cooperative estimation of distribution algorithm (CEDA) to overcome these challenges. The CEDA extends a co-evolutionary framework incorporating a divide-and-conquer strategy. Numerous experiments have been conducted, and the results confirmed that CEDA outperforms hybrid GAs for several SFTS problems.

A large variation in the assembly times is observed among different product types when they are produced by a mixed-model assembly line. The variation reduces production efficiency and may cause a line stoppage. However, the variation of the assembly times can be reduced by installing a bypass line which processes a part of assembly operations of product with relatively longer assembly time. The paper presents that the installation of a bypass line will reduce the total line length of the entire assembly line as well as the variation of the assembly times. An optimization model and its branch and bound algorithm are developed to minimize the total line length. The decision variables are the number of stations for each of main and bypass lines and the product types to be processed at the bypass line. The computational efficiency is discussed through numerical examples.

Dynamic material flow control in mixed model assembly lines

Augmented multi-agent algorithm utilizing intelligent search range detection heuristic to solve assembly line sequencing problem: A case study in the truck industry

A mixed-model assembly line is a type of production line where different models of a product are assembled on. Mixed-model assembly lines can respond to unanticipated changes in product demands quickly without keeping so many inventories. Designing mixed-model assembly line involves solving the traditional problems of the assembly line design (consists of balancing problem, determining cycle time, and the number and sequence of stations) in addition of determining the sequence of products in assembly line. The main goal of this paper is presenting a method in order to determine the sequence of products in mixed-model assembly line by considering Just-in-Time systems. Moreover, supplying some required components from feeding lines is considered. A mathematical model is presented which is capable of specifying the sequence of products in the mixed-model assembly line by considering main criteria and keeping feeding lines balanced. Mathematical model can be used for solving small-size problems. Because the combinatorial nature of sequencing problems typically provides an intractable search space for problems of “real world” size, the search heuristics of simulated annealing and ant colony algorithms are presented and used to find solutions for several problem sets. Experimentations show that the simulated annealing approach outperforms the ant colony approach in objective function performance.