Optimization of two sided assembly line balancing with resource constraint

Two-sided assembly line balancing (2S-ALB) problems are practically useful in improving the production of large-sized high-volume products. Many research has proposed various approaches to study and balance this well-known ALB problem. Although much attention has been given to solve and optimize 2S-ALB, the majority of the research assumed the workstation has similar capabilities. This research has been conducted in an automotive assembly line, where most of the equipment used in assembly is different from one workstation to another. The assumption that all workstation has similar capabilities lead to inefficient resource utilization in assembly line design. This research aims to model and optimize 2S-ALB with resource constraints. Besides optimizing the line balancing, the proposed model also will minimize the number of resources in the two-sided assembly line. The research begins with problem formulation by establishing four optimization objectives. The considered optimization objectives were to minimize the number of workstations, number of mated-workstation, total idle time, and number of resources. For optimization purpose, Particle Swarm Optimization is modified to find the best solution besides reducing the dependencies on a single best solution. This is conducted by replacing the best solution with the top three solutions in the reproduction process. A set of benchmark problems for 2S-ALB were used to test the proposed Modified Particle Swarm Optimization (MPSO) in the computational experiment. Later, the proposed 2S-ALB with resource constraint model and algorithm was validated using a case study problem. The computational experiment result using benchmark test problems indicated that the proposed MPSO was able to search for better solution in 91.6% of the benchmark problems. The good performance of MPSO is attributed to its ability to maintain particle diversity over the iteration. Meanwhile, the case study result indicated that the proposed 2S-ALB with resource constraint model and MPSO algorithm are able to be utilized for the real problem. In the future, the multiobjective optimization problem will be considered to be optimized for other types of general assembly lines

A decomposition based solution algorithm for U-type assembly line balancing with interval data

International audienceBalancing U-type assembly lines under uncertainty is addressed in this paper by formulating a robust problem and developing its optimization model and algorithm. U-type assembly layouts are shown to be more efficient than conventional straight lines. A great majority of studies on U-lines assume deterministic environments and ignore uncertainty in operation times. We aim to fill this research gap and, to the best of our knowledge, this study will be the first application of robust optimization to U-type assembly planning.We assume that the operation times are not fixed but they can vary. We employ robust optimization that considers worst case situations. To avoid over-pessimism, we consider that only a subset of operation times take their worst case values. To solve this problem, we suggest an iterative approximate solution algorithm. The efficiency of the algorithm is evaluated with some computational tests

Ergonomic risk and cycle time minimization for the U-shaped worker assignment assembly line balancing problem: A multi-objective approach

[EN] Workers still perform the bulk of operations in the manufacturing industry. The consideration of the assignment of workers and the reduction of ergonomic risks in U-shaped assembly lines is of paramount importance. However, the objectives of efficient task and worker assignment and a reduction in ergonomic risks are not usually correlated. Moreover, there is limited research in the existing literature into multi-objective approaches in U-shaped assembly lines. We formulate a U-shaped assembly worker assignment and balancing problem to simultaneously minimize cycle times and ergonomic risks. In addition, and due to its simplicity and successful results in flow shop scheduling problems, a Restarted Iterated Pareto Greedy algorithm is designed to optimize both objectives. In this algorithm, a problem-specific heuristic-based initialization is extended to improve the initial solution. Two precedence-based greedy and local search phases are developed to exploit the space around the current solution. Finally, a restart mechanism is proposed to help the algorithm escape from local optima. Comprehensive computational results, supported by detailed statistical analyses, suggest that the proposed multi-objective algorithm outperforms existing methods on a large number of benchmark instances.The authors would like to thank the anonymous reviewers for their helpful comments and constructive suggestions. This work is supported by National Natural Science Foundation of China (No. 51875421, No. 51875420). Ruben Ruiz is partly supported by the Spanish Ministry of Science, Innovation, and Universities, under the project "OPTEP-Port Terminal Operations Optimization"(No.\ RTI2018-094940-B-I00) financed with FEDER funds.Zhang, Z.; Tang, Q.; Ruiz García, R.; Zhang, L. (2020). 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