This paper addresses a multi-objective mathematical model for the mixed-model Two-Sided Assembly Line balancing and worker assignment with different skills. In this problem, the operation time of each task is dependent on the skill of the worker. The following objective functions are considered in the mathematical model: (1) minimizing the number of mated-stations, (2) minimizing the number of stations, and (3) minimizing the total human cost for a given cycle time. Furthermore, maximizing the weighted Line efficiency and minimizing the weighted smoothness are Two indices considered simultaneously in this paper. Since this problem is well-known as NP-hard class, a particle swarm optimization (PSO) algorithm is developed to solve it. The performance of the proposed PSO algorithm is evaluated with a simulated annealing (SA) algorithm existing in the literature over several benchmarked test problems for the conditions of the current problem in terms of running time and solution quality. The results show the proposed algorithm is an efficient algorithm.

Two-Sided Assembly Line Balancing (2S-ALB) is essential to the production of large-sized high-volume products, including automotive production, at Assembly plants. The 2S-ALB problem involves di, erent Assembly resources such as worker skills, tools, and machines required for the Assembly. This research modeled and optimized the 2S-ALB with resource constraints. In the end, besides favorable workload balance, the number of resources can be optimized. For optimization purpose, particle swarm optimization was modi, ed to reduce dependence on a single best solution. This was conducted by replacing the best solution with the top three solutions in the reproduction process. Computational experiment results using 12 benchmark test problems indicated that the 2S-ALB with a resource-constrained model was able to reduce the number of resources for use in an Assembly Line. Furthermore, the proposed Modi, ed Particle Swarm Optimization (MPSO) was capable of searching for minimum solutions to 11 out of 12 test problems. The good performance of MPSO was attributed to its ability to maintain particle diversity over iterations. The proposed 2S-ALB model and MPSO algorithm were validated later using an industrial case study. This research makes a Two-fold contribution: (a) Proposition of a novel 2S-ALB with resource-constrained model and (b) a modified PSO algorithm with enhanced performance.

In this paper, a type II robotic mixed-model Two-Sided Assembly Line balancing problem is considered. This paper presents a new mixed-integer programming model for type II robotic mixed-model Two-Sided Assembly Line balancing to minimize the cycle time, energy consumption, and purchased cost of robots for a given number of workstations. We provided an effective framework for optimizing the multi-objective robotic mixed-model Two-Sided Assembly Line balancing problem considering energy consumption and smoothing workload in the make to order environment to help the decision-makers make the right decisions under stochastic demand. An augmented epsilon constraint and Lp-metric methods were applied to solve the problem, and then, with the help of defining Two vertical and horizontal criteria, we attempt to help the decision-maker to choose a more efficient solution to make the production Line more smooth workload. We demonstrate the efficiency of the proposed method by designing the numerical experiments.

Mixed model Two-Sided Assembly Lines (MM2SAL) are applied to assemble large product models, which is produced in high-volume. So, the sequence planning of products to reduce cost and increase productivity in this kind of Lines is imperative. The presented problem is tackled in Two steps. In step 1, a framework is developed to select and prioritize customer orders under the finite capacity of the proposed production system. So, an Analytic NeTwork Process (ANP) procedure is applied to sort customers’ order based on 11 assessment criteria. In step 2, a mathematical model is formulated to determine the best sequence of products to minimize the total utility work cost, total idle cost, tardiness/earLiness cost, and total operator error cost. After validation of the presented model using GAMS software, according to the NP-hard nature of this problem, a genetic algorithm (GA) and particle swarm optimization (PSO) are used. The performance of these algorithms are evaluated using some different test problems. The results show that the GA algorithm is better than PSO algorithm. Finally, a sign test for the Two metaheuristics and GAMS is designed to display the main statistical differences among them. The results of the sign test reveal GAMS is an appropriate software for solving small-sized problems. Also, GA is better than PSO algorithm for large sized problems in terms of objective function and run time.

Two-Sided Assembly Lines have been extensively studied due to their application in various auto industries. This paper investigates balancing problem type-II, which serves to minimize cycle time and consider learning effect based on a predefined workstation and costs pertaining to the assignment of operators with various skills. To this end, an integrated approach based on discrete event simulation (DES), artificial neural neTwork (ANN), and data envelopment analysis (DEA) is utilized to optimize the performance of Two-Sided Assembly Line balancing (2S-ALB) problem type-II. The developed approach is applied to a real case study. Since many scenarios (suggestions for production Line improvement) are needed for the simulation, the 2k Factorial design of experiment (DOE) is used to reduce their number. ANN and DEA were then used to select the best scenarios. It has been shown that incorporating learning effect and multi-skilled operators can improve the performance of 2S-ALB problem type-II better than does the conventional approach.

Automobile sector forms the backbone of manufacturing sector. Vehicle Assembly Line is important section in automobile plant where repetitive tasks are performed one after another at different workstations. In this thesis, a methodology is proposed to reduce cycle time and time loss due to important factors like equipment failure, shortage of inventory, absenteeism, set-up, material handling, rejection and fatigue to improve output within given cost constraints. Various relationships between these factors, corresponding cost and output are established by scientific approach. This methodology is validated in three different vehicle Assembly plants. Proposed methodology may help practitioners to optimize the Assembly Line using lean techniques.

This paper presents a multi-objective simulated annealing algorithm for the mixed-model Assembly Line balancing with stochastic processing times. Since, the stochastic task times may have effects on the bottlenecks of a system, maximizing the weighted Line efficiency (equivalent to the minimizing the number of station), minimizing the weighted smoothness index and maximizing the system reliability are considered. After solving an example in detail, the performance of the proposed algorithm is examined on a set of test problems. The experimental results show the new approach performs well.