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.

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.

In today’s competitive market, those producers who can quickly adapt themselves to diverse demands of customers are successful. Therefore, in order to satisfy these demands of market, Mixed-Model Assembly Line (MMAL) has an increasing growth in industry. A Mixed-Model Assembly Line (MMAL) is a type of production Line in which varieties of products with common base characteristics are assembled on. This paper focuses on this type of production Line in a stochastic environment with three objective functions: 1) total utility work cost, 2) total idle cost, and 3) total production rate variation cost that are simultaneously considered. In real life, especially in manual Assembly Lines, because of some inevitable human mistakes, breakdown of machines, lack of motivation in workers and the things alike, events are not deterministic, so we consider operation time as a stochastic variable independently distributed with normal distributions; for dealing with it, chance constraint optimization is used to Model the problem. At first, because of NP-hard nature of the problem, multi-objective harmony search (MOHS) algorithm is proposed to solve it. Then, for evaluating the performance of the proposed algorithm, it is compared with NSGA-II that is a powerful and famous algorithm in this area. At last, numerical examples for comparing these two algorithms with some comparing metrics are presented. The results have shown that MOHS algorithm has a good performance in our proposed Model.

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.

This paper presents a new mathematical programming Model for the bi-criteria Mixed-Model Assembly Line balancing problem in a just-in-time (JIT) production system. There is a set of criteria to judge sequences of the product mix in terms of the effective utilization of the system. The primary goal of this Model is to minimize the setup cost and the stoppage Assembly Line cost, simultaneously. Because of its complexity to be optimally solved in a reasonable time, we propose and develop two evolutionary meta-heuristics based on a genetic algorithm (GA) and a memetic algorithm (MA). The proposed heuristics are evaluated by the use of random iterations, and the related results obtained confirm their efficiency and effectiveness in order to provide good solutions for medium and large-scale problems.

In recent years, robots have been an eminent solution for manufacturers to facilitate their process and focus on a variety of their products. As the importance of robot usages, our paper focuses on the Robotics Assembly Line. In this paper, we have considered the cycle time, robot operational costs, robot purchase costs, and robot energy consumptions. In the following, we add robot failure rates to have an efficient and high-quality Assembly Line. The presented Model is a multi-objective problem, therefore, the Linear programming methods as goal programming and augmented ε-constraint method are applied to optimize the problem. In the end, we have considered a case study to examine and show the applicability of the proposed Model on the real situation.