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

A Multi-Objective Optimization Approach for Multi-Head Beam-Type Placement Machines

This paper addresses a highly challenging scheduling problem in the field of printed circuit board (PCB) assembly systems using Surface Mounting Devices (SMD). After describing some challenging optimization sub-problems relating to the heads of multi-head surface mounting placement machines, we formulate an integrated multi-objective mathematical model considering of two main sub-problems simultaneously. The proposed model is a mixed integer nonlinear programming one which is very complex to be solved optimally. Therefore, it is first converted into a linearized model and then solved using an efficient multi-objective approach, i.e., the augmented epsilon constraint method. An illustrative example is also provided to show the usefulness and applicability of the proposed model and solution method.PCB assembly. Multi-head beam-type placement machine. Multi-objective mathematical programming. Augmented epsilon-constraint method

Research Trends and Outlooks in Assembly Line Balancing Problems

This paper presents the findings from the survey of articles published on the assembly line balancing problems (ALBPs) during 2014-2018. Before proceeding a comprehensive literature review, the ineffectiveness of the previous ALBP classification structures is discussed and a new classification scheme based on the layout configurations of assembly lines is subsequently proposed. The research trend in each layout of assembly lines is highlighted through the graphical presentations. The challenges in the ALBPs are also pinpointed as a technical guideline for future research works

Exact and heuristic methods for solving the Robotic Assembly Line Balancing Problem

[EN] In robotic assembly lines, the task times depend on the robots assigned to each station. Robots are considered an unlimited resource and multiple robots of the same type can be assigned to different stations. Thus, the Robotic Assembly Line Balancing Problem (RALBP) consists of assigning a set of tasks and a type of robot to each station, subject to precedence constraints between the tasks. This paper proposes a lower bound, and exact and heuristic algorithms for the RALBP. The lower bound uses chain decomposition to explore the graph dependencies. The exact approaches include a novel linear mixed-integer programming model and a branch-bound-and-remember algorithm with problem-specific dominance rules. The heuristic solution is an iterative beam search with the same rules. To fully explore the different characteristics of the problem, we also propose a new set of instances. The methods and algorithms are extensively tested in computational experiments showing that they are competitive with the current state of the art. (C) 2018 Elsevier B.V. All rights reserved.Borba, L.; Ritt, M.; Miralles Insa, CJ. (2018). Exact and heuristic methods for solving the Robotic Assembly Line Balancing Problem. European Journal of Operational Research. 270(1):146-156. https://doi.org/10.1016/j.ejor.2018.03.011S146156270

An analysis of task assignment and cycle times when robots are added to human-operated assembly lines, using mathematical programming models

Abstract Adding robots to a human-operated assembly line influences both the short- and long-term operation of the line. However, the effects of robots on assembly line capacity and on cycle time can only be studied if appropriate task assignment models are available. This paper shows how traditional assembly line balancing models can be changed in order to determine the optimal number of workstations and cycle time when robots with different technological capabilities are able to perform a predetermined set of tasks. The mathematical programming models for the following three cases are presented and analysed: i) only workers are assigned to the workstations; ii) either a worker or a robot is assigned to a workstation; iii) a robot and a worker are also assigned to specific workstations. The data of an assembly line producing power inverters is used to illustrate the proposed calculations. Both the assignment of tasks and the changes of cycle time are analysed within the AIMMS modelling environment. The computational characteristics of the proposed mathematical programming models are also examined and tested using benchmark problems. The models presented in this paper can assist operations management in making decisions relating to assembly line configuration

A novel strategy for balancing the workload of industrial lines based on a genetic algorithm

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksOne major problem in industrial automation is the workload balancing problem. It consists of making the robots or, more generally, the machines, involved in the assembly process to work exactly the same, either by picking and placing the same number of pieces or by having the same number of operational cycles. This paper presents a novel strategy for solving such a problem by means of an evolutionary algorithm. The specific application of this strategy is to balance the workload of a pick-and-place process developed in the facilities of the industrial company Fameccanica Spa Data within the framework of an industrial project between the company and our research group. The novelties concerning the state-of-the-art contributions are: (1) instead of using an explicit fitness function, the candidate solutions at each iteration are evaluated by using a simulation of the entire process; (2) the parameters optimized are the velocity and acceleration of the robots involved in the line and (3) the strategy includes an algorithm for distributing the workload between the robots during the process.Peer ReviewedPostprint (author's final draft

Assembly and Disassembly Planning by using Fuzzy Logic & Genetic Algorithms

The authors propose the implementation of hybrid Fuzzy Logic-Genetic Algorithm (FL-GA) methodology to plan the automatic assembly and disassembly sequence of products. The GA-Fuzzy Logic approach is implemented onto two levels. The first level of hybridization consists of the development of a Fuzzy controller for the parameters of an assembly or disassembly planner based on GAs. This controller acts on mutation probability and crossover rate in order to adapt their values dynamically while the algorithm runs. The second level consists of the identification of theoptimal assembly or disassembly sequence by a Fuzzy function, in order to obtain a closer control of the technological knowledge of the assembly/disassembly process. Two case studies were analyzed in order to test the efficiency of the Fuzzy-GA methodologies

Modelling and Optimization of Energy Efficient Assembly Line Balancing Using Modified Moth Flame Optimizer

Energy utilization is a global issue due to the reduction of fossil resources and also negative environmental effect. The assembly process in the manufacturing sector needs to move to a new dimension by taking into account energy utilization when designing the assembly line. Recently, researchers studied assembly line balancing (ALB) by considering energy utilization. However, the current works were limited to robotic assembly line problem. This work has proposed a model of energy efficient ALB (EE-ALB) and optimize the problem using a new modified moth flame optimizer (MMFO). The MMFO introduces the best flame concept to guide the global search direction. The proposed MMFO is tested by using 34 cases from benchmark problems. The numerical experiment results showed that the proposed MMFO, in general, is able to optimize the EE-ALB problem better compared to five comparison algorithms within reasonable computational time.  Statistical test indicated that the MMFO has a significant performance in 75% of the cases. The proposed model can be a guideline for manufacturer to set up a green assembly line in future