1,248 research outputs found
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
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
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
Assembly line balancing with cobots: An extensive review and critiques
Industry 4.0 encourages industries to digitise the manufacturing system to facilitate human-robot collaboration (HRC) to foster efficiency, agility and resilience. This cutting-edge technology strikes a balance between fully automated and manual operations to maximise the benefits of both humans and assistant robots (known as cobots) working together on complicated and prone-to-hazardous tasks in a collaborative manner in an assembly system. However, the introduction of HRC poses a significant challenge for assembly line balancing since, besides typical assigning tasks to workstations, the other two important decisions must also be made regarding equipping workstations with appropriate cobots as well as scheduling collaborative tasks for workers and cobots. In this article, the cobot assembly line balancing problem (CoALBP), which just initially emerged a few years ago, is thoroughly reviewed. The 4M1E (i.e., man, machine, material, method and environment) framework is applied for categorising the problem to make the review process more effective. All of the articles reviewed are compared, and their key distinct features are summarised. Finally, guidelines for additional studies on the CoALBP are offered
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āļāļāļāļąāļāļĒāđāļāļāļēāļĢāļŦāļēāļāđāļēāļāļĩāđāđāļŦāļĄāļēāļ°āļŠāļĄāļāļĩāđāļŠāļļāļāđāļāļāļāļēāļĢāļāļĢāļ°āļāļēāļĒāļāļąāļ§āļāļāļāļŠāļīāđāļāļĄāļĩāļāļĩāļ§āļīāļāļāļēāļĄāļ āļđāļĄāļīāļĻāļēāļŠāļāļĢāđ (Biogeography-based Optimization:BBO) āđāļāđāļāđāļĄāļāļēāļŪāļīāļ§āļĢāļīāļŠāļāļīāļāđāļāļīāļāļ§āļīāļ§āļąāļāļāļēāļāļēāļĢāļāļĩāđāđāļāđāļĢāļąāļāđāļāļ§āļāļīāļāļĄāļēāļāļēāļāļāļĪāļāļīāļāļĢāļĢāļĄāļāļēāļĢāļāļāļĒāļāļāļāļāļŠāļīāđāļāļĄāļĩāļāļĩāļ§āļīāļāļāļāđāļāļēāļ°āļāđāļēāļāđāļāļāļāļ§āļēāļĄāļāļĩāđāļāļģāđāļŠāļāļāļāļąāļĨāļāļāļĢāļīāļāļķāļĄ BBO āđāļāļ·āđāļāđāļāđāļŠāļģāļŦāļĢāļąāļāđāļāđāļāļąāļāļŦāļēāļāļēāļĢāļāļąāļāļŠāļĄāļāļļāļĨāļāļĩāđāļĄāļĩāļŦāļĨāļēāļĒāļ§āļąāļāļāļļāļāļĢāļ°āļŠāļāļāđāļāļāļŠāļēāļĒāļāļēāļĢāļāļĢāļ°āļāļāļāđāļāļāļāļāļēāļāļāļĨāļīāļāļ āļąāļāļāđāļāļŠāļĄ āđāļāļĒāļĄāļĩāļ§āļąāļāļāļļāļāļĢāļ°āļŠāļāļāđāļāļģāļāļ§āļāļāļąāđāļāļŠāļīāđāļ 4 āļ§āļąāļāļāļļāļāļĢāļ°āļŠāļāļāđāļāļĩāđāļāļ°āļāļđāļāļāļģāđāļŦāđāđāļŦāļĄāļēāļ°āļŠāļĄāļāļĩāđāļŠāļļāļāđāļāļāļĢāđāļāļĄāđ āļāļąāļ āđāļāđāđāļāđāļāļģāļāļ§āļāļŠāļāļēāļāļĩāļāļēāļāļāđāļāļĒāļāļĩāđāļŠāļļāļ āļāļģāļāļ§āļāļŠāļāļēāļāļĩāļāđāļāļĒāļāļĩāđāļŠāļļāļ āļāļ§āļēāļĄāļŠāļĄāļāļļāļĨāļāļāļāļ āļēāļĢāļ°āļāļēāļāļĢāļ°āļŦāļ§āđāļēāļāļŠāļāļēāļāļĩāļāļēāļāļŠāļđāļāļāļĩāđāļŠāļļāļ āđāļĨāļ°āļāļ§āļēāļĄāļŠāļąāļĄāļāļąāļāļāđāļāļāļāļāļēāļāļŠāļđāļāļāļĩāđāļŠāļļāļ āļāļĨāļāļēāļāļāļēāļĢāļāļāļĨāļāļāđāļŠāļāļāđāļŦāđāđāļŦāđāļāļāļĒāđāļēāļāļāļąāļāđāļāļāļ§āđāļē BBO āļĄāļĩāļŠāļĄāļĢāļĢāļāļāļ°āđāļāļāļēāļĢāđāļāđāļāļąāļāļŦāļēāļāļĩāđāļŠāļđāļāļāļ§āđāļēāļāļąāļĨāļāļāļĢāļīāļāļķāļĄāđāļāļīāļāļāļąāļāļāļļāļāļĢāļĢāļĄāđāļāļāļāļēāļĢāļāļąāļāļĨāļģāļāļąāļāļāļĩāđāđāļĄāđāļāļđāļāļāļĢāļāļāļāļģ II (Non-dominated Sorting Genetic Algorithm II: NSGA-II) āļāļķāđāļāđāļāđāļāļāļĩāļāļāļąāļĨāļāļāļĢāļīāļāļķāļĄāļŦāļāļķāđāļāļāļĩāđāđāļāđāļāļāļĩāđāļāļīāļĒāļĄ āļāļąāđāļāđāļāļāđāļēāļāļāļēāļĢāļĨāļđāđāđāļāđāļēāļŠāļđāđāļāļĨāļļāđāļĄāļāļģāļāļāļāļāļĩāđāđāļŦāļĄāļēāļ°āļŠāļĄāļāļĩāđāļŠāļļāļāđāļāļāļāļēāđāļĢāđāļ āļāļēāļĢāļāļĢāļ°āļāļēāļĒāļāļąāļ§āļāļāļāļāļĨāļļāđāļĄāļāļģāļāļāļ āļāļąāļāļĢāļēāļŠāđāļ§āļāļāļāļāļāļģāļāļāļāļāļĩāđāđāļĄāđāļāļđāļāļāļĢāļāļāļāļģāđāļĨāļ°āđāļ§āļĨāļēāļāļĩāđāđāļāđāđāļāļāļēāļĢāļāļģāļāļ§āļāļŦāļēāļāļģāļāļāļāļāļģāļŠāļģāļāļąāļ: āļŠāļēāļĒāļāļēāļĢāļāļĢāļ°āļāļāļāđāļāļāļāļāļēāļāļāļĨāļīāļāļ āļąāļāļāđāļāļŠāļĄ āļāļēāļĢāļāļąāļāļŠāļĄāļāļļāļĨāļŦāļĨāļēāļĒāļ§āļąāļāļāļļāļāļĢāļ°āļŠāļāļāđāļāļēāļĢāļŦāļēāļāđāļēāļāļĩāđāđāļŦāļĄāļēāļ°āļŠāļĄāļāļĩāđāļŠāļļāļ āđāļāļāļāļēāļĢāļāļĢāļ°āļāļēāļĒāļāļąāļ§āļāļāļāļŠāļīāđāļāļĄāļĩāļāļĩāļ§āļīāļāļāļēāļĄāļ āļđāļĄāļīāļĻāļēāļŠāļāļĢāđAbstractBiogeography-based Optimization (BBO) is an evolutionary metaheuristic inspired by migratory behavior of species among islands. This article presents a BBO algorithm for solving multi-objective mixed-model parallel assembly line balancing problem where four objectives are optimized simultaneously; i.e. to minimize the number of workstations, to minimize the number of stations, a maximization of workload balancing between workstations, and placing an emphasis on maximizing work relatedness. The results from experiments clearly show that BBO promises better performance than does Non-dominated Sorting Genetic Algorithm II (NSGA-II), which indicates another well-known algorithm, in terms of convergence to the Pareto-optimal set, spread of solutions, ratio of non-dominated solutions, and computation time to solution.Keywords: Mixed-model Parallel Assembly Lines, Multi-objective Line Balancing, Biogeography-based Optimizatio
āļāļēāļĢāļāļąāļāļĨāļģāļāļąāļāļāļēāļĢāļāļĨāļīāļāļĢāļāļĒāļāļāđāđāļāļāļŦāļĨāļēāļĒāļ§āļąāļāļāļļāļāļĢāļ°āļŠāļāļāđāļāļāļŠāļēāļĒāļāļēāļĢāļāļĢāļ°āļāļāļāļāļĨāļīāļāļ āļąāļāļāđāļāļŠāļĄāđāļāļāļŠāļāļāļāđāļēāļ
āļāļēāļĢāļāļąāļāļĨāļģāļāļąāļāļāļēāļĢāļāļĨāļīāļāļĢāļāļĒāļāļāđāļāļāļŠāļēāļĒāļāļēāļĢāļāļĢāļ°āļāļāļāđāļāļāļŠāļāļāļāđāļēāļāļĄāļĩāļāļ§āļēāļĄāļŠāļģāļāļąāļāļāļĒāđāļēāļāļĒāļīāđāļāļŠāļģāļŦāļĢāļąāļāđāļāđāđāļāļāļēāļĢāđāļāđāļāļąāļāļŦāļēāļŠāļēāļĒāļāļēāļĢāļāļĢāļ°āļāļāļāļāļĩāđāļĄāļĩāļŦāļĨāļēāļĒāļāļĨāļīāļāļ āļąāļāļāđāđāļŦāđāđāļāļīāļāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļāļŠāļđāļāļŠāļļāļ āļāļķāđāļāļāļąāļāļŦāļēāļāļāļīāļāļāļĩāđāļĄāļĩāļāļ§āļēāļĄāļĒāļļāđāļāļĒāļēāļāđāļĨāļ°āļŠāļĨāļąāļāļāļąāļāļāđāļāļ āđāļāļ·āđāļāļāļāļēāļāđāļāđāļāļāļąāļāļŦāļēāđāļāļ Non-deterministic Polynomial Hard: NP-Hard āđāļāļĒāļāļąāļāļŦāļēāļāļēāļĢāļāļąāļāļĨāļģāļāļąāļāļāļēāļĢāļāļĨāļīāļāļĢāļāļĒāļāļāđāđāļāļāļāļĨāļīāļāļ āļąāļāļāđāļāļŠāļĄāļāļāļŠāļēāļĒāļāļēāļĢāļāļĢāļ°āļāļāļāđāļāļāļŠāļāļāļāđāļēāļāļāļĩāđ āđāļāđāļāļīāļāļēāļĢāļāļēāļāļąāļāļāđāļāļąāļāļ§āļąāļāļāļļāļāļĢāļ°āļŠāļāļāđ 3 āļāļąāļāļāđāļāļąāļāđāļāļāļēāļāļ§āļīāļāļąāļĒāļāļ·āļ āļāļĢāļīāļĄāļēāļāļāļēāļāļāļĩāđāļāļģāđāļĄāđāđāļŠāļĢāđāļāļāđāļāļĒāļāļĩāđāļŠāļļāļāļāļģāļāļ§āļāļĢāļāļĒāļāļāđāļāļĩāđāļĨāļ°āđāļĄāļīāļāļĢāļ§āļĄāļāđāļāļĒāļāļĩāđāļŠāļļāļ āđāļĨāļ°āļāļģāļāļ§āļāļāļĢāļąāđāļāļāļēāļĢāđāļāļĨāļĩāđāļĒāļāđāļāļĨāļāļŠāļĩāļāđāļāļĒāļāļĩāđāļŠāļļāļ āđāļĨāļ°āļāļģāđāļŠāļāļāļāļąāļĨāļāļāļĢāļīāļāļķāļĄāļāļēāļĢāļāļĢāļĢāļāļ§āļāđāļāļāļāļĒāļēāļĒ (Combinatorial Optimization with Coincidence Expand: COIN-E) āļāļķāđāļāđāļāđāļāļāļąāļĨāļāļāļĢāļīāļāļķāļĄāļāļĩāđāļāļĢāļ°āļĒāļļāļāļāđāļĄāļēāļāļēāļ COIN āļĄāļēāđāļāđāđāļāļāļēāļĢāđāļāđāļāļąāļāļŦāļē āđāļāļĒāļāļģāļāļēāļĢāđāļāļĢāļĩāļĒāļāđāļāļĩāļĒāļāļāļąāļāļāļąāļĨāļāļāļĢāļīāļāļķāļĄāļāļĩāđāļĒāļāļĄāļĢāļąāļāđāļāļāļēāļĢāđāļāđāļāļąāļāļŦāļēāļāļēāļĢāļāļąāļāļĨāļģāļāļąāļāļāļēāļĢāļāļĨāļīāļ āđāļāđāđāļāđ NSGA-II, DPSO, BBO āđāļĨāļ° COIN āļāļĨāļāļēāļāļāļēāļĢāđāļāļĢāļĩāļĒāļāđāļāļĩāļĒāļāļāļāļ§āđāļē COIN-E āļĄāļĩāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļāļāđāļēāļāļāļēāļĢāļĨāļđāđāđāļāđāļēāļŠāļđāđāļāļĨāļļāđāļĄāļāļģāļāļāļ āļāđāļēāļāļāļēāļĢāļāļĢāļ°āļāļēāļĒāļāļĨāļļāđāļĄāļāļģāļāļāļāđāļĨāļ°āļāđāļēāļāļāļąāļāļĢāļēāļŠāđāļ§āļāļāļāļāļāļģāļāļ§āļāļāļĨāļļāđāļĄāļāļģāļāļāļāļāļĩāđāļāđāļāļāļāđāļāļĩāļĒāļāļāļąāļāļāļĨāļļāđāļĄāļāļģāļāļāļāļāļĩāđāđāļāđāļāļĢāļīāļāđāļāđāļēāļāļąāļ 91.85, 51.08 āđāļĨāļ° 57.48 āļāļēāļĄāļĨāļģāļāļąāļ āļāļķāđāļāļāļēāļāļāļąāļ§āļāļĩāđāļ§āļąāļāļŠāļĄāļĢāļĢāļāļāļ°āļāļāļāļāļąāđāļ 3 āļāļāļīāļāļāļ°āļāļāļ§āđāļē COIN-E āļĄāļĩāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļāđāļāļāļēāļĢāđāļāđāļāļēāļĢāđāļāđāļāļąāļāļŦāļēāđāļāđāļāļĩāļāļ§āđāļē NSGAII, DPSO, BBO āđāļĨāļ° COINāļāļģāļŠāļģāļāļąāļ: āļāļąāļĨāļāļāļĢāļīāļāļķāļĄāļāļēāļĢāļāļĢāļĢāļāļ§āļāđāļāļāļāļĒāļēāļĒ āļāļēāļĢāļāļąāļāļĨāļģāļāļąāļāļāļēāļĢāļāļĨāļīāļāļĢāļāļĒāļāļāđāļāļāļŠāļēāļĒāļāļēāļĢāļāļĢāļ°āļāļāļ āļāļĨāļīāļāļ āļąāļāļāđāļāļŠāļĄāđāļāļāļŠāļāļāļāđāļēāļCar Sequencing on two-sided assembly line is an important problem in an automotive industry. Researchers and practitioners have attempted several approaches to solve this problem aiming at maximum production efficiency. The problem is considered as an âNP-Hard problemâ. In this paper, three objective functions are considered including 1) minimizing utility work, 2) reducing the number of violation and 3) decreasing the number of color changes. The expansion of Combinatorial Optimization with Coincidence (COIN-E) algorithm is developed from its original version (i.e. COIN). Several well-known algorithms are compared in solving this problem including Non-dominated Sorting Genetic Algorithms (NSGA-II), Discrete Particle Swarm Optimization (DPSO), Biogeography-based Optimization (BBO) and (COIN). The experimental results indicate that COIN-E is efficient and it obtains the values of convergence = 91.85%, spread = 51.08% and ratio = 57.48%, which are significantly superior to NSGA-II, DPSO, BBO and COIN.Keywords: Expanded Combinatorial Optimization with Coincidence, Car Sequencing, Mixed-model Two-sided Assembly Line
Energy Efficient Policies, Scheduling, and Design for Sustainable Manufacturing Systems
Climate mitigation, more stringent regulations, rising energy costs, and sustainable manufacturing are pushing researchers to focus on energy efficiency, energy flexibility, and implementation of renewable energy sources in manufacturing systems. This thesis aims to analyze the main works proposed regarding these hot topics, and to fill the gaps in the literature. First, a detailed literature review is proposed. Works regarding energy efficiency in different manufacturing levels, in the assembly line, energy saving policies, and the implementation of renewable energy sources are analyzed. Then, trying to fill the gaps in the literature, different topics are analyzed more in depth. In the single machine context, a mathematical model aiming to align the manufacturing power required to a renewable energy supply in order to obtain the maximum profit is developed. The model is applied to a single work center powered by the electric grid and by a photovoltaic system; afterwards, energy storage is also added to the power system. Analyzing the job shop context, switch off policies implementing workload approach and scheduling considering variable speed of the machines and power constraints are proposed. The direct and indirect workloads of the machines are considered to support the switch on/off decisions. A simulation model is developed to test the proposed policies compared to others presented in the literature. Regarding the job shop scheduling, a fixed and variable power constraints are considered, assuming the minimization of the makespan as the objective function. Studying the factory level, a mathematical model to design a flow line considering the possibility of using switch-off policies is developed. The design model for production lines includes a targeted imbalance among the workstations to allow for defined idle time. Finally, the main findings, results, and the future directions and challenges are presented
Solving no-wait two-stage flexible flow shop scheduling problem with unrelated parallel machines and rework time by the adjusted discrete Multi Objective Invasive Weed Optimization and fuzzy dominance approach
Purpose: Adjusted discrete Multi-Objective Invasive Weed Optimization (DMOIWO) algorithm, which uses fuzzy dominant approach for ordering, has been proposed to solve No-wait two-stage flexible flow shop scheduling problem.
Design/methodology/approach: No-wait two-stage flexible flow shop scheduling problem by considering sequence-dependent setup times and probable rework in both stations, different ready times for all jobs and rework times for both stations as well as unrelated parallel machines with regards to the simultaneous minimization of maximum job completion time and average latency functions have been investigated in a multi-objective manner. In this study, the parameter setting has been carried out using Taguchi Method based on the quality indicator for beater performance of the algorithm.
Findings: The results of this algorithm have been compared with those of conventional, multi-objective algorithms to show the better performance of the proposed algorithm. The results clearly indicated the greater performance of the proposed algorithm.
Originality/value: This study provides an efficient method for solving multi objective no-wait two-stage flexible flow shop scheduling problem by considering sequence-dependent setup times, probable rework in both stations, different ready times for all jobs, rework times for both stations and unrelated parallel machines which are the real constraints.Peer Reviewe
Optimised configuration of sensing elements for control and fault tolerance applied to an electro-magnetic suspension system
New technological advances and the requirements to increasingly abide
by new safety laws in engineering design projects highly affects industrial
products in areas such as automotive, aerospace and railway industries.
The necessity arises to design reduced-cost hi-tech products with minimal
complexity, optimal performance, effective parameter robustness properties,
and high reliability with fault tolerance. In this context the control system
design plays an important role and the impact is crucial relative to the level
of cost efficiency of a product.
Measurement of required information for the operation of the design
control system in any product is a vital issue, and in such cases a number of
sensors can be available to select from in order to achieve the desired system
properties. However, for a complex engineering system a manual procedure
to select the best sensor set subject to the desired system properties can
be very complicated, time consuming or even impossible to achieve. This is
more evident in the case of large number of sensors and the requirement to
comply with optimum performance.
The thesis describes a comprehensive study of sensor selection for control
and fault tolerance with the particular application of an ElectroMagnetic
Levitation system (being an unstable, nonlinear, safety-critical system with
non-trivial control performance requirements). The particular aim of the
presented work is to identify effective sensor selection frameworks subject to
given system properties for controlling (with a level of fault tolerance) the
MagLev suspension system. A particular objective of the work is to identify
the minimum possible sensors that can be used to cover multiple sensor faults,
while maintaining optimum performance with the remaining sensors.
The tools employed combine modern control strategies and multiobjective
constraint optimisation (for tuning purposes) methods. An important part
of the work is the design and construction of a 25kg MagLev suspension
to be used for experimental verification of the proposed sensor selection
frameworks
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