Assembly Sequence Planning Research Articles

Resource Efficient Configuration of an Aircraft Assembly Line

The design of efficient assembly systems can significantly contribute to the profitability of products and the competitiveness of manufacturing industries. The configuration of a an efficient assembly line can be supported by suitable methodologies and techniques, such as design for manufacture and assembly, assembly sequence planning, assembly line balancing, lean manufacturing and optimization techniques. In this paper, these methods are applied with reference to the industrial case study of the assembly line of a Skycar light aircraft. The assembly process sequence is identified taking into account the analysis of the assembly structure and the required precedence constraints, and diverse techniques are applied to optimize the assembly line performance. Different line configurations are verified through discrete event simulation to assess the potential increase of efficiency and throughput in a digital environment and propose the most suitable configuration of the assembly line.

Optimization of Assembly Sequence Plan Using Digital Prototyping and Neural Network

Carrying out assembly process planning tasks manually is both time-consuming and requires a lot of effort. To gain an insight into the processes that occur during a design and assembly task, time and motion study methods are utilized in both digital prototyping and assembly sequence programming to carry out the investigation. In addition, a brief investigation has been carried out to examine the time savings of using the virtual tools in comparison with the traditional manual method. The whole process comprised of three steps viz. digital prototyping, assembly representation i.e. assembly part relationships and generating optimized assembly sequence plans. The process resulted in 60% saving in time, 63% saving in assembly cost and 150% improvement in the productivity.

Assembly sequence planning based on an improved harmony search algorithm

As a typical, discrete, and NP-hard problem, assembly sequence planning (ASP) has direct impact on assembly quality and costs. So far, lots of graph-based and meta-heuristic approaches cannot solve this problem effectively. This paper proposes an effective ASP algorithm based on the harmony search (HS) algorithm, which has an outstanding global search ability to obtain the global optimum more efficiently. To solve the ASP problem, an improved harmony search (IHS) algorithm is proposed mainly in four aspects: (1) an encoding of harmony is designed based on ASP problems; (2) an initial harmony memory (HM) is established using the opposition-based learning (OBL) strategy; (3) a particular way to improvise a new harmony is developed; and (4) a local search strategy is introduced to accelerate the convergence speed. Finally, the advantage of the proposed ASP algorithm over the competing algorithms in solving ASP problems is verified by two experiments. Moreover, the proposed ASP algorithm is applied to a practical problem, a propeller of azimuth thruster to verify its practicability.

Improved Cat Swarm Optimization Algorithm for Assembly Sequence Planning

Assembly sequence planning (ASP) is a combinatorial optimization problem in which the order for each part and subassembly is determined. This order is then incorporated into an incrementally expanding subassembly and eventu- ally results in a final assembly. To address this problem, we propose an improved cat swarm optimization (CSO) algo- rithm and redefine some basic CSO concepts and operations according to ASP characteristics. The feasibility and the sta- bility of this improved CSO are verified through an assembly experiment. The improved CSO is also compared with par- ticle swarm optimization. Experimental results show that the proposed algorithm effectively solves the ASP problem; thus, the application of the proposed algorithm should enhance ASP level.

Integrated assembly sequence planning and assembly line balancing with ant colony optimization approach

This paper proposes an approach for solving integrated assembly sequence planning and assembly line balancing with ant colony algorithm. In integrated assembly sequence planning and assembly line balancing, the assembly task priority diagram is built based on the assembly information, and the assembly sequences are evaluated in each workstation of the assembly line separately; for calculating the total assembly time in the workstation, besides the assembly task time, the time for changing assembly directions, changing assembly tools, and the time for moving the heavy parts in the workstation are also considered. The fitness function is built and the constraints are discussed; an ant colony algorithm is proposed to carry out assembly sequence planning and assembly line balancing simultaneously, based on the searching mechanism and the pheromone updating mechanism, to obtain the optimal solutions under the given cycle time. Finally, case studies are given to verify the proposed approach.

A weighted assembly precedence graph for assembly sequence planning

Assembly sequence planning is one of the well-known combinatorial optimization problems in manufacturing. An assembly is often represented as an assembly relation graph or precedence graph. The traditional methods are used to generate a large number of feasible assembly sequences and then find the optimal sequence through evaluation. A lot of computation resources are needed. To reduce the complexity of assembly sequence planning, the assembly is converted into a weighted assembly precedence graph considering multiple assembly constraints, i.e., the qualitative and quantitative constraints. The vertices in the weighted precedence graph are the parts or components. The qualitative constraints including the topological and geometrical assembly constraints guarantee to derive the feasible assembly sequences. Some process constraints are also taken as the qualitative constraints. They are represented as the directed edges in the weighted assembly precedence graph. The other assembly constraints, such as the stable support, connector strength, changes of assembly directions, and tools and so forth, are quantified as indices to compute the cost of assembly relations with the fuzzy analytical hierarchy process. The costs are taken as the heuristic information to find the optimal or near-optimal assembly sequences. With the weighted assembly precedence graph, the search space of the optimal assembly sequence will be reduced. We design a minimum spanning tree-based algorithm to detect the optimal assembly sequence based on the weighted assembly precedence graph. The optimal assembly sequences are found in O(n 3) computation time, where n is the number of the discrete parts.

The modified firefly algorithm considering fireflies’ visual range and its application in assembly sequences planning

This paper proposes a modified discrete firefly algorithm (MDFA) to solve the problem of assembly sequence planning. Firstly, to improve the performance of the firefly algorithm (FA), we proposed a MDFA by endowing the fireflies with the capability of changeable visual range. The computing case shows the proposed algorithm is more effective and robust than standard FA, genetic algorithm and particle swarm optimization algorithm. Secondly, a method of how to set parameters for FA and MDFA is proposed. This method is practical in the application of FA to solve discrete problem. Thirdly, to make the sequences more closer to real industrial requirements, a so called process precedence relations (PPR) evaluation function is presented, which not only considering the interference between parts, assembly tools and clamps, but also regarding the assembly order between parts and their reference parts. Finally, the evolution performance of the MDFA is investigated, and the performance of the proposed approach to solve ASP is verified through two cases study.

Assembly sequence planning of rigid and flexible parts

Assembly sequence planning (ASP) is the process of computing a sequence of assembly motions for constituent parts of an assembled final product. ASP is proven to be NP-hard and thus its effective and efficient solution has been a challenge for the researchers in the field. Despite the fact that most assembled products like ships, aircrafts and automobiles are composed of rigid and flexible parts, no work exists for assembly/disassembly sequence planning of flexible parts. This paper lays out a theoretical ground for modeling the deformability of flexible assembly parts by introducing the concept of Assembly stress matrix (ASM) to describe interference relations between parts of an assembly and the amount of compressive stress needed for assembling flexible parts. Also, the Scatter Search (SS) optimization algorithm is customized for this problem to produce high-quality solutions by simultaneously minimizing both the maximum applied stress exerted for performing assembly operations and the number of assembly direction changes. The parameters of this algorithm are tuned by a TOPSIS-Taguchi based tuning method. A number of ASP problems with rigid and flexible parts were solved by the presented SS and other algorithms like Genetic and Memetic algorithms, Simulated Annealing, Breakout Local Search, Iterated Local Search, and Multistart Local Search, and the results and their in-depth statistical analyses showed that the SS outperformed other algorithms by producing the best-known or optimal solutions with highest success rates.

A formal ontology-based spatiotemporal mereotopology for integrated product design and assembly sequence planning

This paper introduces the development of a formal ontology based on spatiotemporal mereotopology in the context of integrated assembly design and sequence planning. The main objective is to make assembly information accessible and exploitable by information management systems and computer-aided X tools in order to support product architects and designer’s activities. Indeed product design information and knowledge as well as the related assembly sequence require a semantic and logical foundation in order to be managed consistently and processed proactively. In this context, product relationships are considered and described in the part-whole theory supported by mereology and its extension, mereotopology. Firstly, a literature survey on formalisms in assembly-oriented design, mereotopological theories and spatiotemporal ontologies is presented so as to highlight the current research issue. Then, a mathematical description approach of product relationships based on mereotopology and temporal relationships is introduced. Built on this, an ontological development of the proposed theory using description logic rules is proposed. Finally, a mechanical assembly, as a case study, is introduced to illustrate the relevance of the proposed ontology.

Assembly system synthesis using association rule discovery

Assembly is the final process for product realization in which various parts, modules, and sub-assemblies are put together to build a product. Designing an assembly system typically involves many issues such as assembly equipment selection, assembly sequence planning, system layout, task assignments, and line balancing. Assembly system synthesis, specifically the choice of assembly resources/equipment, is examined in this paper. An association rule discovery model is employed to extract association relationships between existing and/or previous products and the systems used to assemble them. The extracted knowledge is used to synthesize assembly systems for new products that fall within the scope of their predecessors. The developed method is demonstrated using two examples from the automotive industry. System synthesis results that are consistent with the used assembly data instances are obtained in both examples. The presented systematic system synthesis method supports the utilization of existing legacy data in developing assembly systems for new product generations. This will lead to a significant reduction of time and effort needed to design assembly systems, particularly in applications that feature frequent design changes and updates, as in the automotive industry.

An interoperability CAD assembly sequence plan approach

The assembly sequence plan (ASP) is a relatively new concept that has drawn the attention of many researchers over the last years. In spite of the enormous development of CAD Software in many aspects, the automatic generation of ASP from CAD assembly remains a subject to improve especially with the growing complexity of industrial products. This article presents an ASP generation approach based on CAD assembly data. To generate ASP, the developed method is composed of two mains steps. The first one is the extraction of the CAD assembly data. To identify all possible obstacles during the components’ displacement, an interference analysis process is performed in the second step. An illustrative example of the CAD assembly is detailed in the different stages of this article.

An assembly sequence planning approach with a rule-based multi-state gravitational search algorithm

Assembly sequence planning (ASP) becomes one of the major challenges in product design and manufacturing. A good assembly sequence leads to reduced costs and duration in the manufacturing process. However, assembly sequence planning is known to be a classical NP-hard combinatorial optimization problem; ASP with many product components becomes more difficult to solve. In this paper, an approach based on a new variant of the gravitational search algorithm (GSA) called the rule-based multi-state gravitational search algorithm (RBMSGSA) is used to solve the assembly sequence planning problem. As in the gravitational search algorithm, the RBMSGSA incorporates Newton’s law of gravity, the law of motion, and a rule that makes each assembly component of each individual solution occur once based on precedence constraints; the best feasible sequence of assembly can then be determined. To verify the feasibility and performance of the proposed approach, a case study has been performed and a comparison has been conducted against other three approaches based on simulated annealing (SA), a genetic algorithm (GA), and binary particle swarm optimization (BPSO). The experimental results show that the proposed approach has achieved significant improvement in performance over the other methods studied.

A breakout local search (BLS) method for solving the assembly sequence planning problem

Being one of the main subproblems of the broader Assembly Planning (AP) problem, Assembly Sequence Planning (ASP) is defined as the process of computing a sequence of assembly motions for constituent parts of an assembled final product. ASP is proven to be NP-complete and thus its effective and efficient solution has been a challenge for the researchers in the field. However, despite the existence of numerous works on solving the ASP, the topology, structure, and complexity of the problem׳s search space (i.e., the fitness landscape) has not been studied yet. In this article, the fitness landscape of the ASP problem is analyzed for five typical assembled products through various distribution and correlation statistical measures, which reveals that locally optimal assembly sequences are distributed in the problem׳s landscape nearly uniformly. Based on this result, a suitable optimization algorithm called Breakout Local Search (BLS) is selected and customized for obtaining high-quality solutions to ASP. A number of ASP problems are solved by the presented BLS and other algorithms in the ASP literature, including simulated annealing, genetic algorithm, memetic algorithm, harmony search, hybrid immune systems-particle swarm optimization, as well as by other variants of local search like iterated local search and multi-start local search. Experimental results and their in-depth statistical analyses show that the BLS outperforms other algorithms by producing the best-known or optimal solutions most of the time.

Supplier Selection and Production Planning by Using Guided Genetic Algorithm and Dynamic Nondominated Sorting Genetic Algorithm II Approaches

Through the global supply chain (SC), numerous firms participate in vertically integrated manufacturing, and industrial collaboration and cooperation is the norm. SC management activities, such as delivery time, quality, and defect rate, are characterized by uncertainty. Based on all of the aforementioned factors, this study established a multiobjective mathematical model, integrating the guided genetic algorithm (Guided-GA) and the nondominated sorting genetic algorithm II (NSGA-II), developed in previous studies, to improve the mechanisms of the algorithms, thereby increasing the efficiency of the model and quality of the solution. The mathematical model was used to address the problems of supplier selection, assembly sequence planning, assembly line balancing, and defect rate, to enable suppliers to respond rapidly to sales orders. The model was empirically tested using a case study, showing that it is suitable for assisting decision makers in planning production and conducting SS according to sales orders, enabling production activities to achieve maximum efficiency and the competitiveness of firms to improve.

An Improved Shuffled Frog Leaping Algorithm for Assembly Sequence Planning of Remote Handling Maintenance in Radioactive Environment

Assembly sequence planning (ASP) of remote handling maintenance in radioactive environment is a combinatorial optimization problem. This study proposes an improved shuffled frog leaping algorithm (SFLA) for the combinatorial optimization problem of ASP. An ASP experiment is conducted to verify the feasibility and stability of the improved SFLA. Simultaneously, the improved SFLA is compared with SFLA, genetic algorithm, particle swarm optimization, and adaptive mutation particle swarm optimization in terms of efficiency and capability of locating the best global assembly sequence. Experiment results show that the proposed algorithm exhibits outstanding performance in solving the ASP problem. The application of the proposed algorithm should increase the level of ASP in a radioactive environment.

Novel Approach to Automatically Generate Feasible Assembly Sequence

Today the assembly sequence for the items is regularly completed manually and its definition, typically, is extremely extravagant, not ensuring ideal arrangements. Gathering arrangement arranging utilizing a business framework regularly depends on a master assembly sequence organizer, and it is dominatingly done manually. The difficulties to consequently produce gathering arrangements utilizing CAD models lie as a part of smart thinking and investigation of the displayed assembly information. This work displays a programmed approach expected to characterize gathering sequences, based on the data containing the mates, obstruction and the volume information existing among the parts, which is acquired by the assembly CAD model of the item. This paper exhibits a framework that can examine and use assembly information accessible from a CAD model to produce gathering arrangements. The framework likewise considers client input as a kind of assembly obligation. The framework is equipped for creating a set of positioned attainable assembly arrangement plans for an administrator to assess. A matrix approach has been embraced to process the data held from a CAD model. Obstruction and volume studies are completed amid the formation of assembly sequence plans.

Toward an integrated CAD assembly sequence planning solution

Assembly sequence planning has drawn the attention of many researchers over the last years. Despite, the great development of CAD software in many aspects (simulation algorithms, interfaces, and ergonomics), automatic generation of assembly sequence planning from CAD assembly remains an issue to improve especially with the increasing complexity of industrial products. In this paper, an approach of assembly sequence planning generation from a CAD assembly model is presented. In order to generate feasible assembly sequence planning, the developed approach begins with the exploration of CAD assembly. Then, a collision analysis is performed to identify all possible obstacles during the components’ motion. An example of CAD assembly is presented in all sections of this paper in order to demonstrate the efficiency of the proposed approach.

Studies on Techniques of Integrated House Assembly Simulation

This thesis introduces integrated house assembly simulation platform technology. It is divided into three categories: assembly simulation for the center with product design, assembly simulation for the center with assembly planning and assembly simulation for the center with virtual prototype. According to the characteristics of the integrated house assembly and modeling, it puts forward level assembly analysis method and studies the assembly system planning, assembly sequence planning and assembly path planning simulation technology. The results show that through the production system and assembly operation simulation technology research, it improves the rationality of the integrated house assembly process planning.

Research of the Assembly Model Based on Parts Attribute Semantic

Aiming at the problems in the modeling of assembly sequence planning, this paper proposes a assembly model based on the semantic and two-dimensional matrix which is easy to calculate and expression. The paper complement the parts attribute semantic and it is used to make the assembly model .So the parts about the attributes of parts assembly can be used in the ASP. Assembly relations including Assembly Precedence Relations (APRs), Contact semantic, Connection and Transmission semantic are transformed into a matrix model. Finally shaft structure is used as a example to represent a competitive advantage of this model.

A technical comment on “a review on assembly sequence planning and assembly line balancing optimisation using soft computing approaches”

Assembly line balancing plays a significant role in mass production systems due to production efficiency and productivity. A number of researchers have recently shown great interest in this topic, especially in the area of simple assembly line balancing problems (SALBP), and several academic papers have been published on this topic using different exact, heuristic, and metaheuristic methods. Recently, Rashid et al. (Int J Adv Manuf Technol 59:335–349, 2012) reviewed one decade (2000–2010) of published studies on assembly sequence planning and assembly line balancing optimization, those studies that applied soft computing approaches. The scope of the Rashid et al. review in the area of assembly line balancing problems was reported to be on SALBP. In this paper, we suggest that the review by Rashid et al. and the conclusion drawn regarding SALBP are inaccurate in some parts, and some revisions to Rashid et al. (Int J Adv Manuf Technol 59:335–349, 2012) are necessary. Accordingly, a revision to Rashid et al.’s review paper is proposed, and a guide to future research is presented. Moreover, to have up-to-date information, the review is extended to also include the published studies in the period of 2011–2013.