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Complex products, such as aircrafts and ships, are assembled from many parts and there are many available assembly sequences. Selecting the best from the available assembly sequences is challenging because of many factors, such as assembly performance, assemblability, assembly cost, assembly quality and assembly time. The purpose of this paper is to investigate a new and efficient algorithm aimed at this goal.

A new and efficient algorithm evaluating assembly sequences based on multi‐objective harmonious colony‐decision method is presented. This algorithm mainly includes three key steps: first, presenting the priority relationship between assembly sequences and the coefficient matrix for these objectives: assembly performance, assemblability, assembly cost, assembly quality, assembly time, and so on by several experts; second, calculating the maximum of harmonious values and harmonious priority value; third, if the maximum of harmonious priority value is not negative, the algorithm ends. Then the priority relationships of assembly sequences are obtained and the optimal assembly sequence can be selected.

This algorithm can efficiently support several experts to evaluate assembly sequences according to plenty of evaluation objectives and then to output a harmonious and recognized priority relationship of assembly sequences.

The algorithm is applied successfully to evaluate assembly sequences and select the optimal assembly sequence for component of aircraft's wing with fixtures.

This algorithm provides a new method to synthetically evaluate assembly sequences for complex product according to multi‐objectives.

The purpose of this paper is to find out the significant influence of assembly predicate consideration on optimal assembly sequence generation (ASG) in terms of search space, computational time and possibility of resulting practically not feasible assembly sequences. An appropriate assembly sequence results in minimal lead time and low cost of assembly. ASG is a complex combinatorial optimisation problem which deals with several assembly predicates to result an optimal assembly sequence. The consideration of each assembly predicate highly influences the search space and thereby computational time to achieve valid assembly sequence. Often, the ignoring an assembly predicate leads to inappropriate assembly sequence, which may not be physically possible, sometimes predicate assumption drastic ally raises the search space with high computational time.

The influence of assuming and considering different assembly predicates on optimal assembly sequence generation have been clearly illustrated with examples using part concatenation method.

The presence of physical attachments and type of assembly liaisons decide the consideration of assembly predicate to reduce the complexity of the problem formulation and overall computational time.

Most of the times, assembly predicates are ignored to reduce the computational time without considering their impact on the assembly sequence problem irrespective of assembly attributes. The current research proposes direction towards predicate considerations based on the assembly configurations for effective and efficient ASG.

This paper presents an algorithm for the generation of mechanical assembly sequences.

The algorithm employs an adjacency matrix, and uses three different mathematical patterns of subassemblies to generate automatically all geometrical feasible assembly sequences.

This algorithm cannot only generate automatically all geometrical feasible assembly sequences but also reduce the number of sequences.

Assembly modeling is more completed than that of previous research; the method is able to automatically generate all possible assembly sequences and be implemented easily with program; and this paper makes use of exact mathematical equations describing every subgroup of the three‐subassembly patterns.

The implied assembly constraints of a computer-aided design (CAD) model (e.g. hierarchical constraints, geometric constraints and topological constraints) represent an important basis for product assembly sequence intelligent planning. Assembly prior knowledge contains factual assembly knowledge and experience assembly knowledge, which are important factors for assembly sequence intelligent planning. This paper aims to improve monotonous assembly sequence planning for a rigid product, intelligent planning of product assembly sequences based on spatio-temporal semantic knowledge is proposed.

A spatio-temporal semantic assembly information model is established. The internal data of the CAD model are accessed to extract spatio-temporal semantic assembly information. The knowledge system for assembly sequence intelligent planning is built using an ontology model. The assembly sequence for the sub-assembly and assembly is generated via attribute retrieval and rule reasoning of spatio-temporal semantic knowledge. The optimal assembly sequence is achieved via a fuzzy comprehensive evaluation.

The proposed spatio-temporal semantic information model and knowledge system can simultaneously express CAD model knowledge and prior knowledge for intelligent planning of product assembly sequences. Attribute retrieval and rule reasoning of spatio-temporal semantic knowledge can be used to generate product assembly sequences.

The assembly sequence intelligent planning example of linear motor highlights the validity of intelligent planning of product assembly sequences based on spatio-temporal semantic knowledge.

The spatio-temporal semantic information model and knowledge system are built to simultaneously express CAD model knowledge and assembly prior knowledge. The generation algorithm via attribute retrieval and rule reasoning of spatio-temporal semantic knowledge is given for intelligent planning of product assembly sequences in this paper. The proposed method is efficient because of the small search space.

Assembly sequence optimization is a difficult combinatorial optimization problem having to simultaneously satisfy various feasibility constraints and optimization criteria. Applications of evolutionary algorithms have shown a lot of promise in terms of lower computational cost and time. But there remain challenges like achieving global optimum in least number of iterations with fast convergence speed, robustness/consistency in finding global optimum, etc. With the above challenges in mind, this study aims to propose an improved flower pollination algorithm (FPA) and hybrid genetic algorithm (GA)-FPA.

In view of slower convergence rate and more computational time required by the previous discrete FPA, this paper presents an improved hybrid FPA with different representation scheme, initial population generation strategy and modifications in local and global pollination rules. Different optimization objectives are considered like direction changes, tool changes, assembly stability, base component location and feasibility. The parameter settings of hybrid GA-FPA are also discussed.

The results, when compared with previous discrete FPA and GA, memetic algorithm (MA), harmony search and improved FPA (IFPA), the proposed hybrid GA-FPA gives promising results with respect to higher global best fitness and higher average fitness, faster convergence (especially from the previously developed variant of FPA) and most importantly improved robustness/consistency in generating global optimum solutions.

It is anticipated that using the proposed approach, assembly sequence planning can be accomplished efficiently and consistently with reduced lead time for process planning, making it cost-effective for industrial applications.

Different representation schemes, initial population generation strategy and modifications in local and global pollination rules are introduced in the IFPA. Moreover, hybridization with GA is proposed to improve convergence speed and robustness/consistency in finding globally optimal solutions.

The purpose of this paper is to develop an efficient hybrid method that can collectively address assembly sequence generation (ASG) and exploded view generation (EVG) problem effectively. ASG is an act of finding feasible collision free movement of components of a mechanical product in accordance with the assembly design. Although the execution of ASG is complex and time-consuming in calculation, it is highly essential for efficient manufacturing process. Because of numerous limitations of the ASG algorithms, a definite method is still unavailable in the computer-aided design (CAD) software, and therefore the explosion of the product is not found to be in accordance with any feasible disassembly sequence (disassembly sequence is reverse progression of assembly sequence). The existing EVG algorithms in the CAD software result in visualization of the entire constituent parts of the product over single screen without taking into consideration the feasible order of assembly operations; thus, it becomes necessary to formulate an algorithm which effectively solves ASG and EVG problem in conjugation. This requirement has also been documented as standard in the “General Information Concerning Patents: 1.84 Standards for drawings” in the United States Patent and Trademark office (2005) which states that the exploded view created for any product should show the relationship or order of assembly of various parts that are permissible.

In this paper, a unique ASG method has been proposed and is further extended for EVG. The ASG follows a deterministic approach to avoid redundant data collection and calculation. The proposed method is effectively applied on products which require such feasible paths of disassembly other than canonical directions.

The method is capable of organizing the assembly operations as linear or parallel progression of assembly such that the assembly task is completed in minimum number of stages. This result is further taken for EVG and is found to be proven effective.

Assembly sequence planning (ASP) is performed most of the times considering the geometric feasibility along canonical axes without considering parallel possibility of assembly operations. In this paper, the proposed method is robust to address this issue. Exploded view generation considering feasible ASP is also one of the novel approaches illustrated in this paper.

The purpose of this paper is to present a new generic Petri net (PN) model based on assembly plan for assembly sequence optimization. The model aims to allow modeling the flexible assembly system (FAS) configuration, determining the optimal work in process, lead time, throughput, and utilization of each station. Moreover, it aims to show assembly features (AFs) as being useful in assembly sequence planning.

Sophisticated knowledge of AFs is used to get very few feasible assembly sequences (ASs) rather than all possible ASs for a product. A PN model is developed to find out the near optimal assembly sequence out of the sequences obtained from the AF knowledge. It is also used for design and performance evaluation of FAS. Multiple optimization criteria are used for assembly sequence optimization, keeping in view the line balancing. The PN is optimized using weighted‐WIP when the throughput is bounded by the utilization of the bottleneck machines.

The results achieved from the example show a considerable reduction in the number of feasible ASs for a product. The PN optimization gives minimum WIP corresponding to the maximum production rate. Moreover, the PN model pushes more inventories to the initial assembly phase.

The proposed PN can be easily extended for inclusion of dual kanban, where the managers may adjust the number of kanban cards as per the requirement.

Managers may use the concept of multiple AFs in order to design and operate robot assembly that will result in more efficient sequence planning. Using the PN model, the assembly manager may design, analyze, evaluate, and even optimize the layout of the FAS for minimum WIP, maximum throughput, and reduced lead time. The determination of total WIP, total number of stations in the assembly line, and the number of servers at each station may be helpful in the factory floor management. Line balancing may result in the highest efficiency and the shortest idling time along with ease of management and supervision.

This paper provides a clear insight into how a large reduction in the number of feasible ASs for a product can be obtained using the knowledge of AFs. It also presents a new PN model used for assembly sequence optimization and design and performance analysis of FAS.

There is a rich body of literature on sequencing assembly and on sequencing disassembly, but little that either fuses or contrasts the two, which may be valuable for long-range planning in the closed-loop supply chain and simply convenient in terms of consistency in nomenclature and mathematical formulations. The purpose of this paper is to concisely unify and summarize assembly and disassembly formulae – as well as to add new formulations for completeness – and then demonstrate the similarities and differences between assembly and disassembly.

Along with several familiar assembly-line formulae which are adapted here for disassembly, five (two specific and three general) metrics and a comparative performance formula from disassembly-line balancing are proposed for use in assembly- and disassembly-line sequencing and balancing either directly, through generalization, or with some extension. The size of assembly and disassembly search spaces are also quantified and formulated. Three new metrics are then developed from each of the general metrics to demonstrate the process of using these general formulae as prototypes.

The three new metrics along with several of the original metrics are selectively applied to a simple, notional case study product to be sequenced on an assembly line and then on a disassembly line. Using these analytical results, the inherent differences between assembly and disassembly, even for a seemingly trivial product, are illustrated.

The research adds several new assembly/disassembly metrics, a case study, unifies the evaluation formulae that assembly and disassembly hold in common as well as structuring prototype formulae for flexibility in generating new evaluation criteria for both, and quantifies (using the case study) how assembly and disassembly – while certainly possessing similarities – also demonstrate measurable differences that can be expected to affect product design, planning, production, and end-of-life processing.

The purpose of this paper is to develop a new methodology to find out the best robotic assembly sequence amongst feasible robotic sequences.

The feasible robotic assembly sequences were generated based on the assembly constraints and later and artificial immune system (AIS) was implemented to find out the best assembly sequence.

The paper reveals the best assembly sequence.

Robotic assembly has expanded the process capabilities in the manufacturing world because of the fact that it is faster, more efficient, precise and cost‐effective process than any conventional mechanized process. Since a robotic system is a cost‐intensive one it is necessary to find out the correct and optimal sequence with the constraints of the process in mind while dealing with assembled products with large number of parts.

The purpose of this paper is to present and develop an ontology-based approach for automatic generation of assembly sequences.

In this approach, an assembly sequence planning ontology is constructed to represent the structure and interrelationship of product geometry information and assembly process information. In the constructed ontology, certain reasoning rules are defined to describe the knowledge and experience. Based on the ontology with reasoning rules, the algorithm for automatically generating assembly sequences is designed and implemented.

The effectiveness of this approach is verified via applying it to generate the assembly sequences of a gear reducer.

The main contribution of the paper is presenting and developing an ontology-based approach for automatically generating assembly sequences. This approach can provide a feasible solution for the issue that mathematics-based assembly sequence generation approaches have great difficulty in explicitly representing assembly experience and knowledge.