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Assembly sequence planning (ASP) plays a vital role in assembly process because it directly influences the feasibility, cost and time of the assembly process. The purpose of this study is to solve ASP problem more efficiently than current algorithms.

A novel assembly subsets prediction method based on precedence graph is proposed to solve the ASP problem. The proposed method adopts the idea of local to whole and integrates a simplified firework algorithm. First, assembly subsets are generated as initial fireworks. Then, each firework explodes to several sparks with higher-level assembly subsets and new fireworks are selected for next generation according to selection strategy. Finally, iterating the algorithm until complete and feasible solutions are generated.

The proposed method performs better in comparison with state-of-the-art algorithms because of the balance of exploration (fireworks) and exploitation (sparks). The size of initial fireworks population determines the diversity of the solution, so assembly subsets prediction method based on precedence graph (ASPM-PG) can explore the solution space. The size of sparks controls the exploitation ability of ASPM-PG; with more sparks, the direction of a specific firework can be adequately exploited.

The proposed method is with simple structure and high efficiency. It is anticipated that using the proposed method can effectively improve the efficiency of ASP and reduce computing cost for industrial applications.

The proposed method finds the optimal sequence in the construction process of assembly sequence rather than adjusting order of a complete assembly sequence in traditional methods. Moreover, a simplified firework algorithm with new operators is introduced. Two basic size parameters are also analyzed to explain the proposed method.

The ever‐increasing demand for rationalization of manufacturing activities requires closer co‐operation and integration between design and production planning. Since the impact on the overall cost of production of designing a product for ease of assembly was realized, feedback from assembly planning to the design process has become important for improving the design. Traditionally, planning for assembly takes place after the design of the product has been completed. Often it proves necessary to modify a design, because some aspect is not particularly convenient or efficient under the available assembly facilities. However, considerable time and cost savings can be made by making assembly‐planning information available to design engineers, so that they can take account of assembly factors when designing a product.

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.

To improve the efficiency of end-of-life product’s disassembly process, this paper aims to propose a disassembly sequence planning (DSP) method to reduce additional efforts of removing parts when considering the changes of disassembly directions and tools.

The methodology has three parts. First, a disassembly hybrid graph model (DHGM) was adopted to represent disassembly operations and their precedence relations. After representing the problem as DHGM, a new integer programming model was suggested for the objective of minimizing the total disassembly time. The objective takes into account several criteria such as disassembly tools change and the change of disassembly directions. Finally, a novel hybrid approach with a chaotic mapping-based hybrid algorithm of artificial fish swarm algorithm (AFSA) and genetic algorithm (GA) was developed to find an optimal or near-optimal disassembly sequence.

Numerical experiment with case study on end-of-life product disassembly planning has been carried out to demonstrate the effectiveness of the designed criteria and the results exhibited that the developed algorithm performs better than other relevant algorithms.

More complex case studies for DSP problems will be introduced. The performance of the CAAFG algorithm can be enhanced by improving the design of AFSA and GA by combining them with other search techniques.

DSP of an internal gear hydraulic pump is analyzed to investigate the accuracy and efficiency of the proposed method.

This paper proposes a novel CAAFG algorithm for solving DSP problems. The implemented tool generates a feasible optimal solution and the considered criteria can help the planer obtain satisfactory results.

Environmental problems in manufacturing industries are a global issue owing to severe lack fossil resources. In assembly sequence planning (ASP), the research effort mainly aims to improve profit and human-related factors, but it still lacks in the consideration of the environmental issue. This paper aims to present an energy-efficient model for the ASP problem.

The proposed model considered energy utilization during the assembly process, particularly idle energy utilization. The problem was then optimized using moth flame optimization (MFO) and compared with well-established algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO). A computational test was conducted using five assembly problems ranging from 12 to 40 components.

The results of the computational experiments indicated that the proposed model was capable of generating an energy-efficient assembly sequence. At the same time, the results also showed that MFO consistently performed better in terms of the best and mean fitness, with acceptable computational time.

This paper proposed a new energy-efficient ASP model that can be a guideline to design assembly station. Furthermore, this is the first attempt to implement MFO for the ASP problem.

This paper aims to optimize the assembly sequence planning (ASP) problem using a proposed hybrid algorithm based on Ant Colony Optimization (ACO) and Gray Wolf Optimizer (GWO). The proposed Hybrid Ant-Wolf Algorithm (HAWA) is designed to overcome premature convergence in ACO.

The ASP problem is formulated by using task-based representation. The HAWA adopts a global pheromone-updating procedure using the leadership hierarchy concept from the GWO into the ACO to enhance the algorithm performance. In GWO, three leaders are assigned to guide the search direction, instead of a single leader in most of the metaheuristic algorithms. Three assembly case studies used to test the algorithm performance.

The proposed HAWA performed better in comparison to the Genetic Algorithm, ACO and GWO because of the balance between exploration and exploitation. The best solution guides the search direction, while the neighboring solutions from leadership hierarchy concept avoid the algorithm trapped in a local optimum.

The originality of this research is on the proposed HAWA. In addition to the standard pheromone-updating procedure, a global pheromone-updating procedure is introduced, which adopted leadership hierarchy concept from GWO.

The purpose of this paper is to propose a four‐level hierarchy model for multimedia documents representation to be used during the dynamic scheduling and altering of multimedia contents.

The four‐level hierarchy model (object, operation, timing, and precedence), offers a fine‐grain representation of multimedia contents and is embedded within a research tool, which is called WEBCAP. WEBCAP utilizes the four‐level hierarchy to synchronize the retrieval of objects in the multimedia document employing Allen's temporal relations, and then applies the Bellman‐Ford's algorithm on the precedence graph to schedule all operations (fetch, transmit, process, and render), while satisfying the in‐time updating and all web workload's resources constraints.

The experimental results demonstrate the effectiveness of the model in scheduling the periodical updating multimedia documents while considering a variety of workloads on web/TCP.

WEBCAP should be enhanced to automatically measure and/or approximate the available bandwidth of the system using sophisticated measurement of end‐to‐end connectivity. In addition, WEBCAP should be expanded and enhanced to examine system infrastructure for more real‐time applications, such as tele‐medicine and e‐learning.

WEBCAP can be used as an XML markup language for describing multimedia presentations. It can be used to create online presentations similar to PowerPoint on desktop environment, or used as an interactive e‐learning tool. An HTML browser may use a WEBCAP plug‐in to display a WEBCAP document embedded in an HTML/XML page.

This paper proposed a dynamic scheduling of multimedia documents with frequent updates taking into consideration the network's workload to reduce the packet lost ratio in the TCP flow, especially in the early stages. WEBCAP can be used to guide distributed systems designers/managers to schedule or tune their resources for optimal or near optimal performance, subject to minimizing the cost of document retrieval while satisfying the in time constraints.

In this paper we discuss the problem of defining the manufacturing route taken by parts in terms of their technological features. We look closely at the use of the new computer aided process planning (CAPP) systems, and at the need to define groups of operations which will facilitate the generation of the route sheet. Generating different alternatives must be made easier to avoid calculating a range of incoherent possibilities. Our aim was to define groups of precedence between machine operations, which would allow us to reduce the alternatives and therefore, calculate and study the truly plausible routes while ignoring those which, despite being mathematically computable, would not be technologically coherent. In order to determine these groups of precedence, we evaluated a range of parts, which are produced by metal removal using applied group technology. For each one of the parts we generated the possible manufacturing alternatives, and applying graph theory, we determined which operations generally preceded others. The results obtained allowed us to reduce considerably the computational possibilities and thus save time in generating the route sheet.

The aim of this research is to discuss the benefits of U-shaped layout for production cell operating in variable takt time. Different experiments were conducted using benchmarks to highlight the performance gap between a linear cell and a U-Cell.

The implementation of the production cell, either in a U-shaped or in a straight line layout, is optimized through linear programming based on the number of operators. The two corresponding programs, in Mosel language, use the same approach to not introduce bias in the comparison of results. The study used the authors’ own datasets and other well-known academic benchmarks.

A comparison was conducted between the obtained takt times, with equivalent operating conditions, in both U-Cell and linear cell. A significant increase of the production rate was observed. This increase has often exceeded 10 per cent, reaching 32 per cent. All the experiments show that, with the same number of operators, a cell in a U-shaped layout is always at least as efficient, in terms of attainable production rates, than an equivalent linear cell. Ninety-six per cent of the studied cases give an improvement of production rate. Moreover, the dispersion of the U-Cell results is weaker, which suggests that the U-shaped layout gives better performances in more robust manner.

Results were obtained through a study of various academic benchmarks. The results must be validated on industrial situations.

This paper will be very useful for researchers and practitioners to understand lean implementations and their derived benefits. This paper will allow them to evaluate and analyze the expected benefits of the implementation of the production cell in the U-shaped layout (operating in variable takt time).

U-Cells constitute an appropriate solution for a layout of any kind of production cells with a variable structure (variability of the number of operators, of the organization of the cell, of the takt time […]). When facing a significant variation in the demand, the response consists of adjusting the number of operators assigned to the cell. This study jointly addresses the problem of the U-shaped layout and the operation in variable takt time.