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The 4th industrial revolution (Industry 4.0, I4.0) is based upon the penetration of many new technologies to the industrial world. These technologies are posed to fundamentally change assembly lines around the world. Assembly systems transformed by I4.0 technology integration are referred to here as Assembly 4.0 (A4.0). While most I4.0 new technologies are known, and their integration into shop floors is ongoing or imminent, there is a gap between this knowledge and understanding the form and the impact of their full implementation in assembly systems. The path from the new technological abilities to improved productivity and profitability has not been well understood and has some missing parts. This paper strives to close a significant part of this gap by creating a road map to understand and explore the impact of typical I4.0 new technologies on A4.0 systems. In particular, the paper explores three impact levels: strategic, tactical, and operational. On the strategic level, we explore aspects related to the design of the product, process, and the assembly system. Additionally, the paper elaborates on likely changes in assembly design aspects, due to the flexibility and capabilities that these new technologies will bring. Strategic design also deals with planning and realizing the potential of interactions between sub-assembly lines, kitting lines, and the main assembly lines. On the tactical level, we explore the impact of policies and methodologies in planning assembly lines. Finally, on the operational level, we explore how these new capabilities may affect part routing and scheduling including cases of disruptions and machine failures. We qualitatively assess the impact on performance in terms of overall flow time and ability to handle a wide variety of end products. We point out the cases where clear performance improvement is expected due to the integration of the new technologies. We conclude by identifying research opportunities and challenges for advanced assembly systems.

In this paper, a new modified particle swarm optimization algorithm with negative knowledge is proposed to solve the mixed-model two-sided assembly line balancing problem. The proposed approach includes new procedures such as generation procedure which is based on combined selection mechanism and decoding procedure. These new procedures enhance the solution capability of the algorithm while enabling it to search at different points of the solution space, efficiently. Performance of the proposed approach is tested on a set of test problem. The experimental results show that the proposed approach can be acquired distinguished results than the existing solution approaches.

Purpose: Time and Space assembly line balancing problem (TSALBP) is the problem of balancing the line taking the area required by the task and to store the tools into consideration. This area is important to be considered to minimize unplanned traveling distance by the workers and consequently unplanned time waste. Although TSALBP is a realistic problem that express the real-life situation, and it became more practical to consider multi-manned assembly line to get better space utilization, few literatures addressed the problem of time and space in simple assembly line and only one in multi-manned assembly line. In this paper the problem of balancing bi-objective time and space multi-manned assembly line is proposed. Design/methodology/approach: Hybrid genetic algorithm under time and space constraints besides assembly line conventional constraints is used to model this problem. The initial population is generated based on conventional assembly line heuristic added to random generations. The objective of this model is to minimize number of workers and number of stations. Findings: The results showed the effectiveness of the proposed model in solving multi-manned time and space assembly line problem. The proposed method gets better results in solving real-life Nissan problem compared to the literature. It is also found that there is a relationship between the variability of task time, maximum task time and cycle time on the solution of the problem. In some problem features it is more appropriate to solve the problem as simple assembly line than multi-manned assembly line. Originality/value: It is the first article to solve the problem of balancing multi-manned assembly line under time and area constraint using genetic algorithm. A relationship between the problem features and the solution is found according to it, the solution method (one sided or multi-manned) is defined.

The research on the robotic assembly line balancing problem (RALBP) was originated for the first time nearly three decades ago. This problem is under the umbrella of the assembly line balancing problem in which robots and automated equipment are employed to take on human workers’ roles to form a flexible assembly line. In this review paper, the development and generalisation throughout the time of the RALBP are addressed. To make the review easy to comprehend and effective, the RALBP is first classified based on the types of layouts and then further dividing up according to the 4 M (Man, Machine, Material and Method) concept. The main contributions of different articles are chronologically summarised in the form of a table. Besides, the research contribution precedence diagram is used to illustrate the sequential order and linkage relationship among researches. Finally, from the findings of the review, future research directions are pinpointed and discussed.

This paper presents the novel method variable neighbourhood strategy adaptive search (VaNSAS) for solving the special case of assembly line balancing problems type 2 (SALBP-2S), which considers a limitation of a multi-skill worker. The objective is to minimize the cycle time while considering the limited number of types of machine in a particular workstation. VaNSAS is composed of two steps, as follows: (1) generating a set of tracks and (2) performing the track touring process (TTP). During TTP the tracks select and use a black box with neighborhood strategy in order to improve the solution obtained from step (1). Three modified neighborhood strategies are designed to be used as the black boxes: (1) modified differential evolution algorithm (MDE), (2) large neighborhood search (LNS) and (3) shortest processing time-swap (SPT-SWAP). The proposed method has been tested with two datasets which are (1) 128 standard test instances of SALBP-2 and (2) 21 random datasets of SALBP-2S. The computational result of the first dataset show that VaNSAS outperforms the best known method (iterative beam search (IBS)) and all other standard methods. VaNSAS can find 98.4% optimal solution out of all test instances while IBS can find 95.3% optimal solution. MDE, LNS and SPT-SWAP can find optimal solutions at 85.9%, 83.6% and 82.8% respectively. In the second group of test instances, we found that VaNSAS can find 100% of the minimum solution among all methods while MDE, LNS and SPT-SWAP can find 76.19%, 61.90% and 52.38% of the minimum solution.

Motivated by recent developments to deploy collaborative robots in industrial production systems, we investigate the assembly line balancing problem with collaborative robots. The problem is characterized by the possibility that human and robots can simultaneously execute tasks at the same workpiece either in parallel or in collaboration. For this novel problem type, we present a mixed-integer programming formulation for balancing and scheduling of assembly lines with collaborative robots. The model decides on both the assignment of collaborative robots to stations and the distribution of workload to workers and robotic partners, aiming to minimize the cycle time. Given the high problem complexity, a hybrid genetic algorithm is presented as a solution procedure. Based on extensive computational experiments, the algorithm reveals promising results in both computational time and solution quality. Moreover, the results indicate that substantial productivity gains can be utilized by deploying collaborative robots in manual assembly lines. This holds especially true for a high average number of robots and tasks to be assigned to every station as well as a high portion of tasks that can be executed by the robot and in collaboration.

The assembly line was invented in 1913 and has been in continuous operation ever since. It is the most familiar form of mass production. Both praised as a boon to workers and condemned for exploiting them, it has been celebrated and satirized. (We can still picture Chaplin's little tramp trying to keep up with a factory conveyor belt.) In America's Assembly Line, David Nye examines the industrial innovation that made the United States productive and wealthy in the twentieth century.The assembly line -- developed at the Ford Motor Company in 1913 for the mass production of Model Ts -- first created and then served an expanding mass market. It inspired fiction, paintings, photographs, comedy, cafeteria layouts, and cookie-cutter suburban housing. It also transformed industrial labor and provoked strikes and union drives. During World War II and the Cold War, it was often seen as a bastion of liberty and capitalism. By 1980, Japan had reinvented the assembly line as a system of \"lean manufacturing\"; American industry reluctantly adopted this new approach. Nye describes this evolution and the new global landscape of increasingly automated factories, with fewer industrial jobs in America and questionable working conditions in developing countries. A century after Ford's pioneering innovation, the assembly line continues to evolve toward more sustainable manufacturing.

U-shaped assembly lines have been popularly adopted in electronics and appliances to improve their flexibility and efficiency. However, most past studies assumed that the processing time of each task is fixed and hence just considered the task allocation but ignored worker assignment. In this paper, the processing time of each task depends on the workers and then the cooperative optimization of task allocation and workers assignment is considered in U-shaped assembly line balancing problems to optimize the cycle time. Later, an enhanced migrating birds optimization algorithm (EMBO) is proposed to solve it. In the EMBO algorithm, since this new problem has two subproblems: task allocation and worker assignment, the prevent work designs two neighborhood structures to improve the leader and following birds. Furthermore, the temperature acceptance criteria, to judge whether the neighbor replaces current following bird, are developed to ensure the diversity of population and avoid being trapped in the local optimum. And a competitive mechanism is introduced to increase the probability of the promising birds locating in the front of the line. The proposed algorithm is compared with other well-known algorithms in the literature, and the numerical results demonstrate that the proposed algorithm outperforms other algorithms.

Contemporary assembly line systems are characterized by an increasing capability to offer each client a different product, more tuned to her needs and preferences. These assembly systems will be heavily influenced by the advent of Industry 4.0 technologies, enabling the proposal of business models that allow the late customization of the products, i.e., the customer can modify attributes of its product once the production of it is started. This business model requires that the manufacturing tools are able to make decisions online and negotiate with the customer the changes that can be carried out, according to the workload flowing through the production system. In this work, we analyze the possibilities and limitations of this new paradigm. First, we show that Industry 4.0 systems can autonomously manage the production management process, and then, we present a framework based on tolerance planning strategies (tolerance scheduling problem), to determine which changes can be carried out. The ability of resequencing the production process is also implemented in the case that the operations associated with late customization allow it (i.e., when intermediate buffers are available). This establishes a parallelism with the problem of non-permutation flow shop. We finally discuss future developments necessary to implement these procedures.

Robots are extensively used during the era of Industry 4.0 to achieve high productivity, better quality and lower cost. While designing a robotic assembly line, production managers are concerned about the cost involved in such a system development. Most of the research reported till date did not consider purchasing cost while optimizing the design of a robotic assembly line. This study presents the first attempt to study the cost-oriented robotic assembly line balancing problem with setup times to minimize the cycle time and total purchasing cost simultaneously. A mixed-integer linear programming model is developed to formulate this problem. The elitist non-dominated sorting genetic algorithm (NSGA-II) and improved multi-objective artificial bee colony (IMABC) algorithm are developed to achieve a set of Pareto solutions for the production managers to utilize for selecting the better design solution. The proposed IMABC develops new employed bee phase and scout phase, which selects one solution in the permanent Pareto archive to replace the abandoned solution, to enhance exploration and exploitation. The comparative study on a set of generated instances demonstrates that the proposed model is capable of achieving the proper tradeoff between line efficiency and purchasing cost, and the proposed NSGA-II and IMABC achieve competing performance in comparison with several other multi-objective algorithms.