Human-robot collaborative assembly line balancing problem considering uncertain task time

Type-1 U-shaped Assembly Line Balancing under uncertain task time

An assembly line is a serial production system that is meant to produce high-quality and usually complex products in mass quantities. Assembly lines play a crucial role in determining the profitability of a company, as they are utilised as the final stage of the system prior to shipping. In an assembly line balancing problem, the assembly tasks are allocated to workstations based on their processing times after considering the precedence relationships between them. There is a massive amount of research in the literature using deterministic task processing times, and many other works consider stochastic task times. This research utilises the uncertainty theory to model uncertain task times and considers incompatible task sets constraints. The problem is solved using a simulated annealing algorithm with problem-specific characteristics. Lower bounds are developed to accelerate the simulated annealing algorithm. A restart mechanism, which can escape the local optimum obtained by neighbourhood generation, is proposed. A repair mechanism is integrated to combine the workstations so as to further improve the quality of solutions. The numerical examples and experimental tests demonstrate the powerful solution-building capacity of the proposed simulated annealing algorithm over teaching–learning-based and genetic algorithms. The methodology proposed in this research is applicable to any industry (including the automotive industry) when the historical data on task processing times is very limited.

PurposeThe purpose of this paper is to ascertain and expose considerations related to the division of assembly lines into segments, and to develop a framework for assessing the impact of line segmentation. In particular, the paper aims to investigate the decision regarding the number of stations in each segment, and its impact on costs, throughput, and span of control.Design/methodology/approachThe paper analyzes the literature, identifies the main considerations related to assembly line segmentation, and develops a mathematical model that reflects important factors related to the impact of segment length on the throughput and costs. The paper derives several important bounds on the number of stations (length) of a line's segment, which should be incorporated in the final design of the line sections.FindingsA trade‐off was found between the revenue (and throughput) and the buffer spaces located between each pair of sections of an assembly line. The higher the product price, the shorter the segments are. On the other hand, when buffer costs are higher, the line segments are longer (fewer sections and buffers are used). Interestingly, except of stoppages, the other two dominant factors in the segmentation decision are: absenteeism, and span of control. Using these factors, various upper bounds were found on the number of stations per section (which determine the total number of sections). The tightest upper bound is the active one, and this ensures line sections with a small number of stations.Practical implicationsThe model provides a framework of considerations to help designers of assembly lines and production lines determine the appropriate division of the line into sections and zones, and the best allocation of stations to sections. In addition, finding effective upper bounds for the number of stations in a line section establishes the practicality of exact methods for designing and balancing each section.Originality/valueThis is the first paper to offer a quantitative treatment of the various factors affecting assembly line segmentation. By finding the upper bound on the number of station per section, the paper establishes the practicality of exact methods for designing and balancing each section separately. Moreover, it provides a sound basis for future research related to design of assembly and production lines, and management of sequential processes.

Optimisation of mixed-model assembly line balancing problem under uncertain demand

Purpose – The paper aims to study car assembly line, to show its productivity rate, and to derive a mathematical model for the productivity rate of the assembly line segmented into sections with embedded buffers. Design/methodology/approach – The paper performs productivity calculations based on data obtained from the assembly processes of a car and shows the maximum productivity of the assembly line. The equations of the assembly line productivity, the optimal number of assembly stations, and the necessary number of the assembly line's sections with buffers are derived via the criterion of maximum productivity. Findings – The paper provides the productivity diagram of the assembly line that illustrates various measures of productivity, one that depends on the number of assembly stations, the number of sections in the line, and the capacity of the buffers. The diagram is based on the proposed mathematical equations for the productivity of the assembly line as a function of the assembly technology, number of stations, number of sections, and the capacity of the buffer. Research limitations/implications – Solutions towards increasing the productivity of the assembly line are given based on the results of the study and analysis of the assembly processes in real industrial environments. Practical implications – The paper includes the equation for the productivity of the assembly line, which is segmented into sections with limited capacity of the buffers, thereby enabling the calculation of its maximum productivity and the optimal number of assembly stations. Originality/value – The paper presents an analysis of productivity and a mathematical model for calculating the productivity of the assembly line, which is segmented into sections with embedded buffers of limited capacity. The initial results of the research have been obtained from a real industrial environment.

Promoting low-carbon development (LCD) is one of the basic requirements of building new-type urbanization with Chinese characteristics. Research on the formulation of low-carbon city development roadmap (hereinafter referred to as the “Roadmap”) is a necessity for systematic control of greenhouse gas (GHG) emission and the construction of low-carbon cities. Based on the actual conditions of cities as well as the regional and national development strategies, the Roadmap panoramically describes the strategic objectives, development plans, and key sectors' initiatives for the transformation of city development through planning techniques and solutions. Generally, the formulation of a low-carbon roadmap involves six steps: understanding the present situation of GHG emissions; analyzing the future emission scenarios; setting LCD targets; developing action plans for key sectors; assessing mitigation potential of low-carbon technologies/projects; and proposing measures for implementation. This paper, based on the practical experience in low-carbon city roadmap development and the needs of divisional management, establishes an analytical structure of GHG inventory for seven sectors, so as to link the key sectors for LCD, namely the seven major sectors for emissions reduction proposed by the Intergovernmental Panel on Climate Change (IPCC). Moreover, this paper integrates sectorial (industrial) low-carbon technology needs assessment (TNA) into the formulation of the Roadmap. It also provides several recommendations for better incorporating the two methods into the Roadmap: firstly, to strengthen the core function of GHG inventory by enhanced accounting quality and scientific and systematic analysis of the temporal and sectorial distribution of GHG emissions so as to serve the Roadmap formulation and decision-making. Secondly, to reinforce the derivative function of GHG inventory that serves the LCD planning, assessment, and decision-making by improving the quality and continuity of GHG inventory. Thirdly, to develop the derivative function of GHG inventory by integrating the improved GHG inventory and TNA into the formulation of the Roadmap.

Pareto based artificial bee colony algorithm for multi objective single model assembly line balancing with uncertain task times

Assembly lines have very important role in manufacturing. These are used to manufacture large scale series of products. Developments over the period of time changed the assembly lines from single model lines to more convenient systems with some variations including two assembly lines, ‘n’ assembly lines, customer oriented mixed model, multi model and u shaped lines and many more. In this paper attention is focused to develop the model which is used to assemble the three different types of autos which have some common auto parts. The main objective is to design this model to reduce the time when the different types of autos having some common auto parts are assembled. The optimization problem in this paper is solved by using dynamic programming approach. For this purpose six assembly lines are used having ‘n’ number of stations, two assembly lines are specified for each type of auto. Assembly lines are set in such a way that the common auto part of three types must be assembled at the parallel station of the lines

Swarm robotics is the study of how large number of relatively simple physically embodied agents can be designed such that a desired collective behavior emerges from the local interactions among agents and between the agents and the environment. It is a novel approach to the coordination of large numbers of robots. It is inspired from the observation of social insects such as ants, termites, wasps and bees which stand as fascinating examples of how a large number of simple individuals can interact to create collectively intelligent systems. Social insects are known to coordinate their actions to accomplish tasks that are beyond the capabilities of a single individual: termites build large and complex mounds, army ants organize impressive foraging raids and ants can collectively carry large preys. Such coordination capabilities are still beyond the reach of current multirobot systems. In this research, most recent developments of swarm robot systems are addressed by classifying the primary research axes in terms of principal topic areas that have generated significant levels of research. Specific research scopes in each of primary research axes are also classified and the key open issues in this research scopes are identified. With the goal of bringing some objective grounding to the important areas of the mentioned research scopes, this dissertation presents an empirical analysis on multi-agent foraging task using reinforcement learning algorithms. Foraging task is one of the mostly used test applications in multi-agent systems. The aim of agents in foraging task is to find the pucks (prey) and bring them to the home (nest) location in a environment while satisfying the given constraints. This task is also known as prey retrieval task. Reinforcement learning is used to tackle the modeled multi-agent foraging task. Reinforcement learning has been extensively used in many applications such as industrial control, time sequence prediction, robot soccer competition and more. In this thesis, a multi-agent foraging task is modeled using Webots (1996) simulation software and the reinforcement learning algorithms and policies are tested. One of the challenges that arise in reinforcement learning is the exploration and exploitation dilemma. A novel learning policy (FIFO-list learning policy) is proposed and compared against the available learning policies reported in the literature to tackle the exploration and exploitation dilemma. An improved reinforcement learning algorithm (Cautious-Q learning algorithm) is also proposed and the performance is compared with the available learning algorithms. The proposed learning algorithm is a combined strategy of on-policy learning and off-policy learning. The improved learning algorithm and the learning policy is implemented on real environment with Khepera 2 mobile robots and results are presented.

Purpose The purpose of this paper is to introduce robust optimization approaches to balance mixed model assembly lines with uncertain task times and daily model mix changes. Design/methodology/approach Scenario planning approach is used to represent the input data uncertainty in the decision model. Two kinds of robust criteria are provided: one is min-max related; and the other is α-worst scenario based. Corresponding optimization models are formulated, respectively. A genetic algorithm-based robust optimization framework is designed. Comprehensive computational experiments are done to study the effect of these robust approaches. Findings With min-max related robust criteria, the solutions can provide an optimal worst-case hedge against uncertainties without a significant sacrifice in the long-run performance; α-worst scenario-based criteria can generate flexible robust solutions: through rationally tuning the value of α, the decision maker can obtain a balance between robustness and conservatism of an assembly line task elements assignment. Research limitations/implications This paper is an attempt to robust mixed model assembly line balancing. Some more efficient and effective robust approaches – including robust criteria and optimization algorithms – may be designed in the future. Practical implications In an assembly line with significant uncertainty, the robust approaches proposed in this paper can hedge against the risk of poor system performance in bad scenarios. Originality/value Using robust optimization approaches to balance mixed model assembly line.

Design of a Footwear Assembly Line Using Simulation-based ALNS

The human–robot collaborative assembly line has been applied as an effective strategy in production to further improve the efficiency, adaptability, and flexibility. The assembly line balancing is an important part of assembly line design and optimisation. However, the current research often neglects the key factor of the number of stations, which lacks better guidance for the actual assembly line design and optimisation. To provide a better practical reference, this research first takes the number of stations, the makespan and the balance ratio as the optimisation objectives, and constructs a corresponding model. Then, an improved dual-population genetic algorithm is designed to solve the problem more efficiently. This algorithm optimises multiple operators based on the traditional genetic algorithm. The efficacy of the proposed algorithm is substantiated through numerical experimentation with three traditional heuristic algorithms. The experimental results show that the improved algorithm has superior performance in solving the human–robot collaborative assembly line balancing problem. On the one hand, the average improvement of fitness in different scale cases reaches 3.14% (up to 52.33%), and the distribution of solutions is more stable. On the other hand, the convergence speed is faster, the average reduction of convergence time is 11.50% (up to 88.24%).

The global increase of carbon emissions needs to be reduced to keep incremental global warming below 2 degrees. However, in the last decades, Indonesia’s energy supply and demand have been highly dependent on fossil fuels, contributing to high carbon emissions. Most of the non-renewable energy demand comes from transportation and electrification. While in drilling activity, diesel engines produce carbon emissions for mobilization, drilling operation, and site electrification. Carbon emissions reduction is part of the policy of several giant energy companies to achieve net-zero emissions to support environmental sustainability, echoed by Paris Agreement 2015. This preliminary study aims to comprehensively evaluate the activities that generate carbon emissions in geothermal drilling projects. With the case study, the authors offer a numerical approach of emissions calculation that covers from the planning phase until post-operation calculation, including logistics and equipment used in the drilling project. As a result, the authors provide an integrated analysis regarding efficiency options for geothermal drilling operations. This study also proposes a simple economic analysis to achieve lower carbon emissions. From an operational perspective, emissions reduction could be achieved by making resources movement and transportation more efficient, thus directly reducing fuel consumption and emissions. Furthermore, biodiesel and renewable energy to substitute diesel will lower emissions and energy consumption for regular generators and transportation. Moreover, several operational efficiency options that can help reduce carbon emissions are discussed in this research. Then, the digitalization era also plays an important role in increasing efficiency for lowering carbon footprint. Briefly, this research is considered useful for the industry that seeks to deliver an integrated effort to reduce carbon emissions during a geothermal drilling operation. It will serve as a reference to increase awareness in mitigating emissions in geothermal drilling projects to achieve lower carbon drilling operations.

The role and transmission mechanism of forest resource abundance on low-carbon economic development in the Yangtze River Delta region: Insights from the COP26 targets

Apparel manufacturing is an industry with high energy consumption and carbon emissions. With the development of the low-carbon economy, low-carbon production in the apparel manufacturing industry become more and more imperative. The apparel industry is encountering great challenges in reducing carbon emissions. Garment sewing comprises a large number of processes, machines and operators. However, the existing studies lack quantitative analysis of carbon sources in the sewing process. This study analyzed the carbon emission characteristics in garment sewing production. Evaluation models of carbon emission were established for the sewing process in this research and the factors of fabrics, accessories, sewing machines and operators were included in the models. The results showed that fabrics and accessories were the main sources of carbon emissions in garment sewing production. The second largest carbon emission source was sewing machines, followed by operators. According to the evaluation models, the number of machines, operators and the utilization rate of the machines were related to the balance of the assembly line. A multi-objective optimization model aimed at minimizing the time loss rate and smoothness index of the assembly line was established, and a fast and elitist multi-objective genetic algorithm was used to obtain the solution for carbon emission reduction. The men’s shirt assembly lines, based on three types of workstation layouts (the order of processes, the type of machines and the components of the garment), were applied to verify the effectiveness of the model and algorithm. The results indicated that the total carbon emissions of the three assembly lines based on balance optimization were less than that of the normal assembly line. The assembly line of the workstations arranged in the order of processes was the best assembly line since it had the highest efficiency and the lowest carbon emissions.