Mixed-integer programming for robotic assembly line balancing considering cross-station task and carbon footprint

PurposeThe extensive applications of the industrial robots have made the optimization of assembly lines more complicated. The purpose of this paper is to develop a balancing method of both workstation time and station area to improve the efficiency and productivity of the robotic assembly lines. A tradeoff was made between two conflicting objective functions, minimizing the number of workstations and minimizing the area of each workstation.Design/methodology/approachThis research proposes an optimal method for balancing robotic assembly lines with space consideration and reducing robot changeover and area for tools and fixtures to further minimize assembly line area and cycle time. Due to the NP-hard nature of the considered problem, an improved multi-objective immune clonal selection algorithm is proposed to solve this constrained multi-objective optimization problem, and a special coding scheme is designed for the problem. To enhance the performance of the algorithm, several strategies including elite strategy and global search are introduced.FindingsA set of instances of different problem scales are optimized and the results are compared with two other high-performing multi-objective algorithms to evaluate the efficiency and superiority of the proposed algorithm. It is found that the proposed method can efficiently solve the real-world size case of time and space robotic assembly line balancing problems.Originality/valueFor the first time in the robotic assembly line balancing problems, an assignment-based tool area and a sequence-based changeover time are took into consideration. Furthermore, a mathematical model with bi-objective functions of minimizing the number of workstations and area of each station was developed. To solve the proposed problem, an improved multi-objective immune clonal selection algorithm was proposed and a special coding scheme is designed.

An efficient approach for type II robotic assembly line balancing problems

The total cost of assembly is a critical factor in robotic assembly line balancing, as it encompasses all the costs associated with the assembly line, including initial costs, setup, maintenance, and energy cost. This study introduces a different approach to the robotic assembly line balancing problem, with a dual focus on minimizing both cycle time and overall assembly costs. The effectiveness of the proposed approach is validated through three case study problems taken from the literature and results are compared to traditional assembly allocation methods. For case study 1, 89.4% (42 out of 47) of the solutions achieved a lower total cost, and 34% (16 out of 47) of the solutions utilized fewer workstations; and for case study 2, 96.4% (108 out of 112) of the solutions achieved a lower total cost, and 58.9% (66 out of 112) of the solutions utilized fewer workstations for the same cycle time. These results demonstrate a significant savings in cost and a notable improvement in workstation efficiency for a substantial portion of the solutions. This comprehensive approach allows an effective resource allocation, reduces inefficiencies, and enhances the overall cost-effectiveness and performance of the robotic assembly line. It also supports decision-makers in selecting more sustainable and economically viable assembly line solutions that optimize both productivity and energy efficiency.

Purpose The cost-oriented robotic assembly line balancing problem (cRALBP) has practical importance in real-life manufacturing scenarios. However, only a few studies tackle the cRALBP using exact methods or metaheuristics. This paper aims to propose a hybrid particle swarm optimization (PSO) combined with dynamic programming (DPPSO) to solve cRALBP type-I. Design/methodology/approach Two different encoding schemes are presented for comparison. In the frequently used Scheme 1, a full encoding of task permutations and robot allocations is adopted, and a relatively large search space is generated. DPSO1 and DPSO2 with the full encoding scheme are developed. To reduce the search space and concern promising solution regions, in Scheme 2, only task permutations are encoded, and DP is used to obtain the optimal robot sequence for a given task permutation in a polynomial time. DPPSO is proposed. Findings A set of instances is generated, and the numerical experiments indicate that DPPSO achieves a tradeoff between solution quality and computation time and outperforms existing algorithms in solution quality. Originality/value The contributions of this paper are three aspects. First, two different schemes of encoding are presented, and three PSO algorithms are developed for the purpose of comparison. Second, a novel updating mechanism of discrete PSO is adjusted to generate feasible task permutations for cRALBP. Finally, a set of instances is generated based on two cost parameters, then the performances of algorithms are systematically compared.

In this paper, type II robotic mixed-model two-sided assembly line balancing (RMTALB-II) problem is considered. In recent years, robots have been widely used in assembly systems as called robotic assembly lines where a set of tasks have to be assigned to stations and each station needs to select one of the different robots to process the assigned tasks. Two-sided assembly lines are especially used to produce large-sized high-volume products, such as automobiles, trucks, and buses. In this type of production line, both left side and right side of the line are used in parallel. However, little attention has been paid to solve RMTALB problems. Moreover, according to our best knowledge, there is no published work in the literature on RMTALB. This paper presents a new mixed-integer programming model for RMTALB-II to minimize the cycle time for a given number of mated stations. Since RMTALB problems are in NP-hard class of combinatorial optimization problems, a simulated annealing (SA) algorithm as metaheuristic method is proposed to solve the problem. Some problems are solved by SA and performance of the SA is evaluated by comparing their results with the optimal solution. The computational results show that SA is more efficient.

Metaheuristic algorithms for balancing robotic assembly lines with sequence-dependent robot setup times

The Robotic Assembly Line Balancing Problem (RALBP) is a joint optimisation problem that is concerned with assigning both assembly operations and robots to workstations that are placed within a straight line. RALBP-2 is the particular problem where the cycle time, which is the maximum time spent on a workstation by the product being assembled, is minimised while the number of workstations is fixed. Sequence-dependent setup times are considered which raises the problem of sequencing the operations assigned to each workstation. Both the durations of the operations and the setup times depend on the robot. Two different variants are identified from literature. The first variant assumes that, given a set of types of robots, each type of robot can be assigned to multiple workstations without any limitation. Given a set of robots, the second variant forces each robot to be assigned to at most one workstation. Both assumptions are studied in this paper. The particular case of a given giant sequence of operations is solved thanks to a polynomial optimal algorithm. The latter algorithm, called split, is then embedded in a metaheuristic framework that explores the space of giant sequences. Benchmark data sets from literature are considered in the experimental section. A comparative study with other methods from literature shows the competitiveness of the suggested approach.

Energy consumption and cycle time are two contradictory optimization objectives for the robotic assembly line balancing problem (RALBP). Indeed, minimizing the cycle time leads to choosing the fastest robots, while minimizing the energy consumption leads to choosing the robots with the smallest powers. In the context of RALBP, cycle time minimization has been extensively studied while energy minimization has been much less considered. Studies dealing with simultaneous minimization of the two later are even scarcer. A bi‐objective RALBP considering simultaneous minimization of cycle time and energy consumption is studied in this paper. The energy consumption is calculated based on recent papers from the literature. It includes energy consumed during both operation time and idle time. In this paper, a pseudo‐polynomial case is solved thanks to an exact algorithm called split. This latter enumerates all Pareto‐optimal solutions corresponding to a given giant sequence of operations. Split is then used as a decoder in a metaheuristic operating in a reduced search space where giant sequences encode solutions. An experimental study is performed on instances taken from the literature to test the suggested encoding–decoding scheme. It shows that the suggested approach yields competitive results compared to the literature.

A beam search approach for solving type II robotic parallel assembly line balancing problem

Decomposition-based bi-objective optimization for sustainable robotic assembly line balancing problems

Sustainability and reducing carbon footprint has attracted attention in the oil and gas industry to optimize recovery and increase efficiency. The 4th Industrial Revolution has made an enormous impact in the oil and gas industry and on analyzing carbon footprint reduction opportunities. This allows classification of various reservoir operations, installation of permanent sensors and robots on the field, and reduction of overall power consumption. We present an overview of new AI approaches for optimizing reservoir performance while reducing their carbon footprint. We will outline the significant carbon emissions contributors for field operations and how their impact will change throughout the production's lifecycle from a reservoir. Based on this analysis, we will outline via an AI-driven optimization framework areas of improvement to reduce the carbon footprint considering the uncertainty. We analyzed the framework's performance on a synthetic reservoir model with several producing wells, water, and CO2 injecting wells. Beneficial in reducing carbon emissions from the field is the reuse and injection of CO2 for enhancing hydrocarbon production from the reservoir. One hundred different scenarios were then investigated utilizing an innovative autoregressive network model to determine the impact of these components on the overall carbon emission of the field and determine its uncertainty. The conclusions from the analysis were then incorporated into a data-driven optimization routine to minimize carbon footprint while maximizing reservoir performance. The final optimization results of the showcase outlined the ability to reduce the carbon footprint significantly.

The potential benefits of dietary shift in China: Synergies among acceptability, health, and environmental sustainability

Carbon footprint in building distributed energy system: An optimization-based feasibility analysis for potential emission reduction

We welcome the correspondence from O'Sullivan and Shah [1], which comments on our study [2]. I agree broadly with their points and wish to emphasise that our results should not be interpreted as a minimum carbon footprint for total intravenous anaesthesia (TIVA). A common concern about TIVA is the high consumption of single-use plastic compared with volatile inhalational anaesthesia [3]. Anaesthetists might conclude that TIVA has a greater carbon footprint than sevoflurane anaesthesia and our model assumed ‘worst case’ use of consumables for each of our TIVA scenarios. We included all possible consumables that were used so that our calculations of the total carbon footprint of TIVA overestimated actual practices. By using this approach, and by publishing the carbon footprints of individual items used in our scenarios, clinicians can identify how the footprint of their practice might vary from the scenarios we modelled, as O'Sullivan and Shah have done. In our modelling, we assumed that all TIVA cases use processed electroencephalogram (pEEG), as many anaesthetists, particularly during training, choose to use this to minimise the risk of accidental awareness even in the absence of neuromuscular blockade [4]. As discussed in our study, if pEEG is not used for TIVA, the carbon footprint will reduce by 0.83 kgCO2e (excluding the energy required for the pEEG monitor), equating up to 28.1% of the total carbon footprint of TIVA. In this case, after 11 min, both TIVA scenarios have lower total carbon footprints than median consumption inhalational anaesthesia using sevoflurane. Conversely, if pEEG is also used for volatile anaesthesia to reduce recovery time and volatile anaesthetic agent consumption as highlighted by O'Sullivan and Shah [1] and to reduce accidental awareness [4], then the total carbon footprint of volatile sevoflurane anaesthesia will increase by at least 0.83 kgCO2e. Using our modelled scenarios, if pEEG is used for both TIVA and sevoflurane anaesthesia, then, after 20 min, the highest emitting TIVA scenario has a lower total carbon footprint than the most economical sevoflurane scenario. This 20-min threshold is drastically lower than the 44 min for our original high-emission TIVA scenario vs. minimal consumption sevoflurane without pEEG. It should be noted that data used for sevoflurane consumption were derived from a study that did not use pEEG to titrate sevoflurane but instead measured actual consumption using low fresh gas flow [5]. O'Sullivan and Shah highlight several TIVA practices that use resources more efficiently than our modelled scenarios and we encourage anaesthetists to consider how their practices can be leaner and greener while maintaining patient safety.

<p indent="0mm">In recent years, soil-groundwater system has been severly polluted by inorganic and organic contaminants, while the remediation processes remain challenging and inefficient. Iron/manganese (Fe/Mn)-based nanomaterials are environmentally friendly with high reactivity and tunable physicochemical properties, and thus have attracted increasing attention as an ideal type of agent for soil-groundwater remediation. In-depth understanding of the interfacial mechanism and structure-property relationship that govern the nanomaterial-contaminant reactions will help enable efficient <italic>in situ</italic> remediation of soil-groundwater contamination with minimal ecological cost and carbon footprint. This paper reviews the types and properties of the Fe/Mn-based nanomaterials, and the possible reaction mechanisms, including adsorption, precipitation, oxidation, reduction, and catalysis, between the Fe/Mn-based nanomaterials and environmental contaminants. Moreover, according to the current application status and bottlenecks of Fe/Mn-based nanomaterials in contaminated sites, the optimized strategies (<italic>e</italic>.<italic>g</italic>., hetero-atom doping, surface/interface modification, facet engineering, defect engineering, and construction of composites) to improve the functionality of the Fe/Mn-based nanomaterials are also summarized. In the end, key research directions are pointed out to further the research in this field, such as site implementation and green synthesis of the Fe/Mn-based nanomaterials, and strategies for enhancing the mobility and selectivity of nanomaterials for <italic>in situ</italic> application in complex environment.<italic/>