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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.

The purpose of this paper is to apply particle swarm optimization (PSO) a known combinatorial optimization algorithm to multi‐objective (MO) balancing of large assembly lines.

A novel approach based on PSO is developed to tackle the simple assembly line balancing problem (SALBP), a well‐known NP‐hard production and operations management problem. Line balancing is considered for two‐criteria problems utilizing cycle time and workload smoothing as performance criteria, as well as for three‐criteria problems involving the balance delay time of the line together with cycle time and workload smoothing. Emphasis is on seeking a set of diverse Pareto optimal solutions for the bi‐criteria SALBP.

Experiments carried out on multiple test problems taken from the open literature are reported and discussed. Comparisons between the proposed PSO algorithm and two existing MO population heuristics show a quite promising higher performance for the proposed approach.

Artificial particles (potential solutions “flown” by PSO though hyperspace) are encoded to actual ALB solutions via a novel representation mechanism. A new scheme for generating and maintaining diverse Pareto ALB solutions is proposed. For the case of the two‐criteria ALBPs, the individual objectives are summed to a weighted combination with the weight coefficients being dynamically adapted using a novel weighted aggregation method. This weighted method can be applied on any bi‐criteria optimization problem.

The most economical way of protecting pipelines is through the application of organic coatings and wrappings. To make the protection foolproof at holidays and pores, cathodic protection is also applied. At coating holidays, the pipelines act as the cathode generating hydroxyl ion and hydrogen electrochemically. Extensive studies have been carried out using samples of mild steel pipe coated with reinforced coal tar enamel coating to investigate the role of hydrogen and hydroxyl ion on cathodic disbondment. The testing material is subjected to different environmental conditions like pH, aeration and addition of different anionic species. A mechanism has been proposed to describe the individual role of hydroxyl ion and hydrogen in the process of disbondment.

Manufacturing industries these days gives importance to reduce the energy consumption due to the increase in energy prices and to create an environmental friendly industry. Robotic assembly lines (RALs) are used in an industry for assembling different types of products in an assembly line due to the flexibility it offers to the production system. Since different types of robots are available with different specialization and capabilities, there is a requirement of efficiently balancing the assembly line by allocating equal amount of tasks to the workstations and allocate the best fit robot to perform the allocated tasks. The goal of this paper is to maximize the line efficiency by minimizing the total energy consumption in a U-shaped robotic assembly line.

Particle swarm optimization (PSO) and Differential evolution (DE) are the two evolutionary algorithms used as the optimization tool to solve this problem. Performance of these proposed algorithm are tested on a set of randomly generated problems which are generated using the benchmark problems available in the open literature and the results are reported.

The proposed algorithm are found to be useful to reduce the total energy consumption on an assembly line which maximizes the line efficiency. It is found that DE algorithm could improve the line efficiency than PSO algorithm. Computational time taken by the two algorithm are also reported.

Till date, no research has been reported on optimizing the line efficiency by minimizing the total energy consumption in a U-shaped robotic assembly line systems. Particle swarm optimization (PSO) and Differential evolution (DE) are the two evolutionary algorithms used as the optimization tool to solve this problem.

The purpose of this article is to derive inference for multicomponent reliability where stress-strength variables follow unit generalized Rayleigh (GR) distributions with common scale parameter.

The authors derive inference for the unknown parametric function using classical and Bayesian approaches. In sequel, (weighted) least square (LS) and maximum product of spacing methods are used to estimate the reliability. Bootstrapping is also considered for this purpose. Bayesian inference is derived under gamma prior distributions. In consequence credible intervals are constructed. For the known common scale, unbiased estimator is obtained and is compared with the corresponding exact Bayes estimate.

Different point and interval estimators of the reliability are examined using Monte Carlo simulations for different sample sizes. In summary, the authors observe that Bayes estimators obtained using gamma prior distributions perform well compared to the other studied estimators. The average length (AL) of highest posterior density (HPD) interval remains shorter than other proposed intervals. Further coverage probabilities of all the intervals are reasonably satisfactory. A data analysis is also presented in support of studied estimation methods. It is noted that proposed methods work good for the considered estimation problem.

In the literature various probability distributions which are often analyzed in life test studies are mostly unbounded in nature, that is, their support of positive probabilities lie in infinite interval. This class of distributions includes generalized exponential, Burr family, gamma, lognormal and Weibull models, among others. In many situations the authors need to analyze data which lie in bounded interval like average height of individual, survival time from a disease, income per-capita etc. Thus use of probability models with support on finite intervals becomes inevitable. The authors have investigated stress-strength reliability based on unit GR distribution. Useful comments are obtained based on the numerical study.

Digitalisation is a key driver of the Fourth Industrial Revolution (Industry 4.0) to better understand the opportunities and challenges pertaining to digital transformation; organisations adopt different approaches to dealing with digitisation. The purpose of this chapter is to explore the impact of Industry 4.0 on the procurement process re-engineering and its role within the area of supply chain management. Additionally, the research will examine barriers and challenges involved in the digitalisation of procurement and supply chains and how to overcome them. According to the findings, digitalisation of the procurement process can have several advantages, such as supporting complex decision-making processes and administrative tasks, focusing on strategic decisions and activities, transforming procurement into a strategic interface to support organisational efficiency, effectiveness and profitability and fostering the development of new business models. Furthermore, the study highlighted various influencing factors, challenges and the role of stakeholders impacting the digitalisation of procurement functions and supply chains.

Deals with the dynamic scheduling problems and solutions of job shop comprising six work centres and n components. Concerns the behaviour of the system, with the arrival of urgent orders and normal orders. In a typical manufacturing system, urgent orders are scheduled for processing based on their urgency and given priority over normal orders. Describes an analysis of urgent order processing on the basis of non pre‐emptive priority and pre‐emptive resume priority over normal orders. Enumerates manufacturing system performances which had been analysed for the two most popular scheduling rules – first in first out (FIFO) and shortest processing time (SPT) – through a system simulation program. Concludes by asserting that the simulation program can be used to schedule the manufacturing system dynamically by choosing the appropriate scheduling rule to measure optimal system performance leading to higher productivity.

Design, balancing, and sequencing are the key issues associated with assembly lines (ALs). The purpose of this paper is to identify AL design issues and to develop an integrated methodology for mixed‐model assembly line balancing (MMALB) and sequencing. Primarily, mixed‐model lines are utilized for high‐variety, low‐volume job shop or batch production. Variation of a generic product is important for the manufacturers as the demand is mostly customer driven in the present global market.

Different AL design norms, performance indexes, and AL workstation indexes have been identified in the initial stage of this work. As the paper progresses, it has focused towards an integrated approach for MMALB and sequencing addressed for small‐ and medium‐scale assembly plants. A small‐scale practical problem has been justified with this integrated methodology implemented by MATLAB.

ALs execution in the production floor require many important factors to be considered. Different line orientations, production approaches, line characteristics, performance and workstation indexes, problem definitions, balancing and product sequencing in accordance with the objective functions are needed to be taken into account by the line designer.

This paper has highlighted the important AL design characteristics and also provided an integrated approach for balancing mixed‐model assembly lines (MMALs) combined with sequencing heuristic. The findings of this paper can be helpful for the designers while designing an AL. The integrated approach for balancing and sequencing of MMALs can be used as a functional tool for assembly‐based contemporary industries.

Dimensional quality of sheet metal assemblies is an important factor for the final product. However, the part tolerance is not easily controlled because of the spring back deformation during the stamping process. Selective assembly is a means to decrease assembly tolerance of the assembly from low-precision components. Therefore, the purpose of this paper is to propose a fully efficient method of selective assembly optimization based on an improved genetic algorithm for optimization toolbox (IGAOT) in MATLAB.

The method of influence coefficient is first applied to calculate the assembly variation of sheet metal components since the traditional rigid assembly variation model cannot be used due to welding deformation. Afterwards, the IGAOT is proposed to generate optimal selective groups, which consists of advantages of genetic algorithm for optimization toolbox (GAOT) and simulated annealing.

The cases of two simple planes and the tail lamp bracket assembly are used to illustrate the flowchart of optimizing combinations of selective groups. These cases prove that the proposed IGAOT has better precision than that of GAOT with the same parameters for selective assembly.

The research objective of this paper is to evaluate the changes from rigid bodies to sheet metal parts which are very complex for selective assembly. The method of IGAOT was proposed to the selected groups which has better precision than that of current optimization algorithms.

The objective of this study is to investigate the barriers hindering the integration of lean manufacturing (LM) practices within the furniture industry of Bangladesh. The traditional operational paradigms in this sector have posed substantial challenges to the effective implementation of LM. In this study, the barriers of implementing LM in the furniture business are examined, aiming to provide a systematic understanding of the barriers that must be addressed for a successful transition.

The research reveals that “Fragmented Industry Structure,” “Resistance to Lean Practices” and “Inadequate Plant Layout and Maintenance”, emerged as the foremost barriers to LM implementation in the furniture industry. Additionally, “Insufficient Expert Management,” “Limited Technical Resources” and “Lack of Capital Investment” play significant roles.

The outcomes of this study provide valuable insights into the furniture industry, enabling the development of strategies for effective LM implementation. One notable challenge in lean implementation is the tendency to revert to established practices when confronted with barriers. Therefore, this transition necessitates informed guidance and leadership. In addition to addressing these internal challenges, the scope of lean implementation should be broadened.

This study represents one of the initial efforts to systematically identify and assess the barriers to LM implementation within the furniture industry of Bangladesh, contributing to the emerging body of knowledge in this area.