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To compare the performance of a new hybrid manufacturing system (HMS) with a conventional cellular manufacturing system (CMS). The hybrid system is a combination of the cellular manufacturing and job shop.

A hypothetical manufacturing facility with eight machines and 20 parts is used as a case. Simulation models are developed for two manufacturing systems. A multi‐factor comparison is carried out to test the performance of the systems under different scenarios.

It was found that group scheduling rules (GSR) and the manufacturing system design factors have significant impact on the performance of the system. In particular, the hybrid system shows its best performance when the MSSPT GSR is applied, whereas the cellular system is superior when DDSI is implemented. The results also demonstrate that, by adding non‐family parts to the production schedule of the HMS, significant benefits in the performance measures can be attained.

The conclusion cannot be generalized, as the result is dependent upon the input data and the size of the problem.

The application may be limited to certain industry sectors. Further studies may be needed to identify the appropriate industry.

While the majority of the literature focuses on either a job shop or a pure CMS, this paper has a distinctive approach that allows the combined use of both systems. This could be a useful transitional approach from one system to the other.

The solution quality of a comprehensive machine/part grouping problem, where the processing times, lot sizes and machine capacities are considered, may not be properly evaluated using a binary performance measure. This paper suggests a generalized grouping efficacy index which has been compared favorably with two binary performance measures. A genetic algorithm using the generalized performance measure as the objective is developed to solve the comprehensive grouping problems. The algorithm has been tested using a number of reference problems with processing times being randomly assigned to all operations. The effects of three major genetic parameters (population size, mutation rate and the number of crossover points) have also been examined. The results indicate that, when the computational time is fixed, larger population size and lower mutation rate tend to improve solution quality while the number of crossover points has no significant impact on the final solution.

Boredom is believed to be the common cause of workers' absenteeism, accidents, job dissatisfaction, and performance variations in manufacturing environments with repetitive jobs. Effectively measuring and possibly predicting job boredom is the key to the design and implementation of appropriate strategies to deal with such undesirable emotional state. The purpose of this paper is to present new methodologies to measure and predict human boredom at work.

Two series of mathematical formulations, linear and nonlinear, to describe the variation of human boredom at work are first presented. Given the complexity of human emotions, the authors also present a probabilistic framework based on state‐of‐the‐art Bayesian networks to model employees' boredom at work.

The proposed methods centre on the prediction and measurement of human boredom at work. They enable managers to take proactive actions to deal with human boredom at work. Examples of such actions are task rotation and job redesign.

The proposed methods are verified using a number of cases describing a set of phenomena that may occur in the real world. However, further research is required to demonstrate the validity of the models using real world data.

According to accessible literature, human boredom is being measured by self reporting scales thus far. This study describes and demonstrates analytical approaches to model human boredom at work.

This paper aims to explore issues arising from ṣukūk (Islamic bonds) on blockchain, including Sharīʾah (Islamic law) and legal matters.

A qualitative methodology is used in conducting this research where relevant literature on ṣukūk was reviewed. Through a doctrinal approach, the paper presents analyses on the practice of ṣukūk and ṣukūk on blockchain by discussing its legal, Sharīʾah and regulatory issues. This culminates in a conceptual analysis of blockchain ṣukūk and its peculiar challenges.

This paper reveals that digitizing ṣukūk issuance through blockchain remedies certain inefficiencies associated with ṣukūk transactions. Indeed, structuring ṣukūk on a blockchain platform can increase transparency of underlying ṣukūk assets and cash flows in addition to reducing costs and the number of intermediaries in ṣukūk transactions. The paper likewise brings to light legal, regulatory, Sharīʾah and cyber risks associated with ṣukūk on blockchain that confront investors, practitioners and regulators. This calls for deeper collaboration in research among Sharīʾah scholars, lawyers, regulators and information technology experts.

As a pioneering subject, the paper notes the prospects of blockchain ṣukūk and the current dearth of literature on it. The paper would assist relevant Islamic capital market entities and authorities to determine the potential and impact of blockchain ṣukūk in their respective businesses and the financial system.

Blockchain ṣukūk will assist in addressing issues inherent in classical ṣukūk and in paving the way to innovative solutions that will facilitate and enhance the quality of ṣukūk transactions. For that, ṣukūk would require appropriate regulatory technology to address its governance and regulation peculiarities.

Integrating ṣukūk with blockchain technology will add value to it. The paper advances the idea that blockchain ṣukūk revolutionises ṣukūk and enhances its practice against known inadequacies.

The hospitality industry values segmentation and loyalty programs (LPs), but there is limited research on new methods for segmenting loyalty program members, so managers often rely on conventional techniques. This study aims to use big data-driven segmentation methods to cluster customers and provide a new solution for customer segmentation in hotel LPs.

Using the k-means algorithm, this study examined 498,655 profiles of guests enrolled in a multinational hotel chain’s loyalty program. The objective was to cluster guests according to their consumption behavior and monetary value and compare data-driven segments based on brand preferences, demographic data and monetary value with loyalty program tiers.

This study shows that current tier-based LPs lack features to improve customer segmentation, and some high-tier members generate less revenue than low-tier members. Therefore, more attention should be given to truly valuable customers.

Hotels can segment LP members to develop targeted campaigns and uncover new insights. This will help to transform LPs to make them more valuable and profitable and use differentiated rewards and strategies.

As not all guests or hotel brands benefit equally from LPs, additional segmentation is required to suit varying guest behaviors. Hotel managers can use data mining techniques to develop more efficient and valuable LPs with personalized strategies and rewards.

It is widely acknowledged that the pace of change due to complexity in the competitive environment coupled with advances in technology and innovation is forcing management to rethink strategy formulation and implementation. The purpose of this paper is to discuss convergence in the context of discontinuous competitive environment and possible management responses to changes.

The findings of this paper are based on the analysis of developments in the biotechnology environment. The disruption to pharmaceutical industry is examined from the context of need served.

The rate of change in innovation is leading to the creation of new industries and the disintegration of the industry classifications due to convergence of multiple needs previously served by different industry groupings. As such, firms have to upgrade their environmental scanning systems to detect competitive forces beyond the traditional industrial competitive boundaries.

The paper provides a comprehensive review of convergence and disruptive technologies

The paper highlights the breakdown of barriers in terms of industry classification. Customer's needs could be served by firms in hitherto distinct industry groupings.

The purpose of this paper is to provide a novel domain‐concept association rules (DCAR) mining algorithm that offers solutions to complex cell formation problems, which consist of a non‐binary machine‐component (MC) matrix and production factors for fast and accurate decision support.

The DCAR algorithm first identifies the domain‐concept from the demand history and then performs association rule mining to find associations among machines. After that, the algorithm forms machine‐cells with a series of inclusion and exclusion processes to minimize inter‐cell material movement and intra‐cell void element costs as well as to maximize the grouping efficacy with the constraints of bill of material (BOM) and the maximum number of machines allowed for each cell.

The DCAR algorithm delivers either comparable or better results than the existing approaches using known binary datasets. The paper demonstrates that the DCAR can obtain satisfying machine‐cells with production costs when extra parameters are needed.

The DCAR algorithm adapts the idea of the sequential forward floating selection (SFFS) to iteratively evaluate and arrange machine‐cells until the result is stabilized. The SFFS is an improvement over a greedy version of the algorithm, but can only ensure sub‐optimal solutions. Practical implications – The DCAR algorithm considers a wide range of production parameters, which make the algorithm suitable to the real‐world manufacturing system settings.

The proposed DCAR algorithm is unlike other array‐based algorithms. It can group non‐binary MC matrix with considerations of real‐world factors including product demand, BOM, costs, and maximum number of machines allowed for each cell.

This paper aims to propose an improved Moth Flame Optimization (I-MFO) algorithm to optimize the cost-oriented two-sided assembly line balancing (2S-ALB). Prior to the decision to assemble a new product, the manufacturer will carefully study and optimize the related cost to set up and run the assembly line. For the first time in ALB, the power cost is modeled together with the equipment, set up and labor costs.

I-MFO was proposed by introducing a global reference flame mechanism to guide the global search direction. A set of benchmark problems was used to test the I-MFO performance. Apart from the benchmark problems, a case study from a body shop assembly was also presented.

The computational experiment indicated that the I-MFO obtained promising results compared to comparison algorithms, which included the particle swarm optimization, Cuckoo Search and ant colony optimization. Meanwhile, the results from the case study showed that the proposed cost-oriented 2S-ALB model was able to assist the manufacturer in making better decisions for different planning periods.

The main contribution of this work is the global reference flame mechanism for MFO algorithm. Furthermore, this research introduced a new cost-oriented model that considered power consumption in the assembly line design.

Diabetes is one of the most prevailed chronic diseases in the world. Pro-inflammatory cytokines play a key role in the type 2 diabetes mellitus. Pomegranate seed oil (PSO) has potential anti-inflammatory properties. The purpose of this study is to evaluate the antidiabetic effects of the use of PSO on the expression of peroxisome proliferator-activated receptor-gamma (PPAR-γ), pro-inflammatory biomarkers and lipid profile levels in obese type 2 diabetic patients.

In total, 52 patients were randomly assigned to the PSO (n = 26) and placebo (n = 26) groups. Subjects received daily PSO 3 g placebo (paraffin) in 1 g soft-gel capsules (along with breakfast, lunch and dinner meals) for eight weeks.

Serum levels of fasting blood sugar (FBS) decreased from 161.46 ± 34.44 to 143.50 ± 24.2 mg/dL (p = 0.008), IL-6 decreased from 5.17 ± 2.25 to 4.52 ± 1.90 (p = 0.049) and tumor necrosis factor alpha (TNF-α) significantly decreased from 9.17 ± 4.13 to 7.74 ± 2.44 pmol/mL in PSO group (p = 0.030). However, changes in the expression of PPAR-γ gene, serum levels of hs-CRP and lipid profile levels were not significant.

Lack of PSO concentration measurements and the short duration of the study were the key limitations. Future randomized clinical trials with a longer period of follow-up are needed to assess the potential anti-diabetic effects of PSO.

Administration of PSO in obese type 2 diabetic patients reduced the levels of FBS, interleukin 6 and TNF-α; nevertheless, changes in the insulin, lipid profiles and hs-CRP were not significant.