An ESPC algorithm based approach to solve inventory deployment problem

Global competitiveness has enforced the hefty industries to become more customized. To compete in the market they are targeting the customers who want exotic products, and faster and reliable deliveries. Industries are exploring the option of satisfying a portion of their demand by converting strategically placed products, this helps in increasing the variability of product produced by them in short lead time. In this paper, authors have proposed a new hybrid evolutionary algorithm named Endosymbiotic-Psychoclonal (ESPC) algorithm to determine the amount and type of product to stock as a semi product in inventory. In the proposed work the ability of previously proposed Psychoclonal algorithm to exploit the search space has been increased by making antibodies and antigen more cooperative interacting species. The efficacy of the proposed algorithm has been tested on randomly generated datasets and the results obtained, are compared with other evolutionary algorithms such as Genetic Algorithm (GA) and Simulated Annealing (SA). The comparison of ESPC with GA and SA proves the superiority of the proposed algorithm both in terms of quality of the solution obtained, and convergence time required to reach the optimal /near optimal value of the solution

On translating technology research into industrial applications

Using personal cases he previously experienced, Prof Wong will share with colleagues the influence of environments and market forces that drive the applied technology research into practice. The speech will be delivered in an informal, interactive format, rather than didactic

An investigation of the value recovery process in the automotive remanufacturing industry : an empirical approach

Remanufacturers have been experiencing challenges when optimising the value recovery process mostly due to the uncertainties of cores regarding quality, quantity, arrival time and demand. Hence, the aim of this study is to gather relevant information from the literature and current industrial practice and then define research gaps to improve the decision-making practice for managing value recovery processes in the automotive remanufacturing industry. The case studies used in this paper are an original equipment remanufacturer and a contract remanufacturer. Both companies in the case studies use credit-based systems to take back old cores which can reduce the severity of cores’ unavailability. The ability to access the parts and specifications of the original equipment was the primary factor considered by the contract remanufacturer before deciding to remanufacture the product. In daily operations, the condition of cores was the main factors the OER and the contract remanufacturer considered to make a decision. Finally, the results of this study indicate further research areas from the intersection of industry’s needs and research gaps

Decision makings in key remanufacturing activities to optimise remanufacturing outcomes : a review

The importance of remanufacturing has been increasing since stricter regulations on protecting the environment were enforced. Remanufacturing is considered as the main means of retaining value from used products and components in order to drive a circular economy. However, it is more complex than traditional manufacturing due to the uncertainties associated with the quality, quantities and return timing of used products and components. Over the past few years, various methods of optimising remanufacturing outcomes have been developed to make decisions such as identifying the best End-Of-Life (EOL) options, acquiring the right amounts of cores, deciding the most suitable disassembly level, applying suitable cleaning techniques, and considering product commonality across different product families. A decision being made at one remanufacturing activity will greatly affect the decisions at subsequent activities, which will affect remanufacturing outcomes, i.e. productivity, economic performance effectiveness, and the proportion of core that can be salvaged. Therefore, a holistic way of integrating different decisions over multiple remanufacturing activities is needed to improve remanufacturing outcomes, which is a major knowledge gap. This paper reviews current remanufacturing practice in order to highlight both the challenges and opportunities, and more importantly, offers useful insights on how such a knowledge gap can be bridged

Remanufacturing : a potential sustainable solution for increasing medical equipment availability

The availability of medical equipment contributes significantly to the stability and sustainability of health care systems. However, in some countries, especially the developing ones, medical equipment availability is a major issue that remains unsolved. Hence, this paper explores the root causes of the issue, reviews existing solution approaches and suggests remanufacturing as a sustainable option. An extensive review was first conducted to uncover key factors contributing to the poor availability of medical equipment in developing countries. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was then used to measure the prominence degrees of the key factors and characterise these factors with an aim to differentiate those that are net drivers from those that are driven. Subsequently, factors that can be addressed by remanufacturing were identified, to determine the potential contribution of remanufacturing in addressing the poor medical equipment availability issue. The result shows that remanufacturing can potentially address at least five of the key factors which account for a cumulative total prominence of 43.5%. Remanufacturing is thus, a viable strategy for improving medical equipment availability in developing countries. In addition to remanufacturing, other recommendations were also proposed to help address the issue

An ANN-based approach of interpreting user-generated comments from social media

The IT advancement facilitates growth of social media networks, which allow consumers to exchange information online. As a result, a vast amount of user-generated data is freely available via Internet. These data, in the raw format, are qualitative, unstructured and highly subjective thus they do not generate any direct value for the business. Given this potentially useful database it is beneficial to unlock knowledge it contains. This however is a challenge, which this study aims to address. This paper proposes an ANN-based approach to analyse user-generated comments from social media. The first mechanism of the approach is to map comments against predefined product attributes. The second mechanism is to generate input-output models which are used to statistically address the significant relationship between attributes and comment length. The last mechanism employs Artificial Neural Networks to formulate such a relationship, and determine the constitution of rich comments. The application of proposed approach is demonstrated with a case study, which reveals the effectiveness of the proposed approach for assessing product performance. Recommendations are provided and direction for future studies in social media data mining is marked

Genetic based discrete particle swarm optimization for elderly day care center timetabling

The timetabling problem of local Elderly Day Care Centers (EDCCs) is formulated into a weighted maximum constraint satisfaction problem (Max-CSP) in this study. The EDCC timetabling problem is a multi-dimensional assignment problem, where users (elderly) are required to perform activities that require different venues and timeslots, depending on operational constraints. These constraints are categorized into two: hard constraints, which must be fulfilled strictly, and soft constraints, which may be violated but with a penalty. Numerous methods have been successfully applied to the weighted Max-CSP; these methods include exact algorithms based on branch and bound techniques, and approximation methods based on repair heuristics, such as the min-conflict heuristic. This study aims to explore the potential of evolutionary algorithms by proposing a genetic-based discrete particle swarm optimization (GDPSO) to solve the EDCC timetabling problem. The proposed method is compared with the min-conflict random-walk algorithm (MCRW), Tabu search (TS), standard particle swarm optimization (SPSO), and a guided genetic algorithm (GGA). Computational evidence shows that GDPSO significantly outperforms the other algorithms in terms of solution quality and efficiency

Distributed Approximation of Minimum Routing Cost Trees

We study the NP-hard problem of approximating a Minimum Routing Cost Spanning Tree in the message passing model with limited bandwidth (CONGEST model). In this problem one tries to find a spanning tree of a graph $G$ over $n$ nodes that minimizes the sum of distances between all pairs of nodes. In the considered model every node can transmit a different (but short) message to each of its neighbors in each synchronous round. We provide a randomized $(2+\epsilon)$-approximation with runtime $O(D+\frac{\log n}{\epsilon})$ for unweighted graphs. Here, $D$ is the diameter of $G$. This improves over both, the (expected) approximation factor $O(\log n)$ and the runtime $O(D\log^2 n)$ of the best previously known algorithm. Due to stating our results in a very general way, we also derive an (optimal) runtime of $O(D)$ when considering $O(\log n)$-approximations as done by the best previously known algorithm. In addition we derive a deterministic $2$-approximation

Managing capabilities for research centers in the UK's manufacturing sector : from literature review to a conceptual framework

This study uncovers the knowledge gaps regarding the capability management of research centers in the UK manufacturing sector. The paper presents some key findings from systematic literature review and introduces a novel framework that will improve the decision making process related to capability development and strategy building which are the two main challenges for the UK manufacturing research centers. The findings presented in this paper highlight the need for and the key elements of such a framework and the benefits that it will bring to a research center's capability management, e.g. more effective evaluation of capabilities and comprehensive understanding of development of those capabilities. It also identified knowledge gap related to management of technology capability from a research centre perspective. At the moment there is a lack of standardized framework (or approach) that is easy to use and applicable to research centres in the manufacturing sector. The paper presents findings from systematic literature review and introduces a novel framework that will improve the decision making process related to capability development and strategy building in the manufacturing research centers

The effects of mindfulness-based stress reduction program on the mental health of family caregivers: a randomized controlled trial

<b>Background</b> Caregivers of people with chronic conditions are more likely than non-caregivers to have depression and emotional problems. Few studies have examined the effectiveness of mindfulness-based stress reduction (MBSR) in improving their mental well-being. <p></p> <b>Methods</b> Caregivers of persons with chronic conditions who scored 7 or above in the Caregiver Strain Index were randomly assigned to the 8-week MBSR group (n = 70) or the self-help control group (n = 71). Validated instruments were used to assess the changes in depressive and anxiety symptoms, quality of life, self-efficacy, self-compassion and mindfulness. Assessments were conducted at baseline, post-intervention and at the 3-month follow-up. <p></p> <b>Results </b>Compared to the participants in the control group, participants in the MBSR group had a significantly greater decrease in depressive symptoms at post-intervention and at 3 months post-intervention (p < 0.01). The improvement in state anxiety symptoms was significantly greater among participants in the MBSR group than those of the control group at post-intervention (p = 0.007), although this difference was not statistically significant at 3 months post-intervention (p = 0.084). There was also a statistically significant larger increase in self-efficacy (controlling negative thoughts; p = 0.041) and mindfulness (p = 0.001) among participants in the MBSR group at the 3-month follow-up compared to the participants in the control group. No statistically significant group effects (MBSR vs. control) were found in perceived stress, quality of life or self-compassion. <p></p> <b>Conclusions </b>MBSR appears to be a feasible and acceptable intervention to improve mental health among family caregivers with significant care burden, although further studies that include an active control group are needed to make the findings more conclusive