A bi-objective model in sustainable dynamic cell formation problem with skill-based worker assignment

A bi-objective mathematical model toward dynamic cell formation considering labor utilization

Dynamic Cell Formation Problem (DCFP) seeks to cope with variation in part mix and demands using machine relocation, replication, and removing; whilst from practical point of view it is too hard to move machines between cells or invest on machine replication. To cope with this deficiency, this paper addresses Reconfigurable Dynamic Cell Formation Problem (RDCFP) in which machine modification is conducted instead of their relocation or replication in order to enhance machine capabilities to process wider range of production tasks. In this regard, a mixed integer nonlinear mathematical model is proposed, which is NP-hard. To cope with the proposed model’s intractability, an Imperialist Competitive Algorithm (ICA) is developed, whose obtained results are compared with those of Genetic Algorithm’s (GA’s), showing superiority and outperformance of the developed ICA.

Solving a group layout design model of a dynamic cellular manufacturing system with alternative process routings, lot splitting and flexible reconfiguration by simulated annealing

In this paper a new model is developed to deal with a simultaneous dynamic cell formation and worker assignment problem (SDCWP). Part routing flexibility and machine flexibility and also promotion of workers from one skill level to another are considered. The proposed model is formulated as a single objective nonlinear integer programming which is converted to a linear one. The objective function consists of two separate components. The first part of the objective function is related to machine-based costs such as production cost, intercell material handling cost, machine costs in the planning horizon. The second part is related to human issues and consists of hiring cost, firing cost, training cost and salary. It is the first time that worker assignment and dynamic cell formation are considered simultaneously. To verify the performance of the proposed model, some numerical examples are presented. Computational and sensitivity analysis results imply the significance of SDCWP.

The current research seeks to present a proposed model for the design of a dynamic cellular manufacturing system in the Light Industries Company/Ashtar in Baghdad, one of the Iraqi industrial companies. With the aim of generating an industrial environment capable of accommodating rapid changes in the market to meet the changing and diverse needs and desires of the customer at the lowest possible cost, at the right time and the required quality. To achieve the current research relied on the methods of artificial intelligence, employing them and benefiting from them in the industrial field, especially the genetic algorithm. Accordingly, the current research included a theoretical framework for the dynamic cellular manufacturing system and the genetic algorithm, in light of what was mentioned in the Arab and foreign sources related to them. The research also adopted a field framework to design this system in the researched company using the genetic algorithm, and the researchers chose the freezers and models labs as a field for study, and in light of the results of the programming language program (MATLAB). It was reached to build a proposed model that reflects the virtual reality of this system. On the basis of that, the research reached a set of conclusions, the most important of which is the genetic algorithm that contributed significantly and effectively to finding the optimal cellular path for the dynamic cellular manufacturing system and reducing both the total time and the total cost of production. In light of this a set of proposals were presented showing the importance of shifting from the existing cellular manufacturing system in the field of study to the dynamic cellular manufacturing system.

Cell formation problem mainly addresses how machines should be grouped and parts be processed in cells. In dynamic environments, product mix and demand are changed in each period of the planning horizon. Incorporating such assumption in the model increases flexibility of the system to meet customers' requirements. In this model, to ensure the reliability of the system in presence of unreliable machines, alternative routing process as well as buffer storage are considered to reduce detrimental effects of machine failure. This problem is presented by a nonlinear mixed integer programming model attempting to minimize the overall cost of the system. To solve the model in large scale for practical purposes, a genetic algorithm approach is adopted as the model belongs to NP-hard class of problems. Sensitivity analysis is used to show the validity of the proposed model. Besides, numerical examples are used both, for small-sized and large- sized instances to show the efficiency of the method in finding near optimal solution.

Sustainability accounting, which arises as a result of the failure of the traditional accounting system to meet the information needs for corporate sustainability, has been examined in this study. Through sustainability accounting, which may be useful for companies by transferring useful information, in addition to collecting, classifying and recording documents, sustainability information has also been included in its contents. As a new concept, sustainability accounting has come to the fore because traditional accounting is not sufficient enough to convey information for corporate sustainability. Sustainability accounting in Turkey is subject to a voluntary basis. Developing a sustainability accounting system which increases the reliability and transparency of the enterprises on social and environmental issues enables more accurate determination of product costs and efficiency in resource utilisation. In the traditional approach to accounting, the use of natural resources is neglected and economic development will accelerate with the inclusion of sustainability accounting as it is not enough for companies to use this method alone.

A vibration damping optimization algorithm for solving a new multi-objective dynamic cell formation problem with workers training

New integration of preventive maintenance and production planning with cell formation and group scheduling for dynamic cellular manufacturing systems

Cellular manufacturing system deals with grouping of machines and parts into machine cells and part families with their compatibility. Reduction in part life cycle and variation in product mix and demand creates dynamic condition in the manufacturing systems. In previous work manufacturing companies has faces a lot of problems in meeting the customer requirement in dynamic conditions. Cellular Manufacturing System is a type of manufacturing system which tries to create a flexibility of job type and also productivity in shop floor. Now a day's Dynamic cellular manufacturing system becomes the emerging topic and attracts of lot of attention at different times intervals. This paper gives the brief review about the work which was focused on all published paper on this subject.

This paper presents a novel mixed-integer nonlinear programming model for the group layout (GL) design of a dynamic cellular manufacturing system (DCMS). A novel aspect of this model is concurrently making the cell formation (CF) and GL decisions in a dynamic environment. Another compromising aspect of this model is the utilization of multi-rows layout to locate machines in the cells configured with flexible shapes. The proposed model incorporates several design features including alternate process routings, operation sequence, processing time, production volume of parts, purchasing machine, duplicate machines, machine capacity, lot splitting, group layout, multi-rows layout of equal area facilities, and flexible reconfiguration. A numerical example taken from the literature is solved by the Lingo software to illustrate the performance of this model.

As global entities encounter increasing demands to reconcile economic goals with environmental and social responsibilities, the integration of sustainability accounting and artificial intelligence (AI) provides a scientific framework for enhancing ESG performance. This paper explores the role of AI-enhanced sustainability accounting as a crucial instrument for measuring, analyzing, and improving the effects of organizations on ecological and social systems. By leveraging AI's computational strengths - such as predictive modeling, real-time data analysis, and automated tracking of metrics -sustainability accounting transitions from a static reporting mechanism to a dynamic, decision-oriented field. Notable applications include simulations of carbon footprints based on various scenarios, optimization of resources within a circular economy, and automated adherence to international standards. The research highlights AI's potential to improve transparency and accountability through standardized reporting, while also reducing risks by providing predictive insights into vulnerabilities within supply chains and shifts in regulations. Additionally, machine learning facilitates ethical impact assessments, addressing biases in labor practices and promoting fair AI implementation. Nonetheless, challenges such as data fragmentation, the need for algorithmic transparency, and the requirement for interdisciplinary expertise remain, calling for effective solutions to ensure scalability. Empirical findings suggest that organizations that implement AI-enhanced sustainability accounting experience significant gains in operational efficiency, stakeholder trust, and resilience against climate-related challenges. This study advocates for a comprehensive governance framework that emphasizes ethical AI, interoperable data systems, and collaboration among stakeholders. By framing sustainability accounting as a science-based approach bolstered by AI, this research contributes to the ongoing dialogue on achieving the United Nations Sustainable Development Goals (SDGs) while encouraging innovation in the context of the Fourth Industrial Revolution

Background: Population segmentation models are often used to stratify people into groups for care or services planning purposes. These are typically disease-specific or utilisation-based, and target high-risk patient groups. Singapore’s National Health Group (NHG) has developed a River of Life (RoL) segmentation approach stratifying its population into five segments, namely, Living Well, Living with Illness, Crisis and Complex Care, Living with Frailty and Leaving Well. Building on this approach, Yishun Health (YH), a Regional Population Health System under NHG caring for more than 300,000 residents in Northern Singapore, developed the Needs-Based Sub-Segmentation Model (NBSSM) to further stratify Yishun Zone residents who are living with illness and frailty using additional biological, psychological and social criteria. We evaluated the NBSSM’s ability to stratify residents into distinct risk-groups based on a set of person-centred outcomes. Methods: Yishun Zone residents with chronic illness and/or frailty were first identified to generate ‘Living with Illness’ and ‘Living with Frailty’ groups. These residents were further segmented into 10 sub-segments representing different needs and complexity levels, based on biological complexity i.e. number and severity of chronic diseases, as well as presence or absence of psychosocial criteria i.e. presence of mental illness and social issues. To evaluate the NBSSM, we studied its ability to stratify residents into distinct risk-groups using Charlson’s Comorbidity Index (CCI) scores as well as person-centred outcomes such as healthcare utilisation, and healthcare cost. Statistical distinction between NBSSM segments based on the above outcomes was also tested. Data from a Population Health Survey being conducted by YH, which included health behaviours and more detailed mental and social needs data, will next be integrated into the NBSSM to forecast the needs under each sub-segments to enable services planning for segment-specific interventions. Results: All 314,522 Yishun Zone residents were included for RoL segmentation, of which 53,653 residents known to YH were further sub-segmented by the NBSSM according to their biopsychosocial needs. CCI, healthcare utilisation (i.e. number of Emergency Department visits, Specialised Outpatient Clinic visits, admissions and annual bed-days), and annual total healthcare cost were found to be statistically different across the NBSSM segments (p<0.05). Residents with presence of mental health and social issues had significantly higher risk of healthcare utilization and cost. Discussion & Conclusion: Using only data that was readily available data in our electronic medical record system, the NBSSM was able to augment NHG RoL segmentation and effectively sub-segment the Yishun Zone resident population into mutually exclusive and collectively exhaustive groups with similar magnitude of risk of health needs and outcomes. NBSSM enables development of more targeted and aligned clinical governance and leadership of segment-specific interventions through clearer assignment of segment-specific accountability for population outcomes. Integrating comprehensive needs assessment data from the YH Population Health Survey will enable more coherent health system and services planning. In conclusion, needs-based segmentation models are more person-centred than disease-specified or utilization-only segmentation models. NBSSM enhances our health system’s system transformation and service planning capabilities to direct targeted interventions to meet needs and improve population health outcomes.

Ocean negative carbon emissions: A new UN Decade program

SONNE: Solar-Based Man-Made Carbon Cycle and the Carbon Dioxide Economy