Risk-Acceptance Criteria in Occupational Health and Safety Risk-Assessment—The State-of-the-Art through a Systematic Literature Review

The utilization of risk acceptance criteria (RAC) can help a business to judge whether the risk level concerning any process involved in its working environment is acceptable or not, especially when the risk has a significant societal impact. Thus, the main intention of this study is to make known the current state-of-the-art concerning RACs and to propose new interpretations of it by surveying, for first time, the scientific literature about the RACs associated with the occupational health and safety (OHS) risk-assessment methodologies (RAA). A second objective of this work is the attainment of a prediction for the evolution of the quantity of the publications concerning OHS-RACs, and a third one is the derivation of an algorithm (via a flow-chart) in order to illustrate the process of the formation of new OHS-RACs. The work consists of two parts, (a) exploring and presenting methods of developing RACs in OHS; (b) classifying, analyzing, and benchmarking relevant published scientific articles by surveying the Scopus data base with proper search-hints, through a time interval of 20 years (January2000–December 2019). The review has defined a plethora of RAC-papers with reference to OHS, which is a remarkable percentage in comparison with the other fields aggregated, and this outcome proves that the issue of utilizing RACs is fundamental for the field of OHS. Additionally, it has been deduced that, day after day, there is an increasing tendency for the scientific community to develop and use RACs in the field of occupational safety, as this is evident by their frequent reference to the risk analysis and assessment (RAA) process. Our specific research methodology has been compatible with the PRISMA protocol. A prediction for the evolution of the quantity of the OHS-RAC publications is also given by confirming the Poisson stochastic process. Finally, we propose a generic guideline framework that can contribute to the establishment of new empirically-generated OHS-RACs

Reinforcement Learning-Based Optimization for Sustainable and Lean Production within the Context of Industry 4.0

The manufacturing industry often faces challenges related to customer satisfaction, system degradation, product sustainability, inventory, and operation management. If not addressed, these challenges can be substantially harmful and costly for the sustainability of manufacturing plants. Paradigms, e.g., Industry 4.0 and smart manufacturing, provide effective and innovative solutions, aiming at managing manufacturing operations, and controlling the quality of completed goods offered to the customers. Aiming at that end, this paper endeavors to mitigate the described challenges in a multi-stage degrading manufacturing/remanufacturing system through the implementation of an intelligent machine learning-based decision-making mechanism. To carry out decision-making, reinforcement learning is coupled with lean green manufacturing. The scope of this implementation is the creation of a smart lean and sustainable production environment that has a minimal environmental impact. Considering the latter, this effort is made to reduce material consumption and extend the lifecycle of manufactured products using pull production, predictive maintenance, and circular economy strategies. To validate this, a well-defined experimental analysis meticulously investigates the behavior and performance of the proposed mechanism. Results obtained by this analysis support the presented reinforcement learning/ad hoc control mechanism’s capability and competence achieving both high system sustainability and enhanced material reuse

Risk Prioritization in a Natural Gas Compressor Station Construction Project Using the Analytical Hierarchy Process

Recently, the seamless construction and operation of natural gas pipelines has become even more critical, while the oil and gas industry’s capability to operate effectively with acceptable risks and hazardous situations is mainly dependent on safety. As a result, it is very important to have a wide knowledge of effective management tactics for enhancing implementation of safety regulations and procedures. The problem of assuring workers’ health and safety in the workplace is a crucial component in the endeavor to raise the productivity of labor and the level of competitiveness of building projects. To promote the health, safety, and well-being of workers, issues that are embedded within the concept of sustainability, we propose in this study a safety risk-assessment process that uses the analytical hierarchy process for assigning priorities to risks on construction worksites. This process uses a popular multicriteria method. The success of this strategy was shown by its application to the building of a natural gas compressor plant in Greece. The main contribution of this study is the application of a well-known multicriteria method for assessing risks in a natural gas compressor station construction project and prioritizing hazards to allocate budget for risk-mitigation measures

Safety Considerations by Synergy of HAZOP/DMRA with Safety Color Maps—Applications on: A Crude-Oil Processing Industry/a Gas Transportation System

A collaborative framework by the synergy of Hazard and Operability (HAZOP) process and the Decision-Matrix Risk Assessment (DMRA) in association with safety-color mapping (SCM) is presented, in order to identify critical points and prioritize risks, and also to visualize the occupational safety and health (OSH) situation, at the workplaces (i) of a sour crude-oil processing industry (SCOPI), and (ii) of a measurement and regulatory station (MRS) in a gas transportation system (GTS), situated in Greece. Firstly, the conventional HAZOP analysis is executed in order to identify the potential fault causes of abnormal conditions (deviations) in the plants. The application of the DMRA-modus is valuable to rank the identified risks (hierarchy of risks). In view of the results, both of the HAZOP pattern (for identifying the hazards) and also the DMRA one (for assessing and ranking the risks), SCMs have been derived for the specific workplaces of the SCOPI and the MRS/GTS station, which could be a precious means for safety managers to appraise the urgency of investing limited budgets in measures preventing particular types of deviations, and also protecting the employees

A Joint Stochastic/Deterministic Process with Multi-Objective Decision Making Risk-Assessment Framework for Sustainable Constructions Engineering Projects—A Case Study

This study, on the one hand, develops a newfangled risk assessment and analysis (RAA) methodological approach (the MCDM-STO/DET one) for sustainable engineering projects by the amalgamation of a multicriteria decision-making (MCDM) process with the joint-collaboration of a deterministic (DET) and a stochastic (STO) process. On the other hand, proceeds to the application of MCDM-STO/DET at the workplaces of the Greek construction sector and also of the fixed-telecommunications technical projects of OTE SA (that is, the Greek Telecommunications Organization S.A.) by means of real accident data coming from two official State databases, namely of “SEPE” (Labor Inspectorate, Hellenic Ministry of Employment) and of “IKA” (Social Insurance Institution, Hellenic Ministry of Health), all the way through the period of the years2009–2016.Consequently, the article’s objectives are the following: (i) The implementation and execution of the joint MCDM-STO/DET framework, and (ii) to make known that the proposed MCDM-STO/DET algorithm can be a precious method for safety managers (and/or decision-makers) to ameliorate occupational safety and health (OSH) and to endorse the sustainable operation of technical or engineering projects as well. Mainly, we mingle two different configurations of the MCDM method, initially the Analytical Hierarchy-Process (the typical-AHP), and afterwards the Fuzzy-Extended AHP (the FEAHP) one, along with the Proportional Risk Assessment Technique (PRAT) and the analysis of Time-Series Processes (TSP), and finally with the Fault-Tree Analysis (FTA)

Multicriteria Health and Safety Risk Assessments in Highway Construction Projects

Road building sites are no exception to the fact that construction is one of the most dangerous businesses in the world. There are a number of concerns about the health and safety of the workers at these sites since they combine personnel, machinery, and construction equipment. The purpose of this paper is to determine, analyze, and compare the risks present at road building sites, and how they affect the health and safety of the workers. The study also examines workplace stress and psychosocial risk factors, which may have long-term effects on workers’ physical and mental health. To meet the goals of the research, risk evaluations for a specific construction project were carried out using the Analytic Hierarchy Process (AHP). Using the risk categories and risk factor hierarchy, the AHP compares data pairings. The skills, experience, judgments, and value system of the decision-makers were taken into account while deciding the amount of importance to give each criterion. The final risk rankings were established after calculating the overall priority numerically and running the necessary judgment consistency tests. The most significant risks to the health and safety of workers at road construction sites were identified by the study’s findings. The study additionally showed that psychosocial risk factors were important contributors to workplace stress and may have a negative impact on employees’ health and wellbeing. The results of the present study have important implications for risk management practices in the construction industry. Project managers can implement effective mitigation measures to reduce the likelihood and severity of accidents and injuries by identifying and evaluating the most critical risks associated with road construction sites. The findings also highlight the importance of addressing psychosocial risk factors and workplace stress in improving workers’ health and safety outcomes. Overall, this study underscores the need for a comprehensive approach to risk management that considers the diverse and complex factors contributing to construction site hazards

Efficient priority rules for dynamic sequencing with sequence-dependent setups

Summarization: This article addresses the problem of dynamic sequencing on n identical parallel machines with stochastic arrivals, processing times, due dates and sequence-dependent setups. The system operates under a completely reactive scheduling policy and the sequence of jobs is determined with the use of dispatching rules. Seventeen existing dispatching rules are considered including standard and setup-oriented rules. The performance of the system is evaluated by four metrics. An experimental study of the system is conducted where the effect of categorical and continuous system parameters on the objective functions is examined. In light of the results from the simulation experiments, a parameterized priority rule is introduced and tested. The simulation output is analyzed using rigorous statistical methods and the proposed rule is found to produce significantly better results regarding the metrics of mean cycle time and mean tardiness in single machine cases. In respect to three machine cases, the proposed rule matches the performance of the best rule from the set of existing rules which were studied in this research for three metrics.Presented on: International Journal of Industrial Engineering Computation

Optimal adaptive Kanban-type production control

Summarization: The field of adaptive Kanban-type control policies has attracted considerable attention in the research community over the years. Numerous heuristic control policies have been proposed in the literature for dynamically adjusting the number of kanban cards in a manufacturing system. However, to the authors’ knowledge, none of these approaches comes with guarantees regarding their optimality. In this research, we derive optimal adaptive Kanban-type policies using a dynamic programming approach. We investigate a single-stage system that consists of parallel machines. The demand for end-items is a Markov-modulated Poisson process, meaning that it is stochastic and periodically varying, due to seasonal fluctuations. The situation where the demand follows the Poisson distribution is also examined as a special case. The goal is to minimize the average total cost that consists of holding cost and backorder cost components. The properties of the optimal policy are investigated numerically. This analysis gives strong indications that existing, adaptive heuristics can never be optimal for seasonal demand. An extensive comparative evaluation of the optimal, the standard Kanban, and three adaptive heuristic policies is conducted. The experimental results indicate that the performance of all heuristics deteriorates as the variability of the demand increases. The Adaptive Kanban policy is found to largely outperform all other heuristics and to be a good approximation of the optimal adaptive policy in most cases.Presented on: International Journal of Advanced Manufacturing Technolog

Joint production, inventory rationing, and order admission control of a stochastic manufacturing system with setups

Summarization: In this paper, we examine a single-stage, manufacturing system with setups, which produces a single part type to satisfy demand from two customer classes. We address the problem of coordinating production control, stock rationing, and order admission decisions. The optimal policy, in respect to minimizing holding, backorder, lost sales, and setup costs, is derived by formulating the underlying problem as a Markov Decision Process and solving it by means of Dynamic Programming. The structure of the optimal policy is investigated numerically and, on that basis, a parametric control policy is proposed. The Markov chain model of the single-machine manufacturing system, operating under the proposed policy, is developed. Furthermore, analytical expressions of the steady-state probabilities and of the expected total cost are obtained. The proposed policy is compared to the optimal, as well as to three heuristics, in an extended series of experiments. The numerical results indicate that the proposed policy is a very good approximation of the optimal one, and that it largely outperforms the alternative control policies.Presented on: Operational Research - An International Journa