This paper considers a time window periodic maintenance strategy with different duration windows and job scheduling activities in a single machine environment. The aim is to minimize the number of tardy jobs through the integration of production scheduling and periodic maintenance intervals. A mixedinteger linear programming model (MILP) is proposed to optimize small-sized test instances. Furthermore, an ant colony optimization (ACO) algorithm is developed to solve larger sized test instances. Subsequently, to measure the efficiency of the solutions obtained by ACO, Moore's algorithm is also developed to benchmark with ACO. To test the efficiency and the effectiveness of the ACO algorithm, a set of data for small and large sized problems was generated in which several parameters were adopted and then ten replicates were solved for each combination. The small sized instances were solved by the MILP. Then, the results obtained showed that the proposed ACO was able to obtain the exact solutions within reasonable CPU times, thus, it outperformed the CPLEX solver with respect to CPU. The large sized instances were solved by the Moore's algorithm and compared to ACO. Then, the results obtained showed that the ACO outperforms Moore's algorithm for all the instances tested. It can be concluded that the developed ACOis very efficient and effective in solving the problem considered in this paper. INDEX TERMS Scheduling, MILP, single machine, periodic Maintenance, ant colony. AHMED BADWELAN received the B.Sc. degree in mechanical engineering from the Faculty of Engineering, University of Aden, in 2012, and the M.S. degree in industrial engineering from King Saud University, Saudi Arabia. He is currently pursuing the Ph.D. degree with the
Many occupational injuries occur in the manufacturing industry due to hazardous events. The available studies and statistics on occupational safety in the Kingdom of Saudi Arabia demonstrate the need for improving the work environment by introducing effective techniques for analyzing and assessing safety risks to control the most hazardous events. This study aims to develop a general model for assessing safety risks by integrating Monte Carlo simulation (MCS) and fuzzy set theory (FST) to overcome the uncertainty and unavailability of data on the severity and likelihood of hazards. MCS uses the ModelRisk software for modeling hazards that exhibit randomness and uncertainty and have historical data. In contrast, FST uses a Matlab code to assess expert judgment about hazards featuring epistemic uncertainty or unavailable historical data. The Al-Babtain Pole Factory in Riyadh was selected as a case study in the manufacturing environment to prove the applicability and effectiveness of the developed model. From the 371 hazards identified using the Occupational Health and Safety Assessment Series 18001, only five were analyzed using the two model techniques. The likelihood and severity of these five hazards were collected and analyzed to obtain the risk levels. A list of hazards and their processing priorities were then produced. According to the risk values calculated using both techniques, Hazard5 was found to be the most hazardous event, followed by Hazard1. The results of the proposed model demonstrated the distributions, statistics, percentiles, and risk limits for the selected hazards. These outputs support decision-making and increase the effectiveness and flexibility of safety risk assessments, which means that the proposed model is reliable and applicable for SRA under uncertainty and data unavailability in the manufacturing industry.
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