International Journal of Industrial Engineering: Theory, Applications and Practice
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DETERMINATION OF FLEET SIZE AND ASSIGNMENT OF TRAINS AND ROUTES BY MAXIMIZING THE NUMBER OF TRIPS
No full textTo advance a globally sustainable mobility paradigm, nations are increasingly prioritizing environmentally responsible transportation modes. Within this context, railway systems have assumed paramount significance, encompassing infrastructure development and operational efficiency. The challenge arises in optimizing capacity utilization while minimizing investment costs, given the limitations of available resources. This paper introduces an ingenious integer optimization model aimed at maximizing the frequency of train trips. Simultaneously, it determines the ideal composition of high-speed and conventional train fleets through empirical data analysis. What sets this study apart is its comprehensive approach, encompassing not only trip frequency but also the intricate dynamics of mixed train types, route assignments, and the identification of bottlenecks within railway segments. The findings affirm the model's effectiveness in addressing contemporary global challenges, highlighting its practical applicability
A MODIFIED HEURISTICS FOR THE BATCH SIZE OPTIMIZATION WITH COMBINED TIME IN A MASS-CUSTOMIZED MANUFACTURING
No full textNowadays, mass customization is a modern ideological production strategy that brings numerous new obstacles in areas like batch sizing, cost, and time. This paper proposes the permutation flow shop scheduling problem to minimize time and cost using a heuristic named Analytical Batch Optimization method while maintaining the product sequence order using the Fuzzy Analytic Hierarchy Process (FAHP). This study presents Analytical Batch Optimization that could reduce product Makespan, idle time, job completion time, and the number of changeovers while increasing slack remaining. The proposed approach, namely FAHP along with analytical batch optimization integrating with the setup and maintenance time in this study, is used to compare three cases, viz. without combined time, with combined time, and with combined time-shifted. A case study from benchmark literature is illustrated. The suggested approach is relatively straightforward to comprehend. The results are comparable with earlier studies
AN MCDM-BASED APPROACH TO COMPARE THE PERFORMANCE OF HEURISTIC TECHNIQUES FOR PERMUTATION FLOW-SHOP SCHEDULING PROBLEMS
Get PDFIn the industrial and manufacturing sectors, scheduling is an essential component in the process of determining crucial production cost aspects of corporate strategy. Solving flow-shop problems minimizes the makespan it takes for all jobs to be completed, reducing production costs and boosting output. Therefore, many heuristics techniques have been developed to assist in reaching a good and quick solution. However, newly developed techniques necessitate testing their performance against the classical ones. Therefore, this paper aims to conduct a comparative analytical, computational study of heuristic techniques for solving Permutation Flow-Shop Sequencing Problems and evaluating their performance. Eight techniques were compared by generating a set of problems of varying sizes and then solving them via a developed computer simulation program. Furthermore, a multi-criteria decision-making approach is followed for their performance evaluation. Results of the study revealed that based on six performance evaluation criteria, Dannenbring’s technique is the first best, followed by the Slope Index technique as the second best, then the technique by Campbell, Dudek, and Smith, Hundal, the Time Deviation technique, Palmer, Gupta, and the technique by Jayasankari, Jayakumar, and Vijayaragavan, respectively. This paper puts forward a ranking of the developed techniques for flow-shop problems and a framework for the performance evaluation of new permutation flow-shop scheduling problem methods
A NEW STOCHASTIC SIQR (SUSCEPTIBLE-INFECTED -QUARANTINE-REMOVED) MODEL WITH TWO DELAYS: FORECASTING COVID-19 DIFFUSION
No full textThere have been many efforts to prevent the spread of COVID-19 disease, such as developing medicine and vaccine or studying forecasting epidemic diffusion. In this study, we propose a new stochastic Susceptible-Infected-Quarantine-Removed (SIQR) model with two delays. Unlike the traditional model, the SIQR model considers asymptomatic or pre-symptomatic patients who can transmit the disease. We developed the observation delay to adjust the time differences between the true occurrence, the observation in the real world, and the reaction delay to reflect gradual changes in diffusion trends. Finally, we built a simulation of the complex model using the Gillespie algorithm. We find that in terms of MAPE, RMSE, and MAD, the proposed SIQR model explains COVID-19 epidemic diffusion better than the traditional Susceptible-Exposed-Infected-Released (SEIR). In addition, over a relatively long-term period of time, the SIQR model shows better performance compared to the SEIR model with two delays
OPERATIONAL AND INTELLIGENT ANALYSIS UNDER THE ERGONOMICS APPROACH OF THE PREVALENCE OF MUSCULOSKELETAL DISORDERS IN CONTAINER OPERATORS
Get PDFThis study aims to evaluate the prevalence of musculoskeletal disorders and possibly associated working conditions among dockworkers operating quay cranes. The data on working posture were collected through direct observations, photographs, and videos used in the Rapid Entire Body Assessment. The data on discomfort were collected using the Nordic Musculoskeletal Symptom Questionnaire. First, the questionnaire results showed that musculoskeletal symptoms are highly prevalent, particularly in the lumbar spine, cervical spine, shoulder, and neck. These data were ratified by machine learning analyses that, using logistic regression, achieved a big root mean square error, showing a correlation in the neck, wrists and hands, hip and thigh, and cervical region. Secondly, the final Rapid Entire Body Assessment score was 6, which reveals the median to the high-risk level of worker posture while performing the task
MACHINE LEARNING-ENHANCED GENETIC ALGORITHM FOR ROBUST LAYOUT DESIGN IN DYNAMIC FACILITY LAYOUT PROBLEMS: Implementation of Dynamic Facility Layout Problems
No full textThis paper proposes a new approach for solving dynamic facility layout problems (DFLP) using a genetic algorithm (GA) enhanced with machine learning techniques, namely clustering algorithms. The proposed course aims to design a robust layout that can adapt to changes in the input parameters. Traditionally, the DFLPs are solved using adaptive methods, i.e., the layout from period to period varies. However, in the robust approach, the layout remains the same throughout the different planning periods. The GA is used for generating the solutions, and the machine learning technique is used to cluster the solutions and select the candidate solution to undergo the local search procedure. The proposed approach is tested on a set of benchmark instances and compared with published approaches. The results show that the proposed approach outperforms the existing approaches in terms of solution quality, robustness, and computational efficiency
INTEGRATED HUB LOCATION AND CAPACITATED VEHICLE ROUTING PROBLEM OVER INCOMPLETE HUB NETWORKS
Get PDFHub location problem is one of the most important topics encountered in transportation and logistics management. Along with the question of where to position hub facilities, how routes are determined is a further challenging problem. Although these two problems are often considered separately in the literature, here, in this study, the two are analyzed together. Firstly, we relax the restriction that a vehicle serves between each demand center and hub pair and propose a mixed-integer mathematical model for the single allocation p-hub median and capacitated vehicle routing problem with simultaneous pick-up and delivery. Moreover, while many studies in hub location problem literature assume that there is a complete hub network structure, we also relax this assumption and present the aforementioned model over incomplete hub networks. Computational analyses of the proposed models were conducted on various instances on the Turkish network. Results indicate that the different capacity levels of vehicles have an important impact on optimal hub locations, hub arc networks, and routing design
EFFECT OF PURITY LEVEL OF CO2 SHIELDING ON METAL ACTIVE GAS WELDED JOINT QUALITY
Get PDFThe investigation deals with the study of the effect of the purity level of carbon dioxide shielding gas on the metal active gas weld quality. Studied 99.78 %, 99.95 %, and 99.97 % purity levels of carbon dioxide shielding gas. Factors considered were related to shielding gas purity, moisture, Sulphur, and oxygen content. Welded samples were subjected to ultrasonic testing to assess weld quality. With the reduction in purity level below 99.9 %, it was observed that the weld defect percentage increased in both lab trials and mass manufacturing jobs. The defects recorded were 5% higher when jobs were welded using carbon dioxide supplied from a gas cylinder than that supplied from liquid cryogenic bullets; this established that a higher purity level could be maintained in cryogenic storage and transport of shielding gases. This states helpful references to manufacturing industries for selecting the purity level of shielding gas, with the objective of rework reduction
ROBUST OPTIMIZATION OF STOCHASTIC HYBRID JOB-SHOP SCHEDULING WITH MULTIPROCESSOR TASK
Get PDFDue to the large number of uncertainties in the production workshop, the actual performance of the scheduling scheme deviated significantly from the theoretical value. In order to enhance its anti-jamming capability, this paper developed the robust optimization of stochastic hybrid job-shop scheduling with multiprocessors tasks. Firstly, predictable uncertainties were abstracted into processing time variations and described by scenario analysis in the modeling process. Secondly, based on the analysis of the advantages and disadvantages of traditional robust optimization models, a new Expected Cmax and the Worst scenario Model (ECWM) was proposed. The model improved the single-index robust optimization model and avoided the disadvantage that the Max Regret Model is computationally intensive. Finally, the effectiveness of ECWM is verified by simulation experiments. The results show that the scheduling obtained by ECWM has good average performance and anti-risk ability, which indicates that the model achieves a good balance in scheduling performance enthusiasm and risk resistance
A Hit-Rate Based Dispatching Rule For Semiconductor Manufacturing
Get PDFHit-rate, the percentage of on-time completion, is a very important performance measure in a make-to-order semiconductor fab. This paper presents a dispatching algorithm for such a fab with machine-dedication feature. This feature imposes a constraint on the production route due to the advance of manufacturing technology, and has been rarely addressed in previous literature. A dispatching algorithm, called LBSA, was recently developed for a fab with machine-dedication feature. The LBSA algorithm outperformed many other dispatching methods in terms of hit-rate for short-routing products but not so well for long-routing products. This paper develops a dispatching method that shows a high hit-rate performance for both short-routing and long-routing products