International Journal of Industrial Engineering: Theory, Applications and Practice
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INVESTING IN THE KNOWLEDGE OF SHOP FLOOR WORKFORCE – A SYSTEMIC ANALYSIS
No full textThe objective of this article is to conduct a systemic analysis relating investment in knowledge for a production line to financial losses and returns. A systemic map was created based on the experience of a multidisciplinary group and a review of the literature about knowledge management, people management, and quality management systems. From estimates and initial data of a food production line, a dynamic model was created to simulate scenarios for five years. Concerning the studied production line, in the best-case scenario, within two years and five months after investing the capital, the investor will recover, due to loss reduction, all the investment in knowledge. In five years, the estimated return on investment was approximately twenty times the amount initially invested.
A FORMAL MODEL OF THE AGENT-BASED SIMULATION FOR THE EMERGENCY EVACUATION PLANNING
No full textRecently, the number and size of natural or manmade disasters have increased, leading to significant casualties and enormous economic losses. Disaster is any occurrence that causes damage, ecological disruption, human life or economic loss in a particular area. Massive disasters such as a nuclear disaster can cause a massive damage to the environment and human life. Disaster evacuation is the only way to save the human life after a disaster occurrence. However, the development of an evacuation plan requires many considerations on several factors such as the number of people, the number of shelters, the road network, the traffic congestions and more. The simulation for disaster evacuation can be a useful decision-making tool for the evacuation planner by conducting various evacuation scenarios for analyzing the best evacuation plan. This paper presents a formal model of the agent-based simulation (ABS) for the development of a nuclear disaster evacuation planning tool. In this effort, the agent represents the people that evacuate during the nuclear disaster through the road network. The agent movement behavior is determined by the knowledge and the reward function by implementing the Markov Decision Process (MDP) on the road network. A simulation tool is developed with experiments to demonstrate the effectiveness of the proposed formal model. The simulation results provide an insight for the evacuation planner in designing an efficient evacuation plan in a certain disaster event. The proposed formal model can be used for a useful tool to develop the evacuation strategy
FUZZY LOGIC METHOD CONSIDERING COGNITIVE PROCESS FOR HUMAN RELIABILITY ANALYSIS OF SPACE LAUNCH MISSIONS
No full textHumans play an important role in operation tasks; thus, this paper has proposed a human reliability analysis (HRA) method for space launch missions. There are four performance shaping factors (PSFs) that have considerable influence on human performance based on the analysis of space launch missions. A fuzzy logic system is used as a tool to estimate the human error probability (HEP) of the missions in this study. According to the cognitive reliability and error analysis method, each PSF influences the control mode, which is used as a representation of the HEP. However, it is the cognitive process that can be directly influenced by PSFs and directly influences human performance. Therefore, it is necessary to consider the cognitive process as a part of the HRA in the field of space launch. A fuzzy logic system is used to analyze the process from PSFs to cognitive activities and then to human performance; thus, the HEP can be estimated
DETERMINATION OF AN OPTIMAL PIPELINE FOR IMBALANCED CLASSIFICATION: PREDICTING POTENTIAL CUSTOMER COMPLAINTS TO A TEXTILE MANUFACTURER
No full textThere is an urgent need to reduce customer complaints because they damage reputations and incur losses. This study predicts the likelihood of complaint about a new production order using its intrinsic features. Customer complaints, however, are relatively rare, creating a serious class-imbalanced problem when training a classifier. To overcome this problem, we use a pipeline including the upsampling, the hyper-parameter generation, the classifier, and the evaluation metric. As each strategy involves different tricks in the pipeline, we use the design of experiments (DOE) concept to find, automatically, a suitable combination. A multi-response DOE is used to maximize balanced accuracy and minimize overfitting during training. The experimental results showed that the balanced accuracy of the proposed method for the testing dataset was about 23.6% better than those of the base classifiers and about 7% better than those of the current state-of-the-art methods
ROBUSTNESS OF DISPERSION CONTROL CHARTS IN SKEWED DISTRIBUTIONS
Get PDFThis study examines the relative efficiency and the finite sample breakdown point of eight different estimators in Phase I of the control charting process when outliers occur in non-normal data. The performance of control charts based on these estimators is investigated by using average run lengths under four disturbances in three skewed distributions. The simulation result shows that control charts based on the modified biweight A estimator (D7) and the median of the absolute deviations (MAD) from the median are more robust than those in highly skewed distributions. In practice, in addition to robustness, computational simplicity is another important factor for practitioners when they are choosing control charts. It is thus suggested the control chart based on the MAD should be considered first due to its simplicity and robustness
DYNAMIC AND FLEXIBLE STAFF DEPLOYMENT IN ACCIDENT AND EMERGENCY DEPARTMENTS USING SIMULATION-BASED OPTIMIZATION
No full textAccident and emergency departments experience overcrowding due to staff shortages as well as to variations in patient arrivals and the time required to treat them. Several policies have been developed by hospitals to ensure that patients are not put at clinical risk during overcrowding. These policies suggest flexing nurses from different duties to the overcrowded section. However, the policies do not indicate the details of when exactly the flexing should be activated. We develop a mathematical model to find the optimum levels of triage and treatment queue lengths after which flexing should be activated. The performance indicators of the department are the waiting time targets and the disturbance due to nurse flexing. Because of the lack of closed-form formulations, we propose simulation optimization to solve the problem. By analyzing the model structure, we develop an efficient search procedure of the discrete solution space. We show the application of the proposed method using the data of a large hospital in the UK under different parameter settings. The results show that hospital management should focus on increasing the number of treatment nurses rather than flexing the nurses, and the queue of the service stream that requires tighter staffing should be controlled by an upper limit
FAIR PROFIT DISTRIBUTION IN DELIVERY SERVICE COLLABORATION CONSIDERING SERVICE QUALITY
No full textElectronic commerce (e-commerce) transactions have become quite prevailing over the last decade. Due to the COVID-19 pandemic, consumers have started to make more online purchases, using fast electronic payment and transaction methods. This trend has led to a surge in sales-to-consumers (B2C) and inter-enterprise (B2B) e-commerce. The express or last-mile parcel delivery business of large companies such as Amazon, Alibaba, or Coupang has grown fast, and their market share has risen while the small and medium-sized delivery companies continue to struggle to survive. While anticipation of further growth in e-commerce at rapid rates is still there, small and medium-sized delivery companies shall always look for ways to remain competitive. Collaboration with other companies is a way of their survival in rapid market competition. In general, parcel delivery companies handle and deliver various types of items or products, which are usually mixed in volume, and some require special facilities. Additionally, there are unforeseen troubles during the delivery process, such as loss, damage, or delay, which may quickly reflect the delivery company's reputation and service reliability. When a collaborative delivery system is in place, the frequency of delivery troubles may increase due to differences in delivery processes among participating delivery companies, especially when these participants handle different types of items: regular, oversized/overweighted, and refrigerated items. This study aims to consider defective rates in the delivery service of participating companies and impose a penalty for such defects if any. The multi-objective programming model is proposed to describe the problem considering the delivery by types, defective rate, and penalty) while the collaboration group's profit and individual incremental profit of each participating delivery company can be maximized. The max-sum criterion, max-min criterion, Shapley value allocation, and nucleolus-based allocation methods are used to find an optimal solution and fair profit distribution for the collaboration group. Finally, the effectiveness of the proposed model is demonstrated through an illustrative numerical example
EVALUATION SYSTEM FOR LEAN KNOWLEDGE MANAGEMENT ABILITY BASED ON IMPROVED GRAY CORRELATION ANALYSIS
No full textThe efficient implementation of lean production requires an accurate understanding of effective lean knowledge management. However, the existing literature lacks research on evaluating lean knowledge management abilities. To accurately measure lean knowledge management levels, this study established an evaluation index system, selected an improved gray correlation analysis method to determine index weights at all levels, and built a comprehensive evaluation model based on lean knowledge acquisition, integration, and application. Descriptive statistics were calculated from a questionnaire on the current situation of knowledge management in a lean implementation process. By comprehensively evaluating the lean knowledge management abilities of the 28 surveyed enterprises, it was determined that knowledge management can promote improvements therein. The research results provide a decision-making basis for enterprises to formulate a lean implementation strategy
PERFORMANCE COMPARISON OF META-HEURISTICS FOR THE MULTIBLOCK WAREHOUSE ORDER PICKING PROBLEM
No full textThis study focuses on streamlining the order-picking process in a warehouse. We consider determining the picking sequence of items in a pick-list to minimize the total traveled distance in a multiblock warehouse, where a low-level picker-to-parts manual picking system is employed. We assume that the items are stored randomly in the warehouse. First, we construct a distance matrix of the shortest path between any pair of items. Next, using the distance matrix, we implement two meta-heuristics—the tabu search algorithm and the iterated greedy algorithm—to determine the picking sequence with the minimum total traveled distance. Through a numerical study, the performances of the meta-heuristic algorithms are compared with those of popular rule-based heuristics (S-shape, largest gap, and Combined+) and the best-known solutions. We conducted the numerical study in two stages. In the first stage, we considered a two-block rectangular warehouse, and in the second stage, we considered a three-block rectangular warehouse. The performance of the heuristics was calculated based on the optimal solution when available or the best calculated bound when the optimal solution is not available. We observed that the iterated greedy algorithm significantly outperforms the other heuristics for both stages.
OPTIMAL DESIGNS OF THE VARIABLE PARAMETERS X ̄ CHART WITH ESTIMATED PROCESS PARAMETERS
No full textAmong adaptive charts, the variable parameters chart shows the best performance as it varies all charting parameters. The existing variable parameters chart is designed by assuming that process parameters are known. However, process parameters are rarely known but are usually estimated based on Phase-I data. Hence, the variable parameters chart with estimated parameters is proposed. Formulae to evaluate various performance measures are derived through Markov chains. This paper shows that adopting the optimal charting parameters by incorrectly assuming that process parameters are known results in a large deterioration on the actual performance, especially for small shifts, a small number of subgroups, and small sample sizes. Subsequently, this paper proposes algorithms to obtain the optimal charting parameters for estimated parameters that show better performance, especially for small shift sizes, number of subgroups, and sample sizes. Finally, from an illustrative example, the proposed variable parameters chart is able to detect more out-of-control samples.