International Journal of Industrial Engineering: Theory, Applications and Practice
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EFFECTS OF DIFFERENT POPULATION SYNTHESIS MODELS IN THE EPIDEMIOLOGICAL SEIR (SUSCEPTIBLE-EXPOSED-INFECTED-RELEASED) AGENT-BASED MODEL
No full textThe advent of population synthesis has paved the way for the development of urban microsimulation studies, particularly in epidemiology. Thus, models must utilize synthetic populations that accurately match the actual population of a region, which is essential for large-scale agent-based modelling (ABM) efficiency and validity of large-scale ABMs. Although multiple population synthesis models exist, little is known about whether the errors generated during synthetic population generation can propagate through the simulation model and affect outcomes; this is the aim of this study. The SEIR-ABM is required to simulate the dynamics of disease spread independently using the resulting synthetic population generated through Iterative Proportional Fitting (IPF) and Markov Chain Monte Carlo (MCMC). The performance metrics used to assess the goodness of fit of IPF showed a high percentage of error compared with MCMC, specifically for the type of commuting and spatial locations of synthetic agents. In addition, a slight variation in the working-class group between the population models resulted in different inferences in the disease spread prediction. For instance, the average peaks of new infections in the IPF and MCMC models were 42.2 and 34.7, respectively. The simulation results indicated that inherent errors in social network attributes concede to the mispredictions of epidemiological simulation outcomes
MULTI-OBJECTIVE PATHFINDING FOR AUTONOMOUS ROBOT IN AGRICULTURE
No full textThis paper enhanced the autonomy of an agricultural mobile robot in a structured environment to find an optimal path to perform selective and variable spray of pesticides. To realize these, a robust VRP scheme with variable deterministic demand and nonlinear travel time is designed to navigate the robot while making decisions to fulfill the pesticide demand at each node. Nonlinear time function obtained from experiments necessitates the consideration of three objectives simultaneously. The NSGA-III algorithm is adopted to determine the optimal path for the robot. To achieve this, the mixed integer algorithm is modified to handle the full integer complex routing problem to optimize three test case scenarios. Finally, the NSGA-III has shown the capability to solve the fully integer constraint problem based on the proposed modifications
EVALUATION OF OUT-OF-HOME LAST-MILE DELIVERY METHODS IN TERMS OF SUSTAINABILITY
No full textLast-mile (LM) delivery takes place in the last stage of supply chains in the city centers and directly affects the sustainability of urban areas. Besides affecting the sustainability of the urban, since managing LM delivery is the most complicated and expensive stage in supply chains, it has to be explored, and solutions should be developed in terms of sustainability. In this study, as an alternative to traditional home delivery of parcels, the evaluation of six different out-of-home delivery methods (parcel locker, pick-up point, click & collect, in-car (trunk) delivery, reception/delivery box, and pick-up/drop-off point) has been carried out. The Hesitant Fuzzy Linguistic Term Sets approach has been applied for the evaluation of alternatives with the aim of eliminating the uncertainty and hesitation in expert opinions. Considered criteria in the proposed methodology have been determined with detailed literature research and expert opinions. Click & Collect has obtained the highest value in terms of sustainability among the options analyzed in the study. Sensitivity analyzes have been conducted to assess the results' sensitivity in terms of sustainability
EVOLUTIONARY GAME MODEL ANALYSIS OF COUNTY-LEVEL INTEGRATED HEALTH ORGANIZATIONS IN CHINA
No full textChina is actively encouraging the development of County-level Integrated Health Organizations. This study's goal is to evaluate a balanced approach to patient, government, and county-level hospital involvement in the development of County-level Integrated Health Organizations. To encourage tripartite collaboration, a tripartite evolutionary game model of CIHOs is created, and behavioral tactics and important parameters of the three stakeholders are carefully examined and introduced. Simulation experiments and sensitivity analysis reveal that under government and county-level hospital decisions, patients' choice of graded access is a dominant strategy for themselves. However, the presence of this equilibrium is influenced by the strength of government incentives and the level of county-level hospital participation. It is possible, in certain circumstances, to build CIHOs with the active support of the government, patients who follow graded access, and county-level hospitals who actively participate in the building of CIHOs. However, this requires the interaction of several variables, including the volume of government subsidies, the prestige gain of the government, the benefit of patients who follow graded access, and the cost of labor and financial resources of county-level hospitals
A NOVEL DECENTRALIZED APPROACH FOR PRODUCTION SCHEDULING
No full textThis paper proposes a novel decentralized approach for multi-stage job-shop scheduling. The method divides the larger job-shop scheduling problem into several smaller problems, which various agents solve. The collaboration among these agents ensures the exploration of globally superior solutions while allowing enhanced local exploration. Based on an extensive investigation, the current work shows that the proposed approach outperforms the centralized approach, especially for problems with increasing problem size, in a faster manner. Since the proposed approach is based on decentralized information processing, it is easily adaptable to next-generation cyber-physical manufacturing systems
OPTIMIZING HUMANITARIAN RELOCATION OF CONTAGIOUS AND NON-CONTAGIOUS POPULATIONS DURING THE RECOVERY PHASE: A MODEL FOR MINIMIZING COST AND TIME UNDER UNCERTAINTY: Optimizing Post-Disaster Relocation : A Cost & Time Model Under Uncertainty
No full textIn recent years, there has been a growing significance of research on humanitarian logistics for both researchers and practitioners. This research is crucial for aiding relief operations. While there has been extensive study of mathematical models for disaster operations management in the preparedness and response phases, the recovery phase models still need more attention. One of the significant challenges during the recovery phase is the spread of contagious diseases in the affected area, which necessitates the timely and cost-effective transportation of both contagious and non-contagious populations while preventing further casualties and disease spread. The paper proposes a multi-objective solid transportation model with different conveyance types for the relocation process to address these challenges. The proposed multi-objective model seeks to minimize two essential objectives: the cost and time required for relocation, and includes factors such as transportation, penalties, accommodations, medical expenses, halts, refueling, and maintenance. To account for the unpredictability and vagueness of input data in post-disaster scenarios, the proposed model incorporates fuzzy inputs and introduces a novel defuzzification technique that is validated by comparing it with an existing methodology. The research employs optimization techniques using the LINGO optimizing solver and presents a case study and particular cases that provide valuable management insights for improving decision support systems. Among the optimization techniques, namely the Neutrosophic compromise approach, Goal programming, Fuzzy goal programming, and Global criterion method, the optimal solution is obtained using the Neutrosophic compromise approach. The cost and time objective values obtained using the Neutrosophic compromise approach are 2034725 and 3923, respectively
THE EFFECTS OF PROCESS IMPROVEMENT ON SUSTAINABLE SUPPLY CHAIN MANAGEMENT PERFORMANCE
No full textThis study examines the effects of process improvement on sustainable supply chain management performance via the SCOR model using Lean Six Sigma tools. The transportation company is used as a case study. The critical success factors and reasons for the use of tools are reviewed. In research, it is found that process improvement increases the effectiveness of supply chain management. In addition, sustainability plays a major role in supply chain management. To measure and improve the effectiveness of this model, Lean Six Sigma tools have been used. The paper concludes that firms can have savings in terms of money and time. Furthermore, it is proved that CO2 emissions could be reduced. This paper prompts the efficacy of well-publicized methodologies and evaluates their implementation for process improvement for the firms. The practical application, constraints, and resultant effects of developing Lean Six Sigma were reviewed to give impetus to the methodology
MODELING AND OPTIMIZATION OF AN INTERVAL TYPE 2 FUZZY LOGIC SYSTEM FOR A CERAMIC COATING PROCESS
No full textProcess control is essential in Industry 4.0, and process modeling is an effective way to achieve it. For complex processes with high variability and uncertainty, Interval Type 2 Fuzzy Logic Systems are an efficient alternative, but they lack an appropriate methodology for selecting the Footprint of Uncertainty width. This work proposes a method that uses a genetic algorithm to optimize the Footprint of Uncertainty width and evaluates various Type-Reduction methods. ANOVA and R^2 and R_prediction^2 statistics are used to verify the model, which is applied to a manufacturing process that adjusts the density of a ceramic coating. The results indicate that the optimized model (R^2=0.886) outperforms the non-optimized model (R^2=0.796), linear regression (R^2=0.498), and backpropagation neural networks (R^2=0.641). Additionally, a stability analysis of the proposed model was performed using cross-validation, obtaining an R_prediction^2=0.758, which indicates that the genetic algorithm-based method can be a suitable option for modeling complex processes
DYNAMIC SIMULATION ANALYSIS FOR VARIOUS NUMBERS OF ORDERS IN AN INTEGRATED CAR-MANUFACTURING WAREHOUSE
Get PDFThe order-picking process in a warehouse is critical in managing customer orders, especially in retail stores. It is expensive because fulfilling online orders takes up to 70% of all warehouse activities. Procedures in order picking, including different route selection schemes, can significantly increase yield and reduce costs. The research shows that a suitable routing method can reduce the travel time of the order picker to fulfill the order. However, the number of orders may vary. This paper presented a dynamic simulation analysis based on a real scenario of a various number of orders in an integrated car manufacturing warehouse. The simulation reduced the travel time of the voters by about 44.89%. This simulation model helps to visualize the potential reduction in customer waiting times, leading to increased customer satisfaction
THE INFLUENCE MECHANISMS OF ILLEGITIMATE TASKS ON EMPLOYEES’ SILENCE BEHAVIORS AGAINST THE BACKDROP OF ARTIFICIAL INTELLIGENCE AND FUZZY ALGORITHMS
Get PDFEmployee silence can degrade the working environment and decrease employees’ motivation and commitment to an organization. As a result, it not only affects employees but also reduces the productivity of the organization. However, few studies have investigated the influencing mechanisms of employee silence empirically. This paper studies how illegitimate tasks affect employee silence based on artificial intelligence and fuzzy algorithms. We surveyed 325 employees in several medium-sized enterprises in Jiangsu and Anhui, China. According to the findings, emotional exhaustion partially mediates the relationship between illegitimate tasks and employees’ silence behaviors, and leadership humor can moderate the positive effect of illegitimate tasks on emotional exhaustion. Therefore, situating the mechanisms underlying employees’ silence behaviors in the context of artificial intelligence and fuzzy algorithm research helps researchers understand the relationship between illegitimate tasks and employees’ silence behaviors, thus improving related research on silence behaviors