International Journal of Industrial Engineering: Theory, Applications and Practice
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Optimization of Robotic Mobile Fulfillment System Considering Robot Efficiency and Picker Energy Consumption
No full textRobotic mobile fulfillment system (RMFS) is a human-robot collaborative order-picking system. The substantial workload at the picking stations not only diminishes picker efficiency but also poses a significant risk of injury to their physical health. Therefore, it is essential to incorporate human factors into studying system efficiency optimization. This research analyzes item storage assignment, picker energy consumption, and the working environment to enhance robot efficiency while reducing picker energy expenditure. A two-stage optimization framework is proposed: first, correlation analysis and clustering of items are performed based on different shelf levels, effectively minimizing robot movement, with more pronounced effects observed on multi-level shelves; second, item storage assignments are optimized based on picker energy consumption, revealing that the arrangement of shelves and picking platform (working environment) significantly influences energy expenditure, and several optimal storage configurations that minimizes picker energy consumption. Finally, the results offer tailored decision support for managers with varying operational priorities
Optimized Sustainable Off-Online-Hybrid Channels of Recycling Unwanted Vehicles by Reutilising Forward Sale Networks
Get PDFIn line with continuously improved living standards, the amount of unwanted vehicles (UWVs) is increasing as the consumption of vehicles is being rapidly upgraded. This research aims to design a strategic UWV recycle system with resource-conservation and environmentally-friendly advantages. A nonlinear bi-level programming model is first constructed to optimize cooperation strategies between the manufacturer (leader) and the auto 4S shops (followers), including repurchasing prices and processing ways for distinct types of UWVs, the provided convenience and incentive policies to UWV-holders. Then, an efficient smoothing algorithm is developed to solve this complicated model in virtue of model reformulation and its property analysis. Numerical simulation is employed to reveal their practical implications. Main findings include: Reusing of existing vehicle sales network not only can reduce transportation cost, but also greatly improves sustainability of the UWV recycle system; Differential strategy of processing the UWVs with different damage-aging degrees can greatly improve their utilization rate and the total system profit; Governmental subsidy and differences of user groups both play critical roles in facilitating cooperation of recycle enterprises and efficiency of this system. It is seen that, unlike existing results, this study provides an off-online-hybrid-channel strategy for recycling the UWVs with the synergism of manufacturers and auto 4S shops in the decision-making framework of the Stackelberg game. The optimal strategies can be provided by the developed new model and algorithm, and the values of this research are further validated by numerical simulation
Flexible Job Shop Scheduling with Microgrid Assistance
No full textIn the context of global energy shortage and climate warming, the application of renewable energy in industrial production is an effective strategy to solve the problem. In this paper, a microgrid system composed of photovoltaic energy storage is constructed, which is connected to the flexible workshop together with the power grid. Taking the completion time, economic cost, and carbon emission as the optimization objectives, a multi-objective flexible job shop scheduling and energy allocation model is established, and the improved NSGA-II algorithm is used for optimization and solution. The optimization objective is improved by prioritizing time periods, scheduling the floating processes in the time period with high PV production capacity and low electricity price. Adaptive probability is adopted in crossover and mutation, and critical path mutation is adopted in mutation operation to accelerate the convergence speed and diversity of algorithms; for the energy allocation strategy, branching exact algorithms under the heuristic rules are introduced to obtain the optimal results of the branching paths and integrated into the main algorithm for continuous Solving. The effectiveness of the algorithm is verified by the example dataset of a flexible job shop, and the comparison with the traditional shop scheduling reveals that the optimization effectiveness of the microgrid and flexible job shop depends on the “scale - elasticity - energy consumption” multifaceted characteristics of the production scenarios. By focusing on the operation of different optimization objectives, a coordinated solution is provided for enterprises to cope with different production demands, and the reliability of the algorithm is also verified by comparing with the ordinary NSGA-II algorithm and the GWO algorithm. The study shows that the application of renewable energy to industrial manufacturing scenarios can effectively reduce the production cost and environmental pollution of enterprises, and promote the development of green manufacturing
Capacity Allocation For A Green Supply Chain Using Simulated Annealing
No full textWe consider a green supply chain that minimizes both economic cost and carbon dioxide emissions. The problem is considered a bi-objective optimization problem that requires a comprehensive approach to consider all stages of the supply chain, from design to end-of-life management. It aims to minimize environmental impact while maintaining economic viability. We present a simulated annealing-based optimization model that will be used to build a small Pareto set which is balancing the conflicting objectives of minimizing total economic cost and carbon dioxide emissions. The challenge here is how the decision-maker’s finds the best alternative solution from among the available alternative solutions in the Pareto set. The weighted sum method is used to rank the alternative solutions based on the decision maker experience and then to select an optimal solution. The model consists of three-echelon supply chain (sources, distribution center, retailers). The proposed method is implemented on a case study involving a supply chain model consists of three echelons in Jordan. The results demonstrate the algorithm's ability to identify a distribution model that closely approximates the global optimum.
 
A Time Series Control Chart for Monitoring Abnormal Blood Glucose Levels
Get PDFGlobal diabetes statistics indicate a continuous rise in prevalence and complications, highlighting the need for more effective monitoring and management strategies. Selecting techniques for monitoring blood glucose levels is essential in detecting abnormalities, identifying root causes, and facilitating behavioral adjustments. This study proposes a control chart constructed by using a robust estimator concept, which is suitable for monitoring the autocorrelated blood glucose data as a time-series control chart based on . Its performance is evaluated by using a Monte Carlo simulation under varying parameters and compared with existing charts based on the average run length. Results will show that the proposed chart is the quickest in detecting abnormalities when the data are highly correlated and performs comparably in medium-to-low correlations. It is also applied to real patient self-monitoring data and interpreted with treatment guidelines to support behavioral adjustment. A case study will confirm its capability, particularly when used with physician guidance. The proposed chart provides timely behavior-linked insights, enhancing diabetes management
MODELING OF CROSS-NETWORK COLLABORATION SUPPLY CHAIN AND SOLUTION VIA REINFORCEMENT LEARNING-BASED EVOLUTIONARY ALGORITHM
No full textDespite the increasing importance of collaboration in achieving cost-related advantages for companies, existing studies lack a systematic framework for determining how multiple supply chains can collectively facilitate strategic decision-making. In this study, we propose a multi-network collaborative mixed integer programming model and a reinforcement learning enhanced evolutionary algorithm to optimize cross-enterprise supply chains. The model facilitates collaborative decision-making among supply chains of different enterprises by incorporating collaborative cost management, partner sharing, collaborative transportation, and horizontal logistics. Our algorithm integrates an adaptive reinforcement learning process and an evolutionary structure with multi-branch tree encoding, allowing for the effective accumulation of solving experiences in different states to enhance the efficiency and accuracy of the solving process. We conducted extensive experiments using real data collected from electric automotive manufacturing supply chains. The experimental results show that the obtained solution quality is close to optimal with negligible margin for both small- and large-scale instances. Overall, our proposed approach enables the joint optimization of cross-enterprise collaborative supply chains and holds the potential for improving supply chain management in various industries
Efficient Material Handling: A Novel Adaptive Gating System for Small and Irregular Parts
No full textIn the automotive industry, efficient handling of small and irregular components is imperative for production optimization. Traditional methods involving labor or robotic systems can lead to increased costs. The research introduces an innovative, dynamic gating system, integrating a camera module with computing intelligence to process images and orient parts effectively in linear feeders. Theoretical and experimental results validate the most preferred orientation, with orientations 1 and 2 from group 1 identified as the optimum resting positions. The use of both the Markov model and a hidden Markov model in trap module design yields a robust outcome, effectively attaining the necessary part orientation. The dynamic part feeding system is optimized for frequencies of 45 and 50 Hz with trap angles of 30° from the experimental study. The study quantifies efficiency gains, demonstrating that the proposed system outperforms manual or robotic methods. This study offers a quantitative and comprehensive solution for assembling and packaging small and irregular components in the automotive sector
Single-Employee Scheduling with Continuous Learning Effect: Algorithms and Cost Impact under Classical Performance Measures
No full textIn many real-world scheduling problems, an employee’s productivity fluctuates continuously as learning occurs in human-machine interactions. This paper addresses single-employee scheduling problems considering this continuous learning effect and various objectives. We derive a formula to calculate the makespan and show that the order of the jobs has no effect on the makespan, as opposed to cases where the learning effect is assumed to be discrete. In addition, we demonstrate the optimality of the Shortest Normal Processing Time first (SPT), Earliest Due Date first (EDD), and Shortest Weighted Normal Processing Time first (WSPT) rules for minimizing total completion time, weighted total completion time, and maximum lateness, respectively. A numerical example underscores the key role of the learning rate in task arrangement and provides insights for learning organizations or individuals to manage tasks effectively
Integrating BIM (Building Information Modeling) into Lean Project Construction: A Novel Fuzzy Methodology
No full textThe construction industry has recognized the potential benefits of Lean methodology for improving project outcomes. However, the full utilization of Lean principles remains challenging in the industry. This study aims to address this issue by exploring integrating Building Information Modeling (BIM) into Lean Construction practices to enhance project success. The concept of success in construction projects encompasses various dimensions, including sustainability, time efficiency, cost-effectiveness, and budget adherence. This study focuses on measuring success through the lens of sustainability, emphasizing the need to make construction projects more sustainable and effective. To achieve this, a survey was conducted to collect data on sustainable factors that influence project success. The research analysis focused on a selected company's capacity to utilize its existing BIM software to improve critical sustainable factors. The sustainable factors identified as crucial for project success were the relationship with subcontractors, site cleanliness and tidiness, safety, and solid waste production. Projects can enhance their overall success and sustainability by effectively managing these factors. To evaluate different BIM software options, a comprehensive review of available software was undertaken based on their ability to address the identified sustainable factors. The Fuzzy Weighted LC-BIM TOPSIS was proposed as the evaluation method to determine the best BIM software that could maximize project success. The findings of this study highlight the importance of considering sustainable factors in construction projects and leveraging BIM software to manage them effectively. Integrating BIM into Lean Construction practices can improve project success by enhancing collaboration, streamlining processes, and optimizing resource allocation. The implications of this study suggest that construction industry stakeholders should prioritize adopting lean construction principles and leveraging BIM as a valuable tool to enhance project success. Integrating BIM and Lean methodologies can drive sustainable practices, optimize project performance, and contribute to the long-term success of the construction industry
Designing a Control Chart for Gamma Distribution through Repetitive Sampling with Imprecise Data
No full textThis article introduces an innovative control chart for gamma distribution monitoring in scenarios marked by uncertainty. The study involves determining control chart coefficients—namely, in-control probability, out-of-control probability, and average run lengths. These parameters are derived using the neutrosophic interval method, assuming the normal distribution's symmetrical property. The newly devised control chart's performance is evaluated through measurements of average run lengths across varying process conditions in an uncertain environment. The article explores the chart's behavior in both in-control and out-of-control situations, considering different magnitudes of shifts. Additionally, a comparative analysis with an existing control chart highlights the proposed chart's strengths. A real-world case study from the industrial sector is presented to illustrate practical applicability. Both simulation and real-world examples demonstrate the efficiency of the proposed control chart in swiftly detecting out-of-control processes