International Journal of Industrial Engineering: Theory, Applications and Practice
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SOLUTION OF THE BUFFER ALLOCATION PROBLEM USING THE OVERALL EQUIPMENT EFFECTIVENESS INDICATOR IN A SERIAL PRODUCTION LINE
Get PDFThis paper presents an analysis of the buffer allocation problem on a serial production line with five workstations and four buffer locations with unreliable operating conditions. Originally, this work was used as an optimization criterion to maximize the Overall Equipment Effectiveness indicator; said indicator is used to evaluate the performance of the processes in Lean Manufacturing; a comparison of the generated solution configurations is made with respect to other optimization criteria such as the minimization of the average work-in-process inventory and the maximization of Throughput. Three case studies involving the production line operating in a balanced and unbalanced manner are examined. The evaluation method used in this document is simulation. On the other hand, an exhaustive enumeration of the analyzed solution space is made. The results report the optimal allocation of buffers in the case studies and the differences that exist in the distribution of these in the optimization criteria investigated
Decision Analysis of A Dual Channel Supply Chain Considering Manufacturer’s Online Returns and Online Reviews
No full textWith the gradual development of online shopping, more and more consumers buy products online. The manufacturers who develop online channels often prioritize consumer online reviews. They implement measures such as return services to attract consumers and maximize profits. Online reviews and return service levels are important factors influencing consumers and the profits of the supply chain. To well analyze the impact of these two factors on the dual-channel supply chain, we establish some basic models with the manufacturer’s online return service and online reviews. Then, we analyze optimal decisions under either decentralized or centralized decision-making scenarios. The impact of online review and return service levels on optimal supply chain decisions and profits are further analyzed. Finally, based on the developed model, a revenue-sharing contract coordination scheme is designed to achieve Pareto improvements in the supply chain. The results show that, in any case, the profit gap between centralized and decentralized decision-making increases as the return rate increases. The return rate does not have a linear impact on the profits of manufacturers and retailers, but when the return rate is greater than 0.85, it seriously jeopardizes the interests of manufacturers. In addition, the level of return service has less impact on the supply chain than the perceived quality of online reviews. The overall profit of the supply chain under centralized decision-making is significantly greater than that under decentralized decision-making, and the coordination model can effectively coordinate the supply chain and alleviate conflicts
Navigating the Digital Evolution: I 4.0 Technologies and their Roles in Reconfiguring Dynamic Resource Management Networks
No full textThis research delves into the re-configuration of resource networks, focusing on the promising implications of Industry 4.0 technologies. These technologies, such as Artificial Intelligence (AI), Cloud-ERP Systems (CERP), Big Data Predictive Analytics (BDPA), Internet of Things (IoT), and Blockchain Adoption (BCA), are scrutinized for their potential to enhance efficiency and reconfiguration of resource networks. N=206 participants took part in the survey process. Data analysis based on Smart-PLS revealed some significant findings. AI found no statistical significance with dynamic resource network development; however, cloud findings. The AI found no statistical significance with dynamic resource network development; the cloud showed a significant relation. A significant relationship exists between using Big Data and IoT with dynamic resource network development. However, blockchain adoption (BCA) did not significantly impact resource networks. Overall, this study contributes to the theoretical area of dynamic capability, providing practical insights into the influence of Industry 4.0 technologies
Simulating Wind Speed Time Series by Karhunen-Loève Expansion
No full textThis paper aims to simulate non-Gaussian wind speed time series X(t) with a prescribed probability distribution and a given autocorrelation function (ACF). Given a set of historical observations of wind speed time series, the quantile function of X(t) is fitted by the generalized lambda distribution (GLD), the ACF of wind speed time series is fitted by a weighted sum of products of Gaussian function and cosine function. Then, the marginal transformation is applied to map X(t) to a standard normal space, where the Karhunen-Lo`eve (K-L) expansion method is employed to construct a Gaussian stochastic process Z(t) to match the ACF of X(t). The proposed method features the advantage that the spectral decomposition can be performed analytically, analytical formulae can be derived to calculate eigenvalues and eigenfunctions of the ACF of X(t), and Z(t) can be conveniently constructed by K-L expansion. Finally, case studies are performed to check the proposed method, the results indicate that the K-L expansion and GLD can accurately capture the ACF and distribution function of wind speed time series
Efficient Algorithms for The Multi-Stage Flexible Flow Shop Scheduling Problem with Transportation and Unloading Times
No full textImproving efficiency in multi-stage flexible flowshop operations is essential for modern industrial systems. Optimizing total completion time while simultaneously considering the unloading and transportation times is vital for enhanced productivity and cost-effectiveness. This study delves into the intricate realm of flexible flow shop scheduling, particularly focusing on scenarios prevalent in modern manufacturing processes such as sand casting. Despite limited exploration in prior literature for particular cases, the significance of this problem underscores the pressing need for efficient algorithms and the exploration of new problem properties. Notably, we unveil the symmetry inherent in the problem, where scheduling from the initial stage to the final one, or vice versa, yields identical optimal solutions, thereby augmenting solution quality. Given the strongly NP-Hard nature of the problem, approximate solutions are preferred, especially for medium to large-scale instances. To address this challenge, we propose a novel two-phase heuristic approach, encompassing both a constructive phase and an improvement phase. Leveraging an existing efficient heuristic tailored for parallel machine scheduling, our method extends to efficiently incorporate considerations for unloading and transportation times. The efficacy of the two-phase heuristic lies in its ability to consistently generate high-quality schedules at each stage. Furthermore, we introduce efficient lower bounds derived from estimating the minimum idle time within each stage, drawing from insights in polynomial parallel machine scheduling with a focus on flow time minimization in preceding stages. These lower bounds serve as critical benchmarks for evaluating the performance of the two-phase heuristic against the relative gap performance measure. Extensive experimentation on benchmark test problems underscores the effectiveness of our proposed algorithms, with results demonstrating an average computation time of 9.48 seconds and a mean relative gap of only 2.42% across varying job and stage quantities up to 200 jobs and 10 stages
Tourism Route Association Recommendation Algorithm Based on Changes of User's Interest Characteristics
No full textIn order to improve the ability to recommend tourism routes and increase tourist route satisfaction. This article designs a tourism route association recommendation algorithm based on changes in user interest characteristics. This article provides information on the distribution of tourist routes and Constructs the topology structure of tourism routes. The paper utilizes a multi-block fusion matching method to construct an optimal feature allocation model, Using optimized spatial clustering fuzzy functions to mine preference feature models and Introducing a joint distribution density function to solve the correlation recommendation of tourist routes. The experimental results show that when using this algorithm, the accuracy of the sample set is improved by 1.6% compared to the accuracy of the test set, and the recall rate is improved by 2.9%. Compared with the traditional algorithm, the proposed algorithm has the highest confidence and the best regression effect, which indicates that the proposed algorithm can effectively improve the recommendation efficiency
Unmanned Aerial Vehicle Path Planning Using Water Strider Algorithm
No full textThis paper presents a multi-agent optimal path planning and obstacle avoidance using a water strider algorithm (WSA) based on Sequential Convex Programming (SCP). The outcome is to find optimal collision-free trajectories. The best collision-free trajectories with minimum control effect is needed in the multi-agent route planning technique, which makes use of a centralized WSA algorithm that can guide drones over congested environments while avoiding both static and moving objects. By applying convex constraints on the drones' such as acceleration, velocity input and jerk, the feasibility of the trajectory is ensured. The optimal trajectory path is iteratively created using SCP and followed by WSA. The outcome guarantees the correctness of the linearization. Since the optimization is centralized, it is possible to find a feasible collision-free path, and the results are validated pre-determined formation. It is shown that the WSA algorithm scales with O3(n), where n is the number of drones
Logit Model for Prediction of Financial Health in Automotive Industry
No full textThis article aims to evaluate the financial health of companies operating in the automotive industry in the Slovak Republic and to predict impending bankruptcy using a selected mathematical-statistical method. The research sample consisted of 351 companies from the automotive industry. The basis for the creation of the prediction model is the financial indicators from the previous accounting period. The most significant indicators were selected using LASSO regression. The mathematical-statistical logit method was used to create the model. The success rate of the resulting logit model is assumed to be 78% in predicting the possible bankruptcy of companies in the automotive industry
Supply Chain Responsiveness Evaluation Using Forecasting and An Integrated Rough Z-number-based SWARA-TODIM Method
No full textIn a dynamic environment characterized by rapid market competition and unanticipated changes, supply chains emphasize the cost-effective implementation of responsive strategies. Moreover, enterprises are experiencing the repercussions of substantial economic transformations precipitated by the COVID-19 pandemic. This research paper aims to identify the critical enablers of responsiveness in Vietnam's jewelry supply chain and rank the supply chain responsiveness (SCR) areas. Evaluating and developing in a challenging economic context is essential to support top management in reallocating resources based on a more empirical foundation. This article presents a novel integrated method combining demand forecasting, the rough Z-number, Stepwise Weight Assessment Ratio Analysis (SWARA), and TODIM (Interactive and Multi-Criteria Decision Making in Portuguese). The approach can decrease the number of pairwise comparisons substantially and demonstrate a substantial benefit in managing ambiguous data and ensuring the dependability of the assessment
An Effective Hybrid Novel Genetic and Adaptive Artificial Bee Colony (NG-AABC) Metaheuristic Algorithm for Transforming Concurrent Scheduling Problems
No full textIn Industry 4.0, Automated Guided Vehicles (AGVs) enhance material handling efficiency and cost reduction. However, research on multi-objective scheduling of jobs, tools, automated storage, and AGVs in Flexible Manufacturing Systems (FMS) is limited. This study introduces the Novel Genetic and Adaptive Artificial Bee Colony Algorithm (NG-AABCA) to minimize the makespan, total tardiness, and penalty costs. NG-AABCA integrates cognitive (ε1) and social (ε2) learning factors, often overlooked, to achieve optimal solutions by leveraging external sources like the global optimal solution. This approach expedites convergence and avoids local optima by adjusting parameters iteratively. The Genetic Algorithm component employs elitism and Random-Restart Hill-Climbing to balance solution quality and diversity. Compared to other algorithms, NG-AABCA reduces makespan by 5.3% and tardiness by 8.7%, promising increased productivity and efficient resource use. This robust method aims to transform manufacturing optimization in Industry 4.0, addressing complex scheduling challenges in FMSs effectively