International Journal of Industrial Engineering: Theory, Applications and Practice
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A Two-stage Algorithm for Production Distribution Optimization of Fresh Products
No full textThe rise of e-commerce and the just-in-time system has imposed more stringent demands on fresh product supply chains. This paper addresses the challenges of production and distribution decision-making under uncertainty, considering the vehicle routing problem with time windows (VRPTW). Fresh products are distributed immediately after production, with any remaining perishable products deteriorating before they can be transported. To address these issues, a mathematical model is proposed for optimizing the production and distribution of fresh products. The objective optimization model for production scheduling and VRPTW is classified as an NP-hard problem. To tackle and optimize this complex problem, a two-stage algorithm combining ant colony optimization (ACO) and a fuzzy adaptive genetic algorithm (FAGA) is proposed. The approach begins by determining the critical combination parameters of the algorithm. Subsequently, analysis of the model's results reveals that production and distribution costs decrease significantly when integrated decision-making is employed. Additionally, the vehicle setup cost introduces a turning point in the overall target cost. Finally, a numerical experiment on VRPTW is conducted, with the results demonstrating the effectiveness of the proposed two-stage algorithm
A New Loss Function Based on BURR XII for Using Risk Estimation
No full textAchieving a competitive advantage in today's fast-paced and globally competitive business environment is one of the main goals for organizations across all industries. To attain this, businesses must adopt effective strategies and tools to enhance their operational efficiency, product quality, and overall customer satisfaction. One of the most significant tools that can help businesses achieve this goal is the implementation of quality control methods. Quality control not only ensures that products and services meet predefined standards but also helps reduce costs, increase customer loyalty, and maintain a sustainable competitive edge. In recent years, the importance of loss functions in quality assurance has grown considerably. Loss functions are used to quantify the deviation of a product's performance or quality from its desired target, translating this deviation into a monetary loss. This concept enables businesses to assess the broader impact of poor quality, not only on the organization but also on society as a whole. The monetary value of the loss represents the cost associated with a product's failure to meet expectations, including customer dissatisfaction, warranty claims, and potential reputational damage. Advancements in statistical methodologies, particularly those involving inverted probability density functions (PDFs), have opened new avenues for the application of loss functions. Inverted PDFs allow for a more detailed understanding of quality-related losses. This paper introduces the Inverted Burr XII Loss Function (IBXIILF) as a novel member of the inverted probability loss function family. The IBXIILF provides a robust framework for evaluating and minimizing quality-related losses in various industrial settings. The performance and applicability of the IBXIILF are demonstrated through a comparative study and an industrial example, highlighting its practical relevance and effectiveness in monitoring losses
Optimizing Faculty Hiring in Higher Education Using Model Predictive Control
Get PDFAs in any other sector, the objective of human resource manpower planning in academia is to avoid or to minimize a shortage or surplus of specific types of labor. In academia, the service offered is education, and the labor force is the lecturers and the professors. Hiring faculty members can have a positive socio-economic impact by improving education, driving innovation, supporting the local economy, and enhancing community development. Planning the manpower in academia is crucial for the future of the university. Our main tool is Model Predictive Control, which has received great interest during the last decades in the process industries, especially in chemical processes. Goodwin et al. (2001) report more than 2000 applications of Model Predictive Control. In this paper, we are using Model Predictive Control to obtain the optimal hiring rates for a university given its current and target faculty headcounts. A numerical example shows the effectiveness and the efficiency of the proposed method
State Monitoring of The Machining Process in Multi-Variety and Small-Batch Production Systems Based on Power Data
Get PDFMulti-variety and small-batch production is prevalent in today's manufacturing industries, where identifying the operational state is crucial for achieving efficient and effective manufacturing. However, real-time and intrusive monitoring is challenging due to the nature of multi-variety and small-batch production compared to flowline production. In the context of machining systems, power data not only offers insights into energy consumption but also aids in controlling the production process. Taking into account the characteristics of multi-variety and small-batch manufacturing systems, along with the economic and technological viability of collecting power data, a novel state monitoring method based on power data for multi-variety and small-batch production is proposed. First, to bolster the sample size, power data from the machining process is decomposed using wavelet analysis to extract features across three distinct layers. Then a Dynamic Time Warping (DTW) based workpiece recognizer is established, which calculates the features distance between real-time power signal and predefined templates, thereby facilitating workpiece identification. Thereafter, Recurrence Quantification Analysis (RQA) is applied to the Cross Recurrence Plot (CRP) of the real-time power signals and their corresponding template workpiece powers. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is then utilized to construct an anomaly detection model, which is fed by the outcomes of the RQA. The validity of this proposed methodology is confirmed through experimental validation. A case study demonstrates that the accuracy rates for workpiece recognition and anomaly detection are 98.40% and 98.8%, respectively. This method addresses the issue of limited sample size and provides an in-depth analysis of the input power in the machining system, making it suitable for state monitoring during the machining process within a multi-variety and small-batch production framework. It also has the potential to support dynamic state monitoring and energy optimization in practical machining system applications
A Reinforcement Learning and The Northern Goshawk Optimization Algorithm for Flexible Job Shop Scheduling Problem
No full textThis paper introduces northern goshawk optimization, a novel global search strategy for the flexible job shop scheduling problem. It uses two-stage encoding and random-key-based encoding to transform individual position vectors into flexible job shop scheduling problem solutions. To improve local search, reinforcement learning is integrated, converting the flexible job shop scheduling problem into a Markov decision process with 10 states and 6 rules. A reward function based on optimal completion time guides the search. The proposed hybrid northern goshawk optimization-Q-learning-state-action-reward-state-action framework combines global and local search strengths. Experiments on standard datasets show the algorithm's superior performance, validating its effectiveness and practicality in solving the flexible job shop scheduling problem and real-world production scheduling problems
A Hybrid Pythagorean Fuzzy MCDM Approach for Evaluating Supplier Resilience Capability in The Food Packaging Industry
Get PDFResilience capability in supplier evaluation has increasingly emerged as a critical issue in recent years, highlighted by events such as the COVID-19 pandemic and the Russian-Ukrainian conflict, which have underscored the need for suppliers to be adaptable and robust in the face of various challenges. Accordingly, this study focuses on assessing supplier resilience capability in the food packaging manufacturing industry. The resilience capability concept is defined as a three-dimensional construct based on absorptive capability, response capability and recovery capability for specifically food packaging manufacturing industry. An integrated approach combining the Analytical Hierarchy Process and Weighted Aggregates Sum Product Assessment methods under Interval Valued Pythagorean Fuzzy Set is proposed for evaluating and ranking the suppliers based on their resilience capability. The results of the study revealed that redundancy has the highest rank among other criteria, followed by situational awareness, recovery efficiency, contingency planning, agility, supply chain collaboration, knowledge management, supply chain visibility and correct risk management decisions. At the end of the study, a sensitivity analysis is also performed to demonstrate the robustness and reliability of the decision-making process for assessing and selecting the most resilient supplier in the food packaging manufacturing industry
Multi-Depot General Colored Traveling Salesman Problem with Time Windows in Home Healthcare System: A Medication Delivery Example
Get PDFThis paper focuses on the problem of medication delivery, specifically addressing meeting the medication demands of patients by different pharmacies. Medication delivery, along with the distribution of vaccines and test kits, is a crucial component of home healthcare services, primarily aiming to serve elderly patients and those with physical or psychological disabilities. A significant aspect of these services is the direct delivery of medications from pharmacies to patients' homes. The importance of home healthcare services has grown, particularly during the pandemic, as many patients faced difficulties accessing both prescribed and over-the-counter medications during lockdowns. The medication delivery problem under consideration is modeled as a Multi-Depot General Colored Traveling Salesman Problem with Time Windows (MD-GCTSP-TW). To solve this problem, a mixed integer mathematical model and a metaheuristic algorithm were designed. The effectiveness of these methods was tested on a variety of test problems, demonstrating the metaheuristic's efficiency through promising results
Cooperative Advertising Between O2O Catering Channels: In Perspective of Different Integration Modes
No full textMore catering companies are trying to increase their capacity utilization during off-peak hours through O2O (online-to-offline) platforms. In order to increase the opportunities for potential customers to spend online, platforms often require catering companies to participate in cooperative advertising, but this may harm the catering companies’ profits from offline customers. This research builds different game-theoretic models based on different O2O channel integration modes and obtains the optimal cooperative advertising decisions and profits. In addition, the influence of model parameters (i.e., product attractiveness, advertising interaction, price discounts, and platform listing fees) on optimized advertising decisions and profits is also discussed. The findings indicate that the fully integrated mode yields the highest profits and demand, followed by the partially integrated mode, with the separate mode being the least effective. Factors like product attractiveness and advertising interaction significantly boost consumer demand. This study contributes originality by extending cooperative advertising theories to the O2O catering industry, incorporating a modified demand function that accounts for price discounts and advertising interactions, and comparing different integration strategies through a game-theoretic approach
The Impact of Gauge Cluster Displays on Driver Attention and Mental Workload: An Eye-Tracking Study
Get PDFWith the increasing digitalization of automotive interfaces, optimizing gauge cluster design is crucial for minimizing driver cognitive load and distraction. Gauge clusters contain critical driving-related information linked directly to safety, such as speed, fuel levels, and warning indicators, which drivers must quickly and accurately perceive. This study uses eye tracking to examine the impact of analog and digital gauge clusters on driver attention. Thirty participants viewed driving videos to search for information while their eye movements were recorded. The first experiment compared analog and digital clusters, revealing that digital displays allowed faster visual searches and reduced cognitive effort. The second experiment analyzed digital clusters with various dash types and colors. The results indicate that the dual-dash digital display with a white background was the most effective in the cluster design. This research provides practical guidelines for the cluster design to increase driving engagement and decrease the cognitive workload on drivers. Therefore, the results will encourage automotive interface designers to consider ergonomic directions for gauge clusters from a user-centered perspective
Green Flexible Job Shop Scheduling Based on Improved Golden Jackal Optimization
No full textTo tackle the flexible job-shop scheduling problem (FJSP) in green manufacturing, the multi-objective optimization problem is transformed into a single-objective algorithm through normalization. A model is developed to optimize the objectives of minimizing makespan, machining energy consumption, and completion cost. The Golden Jackal Optimization (GJO) algorithm is enhanced to derive the optimal scheduling scheme. A real-number encoding mechanism is applied to simplify decoding, crossover, and mutation processes. A chaotic sequence is introduced to initialize the population, increasing population diversity and accelerating convergence. Furthermore, crossover and variable neighborhood search mechanisms are integrated to effectively enhance the algorithm's search capability. Comparative analysis based on designed test cases confirms the feasibility and effectiveness of the Improved Golden Jackal Optimization (IGJO) algorithm in addressing the green FJSP. Furthermore, a practical case and statistical testing illustrate the superiority of the IGJO algorithm