Global Path Planning Method for AGV of Warehousing Logistics Based on Improved Ant Colony Algorithm

AGV for warehousing and logistics is an automatic guided vehicle that is used for cargo handling, storage, sorting and other operations in warehousing and logistics scenarios. Due to the complex warehousing logistics scenarios, AGV needs to deal with the complex environment and variable task requirements in warehousing logistics during operation, resulting in low efficiency of path planning. Therefore, a global path planning method for AGV of warehousing logistics based on an improved ant colony algorithm is studied. After analyzing the overall transportation path of warehousing logistics, according to the optimization algorithm of ants' foraging behavior in nature, the pheromone transmission mechanism and behavior rules are simulated, and relevant factors such as path length transportation efficiency. Obstacle avoidance and load balance are considered to adjust the parameters such as pheromone volatilization factor and heuristic information weighting so that the improved ant colony algorithm can better adapt to changes in the warehousing logistics environment and improve the accuracy and reliability of AGV path planning. Through experimental verification, the effectiveness and superiority of the method are proved, the AGV transportation efficiency is improved, and the algorithm has excellent stability and adaptability

The Development of Artificial Intelligence-Based Optimal Route Selection Framework for Rescue Services Process Management

An increase in urban traffic congestion has emerged as a critical bottleneck in the operational efficiency of emergency response systems, leading to substantial delays in rescue service deployment and a measurable increase in roadway mortality rates. Unplanned blockage placements by law enforcement agencies further disrupt traffic flow, elevating the vehicular density and impeding emergency response times. This study presents a data-driven framework that forecasts optimal blockage points and predicts congestion on alternative routes using a combination of operational research strategies and AI-based traffic modeling. The novelty of this work lies in leveraging AI-driven techniques to optimize blockage placement while minimizing disruptions near healthcare and public safety services. The framework employs supervised machine learning models to classify traffic flow (non-congested: 0, congested: 1) based on feature vectors linked to healthcare accessibility, achieving a 99% F1 score on both validation data and real-time traffic monitoring. Additionally, the A-star algorithm is utilized to determine the most efficient alternative routes post-blockage. To enhance practical usability, the framework is integrated into a Graphical User Interface (GUI) application capable of predicting congestion at specific time intervals throughout the day. This system serves as a decision-support tool for local agencies, aiding in strategic traffic planning and ensuring uninterrupted access to critical healthcare services. By mitigating congestion near essential service areas, the proposed approach enhances emergency response efficiency and contributes to overall public safety

Optimal Treadmill Attributes Configuration: A Text Mining Approach

New product development aims to create products aligning with consumer preferences, yet discrepancies exist between consumers’ ideal product attributes and manufacturers’ perceptions. Previous studies primarily utilized regression equations or conjoint analysis to determine product attributes’ impact on consumer utility, with limited discussion on optimal attribute combinations. This study adopts a configurational approach using product attribute configuration as the analysis unit. We hypothesize that an ideal configuration exists for treadmill attributes, where products closer to this configuration achieve higher sales. Using text mining to convert online reviews into numerical data, we construct regression equations based on configuration-ideal distance. Results confirm the existence of an ideal treadmill attribute configuration, providing concrete guidance for new product development

Cooperative Advertising Between O2O Catering Channels: In Perspective of Different Integration Modes

More 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 Combined Effect of Ergonomic Factors on Work and Cognitive Performance in The Automotive Industry

The study aimed to explore the impact of combined ergonomic factors on the work and cognitive performance of final inspection workers (n=18) in the automotive industry. Three levels of ergonomic factors were studied: posture, mental workload, and work shift. The Poison Test and Digit Symbol Substitution Test were used to assess the work and cognitive performance of final inspectors. Results found that posture and mental workload significantly impact work performance (15.22%) and cognitive performance (10.21%), while mental workload and work shift significant-ly impact both (2.98%) and (1.81%). Work shift and posture also substantially impact both (6.61%) and (4.96%). The combined effects of posture, workload, and Work shift significantly impacted both work performance (5.24%) and cognitive performance (7.88%). The study reveals that posture, mental workload, and work shift significantly impact the work and cognitive performance of final inspection workers in the automotive industry

A Hybrid Pythagorean Fuzzy MCDM Approach for Evaluating Supplier Resilience Capability in The Food Packaging Industry

Resilience 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

A Two-stage Algorithm for Production Distribution Optimization of Fresh Products

The 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

Evaluation of Container Terminal Performance with Capacity Constraint Using System Dynamics

Container terminal ports are essential in supporting a country's logistics performance. Thus, it is necessary to improve the port's operation to increase its effectiveness. Port operations involve a lot of stakeholders and variables within the system that require all supporting components to be analysed, so a systemic approach is needed. This study investigates an effective decision to improve the performance of container port operations that utilize the system dynamics methodology. Port logistics performance is represented by the leading indicators of port service (service time and stacking yard usage). The results indicate that gate-out rates of container import flow and inaccurate information sharing related to container stacking systems impact port berthing time and yard occupancy ratio

Grey Three-Way Decision Approach with The Change of Decision Objects

In practical decision problems, some decision objects may enter or exit the decision system, which will affect the decision results. To deal with dynamic decision problems with uncertain information, we construct a dynamic three-way decision method by exploiting three-way decisions, grey numbers, and grey targets. In this paper, firstly, by considering the similarity of positive and negative bullseye distances, we exploit Topsis and grey target to construct a conditional probability of three decisions. Then we propose a dynamic update rule based on the changing of the decision objects and determine the object's evaluation function and the threshold's calculation method. Finally, a case is used to verify the effectiveness and feasibility of the proposed model

Multi-Depot General Colored Traveling Salesman Problem with Time Windows in Home Healthcare System: A Medication Delivery Example

This 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