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This research article presents a low-carbon and environmental protection viewpoint that combines the idea of a cold chain. In this case, the optimisation problem Food distribution system is solved by developing a low-carbon Routing Problem (LRP) model. The researchers seek to identify the minimum costs and carbon emission costs while Carbon tax policies are also presented to examine their effects on carbon emissions. The model is solved by using a hybrid genetic algorithm with heuristic rules, after which the algorithm's efficiency is tested with a globally recognised Proudhon data set. The Cycle evolutionary algorithm can quickly and effectively seek the optimal solution of the model. We combine two kinds of operators, inversion mutation and insertion mutation, in the genetic algorithm to form the combined operator of a hybrid genetic algorithm. Then, the approximate optimal solution is obtained. Meanwhile, many experiments are carried out by setting different carbon tax values and the critical interval values of carbon emissions and optimal. A case study from a third-party logistics company is used to test the applicability of the model in the real world. Results showed that the model and carbon tax policies will benefit business operations and the environment. [ABSTRACT FROM AUTHOR]

This research article presents a low-carbon and environmental protection viewpoint that combines the idea of a cold chain. In this case, the optimisation problem Food distribution system is solved by developing a low-carbon Routing Problem (LRP) model. The researchers seek to identify the minimum costs and carbon emission costs while Carbon tax policies are also presented to examine their effects on carbon emissions. The model is solved by using a hybrid genetic algorithm with heuristic rules, after which the algorithm’s efficiency is tested with a globally recognised Proudhon data set. The Cycle evolutionary algorithm can quickly and effectively seek the optimal solution of the model. We combine two kinds of operators, inversion mutation and insertion mutation, in the genetic algorithm to form the combined operator of a hybrid genetic algorithm. Then, the approximate optimal solution is obtained. Meanwhile, many experiments are carried out by setting different carbon tax values and the critical interval values of carbon emissions and optimal. A case study from a third-party logistics company is used to test the applicability of the model in the real world. Results showed that the model and carbon tax policies will benefit business operations and the environment.

Currently, China's expressway revenue and expenditure imbalance problem is serious. The development of an "Expressway Derivative Economy" (EDE) helps address expressway deficits, ensuring the expressway's sustainable operation. The "Expressway + Logistics Distribution" (ELD) mode is a crucial form of the EDE and enhances expressway traffic flow and asset income. However, the cooperation mechanism among stakeholders remains unclear, hindering the widespread promotion of this mode. This study designs two ELD modes and elaborates on their respective advantages. Therefore, a three-party evolutionary game model involving the government, expressway groups, and logistics enterprises is established. Government "land-use-right" grant and tax incentive policies are formulated to explore the cooperation mechanism among stakeholders. The results indicate that both government "land-use-right" grant and tax incentive policies positively influence the positive evolution of the system. However, when the government "land-use-right" grants reach a high level, the willingness of logistics enterprises to choose entry will decrease due to resource crowding. Comparatively, a higher-level "land-use-right" grant policy significantly enhances the role of government tax incentive policy in promoting the positive development of the system. During tight government funding, it is a feasible policy to prioritize expressway groups by providing more tax incentives. The findings provide theoretical guidance for promoting the ELD mode. [ABSTRACT FROM AUTHOR]

In order to improve the timeliness of logistics distribution, based on the theory of road network smoothness and reliability, the author conducted a study on the optimization of urban logistics distribution and transportation networks based on smoothness and reliability. The concept of logistics distribution and transportation network smoothness and reliability was proposed, and a logistics distribution and transportation network optimization model was established. The solving process of ant colony algorithm was given, and finally, a comparative analysis of a case was conducted. The results showed that: With a 6% increase in total delivery distance, the reliability of the delivery network has increased by 30%. This indicates that when using the model built by the author for distribution network optimization, effective optimization of network smoothness and reliability can be achieved, while only increasing the distance by a small amount. The optimal reliability of a smooth distribution network means that the probability of delivery delays is minimized, which is the most powerful guarantee for the effective accessibility of delivery. Verified the practicality of the constructed model. The proposed logistics distribution network optimization model has practical significance in guiding decision-making for optimizing urban logistics distribution transportation networks and reducing uncertainty in the process of urban logistics distribution. [ABSTRACT FROM AUTHOR]

This study aims to optimize the logistics distribution path of unmanned vehicles under vehicle road collaboration. The vehicle road resistance was calculated, and an optimization objective function for the logistics distribution path of unmanned vehicles was constructed based on the calculation results. The paper uses genetic algorithm to improve the ant colony algorithm, and the combination of the two can accelerate the convergence speed of the algorithm and improve the solving efficiency. Meanwhile, the population diversity of genetic algorithms and the pheromone propagation utilized by ant colony algorithms can also increase path diversity, making optimization results more robust and diverse. The improved ant colony algorithm was employed to optimize the delivery path. Experimental validation demonstrated that the proposed method outperforms others in terms of total delivery delay time and total distance of unmanned vehicle logistics delivery paths, offering higher efficiency and more optimized delivery plans. Specifically, the experimental results revealed that the total delivery delay time of this method is the shortest, not exceeding 30 minutes, and the shortest delivery path is only 100.3 kilometers. [ABSTRACT FROM AUTHOR]

This study was purposed to design a multimodal continuous optimization algorithm based on a scheme agent to address the multidimensional complexity of optimization. An evolutionary sampling method of subarea exploration and multiple exploitations was developed by employing the scheme with variable population size so as to obtain higher optimization speed and accuracy. Second, the distribution plan was quantified into high-dimensional variable parameters based on the characteristics of logistics distribution optimization problems, and a high-dimensional discrete optimization model was constructed. Then, we identified and addressed the prominent issues and malignant virtual changes in the application of continuous algorithms to discrete problems. We have introduced a reasonable mutation mechanism during the optimization sampling process to mitigate this issue. Continuous real coordinate points were transformed across the neighborhood to standard discrete integer coordinate points by normalizing and logicizing the optimization sampling coordinates; also, the discretization of the continuous algorithm was realized. This approach could effectively prevent the algorithm from searching for targets in continuous optimization space, thereby fully reducing the complexity of the objective function distribution after conversion. The experiments showed that the transformed multimodal discrete optimization algorithm effectively addressed the optimization design problem of logistics distribution.

This study was purposed to design a multimodal continuous optimization algorithm based on a scheme agent to address the multidimensional complexity of optimization. An evolutionary sampling method of subarea exploration and multiple exploitations was developed by employing the scheme with variable population size so as to obtain higher optimization speed and accuracy. Second, the distribution plan was quantified into high-dimensional variable parameters based on the characteristics of logistics distribution optimization problems, and a high-dimensional discrete optimization model was constructed. Then, we identified and addressed the prominent issues and malignant virtual changes in the application of continuous algorithms to discrete problems. We have introduced a reasonable mutation mechanism during the optimization sampling process to mitigate this issue. Continuous real coordinate points were transformed across the neighborhood to standard discrete integer coordinate points by normalizing and logicizing the optimization sampling coordinates; also, the discretization of the continuous algorithm was realized. This approach could effectively prevent the algorithm from searching for targets in continuous optimization space, thereby fully reducing the complexity of the objective function distribution after conversion. The experiments showed that the transformed multimodal discrete optimization algorithm effectively addressed the optimization design problem of logistics distribution. [ABSTRACT FROM AUTHOR]

Cross-border e-commerce logistics activities increasingly use multimodal transportation modes. In this transportation mode, the use of high-performance optimizers to provide decision support for multimodal transportation for cross-border e-commerce needs to be given attention. This study constructs a logistics distribution optimization model for cross-border e-commerce multimodal transportation. The mathematical model aims to minimize distribution costs, minimize carbon emissions during the distribution process, and maximize customer satisfaction as objective functions. It also considers constraints from multiple dimensions, such as cargo aircraft and vehicle load limitations. Meanwhile, corresponding improvement strategies were designed based on the Sand Cat Swarm Optimization (SCSO) algorithm. An improved swarm intelligence algorithm was proposed to develop an optimizer based on the improved swarm intelligence algorithm for model solving. The effectiveness of the proposed mathematical model and improved swarm intelligence algorithm was verified through a real-world case of cross-border e-commerce logistics transportation. The results indicate that using the proposed solution in this study, the cost of delivery and carbon emissions can be reduced, while customer satisfaction can be improved. [ABSTRACT FROM AUTHOR]

In this paper, the mathematical model of Vehicle Routing Problem with Time Windows (VRPTW) is established based on the directed graph, and a 3-stage multi-modal multi-objective differential evolution algorithm (3S-MMDEA) is proposed. In the first stage, in order to expand the range of individuals to be selected, a generalized opposition-based learning (GOBL) strategy is used to generate a reverse population. In the second stage, a search strategy of reachable distribution area is proposed, which divides the population with the selected individual as the center point to improve the convergence of the solution set. In the third stage, an improved individual variation strategy is proposed to legalize the mutant individuals, so that the individual after variation still falls within the range of the population, further improving the diversity of individuals to ensure the diversity of the solution set. Based on the synergy of the above three stages of strategies, the diversity of individuals is ensured, so as to improve the diversity of solution sets, and multiple equivalent optimal paths are obtained to meet the planning needs of different decision-makers. Finally, the performance of the proposed method is evaluated on the standard benchmark datasets of the problem. The experimental results show that the proposed 3S-MMDEA can improve the efficiency of logistics distribution and obtain multiple equivalent optimal paths. The method achieves good performance, superior to the most advanced VRPTW solution methods, and has great potential in practical projects. [ABSTRACT FROM AUTHOR]

This study is for optimizing the distribution path problem of cold chain logistics. This study proposes an improved genetic algorithm that introduces natural number coding, elite preservation strategy and adaptive cross-mutation strategy. A cold chain logistics distribution path optimization model is constructed, taking into account various costs, including customer demand, time window requirements, maximum mileage of refrigerated trucks, payload, and other constraints. To address the cold chain logistics distribution path, an improved genetic algorithm is utilized. This study designs experiments to test the performance of improved genetic algorithms and applies the model to an example for experimental analysis. The results show that the improved genetic algorithm has better performance in convergence and convergence speed. From the perspective of distribution cost, the optimization model based on the improved algorithm significantly reduces the total distribution cost compared with that before optimization. The above results show that this study effectively optimizes the cold chain logistics distribution route by improving the genetic algorithm and significantly reducing the total distribution cost. This study not only proves the effectiveness of elite preservation strategy and adaptive cross-variation strategy but also shows the importance of considering various costs and constraints comprehensively. This provides a valuable optimization tool for the cold chain logistics industry, helps to improve efficiency and reduce costs, and has important practical significance. [ABSTRACT FROM AUTHOR]

Logistics distribution is an indispensable part of the modern economy and is crucial for ensuring the efficient operation of the supply chain. With the continuous progress of technology, the application of intelligent scheduling systems in the field of logistics distribution is becoming increasingly widespread. Reinforcement learning, as one of the hot technologies in the field of artificial intelligence, is gradually receiving attention in its application in intelligent scheduling. Reinforcement learning can continuously learn and predict the same thing to enhance memory, while intelligent scheduling requires continuous prediction and optimization of logistics distribution paths. In response to the current problems of slow logistics distribution efficiency and low customer satisfaction, this article analyzed the application of intelligent scheduling in logistics distribution from the aspects of basic data maintenance, basic data review, intelligent scheduling, scheduling result review, distribution information management, and vehicle tracking. By using reinforcement learning, the traffic network weight in logistics distribution was studied to improve logistics distribution efficiency and customer satisfaction. This article analyzed the efficiency of logistics distribution, vehicle tracking accuracy, vehicle scheduling ability, and logistics distribution costs under different logistics distributions. The results showed that the logistics distribution under the integration of reinforcement learning and intelligent scheduling reduced 12.047 km compared to traditional distribution paths, and its distribution cost decreased by 129.718 yuan compared to traditional logistics distribution costs. The efficiency of logistics distribution that integrates reinforcement learning and intelligent scheduling has significantly improved, with optimized distribution costs and paths. It also has a positive effect on improving the utilization rate of logistics distribution vehicles. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the continuous development of smart logistics technology, the status of rural e-commerce logistics and distribution has also increased. This paper combines the intelligent logistics technology framework based on rural e-commerce logistics distribution situation, the selection of rural e-commerce distribution node sites, and distribution path optimization. Furthermore, construct a game model for the rural e-commerce logistics and distribution industry chain with the goal of generating synergy. We examine the traditional rural e-commerce logistics and distribution scheme, distribution node setting, and benefits game based on this basis. The results show that the total cost of the smart logistics distribution program is reduced by 200.7 compared with the traditional distribution program, and the total cost of the smart logistics distribution program saves 5% compared with the traditional distribution program. The average transportation time of smart logistics distribution is 11.22min, which is significantly reduced compared with traditional logistics distribution. The rural smart logistics distribution system for e-commerce that uses benefit sharing and cost sharing profit 1242 is the best program when comparing benefits. To sum up, smart logistics technology plays a significant role in rural e-commerce logistics distribution, which can effectively save logistics distribution costs and enhance distribution efficiency.

The evolution of the logistics and distribution industry, notably the express delivery sector, has significantly increased its prevalence in everyday life. This escalation necessitates an ongoing innovation in logistics strategies and enhancements in service quality, positioning these elements at the heart of industry focus. This study initially addresses the vehicular route planning issue within logistics distribution, selecting Company A as a case study to examine inherent logistical challenges. Subsequently, it develops an optimal route planning model under time constraints. It is converted into a mixed-integer linear programming model through techniques such as variable substitution and segmented linear approximation. This conversion facilitates rapid solutions by mathematical planning solvers. The research contrasts the logistics and distribution performance before and after optimization to assess the efficacy of the proposed strategies. Findings indicate that optimized vehicular route planning achieves a more equitable distribution of delivery tasks, along with substantial improvements in the complexity of transportation routes and the reduction of travel distances. Specifically, the optimization results in a significant decrease in the penalty costs associated with the vehicles, with the penalty cost for the third vehicle involved in the study reduced by 66.40%. This evidence supports the implementation of scientific vehicle scheduling strategies by logistics firms, underscoring the model’s practical implications.

Fresh produce has stricter requirements for supply and distribution due to their short preservation period. Investigating the optimal supply and distribution route of fresh produce in the online to offline (O2O) mode can help the advancement of the fresh industry in the e-commerce sector. This paper established a model of the optimal supply and distribution path for fresh produce in the O2O mode and used a genetic algorithm for solution. By contrasting the distribution distance, time, total cost, and satisfaction before and after optimization, the distribution cost-minimizing path solution was obtained. It was found through simulation experiments that the minimum total distribution cost was 357.57 yuan after the computation by the genetic algorithm; the optimized path reduced the total distribution distance by 47.4 kilometers, the time by 0.95 hours, and the cost by 133.75 yuan, and raised the average consumer satisfaction by 0.12. The results further confirm the correctness of the model constructed in the study and the selected algorithm. [ABSTRACT FROM AUTHOR]

The proposed way of estimating the associated macroeconomic index based on a supply chain network is a novel strategy that might give useful insights for firms and organizations trying to enhance their supply chain logistics operations. To aid in this process, the suggested technique utilizes multiple regression analysis and adaptive extreme learning machine models to determine the relative importance of each indicator in the supply chain logistics decision-making process. Firms may be able to save expenses and boost economic value by employing enhanced genetic algorithms and mathematical modeling to develop a logistics distribution model for urban supply chains based on reconstructive genetic algorithms. It is possible that the paper's study of the issues plaguing the current distribution of logistics in metropolitan areas would prove valuable to organizations that are seeking to improve their own supply chain logistics procedures. This paper appears to take a thorough strategy that might assist assure the accuracy of the forecasting method by preprocessing macroeconomic indicators using imputation, classification, and other approaches to generate a time series consistency model. Some novel ways that may shed light on logistics in the supply chain include using a two-dimensional discrete mesh structure built on wireless sensors to depict the national economic development scenario and using a coding matrix to convey the extent of economic growth. A thorough and all-encompassing way that might aid organizations in making better judgments about their supply chain logistics strategies is to employ multiple regression analysis, an adaptive extreme learning machine model, and other approaches to examine the effect degree of each key indicator. Experiments demonstrating excellent and steady prediction accuracy of the algorithm model are encouraging and point to the possible usefulness of the suggested forecasting approach. The paper's contributions, including its analysis of supply chain logistics cost accounting, determination of the basic path optimization, improvement of the genetic algorithm, and design of the mathematical model of supply chain logistics distribution path decision, all look promising in terms of their potential to help businesses cut costs and boost economic value. The positive outcome of the successful design of the mathematical model demonstrates the possible efficacy of the suggested technique. [ABSTRACT FROM AUTHOR]

In recent years, land–air collaborative logistics delivery is a promising distribution method. It combines the flexibility of unmanned aerial vehicles (UAVs) with the high payload capacity of vehicles, expanding the service range of UAVs while reducing carbon emissions. However, most existing research has focused on path planning and resource allocation for either UAVs or vehicles alone. Therefore, to address the shortcomings of the current research, this paper proposes an intelligent land–air collaboration delivery algorithm for trajectory optimization and resource scheduling. Subsequently, this paper develops a land–air collaboration reliable delivery logistics distribution system, showcasing the driving routes of vehicles and UAVs. Meanwhile, the mode of UAV–vehicle collaboration not only saves operating costs compared to traditional logistics delivery but also achieves energy conservation and emission reduction goals. During the specific design and implementation process of this platform, blockchain technology is integrated into the logistics delivery service to ensure data security and prevent tampering, making the system more efficient and reliable. Finally, through testing and verification of the system's functionalities, its completeness is demonstrated. [ABSTRACT FROM AUTHOR]

In the context of Industry 4.0 and based on the demand of digital logistics construction of industrial enterprises, this paper integrates the concept of digital twin into Refined Logistics Supply Chain construction. Considering the constraints of multi-distribution center, heterogeneous vehicle performance, distribution cost, quasi-shipment certificate and humanized management, Refined Logistics Supply Chain System (RLSCS) and cross-regional scheduling optimization model of logistics vehicles with multi-distribution center were established. The designed model can minimize the transportation cost, reduce the transportation time, and improve the vehicle load rate. An adaptive elite honey badger target algorithm based on cubic mapping mechanism (IHBA), is designed to solve the model. Further, performance evaluation by optimizing test functions, the convergence performance of IHBA algorithm was demonstrated. Finally, the simulation experiment is carried out according to the actual business data and it is compared to eight other optimistic algorithms. The experimental results show that the proposed algorithm is more effective and more robust, and the related models and algorithms can provide research basis for industrial products digital supply chain system.

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Informatica (03505596) is the property of Slovene Society Informatika and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Zeszyty Naukowe SGGW: Ekonomika i Organizacja Logistyki is the property of Warsaw University of Life Sciences (SGGW), Faculty of Economic Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

2E-MDCVRP is a multi-depot, capacity, twoechelon vehicle routing issue. Satellites make it possible to collect and sort orders. In this paper, a 2E-MDCVRP model will be designed to be applied to e-commerce. In the completion stage, the researcher developed a 2E-MDCVRP solution with two stages of completion. The first stage is finding the best route for the satellite to travel to the customer. In the first stage of work, satellites are plotted as centroids in clusters using the k-Means algorithm to obtain k-set clusters. To determine route scheduling using the RNN algorithm in determining travel routes in the second echelon. The second stage uses the trial-anderror method to determine the first echelon's routing schedule. This research aims to minimize the operational application, fuel consumption, vehicle maintenance, vehicle oil change, and handling costs. Moreover, at the end of the paper, the researcher simulate the model that has been built and test the model's effectiveness by comparing it with the classic MDCVRP model, which is similar to the Same-Day Delivery model. The test results show that the 2E-MDCVRP model can make better total distance and cost improvements than the classic MDCVRP model carried out by Same-day Delivery. [ABSTRACT FROM AUTHOR]

In order to improve the centralized planning ability of logistics distribution path data, improve the efficiency of logistics distribution and reduce the cost of logistics distribution, this paper proposes an optimal path selection algorithm based on machine vision. Using machine vision technology to calibrate the coordinates of logistics distribution path, combined with EMD decomposition method and wavelet denoising method to remove redundant data in logistics distribution data, particle swarm optimization algorithm to complete logistics distribution path planning, and ant colony algorithm to realize the optimal path selection of logistics distribution. The experimental results show that the average distribution cost of this method is only 766.7 yuan, the distribution time is less than 0.3 h, and the customer satisfaction is as high as 98%, which shows that this method can effectively optimize the distribution path. [ABSTRACT FROM AUTHOR]

In order to solve the problem of interference caused by the disruption of unmanned aerial vehicle (UAV) capacity to the delivery of end packages, using the concept of interference management, the interference measurement was carried out from the perspective of customer service and delivery cost, and the interference recovery model was established by comprehensively considering factors such as UAV energy consumption, delivery timeliness, flight reliability, and flight safety. The ant colony algorithm was used to solve the problem model. Finally, an example was used to verify the effectiveness of the proposed scheme. The results show that by analyzing three groups of interference scenarios, it is found that when the interference occurs early and there are many disturbed customers, the established recovery model scheme can generate a new delivery path in a short time compared with the original scheme, and improve customer service satisfaction at a lower cost, which is more practical. [ABSTRACT FROM AUTHOR]

In modern logistics, the effective use of the vehicle volume and loading capacity will reduce the logistic cost. Many heuristic algorithms can solve this knapsack problem, but lots of these algorithms have a drawback, that is, they often fall into locally optimal solutions. A fusion optimization method based on simulated annealing algorithm (SA) and binary particle swarm optimization algorithm (BPSO) is proposed in the paper. We establish a logistics knapsack model of the fusion optimization algorithm. Then, a new model of express logistics simulation system is used for comparing three algorithms. The experiment verifies the effectiveness of the algorithm proposed in this paper. The experimental results show that the use of BPSO-SA algorithm can improve the utilization rate and the load rate of logistics distribution vehicles. So, the number of vehicles used for distribution and the average driving distance will be reduced. The purposes of the logistics knapsack problem optimization are achieved. [ABSTRACT FROM AUTHOR]

The location selection of logistics and storage center fundamentally determines the distance between the origin and arrival of goods, which is directly related to whether goods can be transported in a timely and efficient manner, as well as whether distribution costs and storage costs can be minimized. Traditional logistics and warehousing center location solving algorithm is divorced from practical application, and prone to local optimization, slow solving speed and single attention and other problems. In order to reduce the logistics delivery time and logistics cost, and put forward a practical solution for location selection, this paper studied the location selection of logistics storage center in Beijing-Tianjin-Hebei region by improving k-means clustering algorithm and monarch butterfly optimization algorithm. Experimental simulation results show that compared with other optimization algorithms, the improved Monarch optimization algorithm has advantages in solving accuracy, convergence speed and iteration times, and can effectively complete the site selection of logistics storage center, effectively shorten the distance of logistics distribution and improve the efficiency of logistics distribution. [ABSTRACT FROM AUTHOR]

Given the problem of three-sources uncertainty in aero engine degradation process and the assumption of normal distribution of measurement error in existing research, a Wiener process aero engine residual useful life time(RUL) prediction method with logistic distribution of measurement error was proposed. Firstly, the performance degradation model was established when the measurement error obeys the logistics distribution, and the accurate mathematical expression of the RUL distribution was given under the first arrival time. Secondly, in order to improve the utilization of monitoring parameters, a key parameter screening method based on Spearman coefficient was given. Lastly, in view of the lack of historical data and prior information, a parameter estimation method based on maximum entropy unscented particle filtering and conditional expectation maximization algorithm was adopted. Meanwhile after new performance degradation data was obtained, the model parameters could be adaptively updated. With MSE as the evaluation index, the experimental results show that the MSE value of this method is 13.25, and the MSE value is 16.12 lower than that of traditional method, which can effectively improve the prediction accuracy and the engine utilization and safety.

The scheduling of deliveries and the routing of vehicles are of great importance for supply chain operations, as both determine to a great extent the distribution costs, as well as customer satisfaction. The fact that the distribution of goods is being affected by multiple factors, stemming from the demands of transportation companies, customers, and the external environment, has made the vehicle routing problem (VRP) among the most studied topics in operational research. These factors are transformed either to constraints or variables of the problem and finally lead to the creation of different variants of the VRP, formulated and studied by researchers. Moreover, the management of logistics and supply chain operations is being enhanced by the use of algorithms, integrated into information systems, enabling the optimization of real-life distribution cases. This paper presents a methodology for classifying the multiple VRP variants related to freight transportation, that most logistics and distribution companies face in their daily operations, as well as the algorithms solving the various problems. The application of the research methodology concluded to 334 papers, which were further sorted to 263 papers on the subject of freight transportation, aiming to identify the trends of the VRP variants and the applied algorithms, during the last decade. The correlation between the VRP variants and the applied algorithms is also identified. Finally, the paper presents the quantitative and qualitative results of the literature review and discusses the scientific publications with a significant impact on the research community. [ABSTRACT FROM AUTHOR]