Reasonable Routes Research Articles

Handling dynamic capacitated vehicle routing problems based on adaptive genetic algorithm with elastic strategy

With the development of e-commerce platforms, the logistics industry is also booming. The transportation process is at the core of the logistics industry, which influences the timeliness and rationality of logistics distribution. In the real world, there are many dynamic demands in logistics distribution, so logistics distribution should deal with dynamic demands quickly to realize more effective route planning. This paper proposes an adaptive genetic algorithm (AGA) with elastic strategy (AGA-ES) to solve the dynamic capacitated vehicle routing problems (DCVRPs). Firstly, AGA designs an adaptive local search operator, which can adaptively adjust the quantity of nodes that need local search, according to the fitness value of different individuals. At the same time, AGA develops adaptive crossover and mutation operators, which can automatically adjust the crossover and mutation probability based on the different fitness values. The experimental results verify the better performance of AGA on the traditional capacitated vehicle routing problems (CVRPs). After that, AGA combines elastic strategy (AGA-ES) to solve DCVRPs. This paper mainly concerns three types of actual demand information in DCVRPs: distribution nodes increase, distribution nodes decrease, and distribution road conditions change, according to the actual background. AGA-ES could interact with demand information and judge the types of different demands, and then adaptively plan new and reasonable routes after receiving one or more of the above demand information. The verification of benchmark data sets shows that AGA-ES can effectively deal with DCVRPs. Furthermore, this paper collects the longitude and latitude coordinates of 100 SF express stations in Xi’an, constructs the distribution information of these express stations, and verifies that AGA-ES can solve the DCVRPs under the actual background.

A cognitive-based routing algorithm for crowd dynamics under incomplete or even incorrect map knowledge

A cognitive-based routing algorithm is proposed. Concepts like local form and path algorithms are developed. Unlike current mainstream routing algorithms assume that all people know everything about the environment, the proposed algorithm allows people to have a complete or incomplete map knowledge and built up their own map knowledge in a piecemeal fashion. Using a hospital floor plan as the scenario, numerical experiments are conducted by assuming pedestrians to have different levels of map knowledge. Results show that reasonable routes could be frequently found even if pedestrians only have an incomplete knowledge of the network. Also pedestrians generally need to traverse more rooms if having zero or less map knowledge. Hence the proposed algorithm’s effectiveness is validated to some extent.

Self-Driving Tour Travel Route Planning Model Construction Using Recurrent Neural Network

With the continuing increase of people’s living standards, tourism has now become the primary mode of holiday and leisure activity. Scientific and reasonable route planning can best meet people’s essential needs, provide detailed planning in terms of travel time and cost, resolve the impact of various external factors, and realize the complementary benefits of scenic tourism while ensuring that each factor configuration is optimized. External factors such as trip time, cost, travel date, and transport mode all have an impact on route planning. In order to develop a mathematical model and gather other information, it is frequently essential to determine two or more of these influencing elements. The ideal plan for the influencing elements is to first develop an optimal self-driving trip route that meets all of the requirements. This strategy is becoming increasingly popular among the general public, and it focuses on how to spend the least amount of money in order to visit more tourist attractions, construct the most reasonable routes, and provide circumstances for tourists to save time and money. The purpose of this study is to examine the topic of self-driving travel route planning and to suggest the creation of a route planning model. In this paper, a self-driving journey route planning model is built using a recurrent neural network model. The driving route is designed with a high-speed priority strategy, and the travel route selection problem takes into account the consumption condition as well as the quantity of scenic sites. The developed model is used to optimize self-driving trip routes, and the results are promising.

Personalized Route Planning of Rural Ecotourism Using Mobile Computing and Random Forest Algorithm

Tourism has evolved from a few high-end items to a mass market, and it has become an integral part of people’s daily lives. Tourist routes connect tourist sources and destinations, whether they are part of a group tour or a road trip. Tourist routes that are designed scientifically and rationally can improve tourist satisfaction while also highlighting the unique characteristics of various tourist destinations and promoting the long-term development of tourist destinations. Ecotourism is a necessary component of China’s tourism industry’s long-term development, as well as a key component of the country’s low-carbon economy. This paper examines some basic problems in rural tourism planning using the random forest algorithm and proposes planning strategies and methods for its core content in order to promote the development of rural ecotourism. Tourist route optimization is beneficial not only to obtaining reasonable routes to meet tourists’ needs but also to maximizing resource utilization, which is beneficial to environmental protection and balanced economic development.

Street Patrol Routing Optimization in Smart City Management Based on Genetic Algorithm: A Case in Zhengzhou, China

A series of urban law enforcement events involving city inspectors dispatched by the city management department can reflect some problems in smart city management, such as illegal advertising and unlicensed street operation. In this paper, we propose a model for the allocation of city inspectors and the optimization of patrol paths. The objective is to minimize the average response time and the number of inspectors. We also develop a priority-patrol-and-multiobjective genetic algorithm (DP-MOGA) to classify patrol segments according to the frequency of events and develop an improved genetic algorithm to achieve the aforementioned objective. We conduct numerical experiments using patrol data obtained from city inspectors in Zhengzhou, China, to clearly show that the proposed algorithm generates reasonable routes that reduce the average response time of events and the number of patrol inspectors. Furthermore, we test the algorithm for three different time scenarios (roads with different average numbers of events) and demonstrate the efficiency of the algorithm. The experimental results show that our proposed algorithm is more stable and efficient than other existing algorithms.

FUZZY FACTORS CONSIDERATION FOR THE OPTIMIZATION OF THE WASTE COLLECTION ROUTE IN THE TERRITORIAL COMMUNITY

The paper considers the process of the creation of the information technology module for the optimization of the waste collection process in the communities. The given module determines the expediency of inclusion of the waste containerbin to the route of the dust cart, depending on the degree of loading of the container and impact of the external factors on its availability, determined by the intensity of the traffic on the road to the container and state of the road, connected with weather conditions. Objective of the research consists in automatic determination of those waste containers which do not need urgent waste removal, that is an important component of theoptimization problem for the formation of the most reasonable routes for waste transportation. Scientific task of the researchincludes: determination of the factors, influencing the relevance  of the waste removal problem; substantiation of the expediency of using fuzzy logic for this purpose; formation of fuzzy linguistic variables and construction of fuzzy knowledge base; study the efficiency of the process of fuzzy logic conclusion, regarding the determination of the relevance of the containers removal on the base of the suggested influencing  factors; conclusion concerning the rationality of the suggested approach. The process of the relevance  calculation of the inclusion the waste containers into the route of the waste collection, taking into account the degree of their filling and availability by means of fuzzy logic tools is described. Analysis of the external factors, influencing the relevance of the waste collection from the specific container is performed. Fuzzy graph model is suggested, the model presents possible routes of the dust carts, linguistic variables are given, membership functions of their terms are formed, fragment of the knowledge base, supplied by the production rules, processes of the fuzzification and defuzzification are graphically illustrated using Mamdani fuzzy inference algorithm. Calculation of the actuality index of the waste collection is given on the specific example. Testing has been performed on the base of the data of trash enclosures in the city of Rivne. As a result of elimination ща non-actual containersfrom the route the length of the routes was cut by on average 10.43 %. Further improvement of the routes formation is possible, taking into account the degree of importance of the waste collection, for instance, such as from hospitals, schools, recreation spaces and other civil objects.

A comparative study of the impact of sanctions on the oil and cement companies listed in Tehran Stock Exchange: Forecasting and Future Trends

Imposing sanctions can have several malicious effects on a developed or developing economy and Iran is not an exception to this matter. The main purpose of this paper is to determine whether imposing oil sanctions have a significant impact on oil, gas and petroleum companies’ capital structure or not. Furthermore, a comparison between oil industry and cement industry and a prediction of oil companies’ capital structure trend are conducted. The most innovative aspect of our study is to evaluate the influence of sanctions on a firm-specific variable rather than macroeconomic level. To address this problem, we investigate oil and cement companies listed in Tehran Stock Exchange from 2006 to 2018. The leverage ratio indicating capital structure, sanction dummy variable and ROA, tangibility of assets, capital market return, economic growth and inflation rate are dependent variable, independent variable and control variables, respectively. The data are obtained from companies’ financial statements and we use OLS regression to estimate our equations. The results indicate that the 2012 oil sanctions against Iran affect the oil companies’ leverage ratio negatively and the future trend reveals that the share of equity in capital structure will increase. Moreover, we inspect no significant relationship between oil sanctions and cement companies’ capital structure. Therefore, government policymakers should plan strategies to lift or limit the oil sanctions and oil corporate managers should find some reasonable routes for balancing their companies’ capital structure to exploit debt financing benefits.

An Improved HMM-Based Approach for Planning Individual Routes Using Crowd Sourcing Spatiotemporal Data.

With the rapid development of LBSs (location-based services) in recent years, researchers have increasingly taken an interest in trying to make travel routes more practicable and individualized. Despite the fact that many studies have been conducted on routes using LBS data, the specific routes are deficient in dynamic scalability and the correlations between environmental constraints and personal choices have not been investigated. This paper proposes an improved HMM-based (hidden Markov model) method for planning personalized routes with crowd sourcing spatiotemporal data. It tries to integrate the dynamic public preferences, the individual interests and the physical road network space in the same spatiotemporal framework, ensuring that reasonable routes will be generated. A novel dual-layer mapping structure has been proposed to bridge the gap from brief individual preferences to specific entries of POIs (points-of-interest) inside realistic road networks. A case study on Changsha city has proven that the proposed method can not only flexibly plan people’s travel routes under different spatiotemporal backgrounds but also is close to people’s natural selection by the perception of the group.

A new global method for identifying urban rail transit key station during COVID-19: A case study of Beijing, China

The rapid-developed COVID-19 has been defined as a global emergency by the World Health Organization. Meanwhile, various evidence indicates there is a positive correlation between the transmission and population density, especially in closed and semi-closed space. The urban rail transit, as one of the major mode choices for people to commute in big cities, carries thousands of passengers every day with relatively closed and limited space, which provides favorable conditions for the spread of the virus. If the surrounding area of any station was disrupted under COVID-19, not only the individual line but also the entire urban rail transit network will have the risk to be affected. Therefore, it is necessary to identify and explore the distribution law of key stations during the spreading process of the COVID-19 virus in the urban rail transit network during the COVID-19 pandemic. Based on the spatial distribution of epidemic area and the demand of urban rail transit passengers, we have proposed a construction method of the rail transit network and use the improved shortest path algorithm to determine the route diversity index of each station which indicates its importance in the urban rail transit network. On this basis, we identify the key stations of the Beijing rail transit network to ensure that passengers avoid high-risk stations during the epidemic. The results show that the number of reasonable routes between any two stations is 1 to 5 during the COVID-19 pandemic. Moreover, the routes diversity index of the Beijing rail transit network was 1.235 during the COVID-19 pandemic and 2.2574 in the normal period. According to the reasonable route diversity index, we have identified the key stations of the Beijing rail transit network during the COVID-19, such as Qi-Li-Zhuang station.

Two novel cyclohexanone-monocyclic polycyclic polyprenylated acylphloroglucinols from Garcinia multiflora fruits

Two novel cyclohexanone-monocyclic polyprenylated acylphloroglucinol (C-MPAP) derivatives, named norgarmultinones A (1) and B (2), were isolated from the fruits of Garcinia multiflora. Their structures were clarified by NMR, HRESIMS and calculated electronic circular dichroism (ECD) spectra. Compound 2 is the first example of naturally occurring C-MPAPs containing tetrahydropyran moiety by the cyclization of an enolic hydroxyl with an isogeranyl side chain. The reasonable biosynthetic routes of 1 and 2 were also put forward.

SARIMA damp trend grey forecasting model for airline industry

The aim of this paper is to propose a new model that improves the Damp Trend Grey Model (DTGM) with a dynamic seasonal damping factor to forecast routes passengers demand (pax) in the air transportation industry. The model is called the SARIMA Damp Trend Grey Forecasting Model (SDTGM). In the DTGM, the damp trend factor is a static smoothing factor because it does not change over time, and therefore, it cannot capture the dynamic behavior of time series data. For this reason, the modification consists in using the trend and seasonality effects of time series data to calculate a dynamic damp trend factor as time grows. The DTGM damping factor is based on the forecasted data obtained by the GM(1,1) model; otherwise, the SDTGM calculates a seasonal damping factor based on historical data using a large amount of data points for short lead-times. The SDTGM has less uncertainty than the DTGM. The simulation results show that the SDTGM captures the seasonality effect and does not allow the forecast to exponentially grow. The SDTGM forecasts more reasonable routes pax for short lead-times when having a large amount of data points than the DTGM. The United States domestic air transport market data are used to compare the performance of the DTGM against the proposed SDTGM.

Multi-mobile robots and multi-trips feeding scheduling problem in smart manufacturing system: An improved hybrid genetic algorithm

Modern manufacturing systems require timely and efficient production tasks. Any mistakes can have serious consequences which effect the production process obviously. The supply of goods is the beginning of the production process, ensuring that production can proceed normally. Using mobile robots for transportation and supply of production lines can achieve automatic manufacturing. We studied the use of multiple mobile robots to supply multiple production lines. Robots need to return to warehouse when no goods exist. This problem is called a multi-mobile robots and multi-trips feeding scheduling problem. We constructed a mathematical model describing multi-mobile robots and multi-trips feeding scheduling problem, and the objective function is to minimize the transportation cost and waiting cost. To solve this problem, we proposed an improved hybrid genetic algorithm, where a strategy of mixing improved genetic algorithm and tabu search algorithm is adopted to find robots with reasonable routes. Combining genetic algorithm with tabu search algorithm can improve the route planning effect and find a lower cost solution. In the experimental part, it is verified that the proposed algorithm could effectively find reasonable ways for robots to provide services. We also put forward suggestions for the scenarios of using robots in actual production.

Spacer layer design for efficient fully printable mesoscopic perovskite solar cells.

The spacer layer is a key component of fully printable mesoscopic perovskite solar cells, but its precise characteristics are far from being understood in relation to the device design. In the present work, we perform a detailed systematic study on the effects of spacer parameters, such as size of building blocks, layer thickness, etc., on properties of the perovskite filler, insulating ability and performance of fully printable mesoscopic perovskite solar cells by combining the techniques of time-resolved photoluminescence, high-resolution TEM, insulating resistance measurements, impedance spectroscopy and J–V characteristics. Drawing on the deep understanding from these studies, we formulate key principles, which are anticipated to guide the design of the advanced spacer layer for fully printable mesoscopic perovskite solar cells.

Methods to Generate an Array of Novel Rexinoids by SAR on a Potent Retinoid X Receptor Agonist: A Case Study with NEt-TMN.

The methods described in this chapter concern procedures for the design, synthesis, and in vitro biological evaluation of an array of potent retinoid-X-receptor (RXR) agonists employing 6-(ethyl(5,5,8,8-tetramethyl-5,6,7,8-tetrahydronaphthalen-2-yl)amino)nicotinic acid (NEt-TMN), and recently reported NEt-TMN analogs, as a case study. These methods have been extensively applied beyond the present case study to generate several analogs of other potent RXR agonists (rexinoids), particularly the RXR agonist known as bexarotene (Bex), a Food and Drug Administration (FDA) approved drug for cutaneous T-cell lymphoma that is also often prescribed, off-label, for breast, lung, and other human cancers. Common side effects with Bex treatment include hypertriglyceridemia and hypothyroidism, because of off-target activation or inhibition of other nuclear receptor pathways impacted by RXR. Because rexinoids are often selective for RXR, versus the retinoic-acid-receptor (RAR), cutaneous toxicity is often avoided as a side effect for rexinoid treatment. Several other potent RXR agonists, and their analogs, have been reported in the literature and rigorously evaluated (often in comparison to Bex) as potential cancer therapeutics with unique activity and side-effect profiles. Some of the more prominent examples include LGD100268, CD3254, and 9-cis-UAB30, to name only a few. Hence, the methods described herein are more widely applicable to a diverse array of RXR agonists.In terms of design, the structure-activity relationship (SAR) study is usually performed by modifying three distinct areas of the rexinoid base structure, either of the nonpolar or polar sides of the rexinoid and/or the linkage that joins them. For the synthesis of the modified base-structure analogs, often identical synthetic strategies used to access the base-structure are applied; however, reasonable alternative synthetic routes may need to be explored if the modified analog intermediates encounter bottlenecks where yields are negligible for a given step in the base-structure route. In fact, this particular problem was encountered and successfully resolved in our case study for generating an array of NEt-TMN analogs.

Integer programming for urban design

We present an integer program (IP) for urban design that (1) maximizes the floor area; (2) fills building volume with room templates; (3) encodes translational symmetry in urban layout; and (4) constructs economical urban routes. Regardless that integer programming is intensively studied in operational research (OR), its role in solving geometrical and topological problems in urban design was overlooked. Based on a regular grid, our 0–1 IP formulates the sunlight-gain rules, which give urban sites their shapes, especially for residential projects. With predefined plot templates, the gross floor area (volume) within a given site can be maximized under various sunlight requirements. Subsequently, the IP fills each building volume with 2D/3D room templates. Finally, an IP-based algorithm constructs routes that connect all plots and the site’s entrances to public transportation. Both the classical Steiner tree model and the latest coverage network model are extended to create reasonable routes. In addition, this work extends the concept of special ordered sets (SOS) to encode translational symmetry in urban layouts. Encoding layout symmetry can benefit from the solvers’ SOS2 mechanism in the Branch-and-Bound search algorithm. The results indicate that traditional decision making for cities could be partially automated by IP and an abundance of valid solutions are available for designers.

Self-emulsifying drug delivery formulations

As a consequence of modern drug discovery techniques, lipid-based drug delivery systems are the most popular approaches usually used to improve the dissolution and oral bioavailability of hydrophobic drugs. Self-emulsifying drug delivery systems (SEDDS), as one class of these lipid-based systems, are isotropic mixtures of oil, surfactants and co-surfactants where the drug is usually better soluble than in oil alone. The principal characteristic of SEDDS is their ability to form fine oil in water micro-emulsifying drug delivery systems (MEDDS) upon dilution by an aqueous phase through the gastrointestinal tract. SEDDS are considered promising strategy to improve the rate and extent of oral absorption of drugs. However conventional liquid SEDDS have some limitations, solid SEDDS are prepared by solidification of liquid self-emulsifying ingredients into powders or nanoparticles. This overview gives a particular emphasis on the solidification techniques and applications of liquid SEDDS in the pharmaceutical dosage forms to meet a wide range of the formulation requirements achieving reasonable routes of administration with efficient oral bioavailability.

Measuring Route Diversity for Urban Rail Transit Networks: A Case Study of the Beijing Metro Network

Most stations and tracks in metro networks are irreplaceable due to daily operations. If any of them were disrupted, it would impact not only the individual metro line but also the whole metro network. Therefore, metro managers need to have a good understanding of alternative routes between each pair of stations in the metro network. In the event of incidents, metro managers can make use of this information to reroute passengers to minimize the impact of disruptions. This paper aims to develop a route diversity index to address two questions: “how many reasonable routes are there for passengers between any two stations in normal operations or in the event of a disruption?” and “which stations are most vulnerable (i.e., the largest impact to the overall metro network when they are disrupted)?” To implement this measure in practice, definitions of routes and route diversity and a solution algorithm based on characteristics of metro networks are described to calculate the route diversity index. To show proof of the concept, a simple network example and a real-world network based on the Beijing Metro network in China are presented to demonstrate the feasibility of the route diversity index and its application to a real-world metro network.

GC-MS Headspace and Comparative Computational Studies in the Formation and Follow-up Reactions of Nucleophiles in Li-Ion Battery Electrolytes

As societal needs move towards more sustainable and efficient energy landscapes, electrical energy storage is taking a center stage. The need for devices with greater safety, higher energy and power densities, and longer lifetimes is ever increasing, with applications ranging from stationary storage for intermittent sources (e.g. wind and solar) and grid load-leveling, to electrification of transportation and consumer electronics. The development of longer cycle life Li-ion batteries is an important goal for the large-scale deployment of electrified transportation. A key requirement for these devices is an improved solvent-electrolyte system, capable of withstanding the extreme potentials and temperature ranges expected for modern Li-ion cells. We have undertaken fundamental experimental and computational studies to determine the significance of decompositions of linear and cyclic carbonates to commonly observed nucleophilic species in cycled Li-ion battery electrolytes. The formation of these nucleophiles has been widely reported, as have the straightforward follow-up reactions.1 , 2 , 3, although, the mechanism(s) by which these nucleophiles are formed has not been settled. Moreover, the relevant kinetic aspects of these nucleophilic reactions have not been widely explored, nor have possible reactions that proceed at higher temperature or longer timescales. Since these reactions are generally in competition with one another, and Li-ion electrolyte formulations contain multiple solvents in varying concentrations, a view of the relative rates of these reactions is an important piece of formulating a long cycle life electrolyte. Rather than allow these species to react in multiple experimental studies, across temperatures and concentration ranges, computational methods are well suited to determine the relevant reaction kinetics and thermodynamics with a high degree of confidence. For example, products can be oxidized at relatively low cathode potentials, or they can attack cyclic and linear solvents to generate a host of oligomers and other small molecules.4 , 5 Many of the products of these reactions can also be soluble and electrochemically unstable.6 In this report, we will present headspace GC-MS data for a cycled EC-EMC + LiPF6 electrolyte, with consideration toward species found at the anode and cathode. The observed products will then be compared with data from computational studies of electrochemical and chemical reactions of Li-ion battery solvents. From the computational data, we may conclude the most likely pathways for nucleophile generation and follow up solvent decompositions under typical operating conditions of a Li-ion cell. The focus will be on reasonable routes to observed products, many of which have been proposed as the product of solvent oxidation. Solvent transesterfiications, and formation of lithium ethylene dicarbonate (LEDC), poly(ethylene oxide) oligomers and poly(ethylene carbonate-co-ethylene oxide) oligomers will be discussed. Figure 2. Cycle of chemical / electrochemical solvent decompositions.

Study on Search Algorithm of Passenger Travel Route in Railway Transport Network

Study on search algorithm of passenger travel route in railway transport network is of great importance in evaluation on railway operation benefit. Based on features of large-scale network and railway passenger transportation organization, the paper firstly innovatively puts forward the generation method of alternative route set in multiple constraint conditions. K short routes is proposed based on layering network to generate the alternative route set of passenger flow and hybrid genetic algorithm to generate the alternative route set of passenger flow in format of origindestination matrix in order to overcome the shortage of traditional Dijkstra algorithm. Then reasonable routes of passenger flow are selected from the alternative route set. Finally, the algorithm by MATLAB programming can search reasonable route set of 589 passenger flows within 2 minutes in the railway network constituted by 521 stations which verifies the convergence of the algorithm.

Route-Planning Algorithms for Amusement-Park Navigation

This paper describes route-planning algorithms for navigation in amusement parks (e.g. Disneyland). Unlike conventional shortest-path-finding used for traveling salesman problems, the authors provide several algorithms that consider waiting time estimates in real time, exploit the reservation facilities of an attraction such as Fastpass in Disneyland, and balance a series of enjoyment types such as excitement or relaxation. These features make the new shortest-path algorithms more flexible and dynamic for supporting the cognitive aspects of enjoyment. The authors developed a navigation tool as a Web application in which users select their attractions of interest and the application suggests reasonable and enjoyable routes. An experiment was conducted to demonstrate the performance of this application, focusing on well-known attractions in Tokyo Disneyland.