Total Travel Distance Research Articles

A deep reinforcement learning-based approach for the home delivery and installation routing problem

This paper investigates a home delivery and installation routing problem with synchronization constraints stemming from a home industry company in China who provides the last-mile delivery of home decoration and furniture. The company first arranges for products to be delivered from door to door, and later the technicians come to perform the installation service for the customers. The products for each customer must be firstly delivered to the customer by a vehicle and then installed by technicians. The objective is to identify the optimal delivery routes of the vehicles and optimal service routes of the technicians so as to minimize the total travel distance of the delivery and service routes. A deep reinforcement learning method in an Encoder-Decoder fashion with multi-head attention mechanism and beam search strategy is developed to solve the problem. To evaluate the designed method, extensive numerical experiments based on real service networks provided by the company are conducted. The results show that the proposed method can effectively solve the problem, which outperforms some classical strategies, and some meaningful management implications are provided.

Integration of simulation and dispatch modelling to predict fleet productivity: an open-pit mining case

ABSTRACT Predicting the fleet requirement based on fleet productivity estimation is one of the critical parts of a robust long-term mine plan. The dispatch logic that determines the return destination of the empty trucks is significantly important in the overall full and empty travel distances and trucks’ productivity. In this paper, a Monte-Carlo simulation model is presented to mimic the real truck-and-shovel operations and measure trucks’ productivity in terms of Tonne Per Gross Operating Hour (TPGOH). A linear programming model is integrated into the simulation model to optimize the dispatch decision through distance minimization subject to the mine’s production schedule. The historical data records of oil sands mining operations are used to validate model’s performance. The results show significant improvement over the existing mine site’s method with closely matching the real TPGOH and better estimation of the total empty travel distance, as a result of new dispatch model implementation.

Lactobacillus casei Strain Shirota Enhances the Ability of Geniposide to Activate SIRT1 and Decrease Inflammation and Oxidative Stress in Septic Mice.

Gardenia jasminoides Ellis is rich in geniposide, which can be transformed into the anti-oxidant and anti-inflammatory agent genipin. Genipin exhibits greater efficacy than geniposide, but it is unstable and difficult to preserve. In this study, a mouse model for sepsis was established by cecal ligation and puncture, and then we explored the effects and mechanism of Lactobacillus casei strain Shirota (LcS) on the enhancement of the ability of geniposide to reduce sepsis and decrease inflammatory and oxidative levels in mice by the regulation of sirtuin type 1 (SIRT1). The mice were evaluated and analyzed by the open field test, Morris water maze test, flow cytometry, kit assay, qPCR, and western blot. The LcS + geniposide increased the survival rate in mice with sepsis, and increased the total travel distance, number of times the mice stood up, amount of time the mice spent grooming their fur, duration in the target quadrant, and crossing area number. The testing of mouse nerve cells showed that LcS + geniposide reduced the rate of nerve cell apoptosis caused by sepsis. LcS + geniposide also decreased the amount of inflammatory-related indicators of TNF-α, IL-6, and IL-1β, and the oxidation-related levels of malondialdehyde (MDA) in the hippocampi of septic mice, and it increased the oxidase activities of superoxide dismutase (SOD) and catalase (CAT). Additionally, LcS + geniposide increased the SOD1, SOD2, and CAT mRNA expression in the hippocampi of mice with sepsis and decreased the expression of TNF-α, IL-1β, NF-κB, and p53 mRNA. LcS+geniposide also increased the SIRT1 protein expression and decreased the Ac-FOXO1, Ac-NF-κB, and Ac-p53 protein expression in the hippocampi of mice with sepsis. We also observed that LcS + geniposide decreased the inflammatory and oxidative damage in the mice with sepsis. The effect of LcS + geniposide was similar to that of the drug dexamethasone and stronger than the effect of geniposide utilized alone. LcS also enhanced the ability of geniposide to activate SIRT1 and decrease the inflammation and oxidative stress in the septic mice, and it achieved an effect same with that obtained by the use of the drug dexamethasone.

Perancangan Tata Letak Fasilitas Pada Rumah Produksi Taman Eden 100

Small and Medium Enterprise (SME) Taman Eden 100 or CV Andaliman Mangintir is a manufacturing company that produces various andaliman derivative products, including Andaliman Dried Seeds, Andaliman Powder, SikArsik, and Andaliman Seeds. Some of Andaliman's derivative products are already halal certified and some of them do not yet have a halal certificate but already have a PIRT DINKES permit. Thus, it is necessary to have a different room to produce halal products and products that have not been certified halal, so it is necessary to design a layout using the Systematic Layout Planning (SLP) method. The Systematic Layout Planning (SLP) method has several stages in making the layout, namely activity linkage analysis such as Activity Relationship Chart (ARC) analysis, Activity Relationship Diagram (ARD) analysis, and making proposed layouts based on the 5S concept (Seiri, Seiton, Seiso, Seiketsu, and Shitsuke). Based on the research conducted, it was found that the total travel time and distance of Material Handling in the initial layout within 1 month was 3.720 seconds and 2.155 seconds for the time of Andaliman Powder and SikArsik; and 43,69 m and 88,03 m for the distances of Andaliman Powder and SikArsik. By using the Systematic Layout Planning (SLP) method, it is obtained that the total travel time and distance of Material Handling in the proposed layout within 1 month is 2.800 seconds and 1.465 seconds for the time of Andaliman proposed layout within 1 month is 2.800 seconds and 1.465 seconds for the time of Andaliman Powder and SikArsik; and 31,28 m and 74.32 m for the Andaliman powders and SikArsik, resulting in a reduction in Material Handling time and distance of 920 seconds and 12,41 m for Andaliman Powder products; and 690 seconds and 13,71 m for SikArsik products.

Sulfoxaflor causes mortality, decreased locomotion, and altered interactions in pavement ants (Tetramorium caespitum)

This study investigated the exposure effects of sulfoxaflor, a next-generation sulfoximine insecticide, on the viability, locomotor behavior, and nest-mate interactions of the pavement ant (Tetramorium caespitum). Adult worker ants were exposed to 0, 1, 5, 12.5, 25, or 50 mg/L of sulfoxaflor via oral ingestion for 48 h. This short-term exposure to concentrations as low as 1 mg/L had wide ranging effects on multiple locomotive parameters (average speed, mobile average speed, total travel distance), nest-mate interactions (aggression and antennation), and body morphology (abdominal curving). Exposure to sulfoxaflor for 24 h manifested in decreased locomotion, altered intraspecific interactions and the development of abdominal curvature in the 25 and 50 mg/L treatments. Such alterations in mobility and conspecific behavioral parameters would clearly impact the ability of exposed individuals to successfully satisfy resource demands and presents a risk to colony survival.

On Improving the Quality of Solutions in Large-Scale Cooperative Multi-Agent Pathfinding

Scaling up the number of simultaneously moving units in pathfinding problems to hundreds, or even thousands, is well beyond the capability of theoretically optimal algorithms in practice, which is consistent with existing intractability results. Significant scalability can be achieved by trading off solution optimality, which motivates evaluating the quality of suboptimal solutions, especially in instances much larger than can be handled by optimal algorithms. We consider pathfinding in uniform cost grid maps, and we study the solution quality using the three most common quality criteria, total travel distance, sum of actions, and makespan. We focus on MAPP, which has been shown as state-of-the-art in terms of scalability and success ratio (i.e., percentage of solved units) on realistic game grid maps. Until now, the quality of MAPP's solutions had not been as extensively analyzed. We introduced enhancements that significantly improve MAPP's solution quality. For example, its sum of actions is cut to half on average. MAPP becomes competitive in terms of solution quality with FAR and WHCA*, two successful algorithms from the literature. To evaluate the quality of suboptimal solutions in instances beyond the capability of optimal algorithms, we use lower bounds of optimal values to show our solutions have a reasonable quality. For example, MAPP's average total travel distance is 19 percent longer than the lower bound.

The vehicle routing problem with speed optimization for shared autonomous electric vehicles service

The shared autonomous electric vehicles (SAEVs), the integration of car-sharing, autonomous technology, and electrification, are anticipated to be the popular choice for future urban travel, offering more flexible demand-responsive services than traditional shared mobility. This study develops a mixed-integer linear model to optimize the SAEVs service, where the electric vehicles (EVs) are powered by the battery swapping stations (BSSs). The innovation of the model is the speed optimization on the travel arc while optimizing the service routes of the SAEVs fleet. A minimized weighted objective containing the total travel distance, the total travel time and the total energy consumption is considered. An adaptive large neighborhood search (ALNS) algorithm embedded in the speed optimization subroutine is designed to find the optimal solution. The good computational performance of ALNS is demonstrated through numerical comparisons to the solutions found by CPLEX and the best-known solutions of the similar dial-a-ride problem (DARP). The adapted set of instances was employed in the comparative experiment, from which the benefits of speed optimization for saving the objectives are obtained. Sensitivity analysis was performed on the minimum battery level, battery capacity, and the weight of energy consumption in the objective. The results provide SAEVs operators with alternative cost-saving solutions. Larger capacity batteries and lower minimum battery levels help reduce the travel distance, while smaller capacity batteries and feasible lower speeds help to save energy consumption.

Multi-stage optimal design of road networks for automated vehicles with elastic multi-class demand

With the advent of automated vehicles (AVs), new infrastructure planning concepts such as subnetworks of AV-ready roads have been proposed to improve the performance of transportation networks and to promote the adoption of AVs. However, these subnetworks should evolve over time in response to the growing AV demand, which necessitates a multi-stage modeling approach. In this study, we propose multi-stage deployment of AV-ready subnetworks and formulate it as a time-dependent network design problem, which is a bi-level mixed-integer programming problem. The lower level is a simultaneous travel mode and route choice equilibrium with continuous decision variables, and the upper level is a design problem including infrastructure investment decisions to determine which roads to upgrade and include in AV-ready subnetworks for mixed traffic. We use a case study of a real road network to demonstrate the concept. Since computational efficiency is a key factor for solving such large-scale problems, we develop two efficient and tailored evolutionary heuristics to solve the problem, and compare their performance to a computationally demanding Genetic-algorithm-based solution method. The results indicate that the proposed algorithms can efficiently solve this large-scale problem while satisfying constraints in all scenarios, and outperform Genetic algorithm, particularly in the scenario with larger number of stages. Moreover, in all scenarios, deployment of AV-ready subnetworks leads to improvements in network performance in terms of total travel time and cost. However, the improvements are always accompanied with increased total travel distance. The extent of changes depends on AV market penetration rate, AV-ready subnetwork density and timing of densification.

School Bus Routing Problem with a Mixed Ride, Mixed Load, and Heterogeneous Fleet

Special education students are usually routed on different buses than non-disabled general education students. To make the routes more efficient, this study proposes serving general students and special education students on the same bus at the same time (mixed ride) while allowing heterogeneous fleets and mixed loads. ESRI Location-Allocation tools and Google OR-Tools are used for bus stop selection, route generation, and bus stop optimization. Parallel cheapest insertion heuristic and metaheuristic, simulated annealing, are adopted to generate school bus routes. The effectiveness of the mixed ride approach is tested for three schools with 178 synthetic students’ locations data (including 12 with wheelchair) in New York City using a fleet of 14 buses spread over four types. The results show the mixed ride approach achieved 14.32% reduction in total travel distance and 10.46% reduction in total travel time. The mixed ride approach tends to return solutions with fewer vehicles and fewer bus stops, less average travel distance, and shorter average travel time.

Intelligent Multi-Robot System for Collaborative Object Transportation Tasks in Rough Terrains

Human missions on other planets require constructing outposts and infrastructures, and one may need to consider relocating such large objects according to changes in mission spots. A multi-robot system would be a good option for such a transportation task because it can carry massive objects and provide better system reliability and redundancy when compared to a single robot system. This paper proposes an intelligent and decentralized approach for the multi-robot system using a genetic fuzzy system to perform an object transportation mission that not only minimizes the total travel distance of the multi-robot system but also guarantees the stability of the whole system in a rough terrain environment. The proposed fuzzy inference system determines the multi-robot system’s input for transporting an object to a target position and is tuned in the training process by a genetic algorithm with an artificially generated structured environment employing multiple scenarios. It validates the optimality of the proposed approach by comparing the training results with the results obtained by solving the formulated optimal control problem subject to path inequality constraints. It highlights the performance of the proposed approach by applying the trained fuzzy inference systems to operate the multi-robot system in unstructured environments.

Road transportation network hazard sustainability and resilience: correlations and comparisons

Recent national level guidelines and research efforts increasingly suggest the use of resilience and sustainability concepts for natural hazard mitigation and recovery planning at the regional and community levels. This paper aims to distinguish and compare the two abstract concepts in the context of road networks recovering from a hazard event since resilience and sustainability metrics are often interchangeably adopted and can lead to inaccurate equivalencies between the two concepts. A spectrum of resilience and sustainability metrics are defined herein and a research methodology is described to evaluate them probabilistically. A case study demonstration on the road network in the Memphis Metropolitan Statistical Area (MMSA) for a probabilistic suite of earthquake scenarios shows that the correlation between network resilience and sustainability is dependent on the hazard level as well as the graphical abstraction of the real road network. While some correlations are observed between temporal sustainability metrics, such as missed trips and traffic emissions, and resilience metrics, like total travel distance and time, the correlation weakens as the network resolution becomes more sparse. These results show that while for dense networks, time-evolving travel time and travel distance may be used as proxy measures for hazard sustainability evolution, for sparsely connected networks, hazard resilience and post-hazard sustainability remain relatively distinct.

A Hybrid Chemical Based Metaheuristic Approach for a Vaccine Cold Chain Network

One year ago, the coronavirus (COVID-19) virus began spreading around the world. Vaccines have now been developed by a variety companies from different countries. Vaccines must be stored in good condition within cold boxes designed to keep vaccines within acceptable temperature ranges. This paper proposes a hybrid algorithm based on a chemical reaction called Hybrid Artificial Chemical Reaction Optimization Algorithm (HACROA) which has been used to design a vaccine cold chain network in northern Thailand. The scope of this research is to focus on the Office of Disease Prevention and Control Area 1 (Chiang Mai, Thailand). The objective of this research is to rearrange routes to minimize total travel distances. Experiments were conducted to compare the efficiency of the hybrid algorithm with other algorithms in term of the distance. The optimal parameters of the algorithm employed a statistical experiment theory namely full factorial design. Experiment design and analysis were adopted to investigate the factors that affect the performance of this algorithm. HACROA was able to rearrange routes and found a better solution than the other algorithms.

Spatial Extent of Fluorescence Quenching in Mixed Semiconductor–Metal Nanoparticle Gel Networks

AbstractIn this work, mixing and co‐gelation of Au nanoparticles (NPs) and highly luminescent CdSe/CdS core/shell nanorods (NRs) are used as tools to obtain noble metal particle‐decorated macroscopic semiconductor gel networks. The hybrid nature of the macrostructures facilitates the control over the optical properties: while the holes are trapped in the CdSe cores, the connected CdSe/CdS NRs support the mobility of excited electrons throughout the porous, hyperbranched gel networks. Due to the presence of Au NPs in the mixed gels, electron trapping in the gold NPs leads to a suppressed radiative recombination, namely, quenches the fluorescence in certain fragments of the multicomponent gel. The extent of fluorescence quenching can be influenced by the quantity of the noble metal domains. The optical properties are monitored as a function of the NR:NP ratio of a model system CdSe/CdS:Au. By this correlation, it demonstrates that the spatial extent of quenching initiated by a single Au NP exceeds the dimensions of one NR, which the Au is connected to (with a length of 45.8 nm ± 4.1 nm) and can reach the number of nine NRs per Au NP, which roughly corresponds to 400 nm of total electron travel distance within the network structure.

A study on relocation of home field and improvement of game schedules for Nippon Professional Baseball

Professional sports are regarded as a business in Europe and the U.S. lately. Sport scheduling problems have been studied for increased attendance and improvement in game schedules. By contrast, Nippon Professional Baseball (NPB) regards ball games as a performance. Therefore, the improvement of game schedules has not been considered seriously. However, it has been revealed that sufficient attendance for sustainment of baseball league management is not securable only at the present home fields. For that reason, it is fundamentally important to acquire spectators in various regions other than the present home fields for increased attendance. In addition to such management issues, trading of a ball club might also require the relocation of a team's home field. Given that background, it is important to design a game schedule that lessens variation in the travel distance of each team. This study analyzes factors that affect attendance by multiple regression analysis and investigates ball club relocation based on anticipated attendance. Furthermore, we determine a game schedule that minimizes the total travel distance using Tabu Search and propose a method of determining candidate destination cities for the ball clubs to be relocated. Finally, we verify the effectiveness of the proposed method using a numerical experiment.

Storage assignment for newly arrived items in forward picking areas with limited open locations

In e-commerce warehouses, replenishment operations involve the transportation of ordered items from reserve areas to forward picking areas. In this paper, we study where to store these items (called newly arrived items) in open storage locations in forward picking areas prior to picking to minimize the total travel distance for given picking orders. The locations occupied by items previously stored in the forward area are not available. The problem is formulated as an integer program and proved to be NP-hard. We propose a two-stage decomposition algorithm, showing that it provides high-quality solutions quickly. Compared with methods proposed in the literature and used in practice, the travel distance is substantially reduced. Also, our numerical study shows that considering the constraint that items have previously been stored, which has often been ignored in previous research, can improve the picking performance by more than 5%. We apply our proposed algorithm to real data from a Chinese 3PL retailer to show that it significantly outperforms the policy the company currently uses.

Comparing the environmental performance of distributed versus centralized plastic recycling systems: Applying hybrid simulation modeling to life cycle assessment

The plastic waste dilemma has gained international attention because of increased public awareness and the rise of waste import bans. Sorting and recycling technologies that deal with plastic waste streams exist but face challenges in highly dense urbanized regions such as Singapore because of high space requirements. Multiple small-scale plastic sorting and recycling facilities that are distributed closer to points of waste generation offer the possibility of increasing the recovery of plastic-waste streams in urbanized settings. To holistically quantify the environmental performance of this type of system, a life cycle assessment (LCA) is conducted to compare five different scenarios of distributed and centralized sorting and recycling of plastic bottles and takeaway containers in Singapore. A hybrid simulation model is used to simulate the complex dynamics of waste-processing systems which involves waste generation, collection scheduling, sorting, recycling, and disposal of residual materials. The results of the LCA showed that the life cycle environmental impacts to climate change, water depletion, and terrestrial ecotoxicity of the distributed scenarios were higher than the centralized scenarios by 7–30% depending on the impact category. Although the distributed scenarios had lower total travel distances, their reliance on commercial vans led to their higher environmental impacts than the centralized scenarios. The findings of this study provide motivation for future research in applying hybrid simulation models in LCAs of waste-to-resource exchange networks. The data used to carry out the hybrid simulations and the LCA are provided as Supporting Information. This article met the requirements for a Gold-Silver JIE data openness badge described at http://jie.click/badges.

Implementation of a Secure Ride-Sharing DApp Using Smart Contracts on Ethereum Blockchain

The idea of sharing economy gives rise to unique ideas and develops innovative businesses. This article aims to relate the smart city concept by introducing the smart transport system and explores the opportunities of adopting blockchain technology in ride-sharing services. Blockchain technology is a distributed, decentralized public ledger that allows peer-to-peer transactions in a secured way without any third party. This paper proposes a blockchain-based framework from the existing centralized framework for a ride-sharing service and implements the same as a decentralized application (DApp) based on smart contracts on Ethereum Blockchain. Using smart contracts facilitate the users with automated transactions, removes the intermediaries, and enables various activities to be carried out safely and securely. Implementation of smart contracts is done using the Solidity programming language. This DApp uses the min matching algorithm to match riders requesting rideshare to save total travel distance. With the overwhelming growth in the usage of cryptocurrencies, smart contracts usage in applications as proposed in this paper can transform the sharing economy.

Approximate ridesharing of personal vehicles problem

It is important to find ride matches for individuals who participate in ridesharing quickly, and it is equally important to minimize the number of drivers to serve all individuals and minimize the total travel distance of the vehicles. This paper considers the following ridesharing problem: given a set of trips, each trip consists of an individual, a vehicle of the individual and some requirements, select a subset of trips and use the vehicles of selected trips to deliver all individuals to their destinations while satisfying the requirements and achieving some optimization goal. Requirements of trips are specified by parameters including source, destination, vehicle capacity, preferred paths, detour distance and number of stops a driver is willing to make, and time constraints. We consider two optimization problems: minimizing the number of selected vehicles and minimizing total travel distance of the vehicles. We prove that it is NP-hard to approximate both minimization problems within a constant factor if any one of the requirements related to the detour distance, preferred paths, number of stops and time constraints is not satisfied. We give K+22-approximation algorithms for minimizing the number of selected vehicles when the requirement related to the number of stops is not satisfied, where K is the largest capacity of all vehicles.

Demand-predictive storage assignment mechanism for flower auction centers

As the number of daily transactions continues to increase, congestion frequently occurs in flower auction centers. Put system is widely applied in intralogistics operations, which includes distribution and redistribution areas. The uncertain arrivals of demands pose significant challenges for the efficient intralogistics operations in flower auction center. In order to improve performance of the put system, this study newly designs a demand-predictive storage assignment (DSA) mechanism in which uncertain demands are forecasted by constructing ratio time series of each customer. Based on the demand forecasts, the customer locations within the distribution area and the number of locations within the redistribution area are easily determined. Furthermore, a paired redistribution strategy is proposed that enables two customers to share a staging block. A simulation experiment bed is constructed based on a real-life case. The experimental results indicate that the forecasting method outperforms other demand forecast methods in literature with lower forecasting error, and the proposed DSA mechanism reduces the total travel distance compared with the closest open location.

A Hybrid Method Integrating a Discrete Differential Evolution Algorithm with Tabu Search Algorithm for the Quadratic Assignment Problem: A New Approach for Locating Hospital Departments

The facility layout problem (FLP) is a very important class of NP-hard problems in operations research that deals with the optimal assignment of facilities to minimize transportation costs. The quadratic assignment problem (QAP) can model the FLP effectively. One of the FLPs is the hospital facility layout problem that aims to place comprehensive clinics, laboratories, and radiology units within predefined boundaries in a way that minimizes the cost of movement of patients and healthcare personnel. We are going to develop a hybrid method based on discrete differential evolution (DDE) algorithm for solving the QAP. In the existing DDE algorithms, certain issues such as premature convergence, stagnation, and exploitation mechanism have not been properly addressed. In this study, we first aim to discover the issues that make the current problem worse and to identify the best solution to the problem, and then we propose to develop a hybrid algorithm (HDDETS) by combining the DDE and tabu search (TS) algorithms to enhance the exploitation mechanism in the DDE algorithm. Then, the performance of the proposed HDDETS algorithm is evaluated by implementing on the benchmark instances from the QAPLIB website and by comparing with DDE and TS algorithms on the benchmark instances. It is found that the HDDETS algorithm has better performance than both the DDE and TS algorithms where the HDDETS has obtained 42 optimal and best-known solutions from 56 instances, while the DDE and TS algorithms have obtained 15 and 18 optimal and best-known solutions out of 56 instances, respectively. Finally, we propose to apply the proposed algorithm to find the optimal distributions of the advisory clinics inside the Azadi Hospital in Iraq that minimizes the total travel distance for patients when they move among these clinics. Our application shows that the proposed algorithm could find the best distribution of the hospital’s rooms, which are modeled as a QAP, with reduced total distance traveled by the patients.