Minimum Transportation Cost Research Articles

Toward Swimming Speed Optimization of a Multi-Flexible Robotic Fish With Low Cost of Transport

Due to the complex mechanism and fabrication process of flexible materials, it remains extremely challenging for a flexible robotic fish to achieve fast and efficient locomotion. In this article, taking advantage of the passive bending and energy storage properties of flexible materials, we propose an untethered robotic fish with multiple flexible joints to achieve high performance and low Cost of Transport (COT). First, combining rigid links and flexible materials, a compact flexible tail with a simple and efficient structure is proposed. Next, the pseudo-rigid body theory is applied to analyze the deformation of passive joints, and a full-state dynamic model is established. More importantly, an optimization method by adjusting the phase differences of the passive joints is used to obtain high aquatic performance. Finally, extensive simulations and experiments validate the effectiveness of the proposed method, and the robotic fish can achieve a maximum speed of 1.63 body length (BL) per second and a minimum COT of 4.8 J/m (2.87 J/m <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\cdot$</tex-math> </inline-formula> kg). Compared with the multi-joint robotic fish with a similar design, the COT is reduced by up to 81.05% with the basically same aquatic ability. Excitingly, the flexible robotic fish can achieve a COT of 7.36 J/m at 1.23 BL/s, which is 15.72%-36.34% lower than that of the bluefin tuna and is within the range of yellowfin tuna, offering valuable insight into high speed and long endurance applications for underwater robots. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> –This paper is motivated by the design and optimization of an efficient bionic flexible underwater robot with high aquatic abilities, which is conducive to aquatic tasks that require long-time and long-distance sailing, such as underwater topographic exploration, submarine archaeology, and underwater search and rescue. Existing studies of free-swimming bionic underwater robots usually focus on the improvement of swimming speed and rarely consider achieving both high swimming performance and low energy cost. Thus, this paper proposes a bionic underwater robot design with two joints made of flexible materials on the tail to address the problem. Based on detailed analyses of the hydrodynamic force and flexible joint deformation, we propose an effective optimization method for swimming performance. A series of simulations and experiments suggest that the mechatronic design and optimization method are practical and valid. Hopefully, our design and method can provide theoretical guidance for engineers to design and optimize robots with flexible joints.

Exploring Antenatal Health Care Service Utilization in Nepal: A Comprehensive Analysis

The utilization of maternal health care is one of the important factors in reducing the incidence of maternal mortality. Studies in Nepal and in developing countries shows that various demographic, socio-economic and economic factors are associated with ANC service utilization. The main objective of this study is to identify and examine socio-demographic and economic factors affecting antenatal healthcare service utilization in Nepal. This study used data from the Nepal Demographic and Health Survey 2016 and analyzed the data using logistic regression analysis. This research analyzes ANC (antenatal care) concerning dependent variables: age, birth order, religion, ethnicity, educational attainment, province, residence, and wealth indices. Maternal health has been receiving increased attention in the global health, development, and human rights agenda, representing a priority area for accelerating progress on the SDGs and sustaining the gains of the MDGs. Bivariate analysis was conducted for all variables, and some of them were statistically significant at the 5 percent significance level (since p &lt; 0.05). The dichotomous use of modern maternal healthcare utilization, namely whether a woman utilized maternal health care or not, employed dichotomous logistic regression to determine which factors best explain and predict the outcome of maternal health care utilization during pregnancy. A positive estimated value indicates that the odds favor the event and the event is likely to occur, while a negative value indicates that the event is not likely to occur. ANC visits of women decrease with birth order. Based on empirical results, the major factors for ANC services were income, awareness, access to health care services, and birth order attended. The level of awareness among women about the utilization of maternal healthcare in the study area was low. Maternal healthcare service utilization differed across various components of maternity. It would be better to increase awareness about the utilization of modern maternal healthcare for mothers and improve access to healthcare services in their villages to minimize transportation costs and waiting times from the government's side, thus increasing the utilization of maternal health care services in Nepal.

Research on Complex Optimization Models and Efficient Algorithms for Medications Supply Chain during Major Epidemic

The outbreak of the epidemic has had a huge impact on people's lives and economies. Research on the medications supply chain during major epidemic events is a major focus of people's attention and an important part of emergency management work, which is related to the vital interests of people and production enterprises. The supply of medications is an important foundation for the smooth implementation of emergency management work. Based on this, this article conducts empirical research on the multi-product two-level medication supply chain of a pharmaceutical company in Shanghai through optimization models. The medication supply chain consists of three warehouses, each equipped with two types of medications, A and B, which need to be transported to seven disaster-stricken medication demand points. Establish a relevant linear programming model to minimize transportation costs, and then use Lingo software to solve the model. The obtained data results are analyzed, the optimal solution is given, and the model is verified to be the optimal result. Meanwhile, a comparative analysis was conducted on the costs of the supply chain optimization strategy and non-optimization strategy. The research results indicate that the optimal solution not only minimizes transportation costs but also has the shortest transportation path, which can transport medications to various affected areas at the fastest speed, avoiding secondary damage caused by medications shortages in affected areas and ensuring smooth emergency management work.

Developing an Effcient Framework and Mathematical Modelling for Municipal Solid Waste Management in India

An increase in municipal waste has become one of the biggest problems today. Managing waste is a big challenge for every country. Municipal waste management includes transportation and recycling of waste, which needs huge funds. Moreover, significant carbon emissions occur while transporting garbage from one site to another. Thus, this study focuses on reducing the cost due to municipal solid waste (MSW) transportation. The research aims to develop a mathematical model of the supply chain of MSW management. The process of segregation and recycling is also considered in this study. We consider six Indian states and identify the dumping location of the garbage. The study proposes an efficient framework for selecting the segregation hub location and location of MSW treatment plants. The outcomes would be beneficial to achieve an efficient waste management strategy with reduced cost. The numerical simulation results of the model provides the minimum transportation cost as Rs. 4.92 × 106 according to the locations selected for the study. The final strategic decisions is feasible under the constraint sets considered in the model.

A Location Model for the Agro-Biomethane Plants in Supporting the REPowerEU Energy Policy Program

Biomethane represents one of the solutions towards the European Union (EU) energy transition, being capable to decarbonize the EU’s energy system and to reduce the dependence on imported natural gas, as underlined by the “REPowerEU” energy policy program. As its production is expected to expand primarily from biogenic wastes and residues, such as agricultural residues and animal effluents, it is necessary to make its deployment cost-efficient, taking into account factors such as local resources, existing infrastructure, and raw material and investment costs. From this perspective, this paper proposes a replicable predictive model for locating agro-biomethane plants according to raw material potential, relative economic factors, and territorial characteristics. To this end, an analysis was conducted in the Geographic Information System environment, based on location theory. The analysis included testing the minimum transport cost of feedstock in a case study of a rural area in Southern Italy. Three optimal locations for 2 MW size plants were selected where some key element conditions had been identified. The research findings may provide useful information for the EU policymakers in defining more specific energy planning strategies, in accordance with the REPowerEU objectives, addressing the increase in biomethane production by 2030.

Minimization of transportation costs using linear programming

This paper intricately explores the utilization of Linear Programming (LP), a distinguished mathematical optimization technique, within the logistics and transportation sector, a domain persistently pursuing methods to bolster efficiency and curtail expenses. LP, characterized by its adeptness in optimizing a linear objective function subject to linear constraints, emerges as a pragmatic solution. This exposition elucidates how LP can be harnessed to ascertain the most economically viable transportation routes and quantities of goods transported from warehouses to consumers. A case study is introduced to spotlight the pragmatic applicability of LP in authentic scenarios, and the potential fiscal savings corporations can realize through its adoption. While recognizing the merits of LP, such as clarity, versatility, and computational efficacy, the paper also sheds light on its limitations, involving its dependency on certain presumptions, a necessity for precise data, and its concentration on single-objective optimization. The discourse concludes by prospecting the future of LP in transportation, exploring its amalgamation with artificial intelligence, machine learning, multi-objective optimization, and green logistics, intending to underscore the significance of LP in transportation and furnish insights for ensuing research and application.

Optimisation of ship-based CO2 transport chains from Southern Europe to the North Sea

Among the technologies for climate change mitigation, carbon capture and storage is considered as a technically and economically viable option to reduce CO2 emissions from hard-to-abate industrial sectors. When it comes to CO2 logistics, ship-based chains are emerging as an attractive alternative to other CO2 transport modes (e.g., pipelines), as these could exhibit lower operational risk, higher infrastructural flexibility, and lower costs. This work provides insights into the cost of optimal ship-based CO2 transport chains at a European level, by proposing a detailed economic model of CO2 transport by ship, including all the echelons of the infrastructure (i.e., liquefaction, buffer storage, loading, ship, conditioning, and unloading). The final aim is to determine the minimum CO2 transport cost from Southern Europe to North Sea sequestration. Different unloading scenarios (port-to-port, port-to-floating storage and injection, and port-to-direct offshore unloading) and carbon reduction targets are investigated. The minimum unitary transport cost is 26 €/t of CO2 for transporting 103 Mt/y.

A City Logistics Distribution Model: A Physical Internet Approach

The Physical Internet (PI) provides a solution for coping with the continuous rise in urban logistical demands by exploring same-level and cross-level paths. This research suggests a PI-based urban logistical distribution model based on PI distribution that minimizes transportation costs. It also intends to maintain the integrity of the logistical transportation process. This proposed model enhances the flexibility of goods operation and reduces the global cost of logistics’ distribution using an interconnected and open PI logistics distribution mode. This model also explores the functional aspects of the operational problems of city logistical systems and relates to optimization problems. These functionalities are realized by the hierarchical functional structure and are characterized by different cost specifications. The proposed framework provides better integration and cooperation between city logistics warehouses and higher utilization level of logistical infrastructure. The experimental results show that the distribution cost of the PI logistics model is significantly better than that of a traditional logistics model. The elastic and cost advantages grow relatively as the infrastructure for distribution scales up and interruptions from emergencies occur.

Perancangan Rute Pengiriman pada PT. ABC untuk Meminimasi Tingkat Keterlambatan Pengiriman dan Biaya Transportasi Menggunakan Model Mixed Integer Linear Programming

The distribution of vegetables in Indonesia is still constrained by the guarantee of continuity between product quality, the minimum amount of supply, and the timeliness of delivery. Time is a very crucial factor in the distribution of vegetables because vegetables are commodities that experience a rapid decline in quality, so they must reach consumers at the right time. PT ABC is a company engaged in the distribution of fresh fruit and vegetables. The problem faced by PT ABC in distribution activities lies in the delay in delivery. This delay problem can be minimized by optimizing the distribution network so that delivery delays do not occur again. The characteristic of the problem contained in this final project is Vehicle Routing Problem with Heterogeneous Fleet and Time Window. The model that will be used to solve this problem is the Mixed Integer Linear Programming model with the main objective to minimize transportation costs and minimize delivery delays. Problem solving was carried out using the Python programming language and Gurobi Solve. The final result obtained is a reduction in the percentage of delays by 5% and a minimization of transportation costs by 4%.

A co-evolutionary genetic algorithm with knowledge transfer for multi-objective capacitated vehicle routing problems

The capacitated vehicle routing problem (CVRP) aims to find the routes that minimize transportation costs. However, in real-world applications, the equitable workload is essential for the drivers and logistics companies. For assigning an appropriate load for each vehicle, this paper formulates the CVRP with workload balancing as a multi-objective optimization problem (MO-CVRP). The goal is to simultaneously minimize the total length of routes and the variance of loads assigned to each vehicle. Although several multi-objective evolutionary algorithms (MOEAs) have been proposed for the MO-CVRP, they often suffer from slow convergence speeds due to the complexity of the search space, which hinders the identification of clear search directions. To address this issue, the knowledge transfer method is introduced that can leverage search experiences from related problems to expedite the solution process of the current problem. Specifically, a co-evolutionary genetic algorithm with knowledge transfer is proposed, where a simplified version of the traveling salesman problem (TSP) by disregarding the vehicle capacity constraint is utilized to facilitate the quick solution of the original MO-CVRP. To enable information interaction between these two problems, the widely used denoising autoencoder is adopted to construct a transformation relation between solutions from the TSP to the MO-CVRP. Additionally, an adaptive migration strategy is proposed to adaptively select the migration methods and an improved split operator strategy is designed to ensure the feasibility of the solutions. The proposed algorithm is compared with four state-of-the-art MOEAs on MO-CVRP test sets with different scales. Experimental results show that the proposed algorithm exhibits faster convergence speed and achieves solutions with superior convergence.

An agent-based simulation and logistics optimization model for managing uncertain demand in forest supply chains

This paper aims to model and minimize transportation costs in collecting tree logs from several regions and delivering them to the nearest collection point. This paper presents agent-based modeling (ABM) that comprehensively encompasses the key elements of the pickup and delivery supply chain model and presents the units as autonomous agents communicating. The modeling combines components such as geographic information systems (GIS) routing, potential facility locations, random tree log pickup locations, fleet sizing, trip distance, and truck and train transportation. ABM models the entire pickup and delivery operation, and modeling outcomes are presented by time series charts such as the number of trucks in use, facilities inventory, and travel distance. In addition, various simulation scenarios are used to investigate potential facility locations and truck numbers and determine the optimal facility location and fleet size.

Fixed charge 4-dimensional transportation problem for breakable incompatible items with type-2 fuzzy random parameters under volume constraint

Real-life problems are full of uncertainties combined with impreciseness and randomness. Transportation problems become difficult when its parameters are type-2 fuzzy random type. This paper presents a 4-dimensional fixed charge transportation problem for incompatible and compatible breakable items with type-2 fuzzy random parameters under the availability, demand, conveyance’s weight and volume capacity constraint. The model is developed for usual transportation cost minimization objective, where transportation costs, fixed charges, availabilities, demands, conveyances’ weights and volume capacities are type-2 fuzzy random. A type-2 fuzzy random parameter is converted to type-1 fuzzy random one by critical value-based reduction method and then converted to a random parameter using two different methods-generalized credibility measure and centroid method. The random parameter is converted to crisp parameter using probability chance constraint method for constraints and expectation for objective. A detailed conversion procedure from type-2 fuzzy random to crisp is also presented. The reduced deterministic problem is solved by the Generalized Reduced Gradient method using Lingo 11.0 and numerically illustrated. A real-life example is introduced. Particular models are deduced for non-breakable items or different pairwise incompatible items. The sensitivity analyses against different generalized credibility levels of different parameters are presented for total transportation cost. The numerical results show the efficiency of the proposed critical value reduction-generalized credibility-chance constraint, and expectation method. The importance of uncertainty, multi-path and vehicles’ volume capacity in transportation problems is demonstrated.

Analysis of UAV path planning effectiveness and evaluation index scores combined with urban logistics scenarios

Using logistics drones for distribution is an optional solution to the problems of saturated logistics industry, insufficient transportation capacity, and high work pressure on employees in real cities. In a realistic urban environment, it is of practical significance to establish a logistics drone path planning model to improve the efficiency of logistics drones by fully considering the multiple requirements of both logistics service parties, such as time and location, and aiming at minimizing transportation costs. Aiming at this problem, this paper analyzes and studies the path planning problem of unmanned aerial vehicles based on the factors that need to be considered in actual life logistics distribution. Firstly, the actual scenario requirements of logistics UAV applications are analyzed and a path planning algorithm for this scenario is enumerated. Subsequently, mathematical models are established from both path planning and customer satisfaction to assess the performance of logistics drones. Then, a comparative analysis of four existing classical path planning algorithms is conducted, and simulated annealing algorithm is used to optimize logistics delivery scenarios for path planning. Finally, combining the mathematical model and simulated annealing algorithm constructed in the previous article, logistics UAV simulation experiments are conducted in urban distribution scenarios and an ideal path planning model is obtained. The work of this article provides a certain reference basis for logistics companies in the application of logistics UAVs and the selection of path planning methods.

The Line-Haul Feeder Vehicle Routing Problem: A Classification and Review

Today, goods transportation is considered to be one of the most important activities of national economics. Logistics and supply chain play an important role in the industry and services, considering the needs of the people, while there is an increase in the population. In addition, the role of logistics in urban areas, especially in restaurants, grocery stores, etc., is clearly visible. Besides, the final price of the goods is the most important factor that is always considered in service and in production. Due to this important factor, transportation has been found to be one of the most significant and influential factors in determining the price of goods. For these reasons, the newest variant of the vehicle routing problem, called the line feeder vehicle routing problem (LFVRP), is considered in this paper, in which various types of vehicles (large and small vehicles) are used for providing services to customers. In this particular type of delivery issue, these vehicles must start from the warehouse, meet customers, and finally return to the depot. In fact, the issue of LFVRP is related to the fast customer service in urban areas because in this case, all that are considered to be of priority are to minimize transportation costs and overall distribution time for fast customer service, especially in urban areas. Due to the many applications of this problem in the real world, a general review of this problem is conducted, and the versions of this problem are described along with the algorithms for its solution in the paper.

Presenting a Model for Locating and Allocating Multi-Period Hubs and Comparing It With a Multi-Objective Imperialist Competitive Algorithm

Abstract Recently, air pollution has received much attention as a result of reflections on environmental issues. Accordingly, the hub location problem (HLP) seeks to find the optimal location of hub facilities and allocate points for them to meet the demands between source-destination pairs. Thus, in this study, decisions related to location and allocation in a hub network are reviewed and a multi-objective model is proposed for locating and allocating capacity-building facilities at different time periods over a planning horizon. The objective functions of the model presented in this study are to minimize costs, reduce air pollution by diminishing fuel consumption, and maximize job opportunities. In order to solve the given model, the General Algebraic Modeling System (GAMS) along with innovative algorithms are utilized. The results presented a multi-objective sustainable model for full-covering HLP, and provided access to a hub network with minimum transport costs, fuel consumption, and GHG (greenhouse gas) emissions, and maximum job opportunities in each planning horizon utilizing MOICA (multi-objective imperialist competitive algorithm) and GAMS to solve the proposed model. The study also assessed the performance of the proposed algorithms with the aid of the QM, MID, SM, and NSP indicators, acquired from comparing the proposed meta-heuristic algorithm based on some indicators, proving the benefit and efficiency of MOICA in all cases.

Green Distribution Route Optimization of Medical Relief Supplies Based on Improved NSGA-II Algorithm under Dual-Uncertainty

With growing concerns about environmental issues, sustainable transport schemes are receiving more attention than ever before. Reducing pollutant emissions during vehicle driving is an essential way of achieving sustainable transport plans. To achieve sustainable transport and reduce carbon emissions, on the premise of ensuring rescue timeliness, this research proposes a multi-objective distribution route optimization model considering the minimization of transportation cost and transportation risk under dual-uncertainty constraints, providing a practical framework for determining the optimal location of rescue centers and distribution routes in emergencies using fuzzy theory. First, this paper proposes objective functions that innovatively take into account the congestion risk and accident risk during the distribution of medical supplies while introducing the carbon emission cost into the transportation cost and using the fuzzy demand for supplies and the fuzzy traffic flow on the roads as uncertainty constraints. Then, this paper designs a multi-strategy hybrid nondominated sorting genetic algorithm (MHNSGA-II) based on the original form to solve the model. MHNSGA-II adapts a two-stage real number coding method for chromosomes and optimizes the population initialization, crowding distances selection, and crossover and mutation probability calculation methods. The relevant case analysis demonstrates that, compared with the original NSGA-II, MHNSGA-II can decrease the transportation cost and transportation risk by 42.55% and 5.73%, respectively. The sensitivity analysis verifies the validity and rationality of the proposed model. The proposed framework can assist decision makers in emergency logistics rescue.

Assessment of transport operations of a foreign trade enterprise

The key issue of selecting the most rational variant of cargo transportation is the assessment of the efficiency of the transport process. Increasing one’s own profit can be achieved by minimizing transport costs and reducing the time of cargo delivery. This will also allow getting significant advantages in the finished product sales market.

Transport Task Models with Variable Supplier Availabilities

Background: With regard to the definition of an optimal transport plan for some material flow in the logistics system in literature and practice, the classical transport task model is developed. The minimization of total transport costs is usually considered for optimality. Some modifications to the classical transport task have also been developed. Methods: The article uses the methods of linear optimization. Based on these methods, two modified transport task models have been constructed, which consider the possibility of planning in advance the quantities available from the suppliers of the transported cargo. These models are applicable in SCM for pharmaceuticals with a national logistics hub. Furthermore, a solver in MS Excel is used to determine the optimal solution of optimization models. Results: Two new (modified, extended) models of the transport task have been constructed, in which a preliminary planning of the available quantities of the transported cargo at the suppliers is made. These quantities shall be planned in such a way as to ensure a minimum total transport cost. Conclusions: By applying the proposed new transport task models, lower total transport costs for carrying out imported pharmaceuticals can be ensured compared to an application of the classical transport task model.

A Hybridization Approach to Solve the Capacitated Asymmetric Allocation Hub Location Problem

This paper deals with a capacitated asymmetric allocation hub location problem (CAAHLP). We determine the number of hubs, the locations of hubs, and asymmetric allocation of non-hub nodes to hub with the objective of minimum total transportation costs satisfying the required service level. To solve the problem optimally, we present a 0-1 integer programming model and find an optimal solution using CPLEX. As the CAAHLP has impractically demanding for the large-sized problem, a solution method based on combined ant colony optimization algorithm and genetic algorithm is developed which solve hub location problem and node allocation problem respectively. We investigate performance of the proposed solution method through the comparative study. Key Words: Hub Location, Asymmetric Allocation, Ant Colony Optimization, Genetic Algorithm

Hub seaport multimodal freight transport network design: Perspective of regional integration development

To design the multimodal freight transport network of regional hub seaports, and promote the high-quality development of regional economic integration. Combined with the principal component analysis method, the comprehensive evaluation value of the inland cities is determined. Meanwhile, the gravity model is used to obtain the correlation degree between non-core cities and core cities, and the hinterlands of these core cities are divided. The entropy weight TOPSIS model is applied to select the inland hinterland transshipment cities. Taking the minimum total transport costs as the objective function, the regional hub seaport multimodal freight transport network optimization model with space-time constraints is established, and solved by MATLAB software. Taking the freight transport from Chengdu-Chongqing Economic Zone to Yangtze River Delta as an example, a multimodal freight transport network is constructed. It is calculated that Chengdu and Chongqing can be set as freight consolidation centers, and Changsha, Wuhan, Nanchang, Nanjing, Hangzhou, Hefei, Zhengzhou and Xi'an can be set as transshipment nodes. These non-core cities in the Chengdu-Chongqing Economic Zone can be divided into five groups to achieve effective improvement in transport efficiency through group combined transport. This study gives managerial implications for multimodal transport structure optimization and provides reference for the regional integration development between seaports and hinterlands.