Unit Transportation Cost Research Articles

Evaluation of road roughness and its influence on operating parameters in open-pit mines

Hauling ore and waste rock is a major cost factor in open-pit mines, encompassing both road maintenance and off-road truck operation. Road maintenance costs are directly influenced by traffic volume and the size of trucks using the roads. Off-road truck operating costs are impacted by various factors, including those affecting their movement, such as the direct interaction between tires and the road surface. Tire-road interaction significantly affects rolling resistance. Rougher surfaces with deeper grooves caused by track sinks lead to higher rolling resistance. This study employed laser profiling to characterize the surface roughness of five mine haul roads in two large Brazilian iron ore mines. This study evaluated the impact of these road irregularities on various operating parameters for 240-ton off-highway trucks, including rolling resistance, average travel speed, travel time, productivity, and unit transport cost. The results indicate that a 10 cm increase in road surface roughness can lead to a significant increase in rolling resistance (up to 5%), a substantial decrease in average speed (25%), a notable increase in travel time (26%), a decrease in productivity (19%), and a corresponding increase in unit transport cost (21%).

A two-stage optimization model for relief distribution to disaster survivors under two-fold uncertainty

Disasters are unforeseen occurrences requiring extensive transport deployment to support and relieve victims. Sometimes, this transportation is not feasible directly from some supply points to some destination points. Due to this tragedy, it is unclear precisely what is available at supply points, what is needed at destinations, how much transportation capacity there is, and what the routes are like. In this study, we investigate a two-stage multi-item fixed charge four-dimensional transportation problem using the concept of big data theory under the two-fold uncertainties. Here, the model’s parameters such as unit transportation costs, availabilities of items at the suppliers, fixed charges, capacities of conveyances, and demands of the items at the retailers are considered type-2 zigzag uncertain variables. Using big data theory and based on uncertain programming theory, two novel uncertain models are developed such as chance-constrained programming and expected value programming model. These two uncertain models transformed into the deterministic form via uncertainty inverse distribution theory. A critical value based reduction method with three categories (i.e., expected value, pessimistic value, and optimistic value) is applied to reduce the type-2 zigzag uncertain variable to the type-1 zigzag uncertain variable. The genetic algorithm and particle swarm optimization techniques have been proposed to find the optimal solution for the two deterministic models. The efficiency of our proposed approach is demonstrated with a real-life numerical example.

НОВЫЕ ВЫЗОВЫ В ОТНЕСЕНИИ ЗАТРАТ НА ПРОИЗВОДСТВО ТРАНСПОРТНОЙ ПРОДУКЦИИ В УСЛОВИЯХ ИЗМЕНЕНИЯ СТРУКТУРЫ ГРУЗООБОРОТА

The paper provides a brief overview of existing methods for determining the cost of railway transportation for tariff purposes. The relevance of resuming calculations of sectional costs in certain directions of the transport network is considered. Some results of calculations of unit transport costs on the railways of Kazakhstan and Uzbekistan are presented. Existing methods for calculating transport costs depend on the purpose of the calculations and the depth of the planning horizon. Depending on the availability of source data, various cost groupings can be used to calculate transport and economic standards. Transport and economic calculations in Russia, Kazakhstan and Uzbekistan have common historical roots, as they were based on the same scientific school. However, at present, taking into account the independent development of these states, the methodology for these calculations has changed. In this regard, significant difficulties arose in determining the compliance and reliability of transport costs. The division of the transport business into separate components has made it impossible to determine consolidated transportation costs. Moreover, the degree of separation and fragmentation of transport costs varies in different countries. Transport economists are faced with the urgent task of reviving and improving the lost source data for determining transport and economic standards. Without these standards, it is impossible to design a new system of transport tariffs. In this case, transport tariffs play a decisive role in the search for efficient transport corridors, both in direct and local communications. Recommendations are given for changing the system for forecasting traffic volumes and related economic indicators. Ways to solve the problem of matching transport tariffs with the costs of transporting goods are proposed.

Trade imbalance, heavy goods, and pollution

We propose a new welfare effect of trade surplus. Guided by insight from several fields in economics, we provide evidence that an increase in a country's trade surplus alters the unit shipping costs for its trade and the composition of its imports in ways that induce more pollution in the country in equilibrium. This new form of welfare loss of trade imbalance has not been accounted for in the existing international economics literature.

Assessing the economic viability of the Arctic Northeast Passage from 2021 to 2065

ABSTRACT The Northeast Passage (NEP) holds immense potential as a link for maritime transport activities between Europe and Asia, primarily due to the extended sailing season resulting from global warming. However, the economic viability of the Arctic shipping route remains disputed. This study aims to comprehensively evaluate the feasibility of container transportation along the NEP compared to that along the Suez Canal Route (SCR) by using current (2021–2023) and future (2025–2065) scenarios. The results reveal that larger vessels have lower CO2 emissions and costs than small vessels in the NEP, but the costs for larger vessels in the NEP are still higher than those in the SCR throughout both summer and winter seasons under the current scenario. The outcomes also show that a progressive carbon tax scheme will increase the unit shipping costs for all routes in the future scenario, with the NEP being most economically viable during summer. Furthermore, the extended navigable period (NP) bolsters the NEP’s economic cost advantage during a seasonal period. Nevertheless, from a year-round operations standpoint, the NEP remains less competitive than the SCR before 2065. The conclusions drawn from this research serve as a significant resource for decision-makers when formulating operational plans.

Emergency Logistics Facilities Location Dual-Objective Modeling in Uncertain Environments

The uncertainty of post-earthquake disaster situations can affect the efficiency of rescue site selection, material, and personnel dispatching, as well as the sustainability of related resources. It is crucial for decision-makers to make decisions to mitigate risks. This paper first presents a dual-objective model for locating emergency logistics facilities, taking into account location costs, human resource scheduling costs, transportation time, and uncertainties in demand and road conditions. Then, stochastic programming and robust optimization methods are utilized to cater to decision-makers with varying risk preferences. A risk-preference-based stochastic programming model is introduced to handle the potential risks of extreme disasters. Additionally, robust models are constructed for two uncertain environments. Finally, the study uses the Wenchuan earthquake as a case study for the pre-locating of emergency logistics facilities and innovatively compares the differences in the effects of models constructed using different uncertainty methods. Experimental results indicate that changes in weight coefficients and unit transportation costs significantly impact the objective function. This paper suggests that decision-makers should balance cost and rescue efficiency by choosing appropriate weight coefficients according to the rescue stage. It also shows that risk level and robust conservatism can significantly alter the objective function. While stochastic programming models offer economic advantages, robust optimization provides better robustness.

The impact on downstream policy implementation for mineral export products in Indonesia: marine transportation point of view

Indonesia is a country with abundant natural resources, particularly for metal mineral mining. In 2020, Indonesia is recorded to have approximately 1.2 billion tons of bauxite ore reserves, which made Indonesia the 6th largest owner of bauxite reserves in the world. Apart from bauxite, around 52% or 72 million tonnes of world nickel reserves are also located in Indonesia. However, most of the bauxite and nickel mining production have been exported in the form of ore or concentrate, which has a much lower selling value compared to the processed products. Therefore, the Indonesian government imposed a ban on the export of unprocessed minerals through Law Number 3 of 2020. The export ban is intended for mineral mining products, especially bauxite and nickel, to be processed first into semi-finished goods or finished goods, to provide added value in terms of foreign exchange for Indonesia. This study aims to determine the impact on the implementation of the downstream program, especially for nickel mineral product from marine transportation point of view. The method used in this study is optimizing non-linear programming to determine the type and number of fleets needed to transport nickel from the mine site to the smelter and its processed products from the smelter to the importing country by considering the minimum unit transport costs. The output of this research is the configuration of the sea transport fleet required for the implementation of the mineral product downstream program as well as the potential added value of transporting those processed products into the Indonesia‘s balance of payment.

Integration of sales, inventory, and transportation resource planning by dynamic-demand joint replenishment problem with time-varying costs

This paper addresses the joint replenishment problem in the context of sales and operations planning. Unlike traditional ones that assume static demands, unit costs, and transportation capacity, we consider dynamic-deterministic demands from sales plans and time-varying unit costs and transportation capacity to deal with the recent logistics conditions. We then introduce the capacitated dynamic-demand joint replenishment problem with time-varying costs. To efficiently solve this problem, a three-phase approach is proposed: (1) simplifying the problem and determining ideal inventory quantities using mixed integer linear programming, (2) estimating policy variables for each item using the covariance matrix adaptation evolution strategy, and (3) updating the ideal inventory quantities based on evaluated shortages until all demands are satisfied. Our method outperforms conventional approaches with at least 12 times faster solution runtimes in tests with up to 100 items. We also obtain the insight that the cost-efficient replenishment plan changes according to the increase rate of the unit transportation cost.

Technical–Economic Analysis for Ammonia Ocean Transportation Using an Ammonia-Fueled Carrier

This study performed a technical–economic analysis for ship-based ammonia transportation to investigate the feasibility of international ammonia transportation. Ammonia is considered to be a vital hydrogen carrier, so the international trade in ammonia by ship will considerably increase in the future. This study proposed three scenarios for transporting ammonia from the USA, Saudi Arabia, and Australia to South Korea and employed an 84,000 m3 class ammonia carrier. Not only traditional very low sulfur fuel oil (VLSFO)/marine diesel oil (MDO) but also LNG and ammonia fuels were considered as propulsion and power generation fuels in the carrier. A life-cycle cost (LCC) model consisting of capital expenditure (CAPEX) and operational expenditure (OPEX) was employed for the cost estimation. The results showed that the transportation costs depend on the distance. The unit transportation cost from the USA to South Korea was approximately three times higher than that of Australia to South Korea. Ammonia fuel yielded the highest costs among the fuels investigated (VLSFO/MGO, LNG, and ammonia). When using ammonia fuel, the unit transportation cost was approximately twice that when using VLSFO/MDO. The fuel costs occupied the largest portion of the LCC. The unit transportation costs from Australia to South Korea were 23.6 USD/ton-NH3 for the LVSFO/MDO fuel case, 31.6 USD/ton-NH3 for the LNG fuel case, and 42.9 USD/ton-NH3 for the ammonia fuel case. This study also conducted a sensitivity analysis to investigate the influence of assumptions, including assumed parameters.

Optimization strategy of cross-border e-commerce supply chain network based on machine learning

Abstract The rapid development of cross-border e-commerce increases the complexity of supply chain management, and machine learning-based supply chain network optimization strategies are essential for improving efficiency and reducing costs. The study first analyzes the cross-border supply chain network topology and determines the multilayer network structure. Subsequently, an optimization model based on particle swarm algorithm (PSO) is proposed, including the mathematical model of the algorithm and improvement measures. The practical effect of the optimization strategy was verified through example analysis. It is found that the supply chain optimized with particle swarm algorithm has significant Improvement in terms of shipping accuracy, surface transportation ratio, and unit transportation cost. For example, the shipping accuracy of product A in 2022 increased to 95.3% compared to 2021, the proportion of surface transportation increased to 96.5%, and the unit transportation cost decreased by RMB 2.175 per kilogram. This study shows that the particle swarm algorithm can effectively optimize the cross-border e-commerce supply chain network, which is significant in achieving efficient supply chain management.

Chance-Constrained Optimization Formulation for Ship Conceptual Design: A Comparison of Metaheuristic Algorithms

This paper presents a new chance-constrained optimization (CCO) formulation for the bulk carrier conceptual design. The CCO problem is modeled through the scenario design approach. We conducted extensive numerical experiments comparing the convergence of both canonical and state-of-the-art metaheuristic algorithms on the original and CCO formulations and showed that the CCO formulation is substantially more difficult to solve. The two best-performing methods were both found to be differential evolution-based algorithms. We then provide an analysis of the resulting solutions in terms of the dependence of the distribution functions of the unit transportation costs and annual cargo capacity of the ship design on the probability of violating the chance constraints.

A three-phase heuristic for last-mile delivery with spatial-temporal consolidation and delivery options

The parcel demand in last-mile delivery increases sharply with the growth of e-commerce, which has been accelerated by the COVID-19 pandemic. The current delivery scheme follows a first-in-first-out (FIFO) rule, i.e., parcels arriving at different periods for the same customer are likely to become separate deliveries, which incurs a high transportation cost. To address this, an innovative spatial-temporal based consolidation (STC) strategy is proposed to consolidate parcels based on their delivery due dates and spatial distributions to reduce redundant delivery trips and lower the total cost. In addition to reducing the operational cost, improving customer satisfaction by offering diversified delivery preferences is key for enterprises to maintain competitiveness. This paper investigates a new variant of last-mile delivery that integrates parcel consolidation and flexible delivery options, including serving through home delivery and parcel lockers. A bi-objective model is formulated to balance the total cost and the service satisfaction. Then a three-phase heuristic is developed to solve the model. In the three-phase heuristic, the first phase determines the parcel distribution schedule, delivery option and node visiting schedule, while the second one optimizes vehicle routing by solving a series of traveling salesman problems. The third phase improves the solution by applying a general improvement routing procedure. The performance of the three-phase heuristic is demonstrated through extensive computational experiments based on a realistic parcel distribution network in the city of Melbourne, Australia. Experimental results reveal that the STC strategy has significant advantages over the FIFO scheme. Moreover, the advantage increases with the unit transportation cost, vehicle capacity and consumer distribution dispersion.

ActionMixer: Temporal action detection with Optimal Action Segment Assignment and mixers

In this paper, we propose a novel method for dynamic label assignment in temporal action detection (TAD) called Optimal Action Segment Assignment (OASA). The OASA method converts label assignment into an optimal transportation problem by computing the cost matrix between predicted temporal action segments and groundtruths. The unit transportation cost between a predicted temporal segment and a groundtruth pair is defined as the weighted summation of action classification loss and temporal localization loss. Additionally, we deploy Adaptive Estimation of Candidate Segment Number (AE-CSN) to adaptively determine the number of positive samples for each groundtruth. After formulation, the label assignment problem is converted to find a global optimal assignment plan by minimizing the cost. Therefore, OASA eliminates the need for manually designed prior parameters, which exist in fixed label assignment methods, and improves the generalization of the algorithm between different datasets. To evaluate OASA, we also introduce a simple anchor-free temporal action detector called ActionMixer. It consists of two components: Temporal Mixer and Channel Mixer. The Temporal Mixer employs depth-wise convolution layers with large kernels to capture temporal information, while the Channel Mixer mixes and extracts features across the channel dimension. Extensive experiments conducted on the THUMOS-14, ActivityNet-1.3, and EPIC-Kitchens-100 datasets show that ActionMixer equipped with OASA achieves state-of-the-art performance, surpassing other advanced temporal action detection methods.

A multi-objective transportation problem under quantity dependent credit period and cost structure policies in triangular intuitionistic fuzzy environment

One of the major economic activities of a business concern is transportation of its products without any hassle. Therefore, the research on a transportation problem is very important now-a-days. In a classical transportation problem, the amount incurred for shipping the goods from the source to a destination is determined on the basis of unit transportation cost. But now, in many practical cases, it is seen that the unit transportation cost is settled on the basis of transported amount. In such a direction, this article develops a transportation problem to transport the produced items from a production house to some retailers through some distributors. The system has two cost parameters, one is actual unit transportation cost and other is demanded unit transportation cost. The actual unit transportation cost is the cost incurred during the transportation while the demanded unit transportation cost is the cost demanded by the distributors. Here, the demanded unit transportation cost has been decided depending on the amount of transported item. Since in the system, there are more than one distributor, distributors offer a credit period policy to attract the retailers. On the basis of the deterioration of the loaded amount, the transported items are classified into two categories. For the higher deteriorated items, the transportation cost is relatively larger and for the lower deteriorated items, the transportation cost is relatively lower with the increase in the loading amount. Considering the above two categories, two crisp models are developed. Due to the uncertain information of the actual unit transportation cost, the models are illustrated considering the actual unit transportation cost as a triangular intuitionistic fuzzy number. The main objective of this research is to minimize the total cost of the retailers and to maximize the total profit of the distributors simultaneously under the above circumstances. For crispification, two different methods have been used here — one is an existing method namely ordered weighted average operator and other is a developed average value approach. Also, the elitist non-dominated sorting genetic algorithm (NSGA-II) and the fuzzy programming technique have been used to get the optimal solution of this problem. To justify the impact and significance of the proposed models on this decision making system, some numerical studies have been done and it is observed that distributors and retailers both benefit more under the credit period policy than the policy without credit period. This study discusses the situation under which the uncertain parameters are to be expressed in terms of triangular intuitionistic fuzzy numbers. Also, it is identified that more accurate crispification has been done by the average value approach than the existing approach.

Solving Fully Neutrosophic Incompatible Multi-Item Fixed Charge Four-Dimensional Transportation Problem with Volume Constraints

Transportation problem (TP) describes the goods distribution process from production centers to retailers. The transportation-related parameters like demands, availabilities, and unit transportation costs become uncertain due to insufficient data, etc. With the carried items total weight restriction, each vehicle has a volume capacity that restricts the total articles volume. So far, volume restriction has been ignored in TP. Nowadays, with worldwide infrastructural developments, different connecting routes and conveyances are available for transportation. Therefore, conveyances capacities and fixed charges are also uncertain, and hence the decision variables are expected to be uncertain. When both parameters and decision variables are neutrosophic, TP is named fully neutrosophic. Considering these facts, fully neutrosophic multi-item four-dimensional TPs with fixed charges are formulated for cost minimization. Here, items are pairwise incompatible and vehicles volume capacities are imposed. In particular, proposed TP for compatible items is solved. (i) Score and accuracy function and (ii) weighted value function methods are used for deneutrosophication. Converted crisp problems are solved using the generalized reduced gradient method via LINGO 11.0. The methods and solutions are illustrated by a real-life experiment. Significances of several routes and vehicle types are presented. One proposed method gives better results for some existing problems.

Simulation for Appropriate Mean Selection for Can's Approximation Method in Transportation Models

Classical transportation models aim to minimize the total costs of homogeneous goods transport from supply points to demand points, taking into account unit transportation costs. They constitute a special case of network models and employ a technique based on linear programming. Suggested in 2015 and one of the early distribution methods, Tuncay Can’s Approximation Method (TCAM) is based on the geometric averages of unit transportation costs, although it is stated in the theorem that other means than geometric can be used. The aim of this study is to compare the total costs of a transportation model by solving a problem using geometric, arithmetic, square, and harmonic means based on TCAM. The coefficients of the transportation model were obtained randomly by simulation, and the method was repeated on the problem according to the different means and the appropriate means determined.

Fuzzy Transportation Model Technique to Determine the Minimum Total Cost Using a Novel Ranking Function

The transportation problem (TP) is employed in many different situations, such as scheduling, performance, spending, plant placement, inventory control, and employee scheduling. When all variables, including supply, demand, and unit transportation costs (TC), are precisely known, effective solutions to the transportation problem can be provided. However, understanding how to investigate the transportation problem in an uncertain environment is essential. Additionally, businesses and organizations should seek the most economical and environmentally friendly forms of transportation, considering the significance of environmental issues and strict environmental legislation. This research employs a novel ranking function to solve the transportation problem (TP), where fuzzy triangular numbers represent the fuzzy demand and supply (DAS). The fuzzy model is transformed and compressed to a crisp model (CM), and the results are compared using the northwest corner method and the least cost method. In addition, a numerical example of the fuzzy transportation model (FTM) is shown.

A new method to determine the Fermatean fuzzy optimal solution of transportation problems

Transportation Problems (TP) have multiple applications in supply chain management to reduce costs. Efficient methods have been developed to address TP when all factors, including supply, demand, and unit transportation costs, are precisely known. However, due to uncertainty in practical applications, it is necessary to study TP in an uncertain environment. In this paper, we define the Trapezoidal Fermatean Fuzzy Number (TrFFN) and its arithmetic operations. Then we introduce a new approach to solve TP, where transportation cost, supply, and demand are treated as TrFFN, and we call it Fermatean Fuzzy TP (FFTP). We illustrate the feasibility and superiority of this method with two application examples, and compare the performance of this method with existing methods. Furthermore, the advantages of the proposed method over existing methods are described to address TP in uncertain environments.

Optimising fully fuzzy interval integer transshipment problems

In this paper, we focus on solving fully fuzzy interval integer transshipment (FIIT) problems where the unit shipping costs, available supply capacities, and required destination demands are triangular fuzzy interval integers. An innovative method namely, back order sequence method has been developed for finding an optimal solution of the fully FIIT problem. The proposed method provides that the optimal values of decision variables and objective function value for the fully FIIT problem are fuzzy interval integers. A numerical example is presented to illustrate the solution procedure of optimising fully FIIT problems. The optimal solution to the transshipment problem by the proposed method can help the managers to take an appropriate decision regarding transshipments.

A robust model for emergency supplies prepositioning and transportation considering road disruptions

Proper prepositioning of emergency supplies can dramatically improve the efficiency of emergency response work. However, the uncertainties of emergency demands and road conditions bring difficulties to the prepositioning of emergency supplies. This paper proposes a two-stage robust model to locate emergency supply points and preposition the corresponding storage amount of emergency supplies, in which we presented two budget sets to describe the uncertainties of demands and road conditions, respectively. The innovative use of variables in the model to limit road capacities addresses the representation of different road interruption scenarios. We proposed an algorithm based on Benders decomposition by transforming the second-stage model into a binary linear programming model. Computational experiments based on the Sioux Falls network demonstrate the validity of the model and algorithm. We conducted sensitivity analyses for some important parameters in the model, such as two uncertainty control parameters, unit transportation cost, budget for the construction of emergency supply points, etc. We find that the uncertainty of road disruptions has a greater impact on the model than the uncertainty of demands. In addition, when the control parameter of the road disruptions exceeds a certain threshold, its influence on the model remains essentially constant.