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Modular integrated construction (MiC) is an innovative construction method that is widely used in practice, but high transportation cost is an obstacle to its wider implementation. This paper is motivated by the real-world need for a scientific approach for MiC transport scheduling, and addresses the practical problem of how to achieve on-time door-to-door MiC delivery in an economical manner. An innovative integer programming model is formulated with the objective of minimising the total transportation costs, including trailer rental, fuel costs, and worker salaries. A case study based on Hong Kong data validates that the developed model can be efficiently solved for problems of practical size. The results also demonstrate that the derived MiC schedule plan can reduce transportation costs by 58.7% compared with a just-in-time (JIT) strategy, thus bringing great economic benefits to off-site construction practitioners. Useful managerial implications related to on-site MiC storage areas are derived through sensitivity analysis. Overall, this paper sets the ground for the application of mathematical programming methodology to the off-site construction field. Future research may be devoted to MiC transport scheduling under uncertainty. [ABSTRACT FROM AUTHOR]

The complexity of decision-making for companies buying transportation services has increased due to the presence of more options and pricing schedules for transportation. Many companies make transportation and inventory decisions in an uncoordinated way and select only one transportation mode, missing opportunities for logistics cost savings. The experimental study in this paper is based on a real-world decision problem faced by a Scandinavian company that distributes fast-moving consumer goods and wants to determine its transportation strategy. We propose a novel multi-mode lot-sizing model with dynamic deterministic demand to illustrate the cost impact of accurately modelling piecewise-linear transportation costs and allowing a more flexible usage of transportation modes when planning order replenishments. We compare three transportation strategies with increasing degrees of flexibility: two single mode strategies, where one strategy is more flexible than the other, and a multi-mode strategy. We conclude that managers can significantly reduce costs by increasing the flexibility of mode selection in transportation strategies. [ABSTRACT FROM AUTHOR]

To address the protracted blood transportation time prevalent in contemporary urban settings, we proposed a location-routing optimization problem tailored to the distribution of blood within intricate road networks. This involved a comprehensive assessment that encompassed the judicious selection of sites for both stations and blood centers, coupled with the meticulous planning of delivery routes for unmanned aerial vehicles (UAVs) that orchestrate the transportation of blood. First, a model was formulated to minimize the overall cost, including transportation expenses, costs associated with the site, and other relevant costs related to blood transportation vehicles coordinated by UAVs. Subsequently, a two-stage hybrid heuristic algorithm was designed based on the distinctive characteristics of the problem at hand. Moreover, an enhanced k-means algorithm was employed to generate clustering schemes, utilizing the centroid method to address the challenge of location selection for delivery sites effectively. A genetic algorithm enhanced with adaptive operators was employed to address the challenging large-scale NP-hard problem associated with route planning in intricate urban road networks. The results indicated that, compared to the traditional blood delivery model using vehicles, the total blood transportation cost decreased by 12.65% and the overall delivery time was reduced by 37.5% with the adoption of drone-assisted delivery; ultimately, case and sensitivity analyses were conducted to investigate the impact of variables including the number of blood transportation vehicles, UAVs, driver wages, and unit costs of blood transportation vehicles on the location-routing problem. [ABSTRACT FROM AUTHOR]

To address the scheduling challenges associated with the increasing deployment of battery-swapping trucks in open-pit mines, this study proposes a multi-objective scheduling optimization model. This model accounts for the unique characteristics of battery-swapping trucks by incorporating constraints related to battery swapping alerts, the selection of battery-swapping stations, and the impact of ambient temperature on battery capacity. The primary objective is to minimize the total haulage cost and total waiting time. Both a genetic algorithm and an adaptive genetic algorithm are applied to solve the proposed multi-objective scheduling optimization model. The aim is to identify an optimal scheduling solution without violating any model constraints. Results demonstrate that both the basic genetic algorithm and the adaptive genetic algorithm effectively achieve truck transportation scheduling. However, the adaptive genetic algorithm surpasses the basic genetic algorithm, reducing the total transportation costs by 5.6% and total waiting time by 17.4%. It also reduces the number of battery swaps and transportation distance by 15.8% and 1.2%, respectively. The proposed multi-objective scheduling optimization model successfully minimizes the waiting time and transportation costs of battery-swapping trucks while ensuring the completion of production tasks. This approach provides valuable technical support for improving the production and transportation efficiency of open-pit mining operations. [ABSTRACT FROM AUTHOR]

Although there is a strong relationship between housing and transportation (H+T) spending, traditional housing affordability estimates from sub-Saharan Africa exclude transportation costs. Also, the H+T literature has few case studies from small cities and developing countries. This dearth of research makes it difficult to understand the housing burden and provide workable solutions. This paper measures H+T affordability in Koforidua, Ghana, based on rental and transportation costs. A household survey comprising 600 renting households was conducted and analyzed using group comparisons and Ordinary Least Square regressions. The results showed that H+T consumes approximately 56% of household income. This is a huge household burden considering the high unemployment rates in developing countries. Household income and size are significantly associated with H+T spending. Interestingly, smaller households have a marginal propensity to spend on H+T than larger households due to dependency ratio and housing choices. We recommend transport and housing subsidies, and affordable housing to ameliorate the H+T burden. Other policy recommendations and actions have been discussed to guide policymakers. [ABSTRACT FROM AUTHOR]

This study investigates the truck–cargo matching problem in less-than-truckload (LTL) logistics hubs, focusing on optimizing the three-dimensional loading of goods onto standardized pallets and assigning these loaded pallets to a fleet of heterogeneous vehicles. A two-stage hybrid heuristic algorithm is proposed to solve this complex logistics challenge. In the first stage, a tree search algorithm based on residual space is developed to determine the optimal layout for the 3D loading of cargo onto pallets. In the second stage, a heuristic online truck–cargo matching algorithm is introduced to allocate loaded pallets to trucks while optimizing the number of trucks used and minimizing transportation costs. The algorithm operates within a rolling time horizon, allowing it to dynamically handle real-time order arrivals and time window constraints. Numerical experiments demonstrate that the proposed method achieves high pallet loading efficiency (close to 90%), near-optimal truck utilization (nearly 95%), and significant cost reductions, making it a practical solution for real-world LTL logistics operations. [ABSTRACT FROM AUTHOR]

Intelligent logistics and freight transportation is an important part of realizing the intelligence of port terminals. Due to the problems of inaccurate ton bag identification, high costs, large model sizes, and long computation times in traditional freight transportation—issues that hinder meeting real-time requirements on resource-constrained operational equipment—this paper proposes an improved lightweight ton bag detection algorithm, YOLOv8-TB (YOLOv8-Ton Bag), which is optimized based on YOLOv8. Firstly, the improved LZKAC module is introduced to combine with SPPF to form a new SPPFLKZ module, which improves the feature expression performance. Then, with reference to spatial and channel reconstruction convolution and deformable convolution, the C2f-SCTT block is designed for the backbone network, which reduces the spatial and channel redundancy between features in the network. Finally, the C2f-ORECZ block based on a linear scaling layer is designed for the neck, which reduces the training overhead and strengthens the feature learning of the feature extraction network for the targets in the complex background of the harbor and adds the 160 × 160 scale detection head to strengthen small target detection abilities. On the logistics ton bag operation dataset provided by shipping port enterprises, the improved algorithm improves by 3.7% and 5% compared with the original algorithm in mAP50 and mAP50-95, respectively, the model size is reduced by 4.42 MB and the amount of model computation is only 8 G, which is capable of accurately detecting logistics ton bags in real time. The superiority of the method is verified by comparing it with other classical target detection algorithms. [ABSTRACT FROM AUTHOR]

Rock mass discontinuities are an excellent information set for reflecting the geometric, spatial, and physical properties of the rock mass. Using clustering algorithms to analyze them is a significant way to select advantageous orientations of structural surfaces and provide a scientific theoretical basis for other rock mass engineering research. Traditional clustering algorithms often suffer from sensitivity to initialization and lack practical applicability, as discontinuity data are typically rough, low-precision, and unlabeled. Confronting these challenges, II-LA-KM, a learning-augmented clustering algorithm with improved initialization for rock discontinuity grouping, is proposed. Our method begins with heuristically selecting initial centers to ensure they are well-separated. Then, optimal transport is used to adjust these centers, minimizing the transport cost between them and other points. To enhance fault tolerance, a learning-augmented algorithm is integrated that iteratively reduces clustering costs, refining the initial results toward optimal clustering. Extensive experiments on a simulated artificial dataset and a real dataset from Woxi, Hunan, China, featuring both orientational and non-orientational attributes, demonstrate the effectiveness of II-LA-KM. The algorithm achieves a 97.5% accuracy on the artificial dataset and successfully differentiates between overlapping groups. Its performance is even more pronounced on the real dataset, underscoring its robustness for handling complex and noisy data. These strengths make our approach highly beneficial for practical rock discontinuity grouping applications. [ABSTRACT FROM AUTHOR]

This research employs numerical simulations and scenario analysis to assess a supply chain model's economic and environmental performance operating under stochastic market demand, with inventory levels managed by a periodic review (R, s, S) inventory system. The inventory model in this research is designed to determine the minimal inventory levels required to achieve predefined fill rates across various operational constraints. The supply chain's inventory model simulates optimal responses to normally distributed market demand within 365-day periods characterized by mean and two levels of demand variability through two fill rate levels, two workweek schedules, 15 review periods, and 16 lead times. By conducting an extensive analysis of the 192000 simulation experiments of the supply chain under periodic review (R, s, S) inventory system, complex influences between system variables and economic outcomes of supply chain operation measured by ordering, transportation, holding, penalty, and total costs along with greenhouse gas emissions arising from inventory-related transportation according to the ISO 14083 standard are analyzed. The insights from this research have significant practical implications, providing valuable guidance for supply chain managers, researchers, and freight companies offering guidance for improving economic and environmental performance. [ABSTRACT FROM AUTHOR]

Intermodality is regarded as one way of achieving more sustainable transport solutions. To make intermodal transport the preferred solution among shippers, it must be attractive according to the concept of generalized transport costs. An extended model for generalized transport costs is developed which includes elements important for comparing maritime transport solutions in the Artic. This framework forms the basis for a principal discussion on the conditions that make one transport solution preferable to another within the context of maritime logistics. This model is then applied to the context of the Northeast Passage to discuss the necessary requirements for making an intermodal transport solution attractive relative to the current main route from Northeast Asia to Northwest Europe through the Suez Canal. Even though intermodality could be preferable in principle, current solutions cannot compete with either Arctic routes using unimodal solutions with high ice-class vessels or with the Suez route. Due to uncertainty and limited empirical evidence, a sensitivity analysis is conducted, focusing on the variables with the greatest impact on the result. Policymakers and stakeholders can consider the findings to improve transport competitiveness via the Northeast Passage. [ABSTRACT FROM AUTHOR]

Cross‐regional energy dispatch project is a crucial strategy for countries to reduce energy transportation costs and achieve optimal energy allocation. This research constructs a quasi‐natural experiment based on the West‐to‐East Natural Gas Pipeline Project in China. It finds that cross‐regional energy dispatch significantly reduces firms' energy intensity, and that firms in economically developed regions and high‐polluting firms benefit more from it. The mechanism test shows that cross‐regional energy dispatching reduces enterprises' coal consumption, increases the proportion of cleaner energy consumption, and optimizes their energy consumption structure. Cross‐regional energy dispatch accelerates the exit of inefficient enterprises and the development of efficient enterprises. Cross‐regional energy dispatch enhances the green innovation activities of enterprises and accelerates the renewal of production equipment. Governments should encourage cross‐regional energy cooperation and dispatch to achieve efficient use and distribution of energy resources. [ABSTRACT FROM AUTHOR]

The transportation of goods in South Sulawesi remains predominantly reliant on road networks, with truck transport playing a central role in the region's logistics. Despite the introduction of rail infrastructure, its effectiveness as an alternative freight transportation mode has not been comprehensively evaluated. This study assesses the reliability, costefficiency, and environmental impact of both truck and rail transport along the Makassar-Parepare route, employing both qualitative and quantitative methodologies. The findings reveal that road transport demonstrates superior reliability and operational flexibility, with trucks offering lower transportation costs at IDR 404 per ton-kilometre, compared to IDR 511 per ton-kilometre for rail. However, rail transport exhibits increasing cost efficiency over longer distances, particularly beyond 360 kilometres. A modal shift of 30% from trucks to rail has the potential to reduce carbon emissions by 22.6%, contributing to environmental sustainability through a 29.5% reduction in fuel consumption. These results highlight the need for an integrated approach to transport infrastructure development, where economic, environmental, and social dimensions are holistically addressed. Future efforts to enhance rail infrastructure could provide significant long-term benefits by alleviating road congestion and supporting sustainable freight transportation. [ABSTRACT FROM AUTHOR]

Proper road network maintenance is essential for ensuring safety, reducing transportation costs, and improving fuel efficiency. Traditional pavement condition assessments rely on specialized equipment, limiting the frequency and scope of inspections due to technical and financial constraints. In response, crowdsourcing data from connected and autonomous vehicles (CAVs) offers an innovative alternative. CAVs, equipped with sensors and accelerometers by Original Equipment Manufacturers (OEMs), continuously gather real-time data on road conditions. This study evaluates the feasibility of using CAV data to assess pavement condition through the International Roughness Index (IRI). By comparing CAV-derived data with traditional pavement auscultation results, various thresholds were established to quantitatively and qualitatively define pavement conditions. The results indicate a moderate positive correlation between the two datasets, particularly in segments with good-to-satisfactory surface conditions (IRI 1 to 2.5 dm/km). Although the IRI values from CAVs tended to be slightly lower than those from auscultation surveys, this difference can be attributed to driving behavior. Nonetheless, our analysis shows that CAV data can be used to reliably identify pavement conditions, offering a scalable, non-destructive, and continuous monitoring solution. This approach could enhance the efficiency and effectiveness of traditional road inspection campaigns. [ABSTRACT FROM AUTHOR]

Feature-preserving mesh reconstruction from point clouds is challenging. Implicit methods tend to fit smooth surfaces and cannot be used to reconstruct sharp features. Explicit reconstruction methods are sensitive to noise and only interpolate sharp features when points are distributed on feature lines. We propose a watertight surface reconstruction method based on optimal transport that can accurately reconstruct sharp features often present in CAD models. We formalize the surface reconstruction problem by minimizing the optimal transport cost between the point cloud and the reconstructed surface. The algorithm consists of initialization and refinement steps. In the initialization step, the convex hull of the point cloud is deformed under the guidance of a transport plan to obtain an initial approximate surface. Next, the mesh surface was optimized using operations including vertex relocation and edge collapses/flips to obtain feature-preserving results. Experiments demonstrate that our method can preserve sharp features while being robust to noise and missing data. [ABSTRACT FROM AUTHOR]

This paper investigates the inland container transportation problem with a focus on multi-size containers, fuel consumption, and carbon emissions. To reflect a more realistic situation, the depot's initial inventory of empty containers is also taken into consideration. To linearly model the constraints imposed by the multiple container sizes and the limited number of empty containers, a novel graphical representation is presented for the problem. Based on the graphical representation, a mixed-integer programming model is presented to minimize the total transportation cost, which includes fixed, fuel, and carbon emission costs. To efficiently solve the model, a tailored branch-and-price algorithm is designed, which is enhanced by improvement schemes including a heuristic label-setting algorithm, decremental state-space relaxation, and the introduction of a high-quality upper bound. Results from a series of computational experiments on randomly generated instances demonstrate that (1) the proposed branch-and-price algorithm demonstrates a superior performance compared to the tabu search algorithm and the genetic algorithm; (2) each additional empty container in the depot reduces the total transportation cost by less than 1%, with a diminishing marginal effect; (3) the rational configuration of different types of trucks improves scheduling flexibility and reduces fuel and carbon emission costs as well as the overall transportation cost; and (4) extending customer time windows also contributes to lower the total transportation cost. These findings not only deepen the theoretical understanding of inland container transportation optimization but also provide valuable insights for logistics companies and policymakers to improve efficiency and implement more sustainable operational practices. Additionally, our research paves the way for future investigations into the integration of dynamic factors and emerging technologies in this field. [ABSTRACT FROM AUTHOR]

This study examines the complicated logistics optimization issue known as the vehicle routing problem for multi-product and multi-suppliers (VRP-MPMS), which deals with the effective routing of a fleet of vehicles to convey numerous items from multiple suppliers to a set of consumers. In this problem, products from various suppliers need to be delivered to different customers while considering vehicle capacity constraints, time windows, and minimizing transportation costs. We propose a hybrid approach that combines a generalized reduced gradient method to identify feasible regions with a feasible neighborhood search to achieve optimal or near-optimal solutions. The aim of the exact method is to get the region of feasible solution. Then we explore the region using feasible neighborhood search, to get an integer feasible optimal (suboptimal) solution. Computational experiments demonstrate that our model and method effectively reduce transportation costs while satisfying vehicle capacity constraints and relaxed time windows. Our findings provide a viable solution for improving logistics operations in real-world scenarios. [ABSTRACT FROM AUTHOR]

Integrating inventory and transportation decisions is vital in supply chain management and can enable decision-makers to achieve competitive advantages. This study considers a multi-item replenishment problem (MIRP) with a piece-wise linear transportation cost under demand uncertainty, which usually occurs both in retail and production environment when several items must be ordered from a single supplier. Conventionally, two-stage stochastic programming formulation is risk-neutral, and it lacks robustness in the presence of high data variability. Hence, we introduce the Conditional Value at Risk (CVaR) approach for MIRP. Additionally, we deploy both single and multi-cut L-shaped and the sample average approximation method to circumvent the computational complexity to solve large-scale instances. The data-driven simulation study is used to benchmark the results from deterministic, risk-neutral, and risk-averse stochastic models. The results indicate that under higher data variations, the risk-averse model provides better perspectives for a decision-maker. The results show a 40–50% reduction in lost sales with marginal growth in total cost while considering CVaR instead of a risk-neutral approach. [ABSTRACT FROM AUTHOR]

Multi-depot vehicle routing with precedence constraints and selective backhauling, along with practical challenges such as outsourcing, demand splitting, and flexible pickup and delivery formats, is a common issue in the animal feed industry. However, few studies address demand splitting and flexible pickup constraints. The industry struggles with inadequate management, leading to difficulties in efficiently utilizing limited vehicles for both raw material pickup and product delivery. This inefficiency results in higher transportation costs, including subcontractor vehicle expenses. This study proposes the Extended Adaptive Grey Wolf Optimizer to minimize total transportation costs by incorporating essential factors such as multi-depots, outsourcing services, optimization of split delivery and pickup operations, precedence constraints, and selective backhauling. The results show a remarkable 97.6% improvement toward optimality, representing a significant enhancement of 38.1% improvement over current practice. The findings emphasize the benefits to the industry of adopting proper route sequencing and utilizing subcontractor vehicles. [ABSTRACT FROM AUTHOR]

This paper introduces a methodology designed to reduce cost, improve demand coverage, and ensure equitable vaccine distribution during the initial stages of the vaccination campaign when demand significantly exceeds supply. We formulate an enhanced maximum covering problem as a mixed integer linear program, aiming to minimize the total vaccine distribution cost while maximizing the allocation of vaccines to population blocks under equity constraints. Block‐level census data are employed to define demand locations, identifying gender, age, and racial groups within each block using population data. A Lagrangian relaxation technique integrated with a modified Voronoi diagram is proposed to solve the location–allocation problem efficiently. Empirical case studies in Pennsylvania, using real‐world data from the Centers for Disease Control and Prevention and health department websites, were conducted for the first 4 months of the COVID‐19 vaccination campaign. Preliminary results show that the proposed solution algorithm effectively solves the problem, achieving a 5.92% reduction in total transportation cost and a 28.15% increase in demand coverage. Moreover, our model can reduce the deviation from equity to 0.07 (∼50% improvement). [ABSTRACT FROM AUTHOR]

Robotic locomotion in unstructured terrain demands an agile, adaptive, and energy-efficient architecture. To traverse such terrains, legged robots use rigid electromagnetic motors and sensorized drivetrains to adapt to the environment actively. These systems struggle to compete with animals that excel through their agile and effortless motion in natural environments. We propose a bio-inspired musculoskeletal leg architecture driven by antagonistic pairs of electrohydraulic artificial muscles. Our leg is mounted on a boom arm and can adaptively hop on varying terrain in an energy-efficient yet agile manner. It can also detect obstacles through capacitive self-sensing. The leg performs powerful and agile gait motions beyond 5 Hz and high jumps up to 40 % of the leg height. Our leg's tunable stiffness and inherent adaptability allow it to hop over grass, sand, gravel, pebbles, and large rocks using only open-loop force control. The electrohydraulic leg features a low cost of transport (0.73), and while squatting, it consumes only a fraction of the energy (1.2 %) compared to its conventional electromagnetic counterpart. Its agile, adaptive, and energy-efficient properties would open a roadmap toward a new class of musculoskeletal robots for versatile locomotion and operation in unstructured natural environments. Future bioinspired locomotion systems need design's agility and adaptiveness. Here, the authors report a musculoskeletal leg design which leverages multi-layered electrohydraulic actuators to achieve highly energy-efficient and versatile motion. [ABSTRACT FROM AUTHOR]

Objective To support achieving the "dual carbon" goals by clarifying the adaptability and economic feasibility of CO2 pipeline transportation under different phases. Methods This study is set against research on key CCUS-EGR technologies in the carbonate gas reservoirs of PetroChina Southwest Oil and Gas Field. We optimized and established economic estimation models for CO2 pipeline transportation in three phases: gas phase, liquid phase, and supercritical phase. The impacts of flow rate, transportation distance, and transportation volume on the relative total cost were analyzed. Results The findings indicate that: first, the minimum transportation costs follow the order: gas phase pipeline> liquid phase pipeline> supercritical pipeline; second, the economical flow rate ranges are 3.0- 4.0 m/s for gas phase, 1.0-1.8 m/s for liquid phase, and 2.0-2.4 m/s for supercritical phase; third, the relative total cost increases with transportation distance at different conveying distances; fourth, the relative total cost also increases with transportation volume at different throughput, where the transportation cost for pipe diameters above DN200 rises slowly with volume, whereas the cost for DN100 diameter pipes varies significantly with volume. Conclusion By establishing an economic estimation model for CO2 pipeline transportation and analyzing its economic feasibility under different phases, this study provides a solid basis and reference for constructing CO2 pipeline transportation projects. [ABSTRACT FROM AUTHOR]

This paper aims to solve a solid transportation problem, wherein the uncertain parameters related to the problem are represented using triangular Fermatean fuzzy numbers. Fermatean fuzzy sets offer a relatively novel and wider alternative by providing the decision-makers with more versatile means of managing the uncertain information throughout the decision-making process. As per our literature survey, no algorithm exists in the literature for fuzzy solid transportation problems with parameters as triangular Fermatean fuzzy numbers. Therefore, in this study, the existing Vogel's approximation method for the initial basic feasible solution (IBFS) of the traditional transportation problems is extended for the Fermatean fuzzy solid transportation problems. Further, a new method for getting the optimal solution from the obtained IBFS is proposed. The computational complexity and operational efficacy of the proposed algorithm are also discussed. Two application examples have been solved to illustrate the practicality of the proposed algorithm. For the comparative study, the problems are also solved with LINGO 20.0 software. The obtained results of IBFS are found to be significantly lower than those obtained by certain existing methods and the optimal values are comparable with those generated by the LINGO 20.0 optimization solver. Through a comparative analysis, it has been found that the proposed algorithm consistently produces optimal transportation costs for both application examples, adding to the novelty of the proposed work. Finally, the conclusion and future scope of this study are described. Graphical depiction of abstract [ABSTRACT FROM AUTHOR]

This paper establishes a multivariate function model for natural human load-carrying walking in some typical scenarios such as college equipment and material relocation by students and a large amount of identical freight relocation in commercial activities. For classified material relocation needs and constraints, we obtain the relationship between walking speed and load weight for a single person, as well as the time cost for different round trips. By establishing an integer programming model with the minimum total transportation time cost and shelf life as the objective function and the requirements of negative weight and speed as the constraint conditions, we reach the optimal item allocation methods considering time cost and shelf life. We discover that there is an approximate linear relationship between the change in natural walking speed and travel time when the load is small, thus obtaining the time cost of student transportation under different round-trip situations. The Monte Carlo simulation algorithm, which is more efficient compared with other methods such as the integer programming method, is used to obtain the optimal allocation scheme that meets the efficiency and quality requirements. The analysis methods and results can be used as guidance for task scheduling optimization for material relocation in educational organizations as well as commercial agencies. [ABSTRACT FROM AUTHOR]

Aiming at the distributed unrelated parallel machine scheduling problem with machining constraints, this paper proposed an extended empire competition algorithm to solve the problem, which took the total transportation cost, evaluation function for simultaneous shutdown of parallel machines among factories and equilibrium evaluation function for average switching frequency of workpiece types among factories as optimization objectives. Based on the original empire competition algorithm, this algorithm added an initial factory-workpiece sequence group suitable for factory assignment. According to the shortcoming of the traditional empire competition algorithm that it was easy to fall into the local optimum, this paper divided the inferior sequence assimilation into the external assimilation mechanism and the internal assimilation mechanism. The extended empire competition algorithm combined local and global search methods to realize the intelligent search behavior. This paper used the partial matching crossover and single point mutation to update the factory-workpiece sequence group and ensure the diversity of the factory-workpiece sequence. Finally, this paper designed three different scales of 12 examples and verified the effectiveness of the proposed algorithm through simulation experiments. At the same time, this paper verified the advantages of the proposed algorithm in solving the distributed multi-objective unrelated parallel machine scheduling problem by comparing the research results in related fields. [ABSTRACT FROM AUTHOR]

This article proposes a set of information guidance models and corresponding algorithms which are embedded in a traffic information guidance system. Through a systematic analysis, the following results were obtained. When vehicles have different degrees of rationality, it is better to use a differentiation strategy in terms of traffic rewards and punishments; in particular, in a situation in which the strength of interactions between vehicles is different, the platform can guide vehicles to reduce overall transport costs by changing the information of rewards and punishments. The related stability analysis verifies that the information induction can induce the behavioural state of vehicles to stabilize, and the targeted information induction can help the whole system to stabilize. Finally, based on these findings, the traffic inducement strategy of the platform is proposed under different road conditions. [ABSTRACT FROM AUTHOR]

Aiming at the difficulty of measuring various costs and time-consuming elements in multimodal transport, this paper constructs a green vehicle comprehensive multimodal transport model which incorporates transportation, transit, quality damage, fuel consumption, and carbon emission costs and proposes a hybrid embedded time window to calculate the time penalty cost in order to reflect the actual transport characteristics. Furthermore, in order to better solve the model, a hybrid sand cat swarm optimization (HSCSO) algorithm is proposed by introducing Logistic–Tent chaotic mapping and an adaptive lens opposition-based learning strategy to enhance the global search capability, and inspired by the swarm intelligence scheme, a momentum–bellicose strategy and an equilibrium crossover pool are introduced to improve the search efficiency and convergence ability. Through testing nine benchmark functions, the HSCSO algorithm exhibits superior convergence accuracy and speed in dealing with complex multi-dimensional problems. Based on the excellent global performance, the HSCSO algorithm was utilized for multimodal vehicle transportation in East China, and a path with a lower comprehensive cost was successfully planned, which proved the effectiveness of the HSCSO algorithm in green intermodal transport path planning. [ABSTRACT FROM AUTHOR]

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In the realm of urban logistics, optimizing vehicle routes for varied cargo types—including refrigerated, fragile, and standard cargo—poses significant challenges amid complex urban infrastructures and heterogeneous vehicle capacities. This research paper introduces a novel model for the multi-type capacitated vehicle routing problem (MT-CVRP) that harnesses an advanced ant colony optimization algorithm, dubbed Lévy-EGACO. This algorithm integrates Lévy flights and elitist guiding principles, enhancing search efficacy and pheromone update processes. The primary objective of this study is to minimize overall transportation costs while optimizing the efficiency of intricate route planning for vehicles with diverse load capacities. Through rigorous simulation experiments, we corroborated the validity of the proposed model and the effectiveness of the Lévy-EGACO algorithm in optimizing urban cargo transportation routes. Lévy-EGACO demonstrated a consistent reduction in transportation costs, ranging from 1.8% to 2.5% compared to other algorithms, across different test scenarios following base data modifications. These findings reveal that Lévy-EGACO substantially improves route optimization, presenting a robust solution to the challenges of MT-CVRP within urban logistics frameworks. [ABSTRACT FROM AUTHOR]

Rules of international air transportation services that apply to signatories' airlines are defined by air services agreements (ASAs). We assess the impact of bilateral ASAs on air transportation using an unique data set providing direct observations of bilateral air transport cost and time for a standardised good, for 1190 country‐pairs, between 2011 and 2015. Our results show that ASAs reduce transportation cost by 8% while they only impact transportation time for landlocked countries and Regional Trade Agreements (RTA) members. Our estimates also reveal that bilateral trade decreases cost of transport, which highlights the role of backhauling in air transportation. [ABSTRACT FROM AUTHOR]

Containers are commonly thought to be important for international trade but the evidence is thin. I argue that containers were important to U.S. trade expansion but not when they were first adopted. Using previously unused data, I show that U.S. aggregate freight factors did not fall when containers were first adopted. It took a decade before containerised freight rates fell. Falling freight rates matter for trade growth, accounting for 36 percent of the increase in U.S. import share of output from 1972 to 1987 when the impact of containers began to be felt. Institutional factors in port industry delayed containers' significant labour cost savings. Compensation deals to longshoremen kept labour costs up despite rapidly increasing labour productivity in ports. [ABSTRACT FROM AUTHOR]

Purpose: Production and transportation of precast components, as two continuous service stages of a precast plant, play an important role in meeting customer needs and controlling costs. However, there is still a lack of production and transportation scheduling methods that comprehensively consider delivery timeliness and transportation economy. This article aims to study the integrated scheduling optimization problem of in-plant flowshop production and off-plant transportation under the consideration of practical constraints of customer order delivery time window, and seek an optimal scheduling method that balances delivery timeliness and transportation economy. Design/methodology/approach: In this study, an integrated scheduling optimization model of flowshop production and transportation for precast components with delivery time windows is established, which describes the relationship between production and transportation and handles transportation constraints under the premise of balancing delivery timeliness and transportation economy. Then a genetic algorithm is designed to solve this model. It realizes the integrated scheduling of production and transportation through double-layer chromosome coding. A program is designed to realize the solution process. Finally, the validity of the model is proved by the calculation of actual enterprise data. Findings: The optimized scheduling scheme can not only meet the on-time delivery, but also improve the truck loading rate and reduce the total cost, composed of early cost in plant, delivery penalty cost and transportation cost. In the model validation, the optimal scheduling scheme uses one less truck than the traditional EDD scheme (saving 20% of the transportation cost), and the total cost can be saved by 17.22%. Originality/value: This study clarifies the relationship between the production and transportation of precast components and establishes the integrated scheduling optimization model and its solution algorithm. Different from previous studies, the proposed optimization model can balance the timeliness and economy of production and transportation for precast components. [ABSTRACT FROM AUTHOR]

Combining the super‐efficiency model based on data envelopment analysis (DEA) with the Malmquist index model, this paper evaluated the efficiency of the logistics distribution center comprehensively considering the truck traffic restriction and provided decision suggestions to improve the efficiency of the logistics distribution center. This paper takes 20 logistics distribution centers as the research objects and uses economic factors, transportation factors, quality of distribution center business activities, and quality of customer service as the primary input indicators; selects eight indicators such as construction cost, transportation cost, labor cost, road facilities, accessibility, business demand, number of laborers, and customer satisfaction as the secondary input indicators; chooses distribution time and profit as the output indicators; and measures the static efficiency of logistics distribution centers from two perspectives, including the traditional unconstrained super‐efficiency model and the truck‐ restricted conditions, using the super‐efficiency model of data envelopment analysis (DEA). The Malmquist index model was used to measure the dynamic efficiency and change trend efficiency of the logistics distribution center, and a unified and comprehensive analysis was also made. The results of the case study show that the average efficiency of the logistics distribution center in the driving and nondriving restriction area is 0.872 and 0.914, respectively, and the average efficiency in the driving restriction area is about 4.5% lower than that of the nondriving restriction area, and variance is 1.58 times of the latter. Therefore, it can be concluded that the measures of truck driving restriction have an impact on the efficiency of the logistics distribution center, and the results of the super‐efficiency model with the restriction constraint have a greater impact on the logistics efficiency of the logistics distribution center than the traditional unconstrained super‐efficiency model. According to the evaluation results, suggestions on reasonable assignment of labor and other resources input are put forward for logistics distribution centers in areas where driving is restricted to improve efficiency. [ABSTRACT FROM AUTHOR]

Previous studies have demonstrated that tangible and intangible transaction costs influence spatial price differences. The difference between the prices at which producers sell their goods, and the prices at which consumers purchase them is exacerbated by transaction costs. This indicates that farmers receive a lower price from buyers, who then charge consumers a greater price at urban marketplaces. However, existing documentation provides limited information on the relationship between spatial price differences and their drivers. Most research focused primarily on the co-movement of prices and the co-integration test, with little attention paid to the effects of transaction costs on spatial price differences. Thus, the paucity of information in the literature motivated the undertaking of this specific study. Therefore, this study assessed the impact of transaction costs on spatial price differences of maize in the Njombe District of Tanzania. A semi-structured questionnaire was developed and employed to collect information from maize traders. Also, checklists were used to collect information from market leaders. Both descriptive and quantitative analyses were undertaken. In the descriptive analysis, means and percentages were generated. On the other hand, in the quantitative analyses, the Ravalion model which is based on the co-movement of prices, was modified to fit the study in question and estimated. The outputs generated from the modified model were used for inferences. The estimated model results showed that transaction costs (transportation costs, years of schooling, bribery, and the number of maize bags carried in one trip) have significant effects on spatial price differences. Moreover, market leaders revealed that because of the poor quality of the roads, individual farmers find it challenging to deliver their produce to the Njombe market. Consequently, they obtain a low price when they sell their produce at the farm level. The study recommends deliberate measures to reduce transaction costs, such as improving urban and rural infrastructure, simplifying bureaucratic procedures that provide room for corruption, and, finally, promoting large-scale production or collective actions to reduce transaction costs. [ABSTRACT FROM AUTHOR]

As a relatively clean energy source, liquefied natural gas (LNG) is experiencing a growing demand. The uneven global distribution of LNG often compels residents in regions without local sources to import it, underscoring the need to optimize the global LNG transportation network. Therefore, this study formulates a nonlinear mixed-integer programming model for a multimodal transport and storage problem to optimize LNG carrier allocation, LNG storage planning, and LNG transport planning, aiming to minimize the total cost of multimodal transport, minus the rewards offered by ports. In order to facilitate the solving of the model, some linearization methods are used to transform the nonlinear model into a linear model. To assess the efficiency of the linear model, we conduct computational experiments on small-scale instances with five inland cities, medium-scale instances with 15 inland cities, and large-scale instances with 60 inland cities. The results show that all small- and medium-scale instances can be solved to optimality within 427.50 s. Feasible solutions with a maximum gap value of 0.03% for large-scale instances can be obtained within 1 h. In addition, sensitivity analyses are conducted to identify the impacts of the cost of transporting LNG by vehicles, the charter cost of LNG carriers, and the rewards for shipping LNG. In general, higher cost of transporting LNG by vehicles and higher charter cost of LNG carriers lead to a higher objective value. It is also found that when the rewards for shipping LNG increase to a certain extent, such that the additional rewards exceed the additional multimodal transport cost incurred, the amount of LNG unloaded at the subsidized port increases. [ABSTRACT FROM AUTHOR]

Modular integrated construction (MiC) has witnessed increasing adoption in worldwide building projects, driven by various benefits, including improved construction efficiency, reduced on-site labor hours, and minimized waste and pollution. The construction industry has recognized the transportation of prefabricated modules as a huge obstacle to the wide implementation of MiC. In practice, transport planning of prefabricated modules is usually intuitively decided by general contractors based on experience. Intuitive decisions cannot ensure the just-in-time (JIT) module delivery, thereby incurring additional costs of early and late delivery. Motivated by this challenge, this study endeavors to design cost-minimized transportation plans for prefabricated modules using mathematical programming methodology. An integer programming model and a tailored solution method are proposed. Then, we use data from two real building projects in Hong Kong as model inputs to demonstrate the computation of optimal module transport plans using the proposed methodology. Numerical results show that, if the JIT strategy is not considered in module transport planning, the actual total transportation costs will be 21.74% higher than the expected ones. Comparative costs of JIT implementation and nonimplementation indicate the significance of the JIT strategy. Additionally, our proposed method can achieve punctual module delivery and reduce the overall transportation expenses by 10.13%, implying considerable cost savings. [ABSTRACT FROM AUTHOR]

Based on the costs to produce PTX products, a simple computational model is presented to determine the costs to ship and to reform them into hydrogen. In this model, wind farms are the sole energy source for PTX production. As an example, the costs of transporting power‐to‐X (PTX) products NH3, MeOH, liquid organic hydrogen carrier (LOHC), and Fischer‐Tropsch diesel from Chile (Haru Oni) and Morocco (Tarfaya) are determined. These costs are compared with those of hydrogen production at three domestic locations with very good to very moderate wind conditions in the order Dornum (North German coast), Senftenberg (Lausitz), and Ludwigshafen (Rhine Graben). In addition, a cost comparison for Fischer‐Tropsch diesel is made and land requirements for domestic production are determined. [ABSTRACT FROM AUTHOR]

A large amount of cargo is transported between European and Southeast Asian countries. Ships sometimes take different routes when sailing between ports due to the best commercial speed; navigational, economical, and hydrometeorological conditions; and political and military situations. Several routes are available for sailing between Europe and Southeast Asia: sailing the Suez Canal, sailing around the African continent, sailing the Panama Canal, as well as sailing the Northern Sea route. This article analyzes the possible sailing routes between Southeast Asia and Europe and presents a developed methodology for the evaluation of sailing routes. This sea route evaluation methodology is based on a comparative mathematical model that evaluates the main factors of cargo transportation by sea: transportation cost and time, possible maximum ship parameters, transportation energy (fuel) demand, and other possible factors, such as the probability of various restrictions. This paper presents a case study of cargo transportation between Rotterdam (The Netherlands) and Shanghai (China) using different possible sea routes. Assessments of various possible routes are presented; the main topics of discussion and conclusions are formulated. [ABSTRACT FROM AUTHOR]

This paper presents a model for a "hub-and-spoke network design considering congestion and flow-based cost function". The number of hubs and spokes is unknown, and the objective is to minimize the cost (including the transportation cost, lost demand, and facility setup cost). In the post-pandemic era, it is expected to have government-imposed restrictions on the congestion of airports, as a measure of health and safety. Unlike the current literature which considers a monetary penalty for congestion, we consider congestion as an externally imposed factor, which should be modeled as a constraint. We take a gravity-based modeling approach to obtain the desirability of a facility and calculate the demand matrix of the network. To solve the model, a Benders decomposition approach is proposed. Without the Benders decomposition approach, only instances with up to ten nodes were solved within a reasonable time, but with the Benders decomposition approach, instances with up to forty nodes were solved. A heuristic algorithm is developed to have a mechanism for dealing with larger instances. A set of experiments are conducted using data from the Turkish Network dataset to study various aspects of the proposed formulation and different parameters' effects on the performance of the model. [ABSTRACT FROM AUTHOR]

The potential of clean energy is constrained by various social, environmental, and technological challenges. The current work is likely the first to observe the influence of economic complexity on production-based carbon emissions to develop clean and sustainable energy solutions that address climate change apprehensions. The study examines the impacts of clean energy, economic complexity, public service transportation, and improving technology on production-based carbon emissions. This study employ the novel dynamic autoregressive distributive lag simulation method and a novel machine learning technique, Kernel-based regularized least squares, to ascertain the causal effect among variables. The results reveal that economic complexity is escalating environmental contamination. Similarly, public service transportation increases environmental costs, but clean energy and improving technology decrease the production-based emissions in China. These findings offer valuable guidance for China's public transportation system to integrate and consolidate the road, railway, airline transportation, and aviation sectors. [ABSTRACT FROM AUTHOR]

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The review of the study on Edibles and Ownership of very expensive housing will be felt with the continuous high increase in general prices which results in the price of goods that are increasingly scarce in the market, these expensive all the imbalance of demand and supply does not become constant. The economy is currently spinning during the covid-19 pandemic, Social Limitations are the main cause while on the other hand, health is very important when hit by the covid-19 attack. All economic and health joints become sook (stagnant) in the sense that everyone needs rest. However, everyone also needs food, so everyone needs a job to earn income which leads to social welfare, food and board. The city of Ternate is Ternate is currently facing the challenges of high food staple prices and a strong demand for housing, which have made market conditions increasingly unstable. This situation necessitates balanced, measurable policies from all stakeholders in both the short and medium term to stabilize staple food prices. The city also grapples with complex issues such as high transportation costs and long distribution lines, all of which demand effective solutions to control prices and ensure market stability. [ABSTRACT FROM AUTHOR]

Uncertain multi-modal transport path optimization (UMTPO) is a combined optimization non-deterministic polynomial-time hard problem. Its goal is to determine a path with the lowest total transportation cost and carbon emissions from the starting point to the destination. To effectively address this issue, this article proposes a modified dung beetle optimizer (DBO) to address it. DBO is a swarm-based metaheuristic optimization algorithm that has the features of a fast convergence rate and high solution accuracy. Despite this, the disadvantages of weak global exploration capability and falling easily into local optima exist. In this article, we propose a modified DBO called MSHDBO for function optimization and to solve the UMTPO problem. However, for the vast majority of metaheuristic algorithms, they are designed for continuous problems and cannot directly solve discrete problems. Therefore, this article employs a priority based encoding and decoding method to solve the UMTPO problem. To verify the performance and effectiveness of the MSHDBO algorithm, we compared it with other improved versions of the DBO algorithm used in the literature. We confirmed the excellent performance of MSHDBO using 41 benchmark test functions from the IEEE CEC 2017 test suite and IEEE CEC 2022 test suite. Additionally, we compared the MSHDBO algorithm with 10 other state-of-the-art metaheuristic optimization algorithms through a practical UMTPO problem. The experimental results indicated that the MSHDBO algorithm achieved very good performance when solving the UMTPO problem. [ABSTRACT FROM AUTHOR]

In the context of the COVID-19 pandemic, utilizing the potential of online markets to sell products has become increasingly important for creating competitive advantages and ensuring the growth and survival of businesses. The pandemic has disrupted traditional business practices, and with social distancing measures in place, consumers have turned to online channels to meet their needs. As a result, businesses that have adapted quickly to online markets have been able to maintain their customer base and revenue streams. Thus, considering the potential of online markets is of utmost importance in the current pandemic situation. In this regard, the present research aims to provide a practical framework for creating a green and sustainable closed-loop supply chain network (SCLSCN), including the integration of online markets, to assist managers in making decisions that support economic, environmental, and social goals. Accordingly, a multi-objective mixed integer linear programming (MOMILP) optimization model was designed under uncertain demand and disruption caused by natural disasters in Iran's home appliance industry. The study also considered changes in the capacities of online and marketplace sales channels, revealing a significant reduction in costs at each stage. The results show that the increase in demand has a direct impact on the production level, warehousing, and transportation costs, leading to social impacts on the model. However, the current system cannot handle an increase in demand of more than 20%, requiring managers to make decisions to increase production capacity or build new factories. Thus, the study highlights the importance of considering online markets as a means to adapt to disruptions caused by the pandemic and maintain a competitive edge. [ABSTRACT FROM AUTHOR]

Summary: This study underscores the growing significance of multimodal transportation within the cargo sector and its consequential environmental impacts. We present a novel mathematical model for operation scheduling, incorporating variables such as resource availability, customer service benchmarks, and environmental considerations. Our objective is to mitigate transportation expenses and reduce delivery delays. The proposed approach advocates LU decomposition with a pivot strategy for rapid model resolution, adherence to convergence criteria, optimization of cost strategies, and efficient resource utilization. Leveraging the adaptive neural fuzzy inference system (ANFIS) and genetic algorithm (GA), our methodology facilitates learning from past decisions to enhance solutions, aligning supply, and demand efficiently. We evaluate financial and environmental implications across four scenarios, offering insights into the economic and environmental advantages of various transportation modes—trains, ships, airplanes—compared to truck transportation, with a specific focus on CO2 emission impacts. Implementing the ANFIS+GA model in multimodal scenarios yield impressive results: minimal MAPE transportation cost of 0.17%, R2 transportation cost of 0.996, MAPE CO2 emissions of 0.13%, and R2 CO2 emissions of 0.996. By identifying cost‐efficient routes and optimizing resource allocations, our approach enables informed decisions regarding vehicle distribution, supplier selection, and contract negotiations. Additionally, we use LU decomposition to establish the supplier risk threshold, crucial for comparing emission trade variances. Multimodal scenarios typically yield lower emissions, favoring buying emission allowances low and selling them high. Notably, the risk threshold affects low‐emission provider utilization, impacting transportation emissions. With a risk threshold of 0.12 and an emission price of 1.2, our ANFIS+GA‐based multimodal approach achieves a significant −20% deviation in CO2 emissions. [ABSTRACT FROM AUTHOR]

Buses and subways are crucial modes of transportation for residents, yet frequent disturbances pose serious challenges to their daily commutes. To tackle these disruptions and boost the stability of the transportation network, it is vital to accurately measure the resilience of a bus–subway composite network under such events. Therefore, this study utilizes the generalized cost between stations as weights with which to construct a bus–subway weighted composite network. Subsequently, three indicators, namely reachability, path importance, and weighted coreness, are proposed to evaluate the significance of the nodes, thereby combining the improved CRITIC-TOPSIS method to identify the critical nodes. Then, deliberate attacks and preferential restorations are conducted on the nodes, considering their importance and the critical nodes sequences, respectively. Finally, network resilience changes are characterized by the network connectivity coefficient and global accessibility, and the network resilience is compared under different attack and recovery strategies. The research results indicate that resilience is lowest when using reachability sequences to attack and recover the network. The network's recovery is most significant when using the critical nodes sequences. When 70% of the nodes are restored, the network's performance is essentially fully recovered. Additionally, the resilience of a bus–subway network is higher than that of a single bus network. This study applies the generalized cost to weight the transportation network, and considers the impact of multiple factors on the ease of connectivity between the nodes, which facilitates the accurate measurement of the resilience of a bus–subway network and enhances the ability to cope with disruptions. [ABSTRACT FROM AUTHOR]

The present paper introduces a modified flower pollination algorithm (FPA) enhanced by evolutionary operators to solve the uncapacitated facility location problem (UFLP), which is one of the well-known location science problems. The aim in UFLP is to select some locations to open facilities among a certain number of candidate locations so as to minimize the total cost, which is the sum of facility opening costs and transportation costs. Since UFLP is a binary optimization problem, FPA, which is introduced to solve real-valued optimization problems, is redesigned to be able to conduct search in binary domains. This constitutes one of the contributions of the present study. In this context, some evolutionary operators such as crossover and mutation are adopted by the proposed FPA. Next, the mutation operator is further enhanced by making use of an adaptive procedure that introduces greater level of diversity at earlier iterations and encourages intensification toward the end of search. Thus, while premature convergence and local optima problems at earlier iterations are avoided, a more intensified search around the found promising regions is performed. Secondarily, as demonstrated in this study, by making use of the reported evolutionary procedures, FPA is able to run in binary spaces without employing any additional auxiliary procedures such as transfer functions. All available benchmarking instances are solved by the proposed approach. As demonstrated by the comprehensive experimental study that includes statistically verified results, the developed approach is found as a promising algorithm that can be extended to numerous binary optimization problems. [ABSTRACT FROM AUTHOR]

Optimization problems on probability measures in ℝ d are considered where the cost functional involves multi-marginal optimal transport. In a model of N interacting particles, for example in Density Functional Theory, the interaction cost is repulsive and described by a two-point function c ⁢ (x , y) = ℓ ⁢ (| x - y |) where ℓ : ℝ + → [ 0 , ∞ ] is decreasing to zero at infinity. Due to a possible loss of mass at infinity, non-existence may occur and relaxing the initial problem over sub-probabilities becomes necessary. In this paper, we characterize the relaxed functional generalizing the results of [4] and present a duality method which allows to compute the Γ-limit as N → ∞ under very general assumptions on the cost ℓ ⁢ (r) . We show that this limit coincides with the convex hull of the so-called direct energy. Then we study the limit optimization problem when a continuous external potential is applied. Conditions are given with explicit examples under which minimizers are probabilities or have a mass < 1 . In a last part, we study the case of a small range interaction ℓ N ⁢ (r) = ℓ ⁢ (r / ε) ( ε ≪ 1 ) and we show how the duality approach can also be used to determine the limit energy as ε → 0 of a very large number N ε of particles. [ABSTRACT FROM AUTHOR]

The study examined empirical solutions to fish logistics and supply value chain network from selected fishing camps to markets in Warri South, Delta State, Nigeria. Issues relating to high cost of transporting fishes and fisheries products from fishing camps to major markets, poor logistic practices, physical handling and packaging of fishes during transit, are major determinant in the final market price of fish, and also play critical role in the immediate physical satisfaction of the fish consumer. In this study, 100 questionnaires were distributed to fishermen, boat drivers and distributors, eighty (80) questionnaires were returned. Multiple regressions were used for the analysis with statgraphic software version 16.0. The results of the study showed that there was a statistically significant relationship (P < 0.05) between transportation cost of fish and perceived price of fish at a great extent with p-value of 0.01 which is less than alpha level of 0.05. Results also showed that significant relationship existed in the integration of logistics services and this promotes the economic value of fish at a moderate extent at 0.01 which is less than 0.05 alpha value. Finally, recommendations were made for policy implications. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has initiated more telecommuting (or working from home) than ever. This paper provides an economic analysis of the impacts of telecommuting on the residential location choice equilibrium. In particular, this paper employs a simplified monocentric city model of urban spatial structure to analytically examine the impacts of telecommuting on residential location choices and the associated commuting pattern in a city, considering that telecommuting may have positive, zero, or negative impact on individual income level. We found that the income effect of telecommuting and its relative magnitude against the commuting cost govern whether telecommuting will attract more to live in the suburb or the downtown. Moreover, the impacts of housing supply and commuting costs on the residential location choice equilibrium are dependent on the level of telecommuting, where such impacts diminish when the level of telecommuting increases. Furthermore, this paper derives and examines analytical solutions for the optimal level of telecommuting, the optimal housing supply and the optimal transport investment (that reduces commuting cost) in order to improve social welfare or the housing operator's profit in the context of telecommuting. How these optimal solutions are related to the level of telecommuting is also analyzed. This study provides understanding on how telecommuting may affect the joint equilibrium of residential location choice and commuting and how housing supply and transport investment should be adjusted in response to telecommuting. • This paper analyzes the impacts of telecommuting on residential location choices. • The income effect of telecommuting affects where to live. • We analyze the optimal telecommuting level, housing supply and transport investment. [ABSTRACT FROM AUTHOR]