Search Results (75)

Global supply chains often face uncertainties in production lead times, fluctuating exchange rates, and varying tariff regulations, all of which can significantly impact total profit. To address these challenges, this study formulates a multi-country supply chain problem as a Semi-Markov Decision Process (SMDP), integrating both currency variability and tariff levels. Using a Q-learning-based method (SMART), we explore three scenarios: (1) wide currency gaps under a uniform tariff, (2) narrowed currency gaps encouraging more local sourcing, and (3) distinct tariff structures that highlight how varying duties can reshape global fulfillment decisions. Beyond these baselines we analyze uncertainty-extended variants and targeted sensitivities (quantity discounts, tariff escalation, and the joint influence of inventory holding costs and tariff costs). Simulation results, accompanied by policy heatmaps and performance metrics, illustrate how small or large shifts in exchange rates and tariffs can alter sourcing strategies, transportation modes, and inventory management. A Deep Q-Network (DQN) is also applied to validate the Q-learning policy, demonstrating alignment with a more advanced neural model for moderate-scale problems. These findings underscore the adaptability of reinforcement learning in guiding practitioners and policymakers, especially under rapidly changing trade environments where exchange rate volatility and incremental tariff changes demand robust, data-driven decision-making. Full article

In recent years, carbon dioxide (CO₂) emissions have increased at the fastest rates ever recorded. This is a trend that contradicts global efforts to stabilise greenhouse gas (GHG) concentrations and prevent long-term climate change. Over 90% of global transport relies on oil-based fuels. The continued use of diesel and petrol raises concerns related to oil costs, supply security, GHG emissions, and the release of air pollutants and volatile organic compounds. This study explored electric vehicle (EV) charging networks by assessing environmental impacts through GHG and petroleum savings, developing dynamic pricing strategies, and forecasting infrastructure needs. A substantial dataset of over 259,000 EV charging records from Palo Alto, California, was statistically analysed. Machine learning models were applied to generate insights that support sustainable and economically viable electric transport planning for policymakers, urban planners, and other stakeholders. Findings indicate that GHG and gasoline savings are directly proportional to energy consumed, with conversion rates of 0.42 kg CO₂ and 0.125 gallons per kilowatt-hour (kWh), respectively. Additionally, dynamic pricing strategies such as a 20% discount on underutilised days and a 15% surcharge during peak hours are proposed to optimise charging behaviour and improve station efficiency. Full article

Bamboo residues represent an abundant, renewable biomass feedstock that can be converted into hard carbon—an emerging anode material for sodium-ion batteries. This study presents a detailed techno-economic analysis of hard carbon production from bamboo residues across China’s ten most bamboo-rich provinces. Regional feedstock availability was estimated from provincial production statistics, while average transportation distances were derived using a square-root-area-based approximation method. The process includes hydrothermal pretreatment, acid washing, carbonization, graphitization, and ball milling. Material and energy inputs were estimated for each stage, and both capital and operating expenses were evaluated using a discounted cash flow model assuming a 15% internal rate of return. The resulting minimum selling price of bamboo-derived hard carbon ranges from 14.47 to 18.15 CNY/kg. Assuming 10% of bamboo residues can be feasibly collected and processed, these ten provinces could collectively support an annual hard carbon production capacity of approximately 1.04 million tons. The results demonstrate that bamboo residues are a strategically distributed and underutilized resource for producing cost-competitive hard carbon at scale, particularly in provinces with existing bamboo industries and supply chains. Full article

The concept ‘mobility of care’ captures all the daily travel necessary for the upkeep of a household, including trips to grocery stores, health-related appointments, errands, and caring activities for dependents. Since it was originally coined in 2009, a handful of studies have shown how poorly mobility of care trips are captured in transportation surveys. These preliminary analyses also find that care trips comprise a substantial proportion of daily mobility. As women disproportionately engage in ‘mobility of care’ travel, the under-consideration of care trips is argued to result in a gender bias in transport planning. Despite this, transport policy related to mobility of care has received less attention. Given that transport policy shapes how transport systems operate, this paper explores the extent to which recent transport policies consider mobility of care. A critical policy review framework is used to systematically examine seven policy documents (435 pages) from the Toronto Transit Commission (TTC), the largest transit agency in Canada. Results indicate that mobility of care is rarely directly considered or significantly discussed. Instead, transport policy often uses the commute to work as the default trip. Mentions of care destinations and trip characteristics associated with mobility of care are more common in recent years and most frequently discussed in relation to the COVID-19 pandemic or specialized services for seniors and people with disabilities. Policies that likely facilitate mobility of care indirectly are also identified, including fare discounts, transfer windows, and accessibility policies. The review concludes with preliminary recommendations on how transit agencies can more directly plan for mobility of care. Full article

This study examines strategies to enhance transport inclusivity and passenger satisfaction for persons with disabilities in public bus systems in the Philippines. Drawing on data collected through an online questionnaire from 396 persons with disabilities who responded across various regions in the country, this study investigates eight key factors affecting satisfaction: vehicle design, diverse seating options, sensory considerations, assistance services, safety measures, subsidies/discounts, accessibility, and communication and information quality. Structural equation modeling (SEM) was used to analyze the hypothesized relationships between these variables, passenger satisfaction, and intention to reuse public transport. The SEM results revealed that accessibility (β = 0.359, p = 0.005), vehicle design (β = 0.248, p < 0.001), diverse seating options (β = 0.485, p < 0.001), safety measures (β = 0.3867, p = 0.001), and subsidies/discounts (β = 0.447, p < 0.001) significantly influenced passenger satisfaction. In turn, satisfaction had a strong positive effect on the future intention to use public transport (β = 0.760, p < 0.001). However, sensory considerations (β = 0.163, p = 0.225), assistance (β = 0.133, p = 0.519), and communication and information quality (β = 0.171, p = 0.345) were not statistically significant. The model demonstrated a good fit (chi-square/df = 4.03; SRMR = 0.078; NFI = 0.956), supporting the robustness of the proposed framework. These findings suggest that design-centered improvements and subsidies/discounts are critical to inclusive transport experiences, while overreliance on assistance may not guarantee satisfaction. This study recommends promoting autonomy through universal design, enhancing digital and physical accessibility, and increasing public awareness. These insights are intended to guide policymakers and transit authorities in creating a more inclusive, equitable, and user-driven transportation system. Full article

This study demonstrates that small-scale liquefied natural gas (SS LNG) is a viable and cost-effective alternative to High-Speed Diesel (HSD) for power generation in remote areas of Indonesia. An integrated supply chain model is developed to optimize total costs based on LNG inventory levels. The model minimizes transportation costs from supply depots to demand points and handling costs at receiving terminals, which utilize Floating Storage Regasification Units (FSRUs). LNG distribution is optimized using a Multi-Depot Capacitated Vehicle Routing Problem (MDCVRP), formulated as a Mixed Integer Linear Programming (MILP) problem to reduce fuel consumption, CO₂ emissions, and vessel rental expenses. The novelty of this research lies in its integrated cost optimization, combining transportation and handling within a model specifically adapted to Indonesia’s complex geography and infrastructure. The simulation involves four LNG plant supply nodes and 50 demand locations, serving a total demand of 15,528 m³/day across four clusters. The analysis estimates a total investment of USD 685.3 million, with a plant-gate LNG price of 10.35 to 11.28 USD/MMBTU at a 10 percent discount rate, representing a 55 to 60 percent cost reduction compared to HSD. These findings support the strategic deployment of SS LNG to expand affordable electricity access in remote and underserved regions. Full article

Retailing strategy can be considered as the most critical factor for the success of industries. Managing deteriorating products in retail demands a strategic approach aimed at mitigating losses while maximizing profitability. This entails a proactive stance towards identifying products nearing expiration, becoming obsolete or showing signs of deterioration. Offering discounts or promotions can stimulate consumer interest and clear out inventory. The promotion of products within the context of retail management involves a multifaceted approach aimed at increasing awareness, generating interest, and ultimately driving sales. Sustainability helps retailers to develop social as well as economic consistency. Every country and their respective governments are currently working towards sustainable development. New technologies in this direction have been introduced. The present paper introduces a retailing model considering green technology as it is becoming popular to lower environmental risks. The items considered in this study are perishable in nature. As product prices and the promotion of products highly influence demand, a demand pattern dependent on price and promotion is therefore considered. This paper presents a sustainable retail-based inventory model that considers preservation technology to lower the rate of deterioration and increase product shelf life. As carbon emissions is currently the biggest threat to the environment, enforcing a penalty may lower its emissions. Carbon emissions costs due to storage, transportation, and preservation are considered herein. This model studies the effect of various cost parameters on the model. A numerical analysis is performed to validate the result. The results of this study show that the implementation of preservation technology not only increases cycle time but also significantly reduces total cost, hence increasing profit. Sensitivity analysis is performed to show the behaviors of different cost parameters on total cost and decision variables. Mathematica 11 and Maple 18 software are used for graphical representation. Full article

The objective of this study was to address the limitations of biodegradable plastics—low economic benefits and marketing difficulties. To this end, this study analyzed the production processes of two biodegradable plastics: polylactic acid (PLA) and polybutylene adipate terephthalate (PBAT). Based on this analysis, economic, technical, and environmental improvement indicators were constructed, and an optimization model with the three objectives of profit, carbon emission cost, and process risk was established. In this study, we embedded the improved NSGA-III algorithm to obtain the Pareto optimal solution set. We also proposed the entropy-weighted efficiency index (EWEI) for the analysis of transport advantages based on the distribution of biodegradable plastics production, road density, and regional prices. With a production line capacity of 10,000 tons and an 8% discount rate, the 10-year return of PBAT products was 7,039,931.23 yuan higher than that of PLA products. The profit of PBAT products was 488.92 yuan higher than that of PLA products per ton of production. However, PBAT products exhibited higher carbon-emission cost and process risk than PLA products, especially process risk, by 0.11%. The East China region has obvious geographical advantages, but the Southwest region is constrained by limitations in production capacity and the presence of mountainous terrain. Therefore, it is imperative to optimize China’s overall industrial layout of biodegradable plastics, strengthen the profit acquisition of biodegradable plastics, support the sustainable promotion of the biodegradable plastics market, and effectively minimize the environmental pollution caused by traditional plastics. Full article

Sustainable transport is an important trend in smart cities to achieve sustainability development goals. It refers to the use of transport modes with low emissions, energy consumption and negative impacts on the environment. Ridesharing is one important sustainable transport mode to attain the goal of net zero greenhouse gas emissions. The discount-guaranteed ridesharing problem (DGRP) aims to incentivize drivers and riders and promote ridesharing through the guarantee of a discount. However, the computational complexity of the DGRP poses a challenge in the development of effective solvers. In this study, we will study the effectiveness of creating new self-adaptive differential evolution (DE) algorithms based on an old saying to solve the DGRP. Many old sayings still have far-reaching implications today. Some of them influence the organization of management teams for companies and decisions to improve performance and efficiency. Whether an old saying that works effectively for human beings to solve problems can also work for developers to create effective optimization problem solvers in the realm of artificial intelligence is an interesting research question. In our previous study, one self-adaptive algorithm was proposed to solve the DGRP. This study demonstrates how to create a series of self-adaptive algorithms based on the old saying “Two heads are better than one” and validates the effectiveness of this approach based on experiments and comparison with the algorithms proposed previously. The new finding of this study is that the old saying not only works effectively for human beings to solve problems but also works effectively in the creation of new scalable and robust self-adaptive algorithms to solve the DGRP. In other words, the old saying provides a simple and systematic approach to the development of effective optimization problem solvers in artificial intelligence. Full article

Under California’s Clean Miles Standard (or SB 1014), transportation network companies (TNCs) must transition to zero-emission vehicles by 2030. One significant hurdle for TNC drivers is the electric vehicle (EV) acquisition and operating costs versus an internal combustion engine (ICE) vehicle. This study therefore evaluates net TNC driving earnings through EV acquisition pathways—financing, leasing, and renting—along with EV-favoring policy options. Key metrics assessed include (1) total TNC income when considering service fees, fuel costs, monthly vehicle payments, etc., and (2) the time EVs take to reach parity with their ICE counterparts. Monthly comparisons illustrate the earning differentials between new/used EVs and gas-powered vehicles. Our analyses employing TNC data from 2019 to 2020 suggest that EV leasing is optimal for short-term low-mileage drivers; EV financing is more feasible for those planning to drive for TNCs for over two years; EV rentals are only optimal for higher mileages, and they are not an economical pathway for longer-term driving. Sensitivity analyses further indicate that EV charging price discounts are effective in shortening the time for EVs to reach cost parity over ICEs. Drivers may experience a total asset gain when reselling their TNC vehicle after two to three years. Full article

The transition to clean energy is crucial for mitigating the impacts of climate change and achieving sustainable development. Reliance on fossil fuels, which are integral to manufacturing and transportation, remains a major contributor to greenhouse gas (GHG) emissions. Biomass gasification presents a renewable energy alternative that can significantly reduce emissions. However, proper disposal of municipal solid waste (MSW) and agricultural residues, such as date palm waste (DPW), is an increasing global challenge, including in Qatar. This study evaluates the economic feasibility of implementing an MSW and DPW gasification plant for clean electricity generation in Qatar. The country’s growing population and economic development have led to substantial waste production, making it an ideal location for waste-to-energy (WTE) initiatives. Using discounted cash flow (DCF) analysis, the study estimates the capital cost of a 373 MW_th facility at approximately $12.07 million, with annual operating costs of about $4.09 million and revenue of $26.88 million in 2023. The results indicate a net present value (NPV) of $245.77 million, a return on investment (ROI) of 84.80%, a payback period of approximately 5 years over a 20-year project lifetime and a net reduction of 206,786 tonnes CO₂ annually. These findings demonstrate the economic viability of biomass gasification in Qatar while contributing to reduced GHG emissions and advancing the country’s sustainability goals under Qatar National Vision 2030. Full article

The adoption of electric motorcycles is critical for reducing transportation-related greenhouse gas emissions in Indonesia, which reached 674.54 million t of CO₂ in 2023. This study integrates the Theory of Planned Behavior with situational, contextual, and demographic factors to explore the determinants of electric motorcycle adoption intentions and actual usage. Data were collected from 1602 respondents across ten provinces with the highest motorcycle sales using purposive sampling and analyzed through Partial Least Squares—Structural Equation Modeling. Findings reveal that psychological factors—attitude, subjective norms, and perceived behavioral control—significantly influence purchase intentions, while personal moral norms do not. Situational factors such as technology and cost indirectly affect adoption intentions through attitude and perceived behavioral control. Contextual factors show mixed results; government policies effectively shape attitudes and perceived behavioral control, but infrastructure remains inadequate to influence attitudes directly. Demographic analysis highlights gender as a moderating factor, with men showing higher moral-driven adoption intentions. These results imply that the government and manufacturers need to develop the appropriate strategy to foster public interest in adopting electric motorcycles to increase the adoption rate of pro-environmental vehicles. Government policies such as purchase price subsidies, tax reductions, and charging rate discounts can motivate the intention to adopt electric motorcycles. In addition, manufacturers could improve technical performance and reduce the total cost of ownership, such as the purchase price and battery replacement costs. Full article

With the rise of the sharing economy, shared bicycles have become an important component of urban transportation. This paper explores the nonlinear dual oligopoly system for the Cournot model in the bike-sharing market; both operators have maximized profits as their competitive goals. The analysis of pivotal factors influencing passenger preferences, including pricing discounts and comfort levels, is meticulously depicted by a bifurcation diagram. A new chaotic attractor—the shared bicycle attractor—is discovered. The research results indicate that larger discounts and adjustment speeds can cause the system to be in a chaotic state, which is not conducive to the long-term development of operators, although discounts can indeed attract more passengers to a certain extent. On the other hand, the increase in the marginal cost of comfort loss can also make it difficult for enterprises to operate, which requires continuous technological innovation to improve the comfort of cycling. Full article

Globally, the integration of electric vehicles (EVs) in the transportation sector represents a significant step towards achieving environmental decarbonization. This shift also introduces a new demand for electric power within the utility grid network. This study focuses on the design and development of a grid-connected renewable energy system tailored to meet the EV load demands in Taif, Kingdom of Saudi Arabia (KSA). The integration of renewable energy sources, specifically solar photovoltaic (SPV) and wind turbines (WT), is explored within the context of economic feasibility and system reliability. Key considerations include optimizing the system to efficiently handle the fluctuating demands of EV charging while minimizing reliance on conventional grid power. Economic analyses and reliability assessments are conducted to evaluate the feasibility and performance of the proposed renewable energy system. This article discusses the technical sizing of hybrid systems, energy reduction, and net present cost for the selected location. A rigorous sensitivity analysis is performed to determine the impact of major variables such as inflation rate, real discount rate, solar irradiation, and Lack of Power Supply Probability (LPSP) on system performance. The results demonstrate that the Pufferfish Optimization Algorithm (PFO) significantly outperforms other metaheuristic algorithms documented in the literature, as well as the HOMER software. The study found that the grid-connected renewable energy system is the best option for operating EV charging stations at the selected location. The findings underscore the potential for sustainable energy solutions in urban environments like Taif, highlighting the importance of integrating renewable energy technologies to meet growing energy demands with enhanced economic efficiency and system reliability. This initiative seeks to pave the way for a greener and more resilient energy infrastructure, aligning with global efforts towards sustainable development and clean transportation solutions. Full article

The ongoing electrification of the transport sector is expected to cause an increase in electricity demand and, therefore, trigger significant network investments to accommodate it. This paper focuses on investment decision-making for electricity distribution grids and specifically on the strategic and incremental investment network planning approaches. In particular, the former involves network planning with the consideration of a long-term multi-stage study horizon, as opposed to a shorter–term view of the future that applies to the latter case. An investment analysis that is carried out underlines the economic savings generated from adopting a strategic investment perspective over an incremental one. These economic savings are achieved from the fact that the associated fixed investment costs are incurred only once in the horizon under strategic planning. On the other hand, incremental planning involves a series of network reinforcement decisions, thereby incurring the fixed cost multiple times. In addition, sensitivity analyses that are carried out capture the effect of key parameters, such as investment cost, discount rate and investment delay, on the generated economic savings. Full article