Total Profit Research Articles

Blockchain-enabled integrated three-layer bio-circular economy model with wastewater treatment, emissions, and controlled cheese spoilage

ABSTRACT Regulatory and market forces compel companies to integrate transparency and sustainability into their operations. In response to the pressing need, the study proposes a blockchain-enabled, sustainable, integrated three-layer inventory model for the cheese industry under carbon cap-and-trade policy. The ultimate aim is to maximize the integrated total profit of the system with optimal investment in green technology. Prior studies have neglected to account for blockchain costs and spoilage rate control, whereas our findings demonstrate that strategic investments in preservation technology yield substantial reductions in spoilage rates. Results indicate that the downstream end of the supply chain, retail, also plays a crucial role in maintaining total profitability, indicating the significance of collective inventory management along with sustainability initiatives. This study concludes that technology-driven sustainability initiatives, coupled with effective spoilage management, can optimize sustainability outcomes in the cheese supply chain without compromising financial performance.

A Lagrangian relaxation approach for algae-based biofuel supply chain network design under uncertainty and pricing issue.

Biofuel has gained significant attention as a potential source to meet fuel demands instead of fossil fuel. The price of biofuel and alternative fuel have a considerable impact on biofuel demand. Thus, it is important to design a biofuel supply chain network that incorporates the biofuel price into an elastic demand. More precisely, a variable demand, including customer importance level (to the environment), biofuel price, and substituted fuel price, is considered in this research. Furthermore, this research presents a bi-objective mixed-integer quadratic formulation that aims to maximize the total profit of the supply chain and carbon absorption in harvesting areas. The problem is solved by the ε-constraint and lagrangian relaxation methods due to its complexity. Moreover, substituted fuel price uncertainty is addressed by two-stage stochastic programming. Finally, a real case study utilizing the data envelopment analysis approach is applied to assess the efficiency and currency of the addressed model. Several consequences are illustrated in the case study, such as rich areas for exporting algae, suggesting hub locations for biofuel production, etc.

Public transport pricing incentive schemes in a competitive market.

In many countries, public transport (PT) services are offered by multiple operators with different modes, such as trains, metros, and buses, which are interchangeable across a single journey. This paper presents a model with one origin (O)-destination(D) path operated by two operators, each of which is responsible for different parts of the OD journey. One operator competes with potential third-party transport companies by offering discount incentives. Such an abstract settings has not been discussed in the literature. We explore how prices, demand, profits, and social welfare change with discounts through a theoretical analysis and numerical simulations under five scenarios. The results indicate that in all the scenarios the operator offering a discount incentive can always attract more passengers and increase its profits. Moreover, reducing the service time of operators offering discounts contributes to an increase in social welfare. Notably, this paper deduces for the first time that the demand scenario aimed at maximizing social welfare is twice as high as that aimed at maximizing total profit. However, in the scenario of maximum social welfare, the profitability of operators becomes challenging.

Optimizing Multi-Channel Green Supply Chain Dynamics with Renewable Energy Integration and Emissions Reduction

In response to the global imperative of mitigating greenhouse gas emissions (GHGs) and the shifting landscape of business models toward multi-channel structures, this study delves into the intricacies of a green supply chain. Operating through both online and traditional channels with uncertain demands, the producer’s distribution strategy prompts an exploration of supply chain dynamics. Utilizing an integer programming model, this study calculates optimal prices, optimizes total profit, and minimizes transportation costs to curtail carbon dioxide emissions, depending on the transportation mode. Additionally, this study incorporates renewable energy sources into the production and transportation processes to further minimize carbon dioxide emissions. The integration of renewable energy not only supports environmental goals, but also contributes to the overall profitability of the supply chain by reducing energy costs. Employing a theoretical technique for linearization, the model, resolved through the Jimenez and TH methods, demonstrates efficacy in reconciling economic and environmental goals. The Jimenez method enables the transformation of fuzzy parameters into deterministic equivalents, allowing for a more reliable optimization during uncertainty, while the TH method provides an interactive fuzzy multi-objective approach, aligning the model’s dual objectives for both economic and environmental goals. Notably, when transportation costs to both markets are equal, the model prioritizes devices with a lower environmental impact, showcasing adaptability. Furthermore, the proposed solution empowers decision makers to influence pricing and enhance the entire supply chain’s profitability. In conclusion, this research offers nuanced insights, strategically aligning economic viability with environmental sustainability in the discourse on green supply chains.

Analisis Current Ratio, Debt To Equity Ratio, dan Total Asset Turnover Terhadap Profitabilitas pada Subsektor Telekomunikasi yang Terdaftar di Bursa Efek Indonesia Periode 2020-2022

In Indonesia’s economic growth, the telecommunications industry has become a growing company along with the rapid development of technology, information and communication. With this growth, it is necessary to compare various financial statements, especially the balance sheet and income statement to determine the profitability of the company’s. The result of this study indicate that Return On Asset in telecommunications subsector listed on the Indonesia Stock Exchange for the 2020-2022 period has decreased drastically. The research method used is descriptive and assiciative with quantitative approach. The population used in the study is the financial statements of the telecommunications subsector listed on the Indonesia Stock Exchange period 2020-2022. The research object are Current Ratio (X1), Debt to Equity Ratio (X2), Total Asset Turnover (X3) and Profitability (Y). The data analysis techniques used intihs study are descriptive statistics and multiple linear regression. The result showed that partially (t test) Current Ratio has no significant effect on profitability, Debt to Equity Ratio has no significant effect on profitability, and Total Asset Turnover has a negative and significant effect on profitability. However, simultaneously (f test) Current Ratio, Debt to Equity Ratio, and Total Asset Turnover have a significant influence on the profitability of the telecommunications subsector listed on Stock Exchange for the period 2020-2022.

Profit-oriented balancing of two-sided disassembly lines with resource-dependent task times

PurposeAs a result of environmentally conscious production requirements in the world, the concept of disassembly has been a focus of interest by researchers and practitioners over the last two decades. Disassembly is an important process in circular economy to recover and reuse of parts and materials. End-of-life and large-sized products such as minibuses and trucks may be disassembled on two-sided lines. The ability of using both right and left sides of two-sided lines may increase line efficiency and reduce space requirements across the line. This paper aims to address a two-sided disassembly line balancing problem (TSDLBP), which deals with assigning disassembly tasks, various equipments and assistants to the workstations to maximize total net recovery profit of the line.Design/methodology/approachA detailed explanation of the TSDLBP is first presented in the paper. A new 0–1 integer linear programming model is then proposed for the TSDLBP, aiming at maximizing total net recovery profit from disassembly of products. A set of test problems is generated, and an experimental analysis is conducted to make a comparison between traditional one-sided disassembly lines (TOSDL) and two-sided disassembly lines by means of performance improvement rate.FindingsOptimal results are obtained in 132 (81.48%) out of 162 the TOSDL balancing problems, while 92 (56.79%) out of 162 the TSDLBP using the proposed model. Total net recovery profits are compared on 88 problems for which optimal solutions are obtained in both the TOSDL and the TSDLBP. Results showed that implementing two-sided disassembly lines provides 29.18% increment in total net recovery profit compared to the TOSDL. Furthermore, the effects of different parameter levels on the net recovery profit are analyzed using two-way analysis of variance. According to the results, implementing two-sided disassembly line configuration increases total net recovery profit of the line significantly compared to traditional disassembly line configuration.Originality/valueThe use of disassembly lines has become essential because of increasing consumption that results in a huge number of end-of-life products in the world. Two-sided disassembly lines may be preferred for dismantling large-sized products due to their high disassembly capacity and fewer space requirements. This paper proposes a new mathematical model for disassembly line balancing problem. The proposed model differs from the existing models by means of efficiently assigning limited disassembly resources as well as assigning disassembly tasks to the workstations to maximize total net recovery profit of the production system. The model allows decision-makers to consider several resource limitations when balancing their disassembly lines. The paper also provides a comprehensive experimental study to compare traditional and two-sided disassembly lines by means of profitability of disassembly processes.

A portable balloon integrated photovoltaic system deployed at low altitude

This paper proposed a portable balloon-integrated photovoltaic system (BIPVS) deployed at low altitude. The inflatable and deflatable design enhances the proposed system flexibility and mobility, enabling it have a wider range of application scenarios. Case studies were conducted based on cities' data of Vasteras, Vancouver, New York, Shanghai and Hong Kong to evaluate 10,000 BIPVS's annual power generation potential. Mid-to-high latitudes are not suitable for photovoltaic power generation in winter due to snow and ice coverage. Excluding the unsuitable winter months, simulation results show that the average monthly power generation of the BIPVSs amounts to 3.921 GWh, 4.238 GWh, 4.275 GWh, 3.337 GWh, and 3.379 GWh, respectively, during the effective working months within a year, which shows the superior performance of mid-to-high latitudes over their low latitudes. Over the life cycle, the BIPVSs exhibit a cumulative power generation capacity, amounting to 479.492 GWh, 592.18 GWh, 672.105 GWh, 641.155 GWh, and 708.334 GWh, respectively, and their total profits are 79.614 million USD, 37.007 million USD, 75.146 million USD, 12.946 million USD, 107.369 million USD, accompanied by the return on investment of 218.6 %, 101.6 %, 206.3 %, 35.5 %, 294.8 %, respectively. These findings illustrate the significant energy and economic advantages and potential of BIPVS.

Optimizing task assignment and routing operations with a heterogeneous fleet of unmanned aerial vehicles for emergency healthcare services

This paper studies the optimization of task assignment and pickup and delivery operations using a heterogeneous fleet of unmanned aerial vehicles (UAVs). We specifically address the distribution of emergency medical supplies, including medications, vaccines, and essential medical aid, as well as the collection of biological blood samples for testing and analysis. Unique challenges, such as supply shortages, time windows, and geographical considerations, are explicitly taken into account. The problem is first formulated as a mixed-integer linear programming model aimed at maximizing the total profit derived from the execution of a set of emergency healthcare pickup and delivery tasks. An enhanced Q-learning-based adaptive large neighborhood search (QALNS) is proposed for large-scale benchmark instances. QALNS exhibits a superior performance on benchmark instances. It also improves the quality of the solutions on average by 5.49% and 6.86% compared to the Gurobi solver and a state-of-the-art adaptive large neighborhood search algorithm, respectively. Sensitivity analyses are performed on critical factors contributing to the performance of the QALNS algorithm, such as the learning rate and the discount indicator. Finally, we provide managerial insights on the use of the fleet of UAVs and the design of the network.

Profitability of Swedish Red and White × Holstein crossbred cows compared with purebred Holstein cows

An economic analysis was performed using a bioeconomic simulation model based on production, reproduction and longevity indicators to compare the profitability (net income per cow) of Swedish Red and White × Holstein (S × H) cows and pure Holstein (H) cows in commercial dairy farms of the center-south of Córdoba province, Argentina. The reproductive events analyzed in the model were pregnancy, calving and abortion. The longevity events were culling, sale and death. The lactation curve for each cow was modeled with a lactation function. Estimations were made for the first, second, and third and later lactations. Incomes were from milk, culled cows and calves. Costs were from feeding, reproductive program and replacement heifers. The reproductive cost of a H cow per year was greater than that of a S × H crossbred cow (USD 6.3). The replacement costs were also higher in pure H cows than in S × H cows (USD 67.8). The revenues from the sale of calves (USD 12.1) and milk production minus the feed cost (USD 8.2) were greater for S × H cows than H cows. Hence, the S × H cows generated a greater total profit (USD 94.4) per cow per year than H cows. The present results show that, in the studied production systems, S × H crossbred cows are more profitable than H cows.

A Stochastic Sustainable Closed-Loop Supply Chain Networks for Used Solar Photovoltaic Systems: Meta-Heuristic Comparison and Real Case Study

This research presents a novel approach to setting up a sustainable Closed-Loop Supply Chain (CLSC) network for used solar photovoltaic (PV) systems, addressing end-of-life product waste from solar panel installations and manufacturing centers. The model accounts for uncertainties in PV systems and aims to efficiently collect, refurbish, and recycle used solar PV systems, promoting a circular and environmentally responsible waste management strategy. The supply chain network comprises vendors, collection centers, hybrid centers, distribution centers, and manufacturing centers, with objectives to maximize total profit, minimize environmental risk, and maximize service levels by demonstrating the profitable reuse of used solar photovoltaic systems by manufacturers. The epsilon-constraint method is utilized to handle the model's multi-objectiveness and identify Pareto optimal solutions. A case study in Iran is conducted to validate the methodology's performance, comparing results obtained from three meta-heuristic methods: Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Gray Wolf Optimization (MOGWO). The average error rates are 0.0358 for MOGWO, 0.1248 for MOPSO, and 0.2066 for NSGA-II. Sensitivity analysis highlights the significant impact of demand variations on all objective functions. Lastly, the numerical results are discussed to provide managerial insights for informed decision-making.

Online Retailing with Key Opinion Leaders and Product Returns

Key opinion leaders (KOLs) play a crucial role in marketing because of their potential fan market and influence. We analytically study whether a seller should cooperate with a key opinion leader (KOL) by considering the KOL’s two effects: first, the KOL’s existence will increase the number of consumers who are aware of the product; second, a more positive (negative) product review from the KOL will increase (decrease) consumers’ prepurchase belief in product valuation. After buying the product, consumers will realize their actual product valuation, based on which they decide to return or keep the product. We find that when the product valuation posted in the KOL’s product review is exogenous, a more positive review from the KOL benefits the seller but can harm the KOL. As the consumer’s hassle cost of product return increases, both the seller and the KOL can be better off. If the positivity of the KOL’s product review is high, the KOL’s existence will benefit the seller and harm the consumers. Moreover, if the KOL can strategically make the marketing effort to increase their popularity, their marketing effort will decrease as their product review becomes more positive. If the KOL can strategically choose how they present the product, i.e., when the product valuation posted in the KOL’s product review is endogenous, their product review will be less positive than the product review that maximizes the system’s profit, i.e., the total profit of the KOL and the seller. The model extension shows that the result is robust.

Optimal Configuration of Electricity-Heat Integrated Energy Storage Supplier and Multi-Microgrid System Scheduling Strategy Considering Demand Response

Shared energy storage system provides an attractive solution to the high configuration cost and low utilization rate of multi-microgrid energy storage system. In this paper, an electricity-heat integrated energy storage supplier (EHIESS) containing electricity and heat storage devices is proposed to provide shared energy storage services for multi-microgrid system in order to realize mutual profits for different subjects. To this end, electric boiler (EB) is introduced into EHIESS to realize the electricity-heat coupling of EHIESS and improve the energy utilization rate of electricity and heat storage equipment. Secondly, due to the problem of the uncertainty in user-side operation of multi-microgrid system, a price-based demand response (DR) mechanism is proposed to further optimize the resource allocation of shared electricity and heat energy storage devices. On this basis, a bi-level optimization model considering the capacity configuration of EHIESS and the optimal scheduling of multi-microgrid system is proposed, with the objectives of maximizing the profits of energy storage suppliers in upper-level and minimizing the operation costs of the multi-microgrid system in lower-level, and solved based on the Karush-Kuhn-Tucker (KKT) condition and Big-M method. The simulation results show that in case of demand response, the total operation cost of multi-microgrid system and the total operation profit of EHIESS are 51,687.73 and 11,983.88 CNY, respectively; and the corresponding electricity storage unit capacity is 9730.80 kWh. The proposed model realizes the mutual profits of EHIESS and multi-microgrid system.

A two-stage bi-level electricity-carbon coordinated optimization model for China's coal-fired power system considering variable renewable energy bidding

With China's grid-connected large-scale variable renewable energy, coal-fired power system is progressively changing from being the main supplier of electric energy to providing flexibility services. The coal-fired power system must increase operating efficiency during this transition process by considering the technical characteristics of various coal-fired power units. Additionally, the trading strategies of coal-fired power units in the electricity-carbon market are impacted differently depending on the extent of VRE penetration. This paper innovatively considers these two factors and proposes a two-stage bi-level clearing model in electricity-carbon joint market to coordinate the synergistic development of variable renewable energy and coal-fired power. The upper level of the model aims to maximize the total profits of the coal-fired power system and variable renewable energy units, while the lower level of the model minimizes the total purchasing costs. Coal-fired power units and variable renewable energy units compete to sell electric energy, with the latter buying peaking regulation from the former. The research results indicate that the proposed model can significantly reduce 66.79 % carbon emissions, increase 33.96 % non-fossil variable renewable energy integration and optimize the generation structure of coal-fired power system.

Optimizing the Bidding Data for power industry with elastic demand using hybrid Water Cycle Moth Flame Optimization Algorithm

Strategic Optimal Bidding of the data is a compulsory duty for Independent System Operator (ISO) which is the most complicated task that maximizes the profit of the supplier by handling bidding coefficient strategically. This paper endorses a strategy of optimal bidding coefficient data to improve the profit value by latest optimizing technique named hybrid Water Cycle Moth Flame Optimization Algorithm, which achieves a heuristic search thereby obtaining a global search of a stream using Levy flight movement. This method is applied and tested on an Indian-75 Bus system to test and investigate the new strategy whether receiving best solution of profit in comparison with other conventional techniques explained widely. On adding it evaluates the efficacy of the proposed method on the mentioned system through assessing total profit obtained, revenue, power generation, Market Clearing Price and cost of the individual GENCO. In order to show the Statistical Analysis the Box-plot is done to perform the visual data representation of the proposed and conventional methods.

Pricing policies for printed and electronic books in a triple-channel supply chain

Purpose This study aims to investigate the pricing dynamics within a triple-channel supply chain. The publisher can sell printed books (p-books) through bookstores or online direct sales, and electronic books (e-books) are sold directly through the internet. The primary objectives include determining optimal wholesale and final prices for p-books, assessing the profitability of introducing e-books, comparing profits across channels and supply chain modes and identifying optimal demand volumes. Design/methodology/approach The research uses first-order derivatives and the Stackelberg game to analyze the pricing strategies. Two supply chain modes, centralized and decentralized, are considered, and various parameters are examined to understand their impact on prices, demand volumes and final sales profit. Findings The results indicate that the e-book is either not published or is introduced simultaneously with the printed version in both modes. In the decentralized mode, the wholesale price of a p-book is equivalent to the final price in the bookstore channel in the centralized mode. One channel among the three selling channels is used to maximize the total profit in the centralized supply chain, whereas all demand should be fulfilled through either online direct sales or e-book channels in the decentralized mode. Originality/value This paper introduces a comprehensive triple-channel book supply chain model, considering cross-price sensitivities and lag times for e-books. The study provides insights into the dynamics of the book industry and compares them with existing literature, contributing to a broader understanding of the pricing strategies in a triple-channel context.

A 3D multiobjective multi-item eco-routing problem for refrigerated fresh products delivery using NSGA-II with hybrid chromosome

In a developing countries, say India, about 40 percent fresh foods are wasted during transportation and $14 billion of post harvest products are lost due to inefficiency of cold supply chain such as using non-refrigerated vehicle during last mile transport. Hence, temperature control through refrigerated transportation is very essential. But, the by-product of this process i.e., carbon emission (CE) due to transportation and refrigeration is detrimental for the world. Only logistic transportation contributes about 25 percent of global CE. Due to the world wide infrastructural development, there are several available routes for the road transportation among different cities. For the maintenance of these routes, there are some toll plazas for collection of money. Some routes also cross the rail lines. For maximum profit, a supplier distributes the products at the earliest as the retailers fix the product’s price with respect to its freshness. Again, this process generates more CE. With these realities, a 3D multiobjective multi-item eco-routing problems for refrigerated fresh products (3DMOMIERPsfRFPs) are developed, where a refrigerated vehicle starts from a depot (refrigerated) and comes back after distributing the fresh products to retailers as per their demands. The optimal routing plan, vehicle’s velocity and preservation rate are determined so that total profit and CE are respectively maximized and minimized simultaneously. For solution, an NSGA-II with different modified operators (NSGA-IIwDOs) is developed with hybrid chromosomes having discrete and continuous variables together, probabilistic selection, problem-specific crossover and generation-dependent mutation. Some performance metrics are presented through NSGA-IIwDOs on the standard TSPLIB problems. Results of 3DMOMIERPsfRFPs without and with specific delivery time slots, road-dependent vehicle velocity and time constraints are evaluated. Pareto front for multiobjective are depicted. As a particular case, results of a previous investigation are obtained. Some of the managerial decisions are observed.

Capacity Optimization Allocation of Multi-Energy-Coupled Integrated Energy System Based on Energy Storage Priority Strategy

As the global focus on environmental conservation and energy stability intensifies, enhancing energy efficiency and mitigating pollution emissions have emerged as pivotal issues that cannot be overlooked. In order to make a multi-energy-coupled integrated energy system (IES) that can meet the demand of load diversity under low-carbon economic operation, an optimal capacity allocation model of an electricity–heat–hydrogen multi-energy-coupled IES is proposed, with the objectives of minimizing operating costs and pollutant emissions and minimizing peak-to-valley loads on the grid side. Different Energy management strategies with different storage priorities are proposed, and the proposed NSNGO algorithm is used to solve the above model. The results show that the total profit after optimization is 5.91% higher on average compared to the comparison type, and the pollutant emission scalar function is reduced by 980.64 (g), which is 7.48% lower. The peak–valley difference of the regional power system before optimization is 0.5952, and the peak–valley difference of the regional power system after optimization is 0.4142, which is reduced by 30.40%, and the proposed capacity allocation method can realize the economic operation of the multi-energy-coupled integrated energy system.

Enhancing Water Use Efficiency and Carbon Profitability Through the Long-Term Impact of Sustainable Farming Systems

This study aims to enhance water use efficiency, maximize productivity, and minimize environmental impact through the implementation of sustainable agricultural systems using drip irrigation systems. It investigates the effects of biodynamic farming compared to those of organic and conventional methods over a six-year period and focuses on soil properties, water use efficiency, crop yield, and environmental and economic perspectives. Using a biodynamic farming system resulted in an average increase in water use efficiency of 1.96 and 10.67% for maize and 3.62 and 10.68% for faba bean and an increase in maize yield of 1.68 and 0.99%, while the faba bean yield reached 3.25 and 1.57% compared to the organic and conventional farming systems, respectively. The biodynamic system sequestered the highest average soil carbon of 6.16 tons/ha (which is equivalent to 22.45 tons/ha of CO2 emissions), representing a 13% increase compared to the organic system. Additionally, the biodynamic system yielded an increase in total net profit of 5.70 and 21.66% for the maize crop and 6.72 and 22.19% for the faba bean crop compared to the organic and conventional farming systems, respectively. The farming system significantly influenced the soil carbon sequestration and organic carbon.

Chaotic Sea-Horse Optimizer for Profit-Based Unit Commitment to Maximize the Profit of GENCOs in Restructured Power System Considering Reserve Allocation

Objectives: The primary objective of this research work is to solve the Profit Based Unit Commitment (PBUC) problem in Power Generation Companies (GENCOs) in the competitive Electricity market. The energy and spinning reserve allocation are considered to maximize the profit of power generation companies. Methods: A new and recently developed optimization technique of Chaotic Sea-Horse Optimizer (CSHO) is planned for an optimal solution PBUC problem. The control parameters of CSHO are the number of Population is 50, the Chaotic map parameter is 4, the dimensions are 10, and the maximum number of iterations is 100 are considered to achieve the global optimal solutions. The competitive study with standard golden approaches of the LR method and other soft computing techniques such as GA, PSO, etc. are considered for performance evaluations of CSHO. The proposed CSHO is mainly based on the natural behavior of Sea-Horse in the ocean. The MATLAB 15.0 platform is utilized for the expected problem. Findings: The CSHO was tested on a standard IEEE-39 bus system (10 generators and 24 hours) with reserve allocation. The CSHO technique improves the total profit of GENCOs with various test loads and reserve demand. The simulation results like Optimal UC schedule, power generation of thermal plants, fuel cost, startup cost, revenue, and profit are displayed. The percentage of total profit of 29.76% is improved when compared with the mathematical approach of the LR method with 102 second time period. Novelty: The CSHO has currently developed an effective algorithm that easily reaches the global optimal solutions. The CSHO mimics the movement, hunting, and breeding behavior of sea-horses in nature. Implementation of chaotic maps helps to improve the convergence speed of the proposed method. Keywords: Deregulation, Profit Based Unit Commitment, Energy and Reserve Allocation, Cost Minimization, Profit of GENCOs, Chaotic Sea-Horse Optimizer

Resilient shuttle vehicle plan under route disruption scenarios and ticketing policies: a case study of the Qiandaohu archipelago scenic area, China

ABSTRACT Scenic areas in an archipelago context are often accessed with shuttle vehicles to transport tourists between different scenic spots. Vehicle routing and schedule planning could be challenging when route disruptions are considered frequent and fragile areas are protected and conserved. Transport provisions also affect the number, sequence and patterns of visited spots for each tourist, which in turn may not only impact the revenue of the scenic area but also on visitor satisfaction. This paper develops a mixed-integer programming model for this optimisation problem at scenic areas by considering two common ticketing policies: ‘all-inclusive policy’ and the ‘two-part tariff policy’. Experimental results based on the case study of Qiandaohu scenic area in China demonstrate that our method can obtain high-quality solutions for real-sized problems where some routes are disrupted. It is found that ticketing policies may have different impacts on shuttle vehicle planning. Under the all-inclusive policy, the shuttle vehicle planning maximises the total profit. Under the two-part tariff policy, however, the plan needs to maximise the profit to strike a balance between the operational cost and the number of spots visited by tourists. Our study may shed light on the effectiveness of shuttle vehicle planning and ticketing policy selection.