Total Profit Research Articles

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.

Pricing and Replenishment Decision System Based on LSTM

Fresh supermarket vegetables have a very short shelf life, and the quality of vegetables will also be affected by the storage time. If most of the goods are not sold on that day, they will not be sold on the next day. From the demand side, the sales volume of vegetables is related to the supply side of vegetables from April to October. At the same time, the sales space of the supermarket is limited, which makes a reasonable sales portfolio extremely important. The commercial overpricing replenishment decision system based on LSTM analyzes the relationship between daily sales volume and cost-plus pricing for each vegetable category and single product over three years. It predicts the sales volume and wholesale price of each category for the next week, determining replenishment volume and pricing strategy based on forecasted sales volume and average loss rate. The system considers limited sales space and uses the entropy method to calculate weights for key indicators like total profit. It employs the TOPSIS method to sort items, considering forecasted wholesale prices, to select items for pricing decisions. The system aims to maximize total profit while ensuring the supermarket meets minimum display conditions. By assisting supermarkets in formulating more reasonable pricing strategies, it aims to reduce overstock, backlog, or stock shortages, ultimately improving sales volume and operational efficiency.

Environmental and Economic Impact on Scenario-Based Automotive Part Replacement: Case Study of Constant Velocity (CV) Joint Replacement in Korea

The objective of this study is to comprehensively evaluate the environmental and economic impacts of remanufactured constant velocity (CV) joints using a newly proposed framework for automotive parts replacement. This framework utilizes actual vehicle sales data to accurately estimate the demand for CV joint replacements. Specific parameters are established to calculate this demand, enabling a quantitative assessment of both environmental and economic impacts associated with remanufacturing CV joints and other automotive parts. Additionally, sensitivity analyses are conducted to explore the relationship between environmental benefits and economic outcomes, particularly focusing on the preferences for remanufactured parts and their profitability. The results indicate that increasing the proportion of remanufactured CV joints significantly benefits the environment by reducing CO2 emissions, raw material usage, and energy consumption. However, this shift also leads to a decrease in total profits within the CV joint replacement market. To address this trade-off, the study suggests that financial incentives, such as subsidies or tax benefits, are necessary to support the remanufacturing industry and facilitate market expansion. These findings provide valuable insights for policymakers aiming to promote sustainable manufacturing practices through effective subsidy policies.

Dynamic collaborative truck-drone delivery with en-route synchronization and random requests

Coordinated truck and drone delivery is gaining popularity in logistics as it can greatly reduce operation costs. However, existing studies on related operations management problems typically ignore the following important features: (i) the random appearance of requests, which require operators to dynamically respond to the requests; and (ii) the decisions of optimal launch and retrieval locations for trucks and drones instead of fixed to customer locations, which can significantly impact the overall time costs. To tackle these challenges, this study investigates the dynamic collaborative truck-drone routing problem with randomly arriving requests and synchronization on routes. We model the problem as a Markov Decision Process (MDP) and solve the MDP via a reinforcement learning (RL) approach. The proposed RL approach determines: (i) whether each request should be serviced upon arrival, (ii) which truck or drone should be assigned for the request, and (iii) the optimal en-route take-off and landing positions for paired trucks and drones. We further employ a framework of decentralized learning and centralized dispatching in RL to increase performance. Numerical experiments are conducted to assess the proposed solution approach on instances generated based on both the Solomon dataset and real-world operational data of a logistics operator in Singapore over several benchmark algorithms under various battery endurance levels of drones and distinct transportation scenarios including node-based dynamic collaborative truck-drone routing problem, dynamic non-collaborative truck and drone routing problem, and dynamic vehicle routing problem. The results show that our RL solution outperforms the benchmark algorithm in total profit by an average of 28.03 %, and our en-route takeoff and landing scenario outperforms the benchmark scenarios in total profit by an average of 8.43 % in multi-day instances. Additionally, compared to the traditional node-based landing scenario, employing our en-route takeoff and landing strategy can save 0.9 h/(drone*day) of waiting time on average.

Optimal Replenishment Strategy for a High-Tech Product Demand with Non-Instantaneous Deterioration under an Advance-Cash-Credit Payment Scheme by a Discounted Cash-Flow Analysis

This study investigated non-instantaneous deteriorating items because not all products deteriorate immediately. In the high-tech product life cycle, the product demand increases linearly substantially in the growth stage and maintains a near-constant level in the maturity stage. This is a ramp-type demand rate. To satisfy the demand as shortages occur, partial backlogging is necessary. The advance-cash-credit payment scheme, comprising advance, cash, and credit payments, has gained popularity in business transactions to improve cash flow flexibility among supply chain participants. This study explored a partial backlogging inventory model with ramp-type demand for non-instantaneous deteriorating items under generalized payment. The proposed model also incorporated discounted cash flow analysis to account for the time value of the profit function. This study attempted to determine the optimal replenishment strategy to maximize the present value of the total profit. Finally, we conducted a sensitivity analysis to examine the efficacy of the proposed model and gain managerial insights. The optimal total profit rises with an increase in the permissible delay period and sale price but decreases with an increase in ordering and purchase costs. Then, the decision-maker can refer to the managerial insights to choose the appropriate parameter value for the operation.

Research on the Game Model of Green Technology Investment in a Two-Echelon Manufacturing Supply Chain Under Carbon Tax Regulation

Using a Stackelberg game model, this research examines the impact of government-imposed carbon taxes on the sustainability of a two-echelon supply chain. The analysis focuses on the interactions between upstream suppliers and downstream manufacturers, taking into account how manufacturers’ carbon emissions influence market demand, as well as how sustainability levels and price affect this demand. The results show that a centralized system has a lesser potential for total profit development in the supply chain than a decentralized system. To encourage investment in green technologies throughout the supply chain, this paper proposes two coordination mechanisms: a green subsidy and a two-part tariff contract. Numerical study reveals that a win–win scenario for the supply chain requires suppliers to share part of the costs. Furthermore, under a two-part tariff contract, successfully managing fixed costs can result in total profit levels comparable to those in a centralized system. The research also makes strategy recommendations, such as differentiating carbon levies, improving supply chain coordination, and assessing fixed costs. These aim to provide theoretical and practical implications for supply chain management in order to assist businesses and policymakers in effectively responding to the problems of climate change.

Location and Size Planning of Charging Parking Lots Based on EV Charging Demand Prediction and Fuzzy Bi-Objective Optimization

The market share of electric vehicles (EVs) is growing rapidly. However, given the huge demand for parking and charging of electric vehicles, supporting facilities generally have problems such as insufficient quantity, low utilization efficiency, and mismatch between supply and demand. In this study, based on the actual EV operation data, we propose a driver travel-charging demand prediction method and a fuzzy bi-objective optimization method for location and size planning of charging parking lots (CPLs) based on existing parking facilities, aiming to reduce the charging waiting time of EV users while ensuring the maximal profit of CPL operators. First, the Monte Carlo method is used to construct a driver travel-charging behavior chain and a user spatiotemporal activity transfer model. Then, a user charging decision-making method based on fuzzy logic inference is proposed, which uses the fuzzy membership degree of influencing factors to calculate the charging probability of users at each road node. The travel and charging behavior of large-scale users are then simulated to predict the spatiotemporal distribution of charging demand. Finally, taking the predicted charging demand distribution as an input and the number of CPLs and charging parking spaces as constraints, a bi-objective optimization model for simultaneous location and size planning of CPLs is constructed, and solved using the fuzzy genetic algorithm. The results from a case study indicate that the planning scheme generated from the proposed methods not only reduces the travelling and waiting time of EV users for charging in most of the time, but also controls the upper limit of the number of charging piles to save construction costs and increase the total profit. The research results can provide theoretical support and decision-making reference for the planning of electric vehicle charging facilities and the intelligent management of charging parking lots.

A Study on the Production-Inventory Problem with Omni-Channel and Advance Sales Based on the Brand Owner’s Perspective

This study explores a supply chain product-inventory problem with advance sales under the omni-channel strategies (physical and online sales channels) based on the brand owner’s business model and develops corresponding models that have not been proposed in previous studies. In addition, because the brand owner is a member of the supply chain, and has different handling methods for defective products or products returned by customers in various retail channels, defective products or returned products are included in the supply chain models to comply with actual operating conditions and fill the research gap in the handling of defective/returned products. Regarding the mathematical model’s development, we first clarify the definition of model parameters and relevant data collection, and then establish the production-inventory models with omni-channel strategies and advance sales. The primary objective is to determine the optimal production, delivery, and replenishment decisions of the manufacturer, physical agent, and online e-commerce company in order to maximize the joint total profits of the entire supply chain system. Further, this study takes the supply chain system of mobile game steering wheel products as an example, uses data consistent with the actual situation to demonstrate the optimal solutions of the models, and conducts sensitivity analysis for the proposed model. The findings reveal that increased demand shortens the replenishment cycle and raises order quantity and shipment frequency in the physical channel, similar to the online channel during normal sales. However, during the online pre-order period, higher demand reduces order quantity and cycle length but still increases shipment frequency. Rising ordering or fixed shipping costs lead to higher order quantity and cycle length in both channels, but variable shipping costs in the online channel reduce them. Market price increases boost order quantity and frequency in the online channel, while customer return rates significantly impact inventory decisions.

Pharmaceutical procurement profits and universal health coverage: Reform to a sustainable healthcare and financing system

BackgroundIn this study, using Taiwan's National Health Insurance (NHI) as an example of a single-payer system, we examined the extent of pharmaceutical procurement profits (PPP) and evaluated their impact on the financial performance of healthcare institutions. MethodsWe extracted data from financial statements and healthcare service declarations of NHI-contracted hospitals from 2015 to 2021. Financial data concerning PPP, health service profits (HSP), and total operating profits (TOP) from each hospital were analyzed. The impact of PPP on hospitals with positive and negative HSP was further investigated. ResultsThe total PPP across all hospitals studied gradually increased from NT$30.6 billion in 2015 to NT$47.0 billion in 2021. In 2021, 28.1% of all hospitals reported a deficit in HSP. PPP appeared to have a significantly positive impact on the financial performance of these hospitals. It not only enhanced positive profits, but also helped mitigate or completely offset the negative profits from HSP. The effect of PPP seems to be more pronounced for hospitals with larger HSP values, suggesting that larger hospitals benefit more from PPP in absolute terms. DiscussionAverage PPP increased during the study period, increasingly affecting hospitals’ financial stability across all strata. The gap between TOP and HSP in medical centers has gradually widened, suggesting an increase in non-health service profits. In this study, we propose a payment policy reform that fosters sustainability of the healthcare and financing system under universal health coverage and corrects the potential distortions caused by PPP.

Analysis of the Influence of Business Model Innovation on the Operating Capacity of Enterprises: Taking Zhejiang Haers as an Example

This paper takes the case of Zhejiang Haers as the object to explore the impact of business model innovation on the operating capacity of the enterprise, analyzes the impact on the operating capacity of the enterprise through the form of the business model of cooperation between Zhejiang Haers and various types of flow intellectual property (IP), analyzes the enterprise’s inventory turnover, accounts receivable turnover, total asset turnover and net profit to a certain extent, and concludes that the innovation of business model will strengthen the operating capacity of the enterprise. By analyzing the inventory turnover rate, accounts receivable turnover rate, total assets turnover rate, and net profit of the enterprise, it is believed that the innovation of the business model will strengthen the enterprise’s operating ability to a certain extent.

Sustainable supply chain design using a multi-objective linear optimization approach for solid bulk products

This paper proposes a multi-objective optimization model for designing a sustainable logistics system that determines the ideal logistic flow of an ultrafine mineral residue product, tailings, from Brucutu/MG to multiple destinations over a capable multimodal system. These tailings are extracted from iron ore and can be used as a partial substitute for raw materials in construction materials. This substitution can help avoid dangerous dams of iron ore tailings and save natural resources and greenhouse gas emissions in the civil construction chain. However, the distribution incurs logistical costs and resulting socio-environmental impacts. Therefore, logistics plays a fundamental role in making this substitution economically viable. The proposed model aims to maximize total profit while minimizing negative socio-environmental impacts and maximizing the positives. The resulting ideal multimodal logistic system is obtained based on sustainable pillars, demonstrating that a small decrease in profits can yield significant reductions in negative impacts and increases in positive externalities.

Optimal energy management and scheduling of a microgrid considering hydrogen storage and PEMFC with uncertainties

Renewable energy sources have been widely installed and operated in power systems, particularly in microgrids in the form of distributed generation units. This issue requires efficient energy management tools which take into account the inherent uncertainties of such energy resources. Thus, this paper presents a stochastic framework aimed at scheduling the renewable energy-based and thermal units in a coordinated way. The generation units comprise fuel cell units with proton exchange membrane known as PEMFC-CHP producing heat and power, concurrently. Moreover, the uncertainties arising from wind and solar power as well as market prices are characterized by deploying scenario-based optimization. The mentioned framework considers storing hydrogen and the model is presented within a stochastic mixed-integer nonlinear programming (MINLP) framework. The resulting problem is simulated on a modified 33-bus distribution network and tackled using the modified marine predators algorithm (MMPA)algorithm. The obtained results indicate that the revenue increases by more than 5% compared to other optimization algorithms. Furthermore, taking into account CHP will increase the total profit of the system by more than 15%.

Optimal product pricing and lot sizing decisions for an omni-channel supply chain

In an omni-channel supply chain, companies offer customers multiple channels to interact with their brand. This allows for greater outreach and sales, resulting in increased customer loyalty and trust. Furthermore, there has been little discussion on the simultaneous consideration of strategic and tactical decisions under return policies in omni-channel supply chains. To maximize total profit, this research proposes a two-echelon supply chain, comprising a manufacturer and multiple retailers, including traditional, electronic and omni-channels. The non-deterministic demand function takes into account the sales price, refund price, delivery time and order quantities. A heuristic algorithm based on analysis of Karush–Kuhn–Tucker optimality conditions used to solve a nonlinear programming model. This research presents an analysis of the factors influencing the decision variables and profit of the entire supply chain and offers retailers a unique way to evaluate their performance and make operational decisions regarding price, delivery time and order quantities.

Co-Design of a Wind–Hydrogen System: The Effect of Varying Wind Turbine Types on Techno-Economic Parameters

Green hydrogen is crucial for achieving climate neutrality and replacing fossil fuels in processes that are hard to electrify. Wind farms producing electricity and hydrogen can help mitigate stress on electricity grids and enable new markets for operators. While optimizing wind farms for electricity production is well-established, optimizing combined wind–hydrogen systems is a relatively new research field. This study examines the potential profit of wind–hydrogen systems by conducting a case study of an onshore wind farm near the North Sea. Varying turbine types from high wind-speed turbines (with high annual energy production) to low wind-speed turbines (with high full-load hours) are examined. Findings indicate that in a combined hydrogen system, the low wind-speed turbines, which are sub-optimal for mere electricity production, yield lower levelized costs of hydrogen at a higher hydrogen production. Although high wind-speed turbines generate higher profits under current market conditions, at high hydrogen prices and low electricity prices, low wind-speed turbines can yield higher total profit at this site. Therefore, an integrated optimization approach of wind–hydrogen systems can, in certain cases, lead to better results compared to an isolated, sequential optimization of each individual system.

Feasibility Analysis on Tempe Industrial Home (Case in Tempe Yayan Industrial Home, Lebaksiuh Village, Ciawi District, Kuningan Regency)

This study aims to evaluate the financial viability of tempeh home industry businesses by analyzing how production costs affect profitability. The design used in this study is a quantitative descriptive method with a technical survey approach. The data collection techniques include observation, interviews, and questionnaires (questionnaires). The results of this study are as follows: (1) Total production BEP and BEP business price of Tempe Yayan's home industry business in Lebaksiuh Village, Ciawi District, Kuningan Regency, were analyzed. (2) The R/C ratio analysis was used to analyze the profit and loss of the home industry. (3) The total profit of this home industry was calculated as a function of production costs, BEP price, and total profit. (4) Based on the results of the research and discussion conducted regarding the Profit and Loss Analysis of the Tempe YaYan Home Industry Business, future research could focus on a comparative analysis of financial performance and profitability across different tempay home industry ventures in various regions. The research would provide a broader understanding of best practices and challenges faced by similar businesses, offering valuable insights for both entrepreneurs and policymakers. (5) The research could explore the impact of government support and interventions on the growth and success of home industry enterprises, particularly in terms of capital access, production efficiency, and marketing effectiveness.