Total Profit Research Articles

How does the power structure affect the acquisition price and inventory decisions in the reverse supply chain

With growing concerns about environmental sustainability, reverse supply chains play a crucial role in recycling, remanufacturing, and reducing waste. Investigating the power structure’s impact on decisions related to acquisition prices and inventory management can provide insights into the integration of sustainable practices. This study analyzes a reverse supply chain with a supplier and a remanufacturer, in which the supplier provides the remanufacturer with sorted used cores. We investigate the acquisition pricing and inventory decisions under different power structures (supplierled Stackelberg model, remanufacturer-led Stackelberg model) through game theory analysis. We first establish the centralized model to explore optimal acquisition decisions and profit of the total system. Then we evaluate the acquisition quantity, wholesale prices, the profit of supplier and remanufacturer in the supplier-led model and in the remanufacturer-led model. Finally, the impact of power structure on the performance of supplier and remanufacturer are explored by numerical analysis, as well as the supply chain system. Our significant findings show that (i) the total profit of the supplier-led and remanufacturer-led reverse supply chain are less than the centralized model due to the double marginalization; (ii) the remanufacturer-led reverse supply chain is superior to the supplier-led model reverse supply chain; (iii) the selected grades in the supplier-led model is proved the same as that in the remanufacturer-led model, which could not been affected by the wholesale price; (iv) the greater the inventory risk (the uncertainty in demand), the greater the profit share for the remanufacturer (supplier) in the supplier-led (remanufacturer-led) model.

Forecast Horizon of Dynamic Lot Sizing Problem with Perishable Inventory and Multiple Chain Stores: Shipping and Stockout Cost

Perishable products are very common, but managing inventory of perishable products can be very challenging for firms, especially in distribution systems, including multiple chain stores. In this environment, we consider a dynamic lot-sizing problem faced by a distribution center that dis-patches a single perishable product to multiple chain stores. Demand cannot be backlogged, but it does not have to be satisfied; unsatisfied demand means stockout (lost sale). The first step is to transform the total profit function into a special total cost function. Our next step is to explore the properties of the optimal solution and use them to formulate a dynamic programming algorithm to solve the problem. Furthermore, we establish forecast and decision horizon results, which help the operation manager to decide the precise forecast horizon in a rolling decision-making process. Based on the model setting and the methods of dynamic programming, we obtained two interesting findings: (1) the maximized profit objective function is equivalent to the minimized cost objective function, and (2) the famous zero inventory property conditionally holds in the inventory management of perishable products. On an extensive test bed, useful insights were obtained on the impact of the lifetime of the product and cost parameters on the total cost and length of the forecast horizon. Thus, the contributions of this study are as follows: (1) we explore two structure policies in an optimal solution to devise efficient algorithms to reduce computational complexity; (2) we provide a sufficient condition for forecasting and decision horizons; and (3) we determine that, for a given fixed cost, the median forecast horizon first increases with the lifetime of the product and stockout cost and then remains invariable when it reaches a certain level.

A Closed-Loop Supply Chain Inventory Model with Carbon Emissions and Green Technology Investment

This paper proposes an inventory model that integrates a manufacturer and a retailer in a supply chain system. The model employs environmentally friendly technology investments to reduce the emissions produced in the process. Furthermore, the demand at the retailer's end is unpredictable, and green investments affect the average demand levels. This research aims to identify the optimal delivery lot, the number of deliveries, the Safety Factor, the level of green technology, and the collection level to maximize the joint total profit. Numerical examples illustrate the model's practical application, and algorithms are developed to solve the problem. Sensitivity analysis is used to determine the key model parameters' effect on the model's behavior. Green investments have been shown to reduce emissions and increase returns on second-hand goods, thus enhancing the environmental efficiency of supply chains.

Photovoltaic Capacity Management for Investment Effectiveness

The production of photovoltaic utility varies within the day/night cycle. At night, photovoltaic cells do not produce anything. However, their day-light production, unconsumed on a current basis and exported to the grid, is compensated for with supply from the grid at night. This scheme of exploitation is called net-metering and is considered herein. Solar energy produced by a prosumer and fed into the grid has to be equal to the electricity supplied from the grid at night; otherwise, a shortage or waste of photovoltaic production occurs. The above finding leads us to the proposition for the optimal solution of photovoltaic capacity. We derived a closed-form capacity solution to the maximized non-linear profit function. It solves harmonic and 2-point production functions that vary symmetrically around the mean production. To verify the solution methodology, harmonic and 2-point models from empirical production data are estimated. Then, the solution is presented together with its return rate and internal return rate. The main finding is that the unit cost of the grid electricity, photovoltaic capacity unit cost and exploitation time all affect the total profit and return rate values while not impacting the optimal capacity of the photovoltaics. The optimal capacity depends on the prosumer’s energy consumption volume and on the natural conditions of production captured here by the technology efficiency coefficient estimated from the production time series.

Genome-wide associative analysis of reproductive characteristics of a black-and-white Holstein cattle population in the conditions of the Northwestern region

In recent years, genomic assessment and the widespread introduction of modern biotechnological techniques have played a leading role in the implementation of breeding programs. For a long time, increasing milk productivity has been the main goal of breeding. This led to a significant decrease in reproduction in herds. The reproductive indicators of the uterine livestock of cattle can largely reduce the total profitability due to additional costs for manual, veterinary assistance and forced culling. Experienced groups are genotyped using the Bovinesnp50 V3 Beadchip chip are formed from the cows of black-and-white holsteinized breed. For the study, the main reproductive traits of cows were chosen: the age of the first insemination, the insemination indices in front of the first (a), the second (b), the third (c), fourth (d) lactation and the period between hotels. Analysis of associations throughout the genome was carried out using EMMAX software. 2 significant SNP and 5 conditionally significant were found. The CAMK2D candidate gene with the age at the first insemination, the TLN2, WSCD2, GAPT genes with the indicator, the number of insemination and genes WDR36, TDRD10 with the inter -suffering period was identified. Based on the studies, we assume that with age there is a change in the genetic factors affecting animal fertility. The identified candidate genes affect the mechanisms of maturation of follicles, so the growth and development of embryos. The results can be used both for the selection of animals to improve reproduction indicators, and for the search for the fundamental foundations of the formation of fertility in cattle.

Analisis Kelayakan Usaha Ternak Kambing Gibas Di Desa Nglumber Kecamatan Kepohbaru Kabupaten Bojonegoro

This research aims to analyze the income, profits and feasibility of the gibas goat farming business in Nglumber Village, Kepohbaru District, Bojonegoro Regency. This research used as informants 6 gibas goat entrepreneurs in Nglumber Village, Kepohbaru District, Bojonegoro Regency. The analytical tools used in this research are total production costs, revenues, profits and business feasibility. The results obtained from this research are that the average total production costs incurred are IDR 112,362,000 month, the average revenue obtained is IDR 79,916,667/month, and the average profit obtained is IDR 9,430,461/month. The results of calculating 3 (three) feasibility parameters for 6 gibas goat livestock entrepreneurs in Nglumber Village, Kepohbaru District, Bojonegoro Regency, can be seen that the R/C ratio obtained an average value of 1.3 > 1, the B/C ratio obtained an average value equal to 0.13 > 0, and ROI obtained an average value of 7%. Therefore, the gibas goat farming business in Nglumber Village, Kepohbaru District, Bojonegoro Regency is feasible.

A robust bi-level programming model to enhance resilience in supply chain network considering government role, taxing, and raw sale: An iron ore case study

The mining industry plays a substantial role in any economy because of its enormous influence on other sections of a country. Despite the significant challenges in this industry, such as government regulations, taxing policies, raw material sales, and disruptions, there are rare studies that have considered mining supply chain network design. Thus, this paper proposes a mixed-integer linear bi-level programming model under operational and disruption risks to design a robust and resilient mining supply chain network. The government is considered as the leader that aims to decrease the sales of raw materials, boost the export of final products, and maximize its profit. Moreover, the closed-loop mining supply chain is introduced as the follower level that maximizes the total profit. In other words, this study presents a bi-level model that balances the export of raw materials and final products, for the first time. Additionally, a data envelopment analysis approach is applied to rank the candidate mine areas and reduce the model’s complexity. Finally, a real-world case study of the Iranian iron ore supply chain is applied to demonstrate the efficiency and applicability of the addressed model. The results indicate that the effect of raw material price on the upper level is 22% higher than the lower level under the same conditions. Moreover, the exchange rate has a great impact on the objective functions of both levels. Furthermore, Yazd province has great potential to be introduced as a steel pole in Iran.

Analisis Keuntungan Usaha Gula Kelapa Skala Rumah Tangga di Kecamatan Segedong Kabupaten Mempawah

This study aims to determine the magnitude of profits from the business of household-scale coconut sugar in Segedong District Mempawah District. Population in this research is coconut sugar business household scale and selected purposive (intentionally). Data analyst techniques used are total cost, revenue and profit. The results of this study indicate that based on the results of the analysis of the calculation of costs used in the business of coconut sugar household scale. The level of profit of coconut sugar business is Rp. Rp.73.585/day and Rp.1.600/Kg. The analysis shows that this household-scale coconut sugar business is profitable

Data-driven analysis of optimal repositioning policy in bike sharing systems

In this study, we analyze the impact of optimal repositioning policy on the efficiency of bike sharing systems under several performance measures. We first develop a multi-period network flow model to find the optimal repositioning decisions which consist of the origin, destination, and the time of the repositioning that maximize the total profit of a sharing system. We further extend this model to consider demand uncertainty under a stochastic setting. The proposed model is then implemented on the real-world bike-sharing data of New York City, Toronto, and Vancouver. After finding the optimal repositioning policies, we analyze the effect of repositioning on the profit, fulfilled demand, number of required vehicles, and utilization rates of the vehicles. Through computational experiments, we show that repositioning significantly increases the efficiency of a sharing system under these performance measures. In particular, our analyses show that 32% more profit can be obtained and up to 41% more demand can be satisfied with repositioning. Moreover, it is possible to reduce the required fleet size by up to 61% and increase the average utilization rate of the vehicles by up to 21% by employing repositioning.

Optimal Strategy of Unreliable Flexible Production System Using Information System

Background: Optimization approaches and a models can be applied for critical production systems that experience equipment failure, repair delays and product quality control in order to maximize the total profit. We can cite, as an example, flexible manufacturing systems. Methods: Our methodology involves developing a decision model integrated with an information system to coordinate various system operations, ensuring timely response to customer requests. The module of the information system is provided to optimally manage the production flow and parts ordering according to machine availability. The objective is to determine the optimal order thresholds of part batches that maximize the total profit. Results: Numerical results are provided to analyze the influence of system reliability and uncertainty on decision variables, offering insights into the system’s performance and robustness. By using our method, the advancement of the flexible production systems is carried out by addressing key operational challenges and optimizing production processes for enhanced efficiency and profitability. Conclusions: To achieve this, an optimization algorithm is employed to identify optimal solutions that enhance profitability.

IMPACT OF QUANTITY DISCOUNT ON PURCHASING COSTS IN BLENDING TECHNOLOGIES

Blending technologies are important in many sectors of industry, but are most prevalent in the chemical and food industries. They are playing an increasingly important role in the world economy despite the spread of electromobility. Nowadays, in addition to the technological aspects, there is a growing need to look at the logistical aspects, as logistics related costs account for a significant part of the cost of blending technologies. In this research work, the results of the analysis of the impact of quantity discounts, an important aspect of procurement activities related to blending technologies, are presented. A mathematical model is presented that can be used to investigate the impact of quantity discounts of components on profit and product quality. Based on scenario analyses carried out based on the mathematical model, it is demonstrated that the quantity discount can have a significant impact not only on total cost and profit, but also on the quality of the finished blended product.

A bi-level optimization framework for the power-side virtual power plant participating in day-ahead wholesale market as a price-maker considering uncertainty

This paper develops a novel bilevel decision-making framework for a power-side virtual power plant (VPP) comprising refined power to gas (P2G), wherein the upper-level is the cost minimization objective of VPP, while the lower-level is social cost minimization objective of wholesale electricity market. Meanwhile, wind power uncertainty is handled by distributionally robust optimization method. By utilizing Karush-Kuhn Tucker optimality condition and strong duality theory, the bilevel model is interpreted as a tractable single-level mixed integer programming model. Simulation results show that: relative to the models with removing uncertainty and refined P2G device, 1) the proposed approach realizes outstanding economic benefit by obtaining an increase with a rate by 7.47 % and 105.09 % in total actual profit, respectively. Regarding environmental benefit, CO2 emission volume experiences a 0.89 % decline and 0.90 % increase, and the CO2 intensity of the proposed strategy is the lowest with the value of 193. 25kg/MWh. 2) The proposed model can achieve the economic and low-carbon dispatching of the VPP. Wind power curtailment in the proposed model witnesses a trivial increase and reduced by 53.19 %, respectively. 3) The model exhibits merits due to its trade-off between robustness and computational complexity.

Strategic selection between technology spillover and green technology cross-licencing under cap-and-trade policy

ABSTRACT This paper examines the co-opetition strategic selection between two rival manufacturers under a cap-and-trade policy. Based on the classical Cournot game and Nash bargaining game theories, we propose a benchmark model with technology spillover effect and green technology cross-licencing models with both fixed-fee and mixed-fee modes. The results show that manufacturers have the highest outputs in the fixed-fee cross-licencing scenario, and manufacturers’ total profits in the green technology cross-licencing scenarios always outperform that in the technology spillover scenario. Moreover, when the unit carbon quota trading price is high, the technology spillover scenario has the lowest emissions. When the unit cost of governing carbon emissions-related pollution is low, social welfare in the fixed-fee cross-licencing scenario is higher than that in the mixed-fee cross-licencing scenario. Interestingly, the social welfare advantage of the mixed-fee cross-licencing scenario becomes more and more prominent as the unit cost in governing carbon emissions related pollution increases.

A Competitive Newsvendor Problem with Product Substitution under the Carbon Cap-and-Trade System

This study investigates the competitive issues of the newsvendor problem with product substitution under the carbon cap-and-trade system. Building on existing research, this paper introduces the carbon cap-and-trade system under uncertain market demand and considers that the original equipment manufacturer (OEM) can choose to procure raw materials from the contract manufacturer (CM), with both final products being substitutable. Furthermore, we explore the different substitution relationships between OEM and CM products under both pure competitive and co-opetitive modes. For this problem, decision models are developed for various scenarios, and optimal solutions satisfying given conditions are provided. We find that in one-way substitution, under pure competition, an increase in the OEM’s (or CM’s) green investment and substitution rate only leads to an increase in OEM’s (or CM’s) yields, while an increase in the OEM’s (or CM’s) green investment does not necessarily reduce CM’s (or OEM’s) yields. In the co-opetitive mode, an increase in the substitution rate and green investments of both manufacturers may lead to an increase in the yields of both manufacturers. Furthermore, an increase in carbon trading prices does not necessarily inhibit the manufacturer’s yields. Moreover, we find that under the same competition mode, under certain conditions, two-way substitution between OEM and CM can bring better profits to both manufacturers and the entire supply chain. When the two modes are in the same substitution scenario, and the CM cannot substitute for the OEM, the optimal decisions and total supply chain profits of the two modes are equal. Finally, through numerical analysis, we find that neither mode is necessarily optimal when CM can substitute for OEM. Additionally, it is observed that when the same mode is in different substitution scenarios, total supply chain profits may be enhanced in the presence of product substitution.

Large-Scale Conversion of Livestock Blood into Amino Acid Liquid Fertilizer and Dry Protein Feedstuff: A Case Study

Livestock blood, typically considered a waste byproduct of the slaughter industry, has the potential to be a valuable resource in the environmental and agricultural industries owing to its high protein content. This study reports the mechanisms involved in developing a continuous process capable of processing 5 tons/day of livestock blood into high purity amino acid liquid fertilizer and dried protein feedstuff simultaneously. Large-scale processing units were fabricated for the ultrasonic pretreatment and solubilization of proteins, enzymatic degradation of dissolved proteins for amino acid conversion, solid-liquid separation using a membrane filter press to produce high purity amino acid liquid fertilizer, and microwave drying of the solid component to produce dry protein feedstuff. The main processing units were integrated into a continuous, efficient system. The final amino acid liquid fertilizer and dry protein feedstuff contained >20% amino acids and approximately 78% protein, respectively. An economic feasibility analysis of the integrated system based on an annual processing capacity of 3000 tons of livestock blood yielded a total annual profit of 17.4 million euros (5812 euros/ton). This study presents an efficient and profitable approach to repurposing the waste generated by slaughterhouses toward agriculture and feed production.

A comprehensive inventory management model with weibull distribution deterioration, ramp-type demand, carbon emission reduction, and shortages

This study examines the challenges of two warehouses operating under Last-In-First-Out (LIFO) order policies in inventory management, including unpredictable demand patterns, the decay of Weibull distribution, and the need to reduce carbon emissions by adopting green technology. The research addresses various shortage circumstances using advanced inventory modeling techniques to manage ramp-type demand and Weibull distribution deterioration. Additionally, it aims to reduce carbon emissions by incorporating environmentally friendly technologies. By combining advanced inventory modeling with green technology, businesses can effectively manage unpredictable market situations while actively contributing to the global initiative of reducing carbon emissions. It also takes into account various potential backlog scenarios. The proposed model also considers no, partial, and complete shortages and their combinations. This research aims to determine the optimal cycle duration for retailers to increase their total profit while simultaneously investing in green technology. A numerical illustration is provided, and a sensitivity analysis is performed in MATLAB on the optimal solutions concerning parameters to demonstrate applicability.

Integrated optimization of charging infrastructure, fleet size and vehicle operation in shared autonomous electric vehicle system considering vehicle-to-grid

Shared autonomous electric vehicles (SAEVs) are predicted to become a significant solution to reduce global emissions and energy consumption resulting from urban transportation. The centralized operation of SAEVs not only allows large-scale travel demand response but also can provide essential ancillary services to the smart grid through the concept of vehicle-to-grid (V2G). With V2G technology, unused electric vehicles can work as a distributed energy storage facility for the electricity grid to smoothen the intermittent demand. Designing and operating a V2G-enabled SAEV system is challenging. This problem involves complicated planning and operational decisions, as well as time-varying electric tariffs. In this work, a flow-based Integer Linear Programming (ILP) model is formulated for determining the optimal configurations (charging infrastructure and fleet size) and daily operation strategies (serving passengers, relocation, charging/discharging). The developed mathematical model allows for maximizing the total profit, comprising investment cost, revenue from serving passengers, and V2G profit through charging/discharging schedules. A two-stage Benders decomposition-based algorithm is proposed to address the sophisticated ILP problem. Via testing instances in the Manhattan network based on real-world and synthetic data, we have demonstrated the feasibility of our approaches and studied the benefits of integrating V2G in the SAEV system.

Improved cut weight predictions from DEXA scans of lamb carcasses enables more accurate allocation of cuts to processing plans

The value of precise dual energy X-ray absorptiometry (DEXA) cut weight predictions to lamb allocation to cut plans is unknown. Lambs (n = 191) varying in carcase weight (HSCW) and GR (tissue depth over the 12th rib) were DEXA scanned and boned out to weigh retail cuts. Cut weights were predicted using HSCW; HSCW + GR; HSCW + DEXA and HSCW + DEXA image components in GLM models. DEXA improved cut weight predictions in most cuts (P < 0.05). A dataset of 10,000 carcases was then simulated using the associations between HSCW, GR and cut weights, before being truncated to 4500 lambs representing onel day's HSCW distribution. A lamb Carcase Optimisation Tool scenario was developed with 2–3 cut options per carcase section and cut weight thresholds applied to several cuts. Processing costs, market values and actual cut weights were input into the Optimiser to determine carcase allocation to cut options for optimised profits. This scenario was repeated using the predicted cut weights to determine the cut misallocations caused. DEXA-predicted cut weights produced 16.7% and 8.0% less misallocations than HSCW and GR. DEXA produced 20.8% and 14.3% less misallocations than HSCW and GR in shortloins, and 25.5% and 12.9% less in hindquarters. While cut misallocations have little direct impact on total profits, as product is over and under-valued when misallocated, reducing cut misallocations will improve processor compliance when sorting carcases into cut plans- reducing their need to retrim, downgrade and repackage product or the erosion of customer confidence caused by supplying product not meeting market specifications.

Customer-to-customer returns logistics: Can it mitigate the negative impact of online returns?

Customer returns are a major problem for online retailers due to their economic and environmental impact. This paper investigates a new concept for handling online returns: customer-to-customer (C2C) returns logistics. The idea behind the C2C concept is to deliver returned items directly to the next customer, bypassing the retailer’s warehouse. To incentivize customers to purchase C2C return items, retailers can promote return items on their webshop with a discount. We build the mathematical models behind the C2C concept to determine how much discount to offer to ensure enough customers are induced to purchase C2C return items and to maximize the retailer’s expected total profit. Our first model, the base model (BM), is a customer-based formulation of the problem and provides an easy-to-implement constant-discount-level policy. Our second model formulates the real-world problem as a Markov decision process (MDP). Since our MDP suffers from the curse of dimensionality, we resort to simulation optimization (SO) and reinforcement learning (RL) methods to obtain reasonably good solutions. We apply our methods to data collected from a Dutch fashion retailer. We also provide extensive numerical experiments to claim generality. Our results indicate that the constant-discount-level policy obtained with the BM performs well in terms of expected profit compared to SO and RL. With the C2C concept, significant benefits can be achieved in terms of both expected profit and return rate. Even in cases where the cost-effectiveness of the C2C returns program is not pronounced, the proportion of customer-to-warehouse returns to total demand becomes lower. Therefore, the system can be defined as more environmentally friendly. The C2C concept can help retailers financially address the problem of online returns and meet the growing need for reducing their environmental impact.

Extended flexible capacity planning methodology with flight network contribution to the airline fleet assignment problem

Flexible capacity planning is crucial for the airline industry due to the high fixed cost of assets, notably aircraft ownership, and the fluctuating demand, particularly in response to external shocks like pandemics and political crises. Following the negative consequences of such events on the financial positions of almost all airline companies, there is growing interest in developing more responsive capacity planning structures for cost efficiency in the airline industry. In this study, we present a sequential two-phase methodology for flexible capacity planning and airline fleet assignment considering the factors related to fleeting strategies, passenger demand behavior, and contribution of flights in the network. The first phase of the methodology generates potential scenarios to estimate the number of flights to the destinations, based on the data and the reports provided by two prominent associations in the aviation industry, which incorporate various country-related and airline-related factors, including population, income, routes, airports, and connectivity. The second phase identifies the required number of aircraft for each type within the fleet inventory, taking into account the aircraft utilization rates associated with each scenario. The fleet assignment problem is also reformulated to employ the objective of maximizing total profit of the entire flight network by introducing network contribution and nation/region-specific limitations in this phase. A real case study from Turkey is conducted to illustrate efficiency of the proposed methodology. We also present a comparative study to highlight the impact of network contribution and how fleet size affects the outcomes. We have observed that the inclusion of the network contribution aspect and country constraints has led to a significant improvement in financial indicators and connectivity. The results show that the methodology can effectively tackle challenges in airline transportation problems.