Price-dependent Demand Research Articles

Inventory Models for Deteriorative items with Price Dependent Demand Integrated with Stock Dependent Demand

Inventory Models for Deteriorative items with Price Dependent Demand Integrated with Stock Dependent Demand

Ценовые параметры поставки электроэнергии как базис управления спросом на электропотребление в регионе

Energy cost management (more than 45 % in the structure of total costs) can significantly reduce own energy supply costs and improve operational efficiency of an enterprise. The study examines regional electricity price parameters, affected by retail and wholesale electricity markets, in terms of the possibilities for industrial enterprises to implement price-dependent demand management to reduce energy purchase costs. The article aims to distribute Russian regions according to the prospects for reducing energy purchase costs by managing electricity demand schedules. The following methods were utilised: statistical analysis of hourly average electricity prices per year, month and day in the regional context; mathematical modelling and calculation of authors’ coefficients of average electricity prices and coefficients of daily price volatility for assessing prospects for effective energy demand management in Russian regions; construction of positioning maps for grouping and identifying regions where the implementation of price-dependent demand management is possible. Electricity price parameters of all Russian regions were examined. The paper analysed principles of electricity pricing for domestic industrial enterprises, researched the impact of price volatility on energy purchase costs, assessed the contribution of electricity costs to total energy consumption costs of enterprises. Coefficients of average electricity prices, coefficients of daily price volatility and coefficients of efficiency of price-dependent electricity consumption were applied to study electricity price parameters in the regional context. As a result, the article presented a map of electricity price parameters, where regions are grouped according to the possibility of implementing demand management mechanisms. Additionally, specific practical recommendations on price-dependent demand management for electricity consumption of industrial enterprises were given for each identified regional group.

Approaches to solving the problems of development of Irkutsk power system and improving the reliability of consumers power supply

In the power system of the Irkutsk region since 2020, there has been a significant increase in the maximum electrical load, which amounted to more than 1.2 GW. As a result, the available reserves of power generating capacity and capacity of power grid were exhausted. In the coming years, situations of occurrence of an uncovered power shortage during periods of maximum loads are possible. It is proposed to solve the problems by increasing the flexibility of the power system, reducing the peak consumption and levelling the load schedule, including through price-dependent demand response for electric energy and power through the development of regulatory legal acts in the field of electricity supply, technological connection and improving the payment system for electricity and power, including at the level of utility and household consumers.

The effect of rebate value and selling price-dependent demand for a four-level production manufacturing system

<p style='text-indent:20px;'>Price rebate is only permitted when purchases made by the customer exceed a predefined limit and they later buy other items from the purchaser. There are various forms of rebate used by production companies. This study provides a deteriorating inventory model of four-level production rates and derives the rebate-value-based demand with the product selling price under shortages. This model gives preference to optimal replenishment time, ordering quantity, rebate value, and selling price while maximizing total profit. This model first explores and discusses the demand function, which discretely hinges on the selling price of rebate value, followed by discussions on demand based on the selling price. This study proposes a solution through unique propositions and the construction of two algorithms that are suitable for four-level production; this has not yet been explored in-depth in the literature. Illustrative examples and a sensitivity analysis demonstrate the applicability of the proposed algorithms; the customer decides to buy a product that is larger than the minimum suitable for a price rebate and the buyer can then deal with a higher price rebate. The benefit of rebate marketing helps production companies increases conversion rates and encourages customers to purchase goods. This model demonstrates that proposing rebates can consume substantial pricing and inventory inferences and can result in a substantial increase in profit.</p>

Intelligent control system to increase the flexibility of power systems – the direction of development and a way to solve problems in the Irkutsk power system

In the power system of the Irkutsk region since 2020, there has been a significant increase in the maximum electrical load, which amounted to more than 1.2 GW. As a result, the available reserves of power generating capacity and capacity of power grid were exhausted. In the coming years, situations of occurrence of an uncovered power shortage during periods of maximum loads are possible. It is proposed to solve the problems by increasing the flexibility of the power system, reducing the peak consumption and levelling the load schedule. Including through price-dependent demand response for electric energy and power through the development of regulatory legal acts in the field of electricity supply, technological connection and improving the payment system for electricity and power, including at the level of utility and household consumers.

Joint effects of production quality improvement of biofuel and carbon emissions towards a smart sustainable supply chain management

Controlling carbon emissions and improving biofuel generation are crucial for every manufacturer. The responsible managers’ primary concern is to increase profit and form a sustainable supply chain. Further, supply chain managers select appropriate combinations when dealing with minimizing waste, quality improvement of biofuel, and multi-mode transportation. The study’s objective is to show the combined effects of improved quality of biofuel and controlling carbon emissions in a smart three-echelon sustainable supply chain management. In this model, one-manufacturer, one-supplier, and multi-retailers are contemplated. When the supplier makes impure biofuel, it transports to the manufacturer for pure biofuel. A random production rate is applied through a smart production system; still, impure biofuel is produced. For that reason, a two-stage inspection policy with a variable manufacturing rate is considered to make the production process flexible such that the quantity of impure biofuel is reduced and impure biofuel treated as a waste. After production, the manufacturer transports pure biofuel to multi-retailers. A variable selling price-dependent demand is introduced for the maximization of profit. The carbon emissions are considered, which are associated with different operational activities of inventory such as preparation of setup of suppliers, manufacturers, transportation of products, and holding stock at manufacturers and the retailer’s end. The retailers keep up the actual order as on demand to the manufacturer to save from the excess holding costs. The model has been solved with a specific algebraic procedure to obtain the global optimum solution. Four numerical experiments are conducted to ensure the model’s effectiveness and profit maximization. The results indicate that these three echelons’ smart production significantly minimized impure biofuel and carbon emissions, positively impacting the environment and the finance, attached to the inventory. The proposed integrated system’s validity is illustrated with sensitivity analysis, numerical examples, and graphical representation. In addition, various worthy managerial insights based on the study are provided.

A Reinforcement Learning Approach to Price Cloud Resources With Provable Convergence Guarantees.

How to generate more revenues is crucial to cloud providers. Evidences from the Amazon cloud system indicate that "dynamic pricing" would be more profitable than "static pricing." The challenges are: How to set the price in real-time so to maximize revenues? How to estimate the price dependent demand so to optimize the pricing decision? We first design a discrete-time based dynamic pricing scheme and formulate a Markov decision process to characterize the evolving dynamics of the price-dependent demand. We formulate a revenue maximization framework to determine the optimal price and theoretically characterize the "structure" of the optimal revenue and optimal price. We apply the Q -learning to infer the optimal price from historical transaction data and derive sufficient conditions on the model to guarantee its convergence to the optimal price, but it converges slowly. To speed up the convergence, we incorporate the structure of the optimal revenue that we obtained earlier, leading to the VpQ-learning ( Q -learning with value projection) algorithm. We derive sufficient conditions, under which the VpQ-learning algorithm converges to the optimal policy. Experiments on a real-world dataset show that the VpQ-learning algorithm outperforms a variety of baselines, i.e., improves the revenue by as high as 50% over the Q -learning, speedy Q -learning, and adaptive real-time dynamic programming (ARTDP), and by as high as 20% over the fixed pricing scheme.

Fuzzy Optimal Inventory and Shipment Policy on Non- Coordinated Supply Chain Under Quadratic Price Dependent Demand

Abstract: Inventory management in the supply chain has been discovered to be the key to long-term success. Inventory is important in the supply chain because it balances supply characteristics and consumer demand. In this non-coordinated supply chain model, the merchant determines the appropriate order quantity based on the various expenses involved. Under quadratic price dependent demand, an extension of the lagrangian method is used to optimize fuzzy EOQ, total variable cost of the relevant retailer, and the supply chain. To support the model, a numerical case is solved.

Optimization of a non-instantaneous deteriorating inventory problem with time and price dependent demand over finite time horizon via hybrid DESGO algorithm

This work presents an inventory model with partial backlogged shortages for deteriorating items over finite time horizon. Here, the deterioration rate is considered as a random variable which follows three-parameter Weibull distribution. Also, the demand rate is dependent on time and selling price of the product. The aim of this work is to determine the optimal selling price of the product, optimal number of replenishment cycle and maximum shortage level which maximize the retailer's total profit over the finite time horizon. To maximize the total profit, a new hybrid algorithm is proposed which is based on differential evolution and social group optimization algorithms. This algorithm is named as DESGO algorithm. In this context, a numerical problem, based on the proposed inventory model, is considered and solved by DESGO algorithm. To test the performance and efficiency of the proposed algorithm, the problem is solved by some other popular metaheuristic algorithms available in the existing literature and the obtained numerical results are compared with that of the DESGO algorithm. To justify the comparison, two different non-parametric statistical tests and also ANOVA test are conducted. From these tests, it is observed that the proposed hybrid algorithm performs well. Moreover, the impact of changes of different model parameters on the best-found policy is studied graphically through the sensitivity analysis. Finally, the work is concluded with some fruitful future scopes.

An online algorithm for the inventory retrieval problem with an uncertain selling duration, uncertain prices, and price-dependent demands

We study a multi-period inventory retrieval problem with uncertain selling duration, uncertain future prices, and price-dependent demands. At the beginning of a finite selling horizon, a fixed amount of inventory is available for sale in future periods. In each period, the decision-maker observes the current market price and makes the retrieval decision, and only at the beginning of the last period, he knows that it is the last period. The objective is to maximize the expected total revenue and the difficulty stems from the lack of information on probability distributions of both the selling duration and future market prices, which also causes uncertainty in the random demands. Proposing an online algorithm ALG-IR to solve the problem based on competitive analysis theory, we derive its constant competitive ratio, which is optimal under certain conditions, showing the robust performance of the algorithm. We further indicate that, when applying other price-dependent demand models or considering the impact of reference prices on the current demand, the adjusted algorithm ALG-IR-M retains all the theoretical properties. When considering the holding cost of inventory, a similar algorithm ALG-IR-H is proposed to solve the problem. Through numerical studies, we show the algorithm achieves good performance in both optimality and robustness, especially when the market is not over-supplied or the selling duration will not suddenly end at an early period.

How a novel option contract helps a green product to enter a traditional product's retailing channel: A mathematical modeling approach

Multi-sourcing is a practical strategy in retail channels that allows retailers facing uncertainties to respond more effectively to consumers' needs by distributing market demand across multiple supply sources. In this respect, this research investigates a price-setting retailing channel, including two manufacturers (a traditional manufacturer and a green manufacturer) and one retailer where end-of-life products can be collected. The retailer faces an uncertain price-dependent demand and sets orders from both supply directions based on a low-cost ordering approach. The primary purpose of this study is to develop an optimal solution to allow the green product to enter the retailing channel of the non-green product. Accordingly, this research develops and compares two different contractual mechanisms from the standpoint of the green manufacturer/retailer and the supply chain (SC): (1) a penalty-based contract and (2) a modified call option contract mixed with a cost-sharing mechanism. The main contribution of the current paper is to investigate an option contract for the effective management of a closed-loop supply chain and combine it with a cost-sharing contract as a new approach. Another contribution is that the developed model considers two practical channels for collecting and returning obsolete products to the green manufacturer: (1) the retailer is responsible for collecting and returning obsolete products, and (2) collecting and returning obsolete products is outsourced to a third-party logistics provider (3PL). Eventually, the models' efficiency is verified by investigating different numerical experiments, discovering that the proposed reservation-based contract outperforms the penalty-based contract for both parties' profitability and retailing channel's improvement. Besides, the present study finds that hiring 3PL significantly increases the green manufacturer's profit compared to other scenarios.

Rolling horizon-based heuristics for solving a production-routing problem with price-dependent demand

This paper studies a production-routing problem with price-dependent demand (PRP-PD). The problem is formulated as a mixed-integer nonlinear program (MINLP) and solved within an outer approximation (OA) framework where the primal and master problems are solved iteratively. In order to solve the computationally expensive master problem, which is a mixed-integer program (MIP), four different rolling horizon-based heuristics are developed. The efficiency of the proposed heuristic algorithms is benchmarked against the existing methods in the literature to solve the PRP-PD for both linear and nonlinear demand functions. Our results show that by adopting proper freezing and simplification strategies, the rolling horizon-based heuristic can solve the large problem instances significantly faster than the existing methods in the literature, with little to no reduction in solution quality. For large problem instances with linear demand functions, the proposed heuristic provides solutions within 1.5–3 min. The quality of these solutions deviates at most 3.5% from the solutions provided by the existing exact methods, which take more than 2 h. Our solutions are also higher in quality compared to the near-optimal solutions provided in the literature, and they are achieved with a 90% reduction in computational time. With nonlinear demand functions, one-third of the instances require more than 2 h to be solved by the exact methods in the literature, while they are solved within 0.5–3.5 min using our heuristic, with less than 2% deviation in solution quality.

Application of the cybernetic approach to price-dependent demand response for underground mining enterprise electricity consumption

The article considers a cybernetic model for the price-dependent demand response (DR) consumed by an underground mining enterprise (UGME), in particular, the main fan unit (MFU). A scheme of the model for managing the energy consumption of a MFU in the DR mode and the implementation of the cybernetic approach to the DR based on the IoT platform are proposed. The main functional requirements and the algorithm of the platform operation are described, the interaction of the platform with the UGME digital model simulator, on which the processes associated with the implementation of the technological process of ventilation and electricity demand response will be simulated in advance, is shown. The results of modeling the reduction in the load on the MFU of a mining enterprise for the day ahead are given. The presented solution makes it possible to determine in advance the necessary power consumption for the operation of the main power supply unit, manage its operation in an energy-saving mode and take into account the predicted changes in the planned one (e.g., when men hoisting along an air shaft) and unscheduled (e.g., when changing outdoor air parameters) modes. The results of the study can be used to reduce the cost of UGME without compromising the safety of technological processes, both through the implementation of energy-saving technical, technological or other measures, and with the participation of enterprises in the DR market. The proposed model ensures a guaranteed receipt of financial compensation for the UGME due to a reasonable change in the power consumption profile of the MFU during the hours of high demand for electricity, set by the system operator of the Unified Energy System.

Coordination and Decision of Supply Chain Under

In order to investigate supply chain coordination and decision under customer balking and stochastic demand, the article considers a two-echelon supply chain consisting of one manufacturer with risk-neutral and one retailer with risk-neutral and develops two models in a centralized and a decentralized system and the three contracts are designed to coordinate supply chain and the optimal price and customer balking strategies are obtained. The results show that the revenue and cost-sharing contract can coordinate supply chain under customer balking and price-dependent demand and achieve the Pareto-improvement; the expected sales quantity and expected reduced sales quantity are influenced conversely by the threshold of inventory and probability of a sale under customer balking. In addition, numerical analysis is given to verify the effectiveness of revenue and cost-sharing contract and the paper gives some managerial insights and puts forward to the future work at last.

Inventory decisions on the transportation system and carbon emissions under COVID-19 effects: A sensitivity analysis

The COVID-19 pandemic has created multiple problems in the existing transportation system. The contribution of this study is to guide logistics managers as they make ordering decisions within a disrupted transportation system. In the overall supply chain system, inventory decisions have been either compromised or challenged. Traditional inventory decisions that consider preplanned transportation facilities (and speeds) are currently becoming obsolete, predominantly in post-COVID times due to delays in the delivery of products and higher delivery costs. Therefore, businesses such as retailers must align ordering and pricing decisions to maintain a sustainable profit. To address this issue, this study investigates optimum inventory decisions under the pandemic’s effects while considering the transportation cost as proportional to COVID-19 intensity. This study also considers product deterioration, time-dependent holding costs, price-dependent demands, and carbon emissions from vehicle operation and intends to establish a harmonious relationship among these attributes. The optimization of green technology investment is studied to reduce emissions due to transportation. Some theoretical derivations and numerical examples are given, and they are followed by a sensitivity analysis to extract important managerial insights into the effect of COVID-19. The manager can set an optimal selling price and the cycle length by carefully planning the number of trips in considering the rate of the outbreak and its effect on the increasing transportation cost.

Dynamic pricing, vehicle relocation and staff rebalancing for station-based one-way electric carsharing systems considering nonlinear charging profile

ABSTRACT This study focuses on the imbalance of vehicles in one-way station-based electric carsharing systems (OSECSs). A simulation-based optimization framework is proposed to determine optimal OSECS operation strategies, including dynamic pricing, vehicle relocation, and staff rebalancing. In this framework, the simulation module considers the nonlinear charging profiles for electric vehicles (EVs), EV-dependent road congestion, and price-dependent elastic demands to evaluate the performance of operation strategies. The optimization module triggered by the simulation model provides the optimized configuration of operation strategies based on the genetic algorithm and simultaneous perturbation stochastic approximationTo eliminate the decision variable combinatorial explosion for the optimization problem, we apply the K-means clustering algorithm to cluster stations into small groups of cluster zones, reducing the dimensions of decision variables. A case study in Chengdu demonstrates the proposed simulation-optimization framework’s efficiency and further analyzes the impact of different operation strategies, nonlinear charging mode, and EV-dependent road congestion for the OSECSs.

Quadratic quantity discount contract under price-dependent demand and consumer returns

This study introduces a new type of supply contract, the quadratic quantity discount contract, whose average transfer payment is a quadratic function. Thus, both the total and average quantity discounts increase nonlinearly in order quantity, unlike the typical linear quantity discount contract, whose total discount is nonlinear but average payment is a linear function of order quantity. We reveal the quadratic quantity discount contract’s unique properties by comparing it with linear quantity discount and wholesale price contracts. Our investigation, conducted in basic and closed-loop supply chain situations, reveals the potential of the quadratic quantity discount contract to be considered a serious choice for supply chain managers in various situations. Specifically, we reveal that the quadratic quantity discount contract always coordinates the supply chain, achieving the first-best supply chain profit while always enhancing the profits of all players. This is the most important difference from the typical linear quantity discount contract that entails a profit distribution problem and bargaining power issues. Further, unlike other contracts, its profit improvement performance is predictable; moreover, it offers the acceptable wholesale price with an additional nonlinear average discount. However, when the supplier can exercise its power and only considers its own profit, the typical linear quantity discount contract can be considered the better option, as it produces superior profits for the supplier, but the profits of the retailer and supply chain are sacrificed in this case. If the supplier considers the supply chain’s long-term health and wants voluntary compliance from other players, the quadratic quantity discount contract is the optimal choice.

A profit maximization single item inventory problem considering deterioration during carrying for price dependent demand and preservation technology investment

This paper addresses a single item two-level supply chain inventory model considering deterioration during carrying of deteriorating item from a supplier’s warehouse to a retailer’s warehouse as well as deterioration in the retailer’s warehouse. The model assumes preservation technology in the retailer’s warehouse to prevent the rate of deterioration. An upper limit for the preservation technology investment has been set as a constraint to the model. The model maximizes the retailer’s profit per unit time, simultaneously calculated optimal order quantity. A price dependent demand and storage-time dependent holding cost is considered to develop the model. Some theorems are proven to get optimal values of the total cost. A numerical problem is workout as per the developed algorithm and with the help of MATLAB software to study the applicability of our theoretical results.

Controlling defective items in a complex multi-phase manufacturing system

In manufacturing systems, defective items are produced for machine drift and error. Usually, an imperfect production rate is random, and if the items are not reworked, these are considered trash and harm the environment. The proposed model aims to reduce waste by reworking defective products and maximizing profit. For profit maximization or overall cost minimization of the manufacturing system, setup cost has significant. A discrete investment for each phase is introduced with an inequality investment constraint for reducing the setup cost. Selling price-dependent demand is trained for more generalized applications for various industries. The proposed model is a multi-phase manufacturing system with optimum batch size, selling price, and investment with an irregular, imperfect production rate. Defects are detected at the first inspection, and the reworked items are checked if the reworked items are all non-defective in the second inspection. The model conducts a two-stage inspection. One is for detecting defective items, and another is for checking if all items are not defective after reworking. The model is solved with the Karush–Kuhn–Tucker (KKT) method, and the global maximum profit is obtained. The model shows that all investments should be assigned to maximize the profit and the optimal solution. Reducing setup cost with the investment is better than a constant setup cost.

A study of a lock fuzzy EPQ model with deterioration and stock and unit selling price-dependent demand using preservation technology

A study of a lock fuzzy EPQ model with deterioration and stock and unit selling price-dependent demand using preservation technology