Distributed Price Adjustment Based on Convex Analysis

In this paper, we suggest a distributed process of price adjustment toward a partial market equilibrium. As the main contribution, our algorithm of price adjustment is computationally efficient and decentralized. Its convergence properties are crucially based on convex analysis. The proposed price adjustment corresponds to a subgradient scheme for minimizing a special nonsmooth convex function. This function is the total excessive revenue of the market’s participants and its minimizers are equilibrium prices. As the main result, the algorithm of price adjustment is shown to converge to equilibrium prices. Additionally, the market clears on average during the price adjustment process, i.e., by historical averages of supply and demand. Moreover, a global rate of convergence is obtained. We endow our algorithm with decentralized prices by introducing the trade design with price initiative of producers. The latter suggests that producers settle and update their individual prices, and consumers buy at the lowest purchase price.     

Seller’s optimal credit period and replenishment time in a supply chain with up-stream and down-stream trade credits

In practice, a supplier often offers its retailers a permissible delay period M to settle their unpaid accounts. Likewise, a retailer in turn offers another trade credit period N to its customers. The benefits of trade credit are not only to attract new buyers who consider it a type of price reduction, but also to provide a competitive strategy other than introduce permanent price reductions. On the other hand, the policy of granting credit terms adds an additional cost to the seller as well as an additional dimension of default risk. In this paper, we first incorporate the fact that trade credit has a positive impact on demand but negative impacts on costs and default risks to establish an economic order quantity model for the seller in a supply chain with up-stream and down-stream trade credits. Then we derive the necessary and sufficient conditions to obtain the optimal replenishment time and credit period for the seller. Finally, we use some numerical examples to illustrate the theoretical results.     

基于多目标规化粮食最低收购价格模型的研究

基于多目标优化算法,考虑影响粮食最低收购价格的农民收入,种植面积,粮食产量,贸易差距,市场稳定等多个因素,从最优性和重要性两个方向进行改进,提出了具有模糊性的多目标优化算法;然后基于2006年至2016年稻谷和小麦最低收购价格进行研究,发现历年粮食最低收购价格总体处于模型求解范围内,结果表明该算法有效可行.最后利用算法对我国2017年粮食最低收购价格进行预测.     

Parameter estimation for a price series model by solving a linear SDE with a Poisson component

A model of a series of price increments with jumps is constructed based on a linear stochastic differential equation with a Poisson component. Some estimates of unknown parameters of the model and SDE are obtained by using the method of moments. A statistical simulation algorithm for solving an SDE with a Poisson component in general form is proposed. Results of numerical experiments are given.     

Optimal economic growth with foreign trade and a learning industry

Ryder (Ref. 1) has developed a simple two-sector macroeconomic model incorporatinglearning by doing as a determinant of the growth of productivity-enhancing experience and its effect on foreign trade. In this paper, optimal foreign trade and capital allocation policies are determined, in the context of the above model, for ranges of the international trade price not considered by Ryder. An extension of Ryder's model to include a dual trade price structure is briefly discussed. A specific numerical example is used to ascertain the configuration of the various features occurring in the extremal fields at different price levels.     

The optimal pricing and ordering policy for an integrated inventory model when trade credit linked to order quantity

In traditional inventory models, it is implicitly assumed that the buyer must pay for the purchased items as soon as they have been received. However, in many practical situations, the vendor is willing to provide the buyer with a permissible delay period when the buyer’s order quantity exceeds a given threshold. Therefore, to incorporate the concept of vendor–buyer integration and order-size-dependent trade credit, we present a stylized model to determine the optimal strategy for an integrated vendor–buyer inventory system under the condition of trade credit linked to the order quantity, where the demand rate is considered to be a decreasing function of the retail price. By analyzing the total channel profit function, we developed some useful results to characterize the optimal solution and provide an iterative algorithm to find the retail price, buyer’s order quantity, and the numbers of shipment per production run from the vendor to the buyer. Numerical examples and sensitivity analysis are given to illustrate the theoretical results, and some managerial insights are also obtained.     

A closed economy with exogenous uncertainty,different levels of information,money, futures and spot markets

AMoney Game is a model of trade (and production) in which there is a distinguished commodity (real or fiat) which is used as a money; and in which complete rules for the formation of price, as a result of individual strategies, are given. The explicit definition of the games does not necessarily imply the type of solution concept to be used. In this paper we limit our enquiry to the noncooperative equilibrium and its relationship with the competitive equilibrium.     

信用期下时滞变质品供应链库存和定价的Stackelberg均衡策略

在允许缺货和考虑资金机会成本情况下,根据时滞变质品的基本库存模型,分别构建了信用期下供应商为领导者(SL)和零售商为领导者(RL)的Stackelberg博弈模型。通过分析SL和RL下的Stackelberg博弈模型唯一均衡解,得到两个模型中均衡解的解析表达式。最后,根据数值算例分析得出:(1)在SL供应链中信用期并不总使整个供应链协调,然而在RL供应链中信用期的协调效果较好;(2)延长信用期或增加零售价格均能刺激零售商多订货;(3)在两个模型中,零售价格均随变质时刻递增,且整条供应链达到Pareto改进;(4)SL中供应商变动信用期与RL中零售商变动价格相比,SL供应链收益更高;而当信用期和价格固定,其他参数变动时,RL供应链收益更大。     

A note on the optimization of constrained design problems

This paper describes a new algorithm for solving constrained optimization problems, based on a method proposed by Chattopadhyay. The proposed algorithm replaces the original problem withm constraints,m>1, by a sequence of optimization problems, with one constraint. Here, we modify the algorithm given by Chattopadhyay in order to make it applicable for a larger class of optimization problems and to improve its convergence characteristics.     

Stable convergence of semimartingales

Under a nesting condition on the sequence of histories, stable weak convergence of semimartingales to processes with conditionally independent increments is considered. Apart from ensuring the stability property, the nesting condition is more natural in some applications than an alternative measurability condition which appears in the literature.     

Optimal trade credit and order quantity when trade credit impacts on both demand rate and default risk

In practice, to attract new buyers and to avoid lasting price competition, a seller frequently offers its buyers a permissible delay in payment (ie, trade credit). However, the policy of granting a permissible delay in payment adds an additional dimension of default risk to the seller. In contrast to previous researchers for finding optimal solutions to buyers, we first propose an economic order quantity model from the seller's prospective to determine its optimal trade credit and order quantity simultaneously. In addition, we incorporate the important and relevant fact that trade credit has a positive impact on demand rate but a negative impact on receiving the buyer's debt obligations. Then the necessary and sufficient conditions to obtain the seller's optimal trade credit and order quantity are derived. An algorithm to determine the seller's optimal trade credit is also proposed. Finally, we use some numerical examples to illustrate the theoretical results and to provide some managerial insights.     

Limit theorems for the canonical von Mises statistics with dependent data

We study the limit behavior of the canonical (i.e., degenerate) von Mises statistics based on samples from a sequence of weakly dependent stationary observations satisfying the ψ-mixing condition. The corresponding limit distributions are defined by the multiple stochastic integrals of nonrandom functions with respect to the nonorthogonal Hilbert noises generated by Gaussian processes with nonorthogonal increments.     

A note on super-hedging for investor–producers

We study the situation of an agent who can trade on a financial market and can also transform some assets into others by means of a production system, in order to price and hedge derivatives on produced goods. This framework is motivated by the case of an electricity producer who wants to hedge a position on the electricity spot price and can trade commodities which are inputs for his system. This extends the essential results of Bouchard and Nguyen (Math Finance, 2011) to continuous time markets. We introduce the generic concept of conditional sure profit along the idea of the no sure profit condition of Rásonyi (Optimality and risk-modern trends in mathematical finance: the Kabanov Fetschrift, 2009). The condition allows one to provide a closedness property for the set of super-hedgeable claims in a very general financial setting. Using standard separation arguments, we then deduce a dual characterization of the latter and provide an application to power futures pricing.     

The capital cost of holding inventory with stochastically mean-reverting purchase price

Most models of inventory control assume that the per unit purchase price is constant. The capital cost of holding inventory can then be taken into account by adding a fixed interest rate, r, times the purchase price, C, to the out-of pocket holding cost. However, it is not uncommon that the purchase price varies over time. How the capital cost then should be calculated is the focus of the present paper. The paper studies the common single-item inventory model with a fixed set-up cost and assumes that the stochastic purchase price follows the mean-reverting Ornstein–Uhlenbeck process. Methods for computing an adjusted interest rate, r, are suggested along with modifications of well-known heuristics and formulas for lot-sizing. Simulation tests, where the optimal policy has been compared to policies obtained using modified versions of the Silver–Meal method, the Part Period algorithm and the EOQ formula, suggest that r should be estimated as the sum of the unadjusted interest rate and the average expected purchase price decrease, measured over a period between 1/3 and 2/3 of the length of the order cycle.     

Dynamic lot-sizing with price changes and price-dependent holding costs

This paper proposes a new formulation of the dynamic lot-sizing problem with price changes which considers the unit inventory holding costs in a period as a function of the procurement decisions made in previous periods. In Section 1, the problem is defined and some of its fundamental properties are identified. A dynamic programming approach is developed to solve it when solutions are restricted to sequential extreme flows, and results from location theory are used to derive an O(T²) algorithm which provides a provably optimal solution of an integer linear programming formulation of the general problem. In Section 2, a heuristic is developed for the case where the inventory carrying rates and the order costs are constant, and where the item price can change once during the planning horizon. Permanent price increases, permanent price decreases and temporary price reductions are considered. In Section 3, extensive testing of the various optimal and heuristic algorithms is reported. Our results show that, in this context, the two following intuitive actions usually lead to near optimal solutions: accumulate stock at the lower price just prior to price increase and cut short on orders when a price decrease is imminent.     

Retailer’s optimal pricing and lot-sizing policies for deteriorating items with partial backlogging

Pricing is a major strategy for a retailer to obtain its maximum profit. Therefore, in this paper, we establish an economic order quantity model for a retailer to determine its optimal selling price, replenishment number and replenishment schedule with partial backlogging. We first prove that the optimal replenishment schedule not only exists but also is unique, for any given selling price. Next, we show that the total profit is a concave function of p when the replenishment number and schedule are given. We then provide a simple algorithm to find the optimal selling price, replenishment number and replenishment timing for the proposed model. Finally, we use a couple of numerical examples to illustrate the algorithm.     

Scaling transformation and probability distributions for financial time series

The price of financial assets are, since [Bachelier L. Annales de l'Ecole Normale Supérieure 1900;3:XVII:21–86], considered to be described by a (discrete or continuous) time sequence of random variables, i.e., a stochastic process. Sharp scaling exponents or unifractal behavior of such processes has been reported in several works [Mandelbrot BB. J Business 1963;36:394–419; Peters EE. Chaos and order in the capital markets. New York: Wiley, 1991; Mantegna RN, Stanley HE. Nature 1995;376:46–49; Evertsz CJG. Fractals. 1995;3:609–616; Bouchaud JP, Potters M. Théorie des risques financiers. Aléa Saclay, 1997]. In this paper we investigate the question of scaling transformation of price processes by establishing a new connection between non-linear group theoretical methods and multifractal methods developed in mathematical physics. Using two sets of financial chronological time series, we show that the scaling transformation is a non-linear group action on the moments of the price increments. Its linear part has a spectral decomposition that puts in evidence a multifractal behavior of the price increments.     

Massively parallel computation of large-scale spatial price equilibrium models with discriminatory ad valorem tariffs

In this paper, we develop a perfectly competitive spatial equilibrium model in price and quantity variables in the presence of discriminatory ad valorem tariffs, a widely used trade policy instrument. We derive the equilibrium conditions and formulate them as a variational inequality problem. An algorithm is then proposed for the computation of the equilibrium pattern and convergence results established. The algorithm resolves the problem into very simple subproblems, each of which can be solved simultaneously and in closed form. Finally, the algorithm is implemented on the massively parallel Thinking Machines CM-2 and CM-5 architectures, known as the Connection Machines, and numerical results presented.     

Charging scheduling problem of an M-to-N electric vehicle charger

This study investigates a real case of charging scheduling of an electric vehicle charger with multiple ports called M-to-N charger. The charger is designed for a multi-unit dwelling facility and can charge N electric vehicles simultaneously despite the supplied charging capacity being limited to only M electric vehicles. The electric vehicles arrive at the charger randomly and stay for their desired length of time, during which they must be charged as much as possible with minimum electric cost. The scheduling problem considers four objectives: maximizing the total charging amount, minimizing the total charging cost, minimizing the charging completion time, and maximizing the charging balance among the electric vehicles. A mixed-integer linear programming model and a relaxation-based heuristic algorithm are developed. Computational experiment results show that the proposed heuristic algorithm can generate schedules within 8 s for this case study by using an open-source linear programming solver. Compared with the mixed-integer programming algorithm, the proposed heuristic algorithm can provide solutions with less than 7% charging amount gap and 4% price gap. The proposed heuristic algorithm is successfully implemented in a real M-to-N charger.     

A multiple replenishment contract with ARIMA demand processes

This paper is concerned with a multiple replenishment contract with a purchase price discount in a supply chain. The chain is composed of one supplier, one buyer and consumers for a product. The replenishment contract is based upon the well-known (s, Q) policy, but allows us to contract replenishments at a future time with a price discount. Owing to the larger forecast error of future demand, the buyer should keep a higher level of safety stock to provide the same level of service as the usual (s, Q) policy. However, the buyer can reduce his purchase cost by ordering a larger quantity at a discounted price. Hence, there exists a trade-off between the price discount and the inventory holding cost. For the ARIMA demand processes, we present a model for the contract and an algorithm to find the number of the future replenishments. Computational experiments show that the algorithm finds the global optimum solution very quickly.