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Product deterioration is a common phenomenon and is overlooked in most contemporary research on the newsboy problem. In this study, we have considered product deterioration in a production–inventory newsboy model based on multiple just-in-time (JIT) deliveries. This model is solved by a classical optimization technique for the manufacturer production size, wholesale price, replenishment plan, and retailer order policy using a distribution-free approach. Moreover, in order to improve business and entice more customers, a return policy and a post-sale warranty policy is adopted in the model. Theoretical development and numerical examples are provided to demonstrate the validity of this approach.

Online retail of agricultural products is an emerging form of online shopping that has enormous value for researching sustainable agricultural product logistics and the sustainability of e-commerce. By reviewing these practices in China, this paper summarizes three models of online retail of agricultural products: community group buying, prepositioned warehousing, and a mixed model in which the former two are carried out simultaneously. This paper considers the uncertainty of demand and applies the newsboy model to obtain the expected profit function of the three models. The paper proves that the objective functions of the optimization models are all convex functions of the supply capacity. The optimal supply strategy and the expression for each business model are then derived. Next, the intervals for enterprises to choose the profit-optimal business model are given and visually demonstrated through graphs. These findings lead to managerial insights: in economically underdeveloped regions, it is appropriate for enterprises to conduct community group buying businesses; in economically developed regions, it is appropriate for enterprises to conduct prepositioned warehouse businesses; and in regions with average economic development, it is appropriate for enterprises to conduct both businesses. Finally, this paper verifies the optimal supply strategy for the online retail model for agricultural products through numerical experiments and sensitivity analyses for different cost parameters.

In order to maximize the benefit of building supply chain, the topology optimization method of building digital supply chain based on genetic neural network is studied. According to the overall structural characteristics of the building digital supply chain, customer demand data, supplier sales data, manufacturer production management data, and environmental policy exception data are collected to form a building digital supply chain data set. As input data, a building digital supply chain demand prediction model based on improved genetic LSTM neural network is constructed. Capture the needs of the building digital supply chain; according to the demand of suppliers, manufacturers and customers in building digital supply chain, a nonlinear 0–1 mixed integer programming model based on the newsboy model is established. According to the various information provided by the supplier, the ant colony algorithm is used to calculate the optimal supplier. After all suppliers are identified, the topology of the digital supply chain is constructed. So far, the optimization of the supply chain has been completed. The experimental data show that this method can accurately predict the demand of construction projects, the maximum error is less than 1.5%, and can obtain the best supplier selection results. Compared with before optimization, the profit of the structural digital supply chain after topology optimization increases the most.

In the present contribution, the innovative nonlinear state marginal price vector model introduced in Toll and Kintzel (CEJOR 27(4):1079–1105, 2019) (plus Errata herein) is enriched to include budgeting problems under agency conflicts. Under asymmetric information, a company owner as principal can only rely on information transmitted to her from her managers as agents. In the related modeling, it is assumed that slack and capital rationing are optimal. The governing budgeting relations are integrated into a nonlinear framework furnished by a multi-period newsvendor approach and are solved numerically by means of a two-step valuation procedure based on two successive nonlinear convex optimizations. The capital market is assumed to be imperfect. As case study, the M&A-valuation case of a merger of two IT-service companies is considered subjected to optimal combined dimensioning of capacities and budgets under stochastic demand. On balance, by addressing agency conflicts within the well-established nonlinear framework, the practical application field of the valuation procedure is widened.

In this study, we consider a carbon emission cap-and-trade system in which the policymaker decides the cap for carbon emissions for each company and also has the power to regulate the carbon price in the carbon trading market for the purpose of minimizing total carbon emissions. We assume that there are n companies regulated in terms of carbon emissions by the policymaker, each of which emits carbon when producing its own product. After learning the carbon cap and carbon price regulated by the policymaker, each company makes simultaneous pricing and production decisions using the quick response strategy, and can trade some of its carbon emissions in the carbon market at the carbon price set by the policymaker, if the carbon emissions are below the cap. We model this non-cooperative game between the policymaker and companies as a Stackelberg game in which the policymaker is the leader and the companies are the followers. We show that there exists an equilibrium for the policymaker’s carbon pricing decisions and each company’s production and pricing decisions. From this equilibrium, we derive a carbon cap for the company at which the amount of traded carbon emissions is zero. This implies that some company’s production and pricing decisions, even under carbon emission restrictions, will be equal to those without the carbon emission restrictions. Also, we find that companies participating in the carbon cap-and-trade system would reduce their carbon emissions through reduced production, but can have a chance to improve profit through control of the product’s selling price.

In the present contribution we present a nonlinear extension of the innovative linear investment-oriented company valuation method and so-called state marginal price vector model of Toll which represents a two-step procedure separated into a base and a valuation approach. As novel aspect we address nonlinear synergy effects in M&A’s. For this purpose we introduce a nonlinear framework within a semi-discrete convex optimization approach. As capital market assumption we simulate an imperfect market. To demonstrate the usefulness of the method, we address a case study of a merger of two IT-service companies. The related valuation and dimensioning of capacities is done by solving a multi-period newsvendor model under stochastic demand. The demonstrated nonlinear framework is shown to be suitable for a wide range of business valuation tasks.

We consider a multi-period staffing problem in a single-shift call center. The call center handles inbound calls, as well as some alternative back-office jobs. The call arrival process is assumed to follow a doubly non-stationary stochastic process with a random mean arrival rate. The inbound calls have to be handled as quickly as possible, while the back-office jobs, such as answering emails, may be delayed to some extent. The staffing problem is modeled as a generalized newsboy-type model under an expected cost criterion. Two different solution approaches are considered. First, by discretization of the underlying probability distribution, we explicitly formulate the expected cost newsboy-type formulation as a stochastic program. Second, we develop a robust programming formulation. The characteristics of the two methods and the associated optimal solutions are illustrated through a numerical study based on real-life data. In particular we focus on the numerical tractability of each formulation. We also show that the alternative workload of back-office jobs offers an interesting flexibility allowing to decrease the total operating cost of the call center.

We address the problem of hedging inventory risk for a short life cycle or seasonal item when its demand is correlated with the price of a financial asset. We show how to construct optimal hedging transactions that minimize the variance of profit and increase the expected utility for a risk-averse decision maker. We show that for a wide range of hedging strategies and utility functions, a risk-averse decision maker orders more inventory when he or she hedges the inventory risk. Our results are useful to both risk-neutral and risk-averse decision makers because (1) the price information of the financial asset is used to determine both the optimal inventory level and the hedge, (2) this enables the decision maker to update the demand forecast and the financial hedge as more information becomes available, and (3) hedging leads to lower risk and higher return on inventory investment. We illustrate these benefits using data from a retailing firm.

Competitive products frequently face volatile market demand, including most seasonal goods. Simultaneously, retailers of seasonal goods frequently provide customers with a choice of prompt or scheduled delivery. Generally, scheduled delivery services are collectively shipped and realized at the end of the selling season. Moreover, because scheduled delivery can reduce delivery costs, a price discount is commonly offered by retailers as an incentive to encourage customers to select scheduled delivery. Accordingly, this study modifies the traditional newsboy model to tackle the ordering and pricing problem involving seasonal goods with lognormal random demand and two delivery options. Additionally, a scheduled delivery willingness function dependent on discount rate and waiting time until the goods are received is proposed to assess the profitable effect on delivery way changing. Finally, an effective and practical economic ordering and pricing model is developed for jointly determining the optimal order quantity and discount rate to maximize the retailer's expected profits. This study presents numerical examples demonstrating that the profit from offering delivery options clearly exceeds that from only offering prompt delivery.

Competitive products frequently face volatile market demand, including most seasonal goods. Simultaneously, retailers of seasonal goods frequently provide customers with a choice of prompt or scheduled delivery. Generally, scheduled delivery services are collectively shipped and realized at the end of the selling season. Moreover, because scheduled delivery can reduce delivery costs, a price discount is commonly offered by retailers as an incentive to encourage customers to select scheduled delivery. Accordingly, this study modifies the traditional newsboy model to tackle the ordering and pricing problem involving seasonal goods with lognormal random demand and two delivery options. Additionally, a scheduled delivery willingness function dependent on discount rate and waiting time until the goods are received is proposed to assess the profitable effect on delivery way changing. Finally, an effective and practical economic ordering and pricing model is developed for jointly determining the optimal order quanti