When to Lock the Volatile Input Price? Procurement of Commodity Components Under Different Pricing Schemes

Problem definition: We study a two-stage supply chain, where the supplier procures a key component to manufacture a product and the buyer orders from the supplier to meet a price-sensitive demand. As the input price is volatile, the two parties enter into either a standard contract, where the buyer orders just before the supplier starts production, or a time-flexible contract, where the buyer can lock a wholesale price in advance. Moreover, we consider three selling-price schemes: Market Driven, Cost Plus, and Profit Max. Academic/practical relevance: This problem is motivated by real practices in the cloud industry. Our model and optimization approach can address similar problems in other industries as well. Methodology: We assume that the input price follows a geometric Brownian motion. To determine the optimal ordering time, we propose an optimization approach that is different from the classic approach by Dixit et al. ( 1994 ) and Li and Kouvelis ( 1999 ). Our approach leads to deeper analytical results and more transparent ordering policy. Through a numerical experimentation, we compare profitability of different parties under different contracts, pricing schemes, and market conditions. Results: The buyer’s ordering policy is determined by a threshold policy based on the current time and input price; the optimal threshold depends on not only the drift and volatility of the input price but also, their relative magnitude. The supplier’s optimal procurement time should be determined by analyzing a trade-off between the holding cost of storing the components and the future input-price movement. Managerial implications: Under the Profit-Max and the Cost-Plus pricing schemes, the time-flexible contract is a Pareto improvement compared with the standard contract, whereas under the Market-Driven pricing scheme, the supplier may be better off under the standard contract. Moreover, although the most favorable scenario for the buyer is under the Profit-Max pricing scheme, the most favorable scenario for the supplier oftentimes is under the Cost-Plus pricing scheme. Furthermore, this study provides valuable insights into impacts of various characteristics of the component market, such as the trend and volatility of the input price, on the expected profit of the supply chain and its split between the two parties.

Carbon Pricing and Complementary Policies—Consistency of the Policy Mix for Decarbonizing Buildings in Germany

While it is widely acknowledged that carbon pricing plays an important role in driving the transition towards a low-carbon energy system, its interaction with complementary instruments is discussed controversially. The analysis of combining carbon pricing with complementary policies has been mostly focused on the electricity sector, while the role of carbon pricing in the buildings sector has received only minor interest. In view of the newly introduced carbon pricing scheme for the buildings and transport sector in Germany, we analyze the interactions between the carbon pricing scheme with the existing policy instruments and assess the consistency of the policy mix for decarbonizing the buildings sector. Our analysis finds that the introduction of carbon pricing has a reinforcing effect on the instrument mix and adds to the consistency of the policy mix. The results highlight the importance of complementary policies in order to achieve deep decarbonization in the buildings sector. We conclude that carbon pricing, preferably implemented as a tax with a predictable and increasing price level, needs to be supplemented with a powerful mix of complementary measures.

Simulation-Based Dynamic Traffic Assignment with Continuously Distributed Value of Time for Heterogeneous Users

The value of time (VOT) attribute is usually utilized to represent the trade-off between time and monetary expenses in transportation problems. A good representation of VOT is essential for evaluation of any road pricing scheme. Conventionally, in dynamic traffic assignment models, VOT is considered as either constant or finite discrete among travelers because of memory and computational limitations, which in turn could introduce bias in the results. This research explicitly models the individual bi-criteria dynamic user equilibrium (IBDUE) problem and presents a distinct simulation-based solution algorithm that enables individual-based traffic assignment within reasonable run time with a successful implementation of variable and continuously distributed VOT in a simulation-based dynamic traffic assignment package. Numerical analysis reveals that the constant and discrete VOT models tend to overestimate toll road usage compared with the continuous VOT model when the toll charge is low, and underestimate it when the toll charge is high, which reflects previous studies. In the meantime, an experiment on a real-world congestion pricing scheme demonstrates the capability of the proposed algorithm on evaluating flow-dependent pricing schemes.

Welfare and research and development incentive effects of uniform and differential pricing schemes

This paper is about the application of optimization methods to the analysis of three pricing schemes adopted by one manufacturer in a two-country model of production and trade. The analysis focuses on pricing schemes—one uniform pricing scheme, and two differential pricing schemes—for which there is no competition coming from the so-called parallel trade. This term denotes the practice of buying a patented product like a medicine in one market at one price, then re-selling it in a second so-called gray market at a higher price, on a parallel distribution chain where it competes with the official distribution chain. The adoption of pricing schemes under which parallel trade does not arise can prevent the occurrence of its well-documented negative effects. In the work, a comparison of the optimal solutions to the optimization problems modeling the three pricing schemes is performed. More specifically, conditions are found under which the two differential pricing schemes are more desirable from several points of view (e.g., incentive for the manufacturer to do Research and Development, product accessibility, global welfare) than the uniform pricing scheme. In particular, we prove that, compared to the uniform pricing scheme, the two differential pricing schemes increase the incentive for the manufacturer to invest in Research and Development. We also prove that they serve both countries under a larger range of values for the relative market size, making the product more accessible to consumers in the lower price country. Moreover, we provide a sufficient condition under which price discrimination is more efficient from a global welfare perspective than uniform pricing. The analysis applies in particular to the case of the European Single Market for medicines. Compared to other studies, our work takes into account also the possible presence in all the optimization problems of a positive constant marginal cost of production, showing that it can have non-negligible effects on the results of the analysis. As an important contribution, indeed, our analysis clarifies the conditions—which have been overlooked in the literature about the mechanisms adopted to prevent parallel trade occurrence—that allow/do not allow one to neglect the presence of this factor. Such conditions are related, e.g., to the comparison between the positive constant marginal cost of production, the parallel trade cost per-unit, and the maximal price that can be effectively charged to the consumers in the lower price country.

A Comprehensive Analysis of Demand Response Pricing Strategies in a Smart Grid Environment Using Particle Swarm Optimization and the Strawberry Optimization Algorithm

In the modern world, the systems getting smarter leads to a rapid increase in the usage of electricity, thereby increasing the load on the grids. The utilities are forced to meet the demand and are under stress during the peak hours due to the shortfall in power generation. The abovesaid deficit signifies the explicit need for a strategy that reduces the peak demand by rescheduling the load pattern, as well as reduces the stress on grids. Demand-side management (DSM) uses several algorithms for proper reallocation of loads, collectively known as demand response (DR). DR strategies effectively culminate in monetary benefits for customers and the utilities using dynamic pricing (DP) and incentive-based procedures. This study attempts to analyze the DP schemes of DR such as time-of-use (TOU) and real-time pricing (RTP) for different load scenarios in a smart grid (SG). Centralized and distributed algorithms are used to analyze the price-based DR problem using RTP. A techno-economic analysis was performed by using particle swarm optimization (PSO) and the strawberry (SBY) optimization algorithms used in handling the DP strategies with 109, 1992, and 7807 controllable industrial, commercial, and residential loads. A better optimization algorithm to go along with the pricing scheme to reduce the peak-to-average ratio (PAR) was identified. The results demonstrate that centralized RTP using the SBY optimization algorithm helped to achieve 14.80%, 21.7%, and 21.84% in cost reduction and outperformed the PSO.

Resource tokenization for crowdfunding of wireless networks

This paper presents a novel blockchain-based spectrum tokenization method used to crowdsource wireless network deployment projects. Crowdsourcing is a method of financing certain projects and ideas through the funds collected by individuals or businesses in an open marketplace. The method presented in this paper finances the wireless network deployment projects belonging to service providers or governments. The method tokenizes proposed novel wireless resource units, and sells these units to investors. A new Value Unit Per User (VUPU) resource unit is introduced with a new pricing scheme depending on a load of a base station. A novel Proof of Data Load (PoDLO) consensus algorithm is proposed which is used to verify data and traffic load of a base station. Device Diversity Factor (DDF) and Subscriber Unique Permanent Identifier (SUPI) Factor (SUF) are proposed new ways to determine the value of a base station and a network cluster.