Revenue management in resource exchange seller alliances

The purpose of this paper is to obtain insight into conditions under which a resource exchange alliance can provide greater profit than the setting without an alliance, and to propose a model to design a resource exchange alliance. We first consider a setting in which customers want a combined product assembled from products sold by different sellers. We show that without an alliance the sellers will tend to price their products too high and sell too little, thereby foregoing potential profit, especially when capacity is large. This provides an economic motivation for interest in alliances, because the hope may be that some of the foregone profit may be captured under an alliance. We then consider a resource exchange alliance, including the effect of the alliance on competition among alliance members. We show that the foregone profit may indeed be captured under such an alliance. The problem of determining the optimal amounts of resources to exchange is formulated as a stochastic mathematical program with equilibrium constraints. We show how to determine whether there exists a unique equilibrium after resource exchange, how to compute the equilibrium, and how to compute the optimal resource exchange.Alliance, resource exchange, pricing, revenue management, stochastic mathematical programming with equilibrium constraints, non-cooperative game

Revenue management in resource exchange seller alliances

The purpose of this paper is to obtain insight into conditions under which a resource exchange alliance can provide greater profit than the setting without an alliance, and to propose a model to design a resource exchange alliance. We first consider a setting in which customers want a combined product assembled from products sold by different sellers. We show that without an alliance the sellers will tend to price their products too high and sell too little, thereby foregoing potential profit, especially when capacity is large. This provides an economic motivation for interest in alliances, because the hope may be that some of the foregone profit may be captured under an alliance. We then consider a resource exchange alliance, including the effect of the alliance on competition among alliance members. We show that the foregone profit may indeed be captured under such an alliance. The problem of determining the optimal amounts of resources to exchange is formulated as a stochastic mathematical program with equilibrium constraints. We show how to determine whether there exists a unique equilibrium after resource exchange, how to compute the equilibrium, and how to compute the optimal resource exchange

A combined computational and experimental investigation of the [2Fe–2S] cluster in biotin synthase

Biotin synthase was the first example of what is now regarded as a distinctive enzyme class within the radical S-adenosylmethionine superfamily, the members of which use Fe/S clusters as the sulphur source in radical sulphur insertion reactions. The crystal structure showed that this enzyme contains a [2Fe–2S] cluster with a highly unusual arginine ligand, besides three normal cysteine ligands. However, the crystal structure is at such a low resolution that neither the exact coordination mode nor the role of this exceptional ligand has been elucidated yet, although it has been shown that it is not essential for enzyme activity. We have used quantum refinement of the crystal structure and combined quantum mechanical and molecular mechanical calculations to explore possible coordination modes and their influences on cluster properties. The investigations show that the protonation state of the arginine ligand has little influence on cluster geometry, so even a positively charged guanidinium moiety would be in close proximity to the iron atom. Nevertheless, the crystallised enzyme most probably contains a deprotonated (neutral) arginine coordinating via the NH group. Furthermore, the Fe···Fe distance seems to be independent of the coordination mode and is in perfect agreement with distances in other structurally characterised [2Fe–2S] clusters. The exceptionally large Fe···Fe distance found in the crystal structure could not be reproduced

The Sample Average Approximation Method for Stochastic Discrete Optimization

In this paper we study a Monte Carlo simulation based approach to stochastic discrete optimization problems. The basic idea of such methods is that a random sample is generated and consequently the expected value function is approximated by the corresponding sample average function. The obtained sample average optimization problem is solved, and the procedure is repeated several times until a stopping criterion is satisfied. We discuss convergence rates and stopping rules of this procedure and present a numerical example of the stochastic knapsack problem