Fixed cost allocation based on the principle of efficiency invariance in two-stage systems

Fixed cost allocation among groups of entities is a prominent issue in numerous organisations. Addressing this issue has become one of the most important topics of the data envelopment analysis (DEA) methodology. In this study, we propose a fixed cost allocation approach for basic two-stage systems based on the principle of efficiency invariance and then extend it to general two-stage systems. Fixed cost allocation in cooperative and noncooperative scenarios are investigated to develop the related allocation plans for two-stage systems. The model of fixed cost allocation under the overall condition of efficiency invariance is first developed when the two stages have a cooperative relationship. Then, the model of fixed cost allocation under the divisional condition of efficiency invariance wherein the two stages have a noncooperative relationship is studied. Finally, the validation of the proposed approach is demonstrated by a real application of 24 nonlife insurance companies, in which a comparative analysis with other allocation approaches is included

Allocating the fixed cost:an approach based on data envelopment analysis and cooperative game

Allocating the fixed cost among a set of users in a fair way is an important issue both in management and economic research. Recently, Du et al. (Eur J Oper Res 235(1): 206–214, 2014) proposed a novel approach for allocating the fixed cost based on the game cross-efficiency method by taking the game relations among users in efficiency evaluation. This paper proves that the novel approach of Du et al. (Eur J Oper Res 235(1): 206–214, 2014) is equivalent to the efficiency maximization approach of Li et al. (Omega 41(1): 55–60, 2013), and may exist multiple optimal cost allocation plans. Taking into account the game relations in the allocation process, this paper proposes a cooperative game approach, and uses the nucleolus as a solution to the proposed cooperative game. The proposed approach in this paper is illustrated with a dataset from the prior literature and a real dataset of a steel and iron enterprise in China

Contest Theory and its Applications in Sports

This paper outlines how the theory of contests is applied to professional team sports leagues. In the first part, we present the traditional Tullock contest and explain some basic properties of the equilibrium. We will then extend this static contest to a two-period model in order to analyze dynamic aspects of contests. In the second part, we will present applications of contest theory in sports. In particular, we will show how the Tullock framework is applied to models of team sports leagues. For this purpose, we will first explain the value creation process in team sports leagues and show how club revenues are related to the contest success function. Then, we present some basic modeling issues; for instance, we show how the assumption of flexible vs. fixed talent supply depends on the league under consideration and how it influences the equilibria. Furthermore, we explicate the effect of revenue sharing on competitive balance in the different models. Then we address the relationship between competitive balance and social welfare. Finally, we illustrate why many clubs tend to "overinvest" in playing talent in many team sports leagues.Contest theory, Tullock contest, sports leagues, competitive balance, revenue sharing, social welfare, overinvestment

The Champions League and the Coase Theorem

The Coase Theorem is both one of the simplest and most profound ideas in economics. Coase’s insight was first expressed in print as a theorem by George Stigler, following the publication of the famous article “The Problem of Social Cost” by Nobel Laureate Ronald Coase (1960). Stigler stated it thus: “with zero transactions costs, private and social costs will be equal”. In this paper the Coase Theorem is approached through the medium of a sports league. While Coase’s article dates from 1960, a colleague at Chicago University published a discussion of the market for baseball players in 1956 which almost completely anticipates the more famous paper (Rottenberg (1956)).Coase

Supply constrained location-distribution in not-for-profit settings

Inspired by the World Food Programme's activity in the post-civil war food crisis in Angola, this study proposes a systematic approach to address the location distribution problem in not-for-profit settings, where a limited volume of supply has to be allocated to different demand regions. The use of utility functions is key in our framework because it allows the decision-maker to establish priorities by representing the heterogeneous effects of distributing supply to different demand locations (location effect) and to different individuals in the same demand location (diminishing returns effect). We propose the use of two fractional objectives with the utility functions embedded into them: an efficiency measure and a new inequity measure related to the Gini coefficient. The suggested problem has the form of a bi-objective integer linear fractional program and our resolution optimization technique is designed to solve for multiple fractional objective measures. Novel analytical results for the worst-case performance of the proposed resolution technique are provided. Our numerical experiments assess computational efficiency and provide concrete managerial prescriptions. Finally, an illustrative application of our approach in the context of the food crisis in Angola is presented based on an efficiency-inequity trade-off analysis.This research was partially supported by Sejong University[Grant 20180391] (Park) and Purdue University [Doug andMaria DeVos] (Berenguer)

Optimal sampling designs for large-scale fishery sample surveys in Greece

The paper presents the quality problem of fishery statistics produced by the conducted land-based and sea-going, large scale sample surveys of the survey programme of the Institute of Marine Biological Resources of the Hellenic Centre for Marine Research (IMBR/HCMR) in Greece, and the optimality strategies developed in their sampling designs for the maximization of precision of the calculated sample estimates for a given cost of sampling.The optimality problems of the sampling designs of the individual large scale sample surveys are described, and the optimality solutions developed under the sampling variance structure are explained.The paper deals with the optimization of the following three large scale sample surveys: biological sample survey of commercial landings (BSCL), experimental fishing sample survey (EFSS), and commercial landings and effort sample survey (CLES)

Fair task allocation in transportation

Task allocation problems have traditionally focused on cost optimization. However, more and more attention is being given to cases in which cost should not always be the sole or major consideration. In this paper we study a fair task allocation problem in transportation where an optimal allocation not only has low cost but more importantly, it distributes tasks as even as possible among heterogeneous participants who have different capacities and costs to execute tasks. To tackle this fair minimum cost allocation problem we analyze and solve it in two parts using two novel polynomial-time algorithms. We show that despite the new fairness criterion, the proposed algorithms can solve the fair minimum cost allocation problem optimally in polynomial time. In addition, we conduct an extensive set of experiments to investigate the trade-off between cost minimization and fairness. Our experimental results demonstrate the benefit of factoring fairness into task allocation. Among the majority of test instances, fairness comes with a very small price in terms of cost