Cost Allocation Scheme Research Articles

A generalized data envelopment analysis approach for fixed cost allocation with preference information

Exploring a commonly acceptable fixed cost allocation (FCA) scheme is a challenging yet significant task, since one may contend with conflicting objectives. In this paper, we propose a new data envelopment analysis based FCA technique. The proposed approach aims to balance individual efficiency guarantees and collective preference objectives simultaneously. Particularly, a preference value loss (PVL) index is explicitly constructed to quantify the impact of preference considerations. Moreover, our generalized FCA strategy guarantees a minimal PVL and provides a reasonable and realizable lexicographic evaluation procedure to single out the final FCA scheme. Interestingly, the efficiency invariance and efficiency variance approaches from the prior literature are special cases of our proposed approach. Finally, the proposed approach is validated with a numerical example and an empirical application to bank maintenance fee distribution.

The Basic Core of a Parallel Machines Scheduling Game

Problem definition: We consider the parallel machine scheduling (PMS) under job-splitting game defined by a set of manufacturers where each holds uniform parallel machines and each is committed to produce some jobs submitted to her by her clients while bearing the cost of the sum of completion times of her jobs on her machines. An efficient algorithm for this scheduling problem is well known. We consider the corresponding cooperative game, where the manufacturers are players that want to join forces. We show that collaboration is profitable. Yet, the stability of the cooperation depends on the cost allocation scheme; we focus on the core of the game. Methodology/results: We prove that the PMS game is totally balanced and its core is infinitely large, by developing a sophisticated methodology of linear complexity that finds a line segment in its symmetric core. We call this segment the basic core of the game. Managerial implications: This PMS game has the potential for various applications both in traditional industry and in distributed computing systems in the hi-tech industry. The formation of a partnership among entrepreneurs, companies, or manufacturers necessitates not only a plan for joining forces toward the achievement of the ultimate goals, but also an acceptable agreement regarding the cost allocation among the partners. Core allocations guarantee the stability of the partnership as no subset of players can gain by defecting from the grand coalition. Funding: This work was supported by the Henry Crown Israeli Institute for Business Research, the Coller Foundation, and the Israel Science Foundation [Grant 1489/19].

The coalitional aggregation of wind energy: A stochastic duality approach

The inherent uncertainty of wind power poses a major challenge to modern electricity grid. This paper mainly investigates a scenario where a crowd of wind power producers form a coalition by cooperatively offering their aggregated power output, aiming to mitigate these risks created by the inherent uncertainty of wind power. The main results are as follows. Firstly, a stochastic two-stage programming problem is proposed to formulate the capacity management with respect to a wind power producers’ coalitional game. Secondly, we give a constructive proof to illustrate that a wind power producers’ coalitional game has a nonempty core using the theory of cooperative game. Meanwhile, we also show that the form of a coalition always reduces their expected costs. Thirdly, we propose a stochastic duality approach to design a fair cost allocation scheme for players of a wind power producers’ coalition. Finally, we consider a concave function structure, indicating that there exists economies of scale, and extend this proposed duality theory to the wind power producers’ coalitional game under a concave capacity investment cost structure. Furthermore, we find that a wind power producers’ coalition is balanced under the concave capacity investment cost function. Our results provide an incentive for collaboration strategies in wind power setting, and promote sustainable development of wind power.

Cooperative game theory approach to develop an incentive mechanism for biopesticide adoption through farmer producer organizations

Biopesticides have been recognized as viable alternatives to chemical pesticides in controlling agricultural pests for plants and reducing harmful chemical residues. However, small and marginal farmers are facing challenges while adopting biopesticides, namely, high cost and complicated application techniques, resulting in a low level of farmer acceptance. Accordingly, Farmer Producer Organizations (FPOs), voluntarily formed by farmers, develop mutual technical assistance among their members to solve the technical problems of biopesticide adoption. This study assumes that as a new form of farmer cooperative, FPOs have the potential to promote biopesticide adoption through the implementation of collective pesticide adoption (CPA). Along this line, this paper uses a cooperative game-based hybrid method to develop an incentive mechanism of biopesticide adoption for FPOs to implement CPAs. First, we construct a CPA decision model for mixed pesticides (i.e., biopesticides and chemical pesticides) based on multichoice goal programming (MCGP) to compromise the conflicting objectives regarding cost efficiency and chemical residue reduction, thereby obtaining the optimal total cost of pesticide adoption. Second, recognizing the optimal total adoption cost as a baseline, we devise a cooperative game-based cost allocation scheme to maintain farmers' voluntary participation in FPOs. This study demonstrates that the CPA implemented based on our proposed models can at least match if not surpass the economic and environmental performance of farmers' independent pesticide adoption (IPA). We further demonstrate that the proposed cooperative game solution is more suitable for the FPO's cost allocation issue than the eminent solutions, such as the Shapley value.

Allocating a fixed cost as a reduction of outputs in a non-cooperative decision-making environment: A data envelopment analysis approach

The fixed cost generated from constructing a common platform by all units needs to be allocated among these units in a reasonable way in many management practices. As market information is often incomplete, data envelopment analysis (DEA) approaches have been successfully used in fixed cost allocation problems. The fixed cost was treated as an input measure in previous studies. However, it has no effect on the original inputs and outputs when making the allocation decision after an operating cycle. In this case, it can be regarded as a reduction of outputs rather than an input. This paper developed a new DEA approach based fixed cost allocation model in a decentralized decision-making environment that treats the fixed cost as a reduction of outputs. Unlike the existing approaches addressing efficiencies, the proposed model considers the cost itself in the allocation process. An iterative procedure is then presented to derive the average game cost allocation scheme. A classical numerical example from the prior literature is given to show the applicability of the proposed model. The simulation results show that the developed approach can obtain a unique allocation scheme.

A new benefit-based transmission cost allocation scheme based on capacity usage differentiation

This paper proposes a new transmission cost allocation (TCA) scheme, which includes two parts. The first part deals with identifying the transmission capacity that should be allocated. In this section, a novel scheme is introduced, which determines the beneficial capacity based on various operation conditions over the period, during which the transmission capacity is expected to be fully used. Then, only the cost of the beneficial capacity rather than total transmission capacity is allocated to users. This approach towards TCA brings about important advantages: it promotes transmission sustainability and incentivizes more transmission investments while encouraging users to utilize transmission systems optimally and preventing transmission owners from overinvesting. The second part proposes a new benefit-based algorithm for TCA. This algorithm enjoys significant benefits over existing benefit-based methods: none of the current benefit-based TCA procedures can always generate allocation factors, making them impractical, but the proposed method allocates transmission costs in any transmission network, so it can be applied to real systems. Additionally, unlike the existing benefit-based approaches, the proposed method allocates transmission costs by considering users' benefits of the entire beneficial capacity rather than a minor fraction of it. Numerical examples demonstrate the applicability and validity of the proposed TCA scheme.

Cost allocation in integrated community energy systems — Performance assessment

Integrated community energy systems (ICESs) are a modern development of local energy systems by integrating distributed energy resources and local communities. Cost allocation is one of the key issues affecting the success of ICESs. Costs should be allocated to those who cause them, and benefits to those who make the investments. A well-designed cost allocation approach will therefore contribute to a successful implementation and sustainable development of ICESs. This paper presents a general framework for designing cost allocation schemes in ICESs. Various cost allocation methods are proposed to compute the energy bills for local community members in an ICES. In addition, the cost reflectiveness of different cost allocation methods has been computed based on a case study of an ICES to gain insights into how well the costs are allocated. Next to this, the same is also done for the cost predictability to investigate how the energy costs would change in the long term. The results showed that methods with a single energy charging component perform the best in terms of the two criteria. Our assessment can facilitate local community members in selecting a method that satisfies their requirements. Overall, this research contributes to a successful implementation of cost allocation in an ICES.

Fixed cost allocation considering the input-output scale based on DEA approach

As independent and decentralized entities often share common resources, it is necessary to allocate common costs across these subunits. When there is a lack of specific price information, data envelopment analysis (DEA) approaches have been successfully used to resolve fixed cost allocation problems. To this end, this paper develops a fixed cost allocation scheme based on decision-making unit (DMU) input–output scales that considers both the input consumption and output production scales. The allocation scheme is derived from a one-dimensional case to a general case, in which the input–output scale is calculated based on a common set of weights (CSW) determined using a DEA approach. The proposed method is demonstrated using a classical numerical example from the prior literature and an empirical application that has been applied in previous studies, from which it is found that the fixed cost allocation scheme is more acceptable to the involved DMUs.

Network Connectivity Game

We investigate the cost allocation strategy associated with the problem of providing service /communication between all pairs of network nodes. There is a cost associated with each link and the communication between any pair of nodes can be delivered via paths connecting those nodes. The example of a cost efficient solution which could provide service for all node pairs is a (non-rooted) minimum cost spanning tree. The cost of such a solution should be distributed among users who might have conflicting interests. The objective of this paper is to formulate the above cost allocation problem as a cooperative game, to be referred to as a Network Connectivity (NC) game, and develop a stable and efficient cost allocation scheme. The NC game is related to the Minimum Cost Spanning Tree games and to the Shortest Path games. The profound difference is that in those games the service is delivered from some common source node to the rest of the network, while in the NC game there is no source and the service is established through the two-way interaction among all pairs of participating nodes. We formulate Network Connectivity (NC) game and construct an efficient cost allocation algorithm which finds some points in the core of the NC game. Finally, we discuss the Egalitarian Network Cost Allocation (ENCA) rule and demonstrate that it finds an additional core point.

A new congestion cost allocation method in a deregulated power system using weighting of contractual preferences and geographical location of users

The restructuring of a power system from its monopolistic market to a deregulated system introduces important power flow considerations such congestion in the transmission system due to economic factors like competition in generation. Transmission congestion leads to additional cost for the transmission operator. This paper elaborates a method allocation of this cost amongst users based on their individual utilization of transmission assets. The congestion is relieved by influencing the choices of the consumers in the energy market. The paper proposes a cost allocation scheme based on power flow tracing method, by distinguishing the part of the cost related to the consumers’ market preferences, as opposed to the cost due to their geographical locations in the network before weighting them into a unique cost. This can bring about more transparency and efficiency with regard to payments made by producers and consumers, and also guides them towards choices that alleviate congestions in network.

Price-Directed Cost Sharing and Demand Allocation Among Service Providers with Multiple Demand Sources and Multiple Facilities

Problem definition: We consider capacity sharing through demand allocation among firms with multiple demand sources and multiple service facilities. Firms decide on the allocation of demand from different sources to different facilities to minimize delay costs and service-fulfillment costs possibly subject to service-level requirements. If firms decide to operate collectively as a coalition, they must also decide on a scheme for sharing the total cost. Academic/practical relevance: We study capacity sharing through demand allocation in service systems in the presence of service-fulfillment costs. Our problem is motivated by service collaboration in healthcare involving public–private partnerships. Methodology: We formulate the problem as a cooperative game and identify a cost allocation that is in the core. Results: The cost-allocation scheme we identify is price-directed, and the cost of each firm consists of three components: (1) the delay cost incurred within the firm; (2) a cost paid for the capacity used by the firm at facilities owned by other firms; and (3) a payment received for fulfilling demand of other firms at facilities owned by the firm. Interestingly, we show that the cost-allocation scheme is equivalent to a market equilibrium—that is, it can be implemented in a decentralized fashion. We extend our analysis to settings where the capacity of each facility is endogenously determined and to settings where a service-priority policy is deployed. Our results are robust to a variety of generalizations: partial sharing, partial transfer, facilities modeled as general queueing systems, and convex delay costs. Managerial implications: Our findings provide guidelines for and insights into how to carry out demand allocation and cost sharing among different firms in the presence of service-fulfillment costs to foster service collaboration. In particular, the equilibrium market prices can be viewed as the prices/subsidies for service collaboration in a public–private partnership.

Aggregation-Based Colocation Datacenter Energy Management in Wholesale Markets

In this paper, we study how colocation datacenter energy cost can be effectively reduced in the wholesale electricity market via cooperative power procurement. Intuitively, by aggregating workloads and renewables across a group of tenants in a colocation datacenter, the overall power demand uncertainty of the colocation datacenter can be reduced, resulting in less chance of being penalized when participating in the wholesale electricity market. We use cooperative game theory to model the cooperative electricity procurement process of tenants as a cooperative game, and show the cost saving benefits of aggregation. Then, a cost allocation scheme based on the marginal contribution of each tenant to the total expected cost is proposed to distribute the aggregation benefits among the participating tenants. Besides, we propose proportional cost allocation scheme to distribute the aggregation benefits among the participating tenants after realizations of power demand and market prices. Finally, numerical experiments based on real-world traces are conducted to illustrate the benefits of aggregation compared to noncooperative power procurement.

Reduction of nitrogen pollution in agriculture through nitrogen surplus quotas: an analysis of individual marginal abatement cost and different quota allocation schemes using an agent-based model

Nitrogen (N) pollution has mostly been controlled using command-and-control instruments. However, nitrogen surplus permits (NSPs), which are tradeable, can be more cost-efficient in addressing the problem. To model this instrument, we calculated the individual marginal abatement cost curve for a sample of about 3,400 Swiss farms using farm-optimization models implemented in the agent-based agricultural sector model SWISSland. We also used SWISSland to analyze the effects of two NSP distribution systems (grandfathering and land-based allocation) on different farm types. The results showed that different farm types range in their abatement costs to reduce N surplus from an average of −0.04 CHF kg−1 N on arable farms to 51.06 CHF kg−1 N on special crop farms. We also found that N surpluses hardly explain the level of abatement costs. The biggest differences in effects of the distribution scheme were found in intensive livestock farm types such as pig or poultry farms.

Stochastic Risk Assessment with a Lagrangian Solution for the Optimal Cost Allocation in High-Speed Rail Networks

In large-scale high-speed rail networks (HSRNs), the occurrence of occasional malfunctions or accidents is unavoidable. The key issue considered in this study is the optimal allocation of the maintenance costs, based on the stochastic risk assessment for HSRNs. Inspired by the theoretical risk evaluation methods in the complex network, three major factors, including the local effects, global effects, and component self-effects are considered in the process of assessing the impact on the network components (nodes or lines). By introducing the component failure occurrence probability, which is considered to be an exponential function changing with the component maintenance costs, a feasible stochastic risk assessment model of the HSRNs together with the component impact assessment is proposed that can better unify the impact assessment of both the high-speed rail stations and railways. An optimal allocation algorithm based on a Lagrangian relaxation approach is designed. Correspondingly, the optimal cost allocation scheme can be determined using the algorithm to eliminate the various HSRN risks under the given costs. Furthermore, a real-world case study of the HSRNs in eastern China is illustrated. Compared with the genetic algorithm, the simulation shows that the approach can solve the optimal cost allocation problem to more effectively reduce the risks of large-scale HSRNs in practice.

Co-Opetition in Service Clusters with Waiting-Area Entertainment

Problem definition: Unoccupied waiting feels longer than it actually is. Service providers operationalize this psychological principle by offering entertainment options in waiting areas. A service cluster with a common space provides firms with an opportunity to cooperate in the investment for providing entertainment options while competing on other service dimensions. Academic/practical relevance: Our paper contributes to the literature by being the first to examine co-opetition in a service setting, in addition to developing a novel model of waiting-area entertainment. It also sheds new light on the emerging practice of service clusters and small-footprint retailing. Methodology: Using a queueing theoretic approach, we develop a parsimonious model of co-opetition in a service cluster with a common space. Results: By comparing the case of co-opetition with two benchmarks (monopoly and duopoly competition), we demonstrate that a service provider that would otherwise be a local monopolist can achieve higher profitability by joining a service cluster and engaging in co-opetition. Achieving such benefits, however, requires a cost-allocation scheme that properly addresses an efficiency-fairness tradeoff—the pursuit of fairness may backfire and lead to even lower profitability than under pure competition. Managerial implications: We show that as much as co-opetition facilitates resource sharing in a service cluster, it heightens price competition. Furthermore, as the intensity of price competition increases, surprisingly, service providers may opt to charge higher service fees, albeit while providing a higher entertainment level.

Allocating a fixed cost across decision making units with explicitly considering efficiency rankings

Fixed cost allocation (FCA) is one of the most important applications of the data envelopment analysis (DEA). Numerous studies on this problem have appeared in the literature in the last two decades; however, almost all of them are based on either efficiency invariance or efficiency maximization, both of which focus mainly on efficiency scores. It is notable that the efficiency score is more an intermediate indicator and the corresponding efficiency ranking is actually the main focus. Motivated by this observation, this paper aims to propose a new approach for allocating the fixed cost across decision making units (DMUs) based on efficiency ranking. To this end, the efficiency ranking intervals are explicitly determined by taking allocated costs and feasible weights into account. The results indicate that each DMU can be ranked at any position ranging from the best one to the worst n, and there are fixed cost allocation schemes that can determine the best efficiency ranking of one for each individual DMU simultaneously under a set of common weights. Furthermore, we define a new comprehensive satisfaction degree based on the interval of the upper and lower bounds of the allocated costs. Then, we generate the final allocation scheme using an iterative procedure to maximize the satisfaction degree across all DMUs. Finally, the proposed approach is applied to a classic numerical example from prior literature and an empirical study from the real world to illustrate its usefulness and efficacy.

Decision Support for Collaboration of Carriers Based on Clustering, Swarm Intelligence and Shapley Value

Transportation costs constitute an important part in providing services and goods to customers. How to reduce transportation costs has a significant influence on competitive advantage of carriers. Although a lot of vehicle routing problems (VRP) and their variants have been extensively studied to reduce transportation costs via optimization of vehicle routes, little research focuses on how to achieve lower transportation costs through cooperation of carriers while fulfilling customer requests. This article aims to develop a decision support framework to facilitate cooperation of carriers to reduce transportation costs further based on information sharing, clustering requests, swarm intelligence, and the Shapley value cost allocation scheme. Two decision models for two carriers are compared: one reflecting the scenario without cooperation between the two carriers and the other one reflecting the scenario with cooperation between the two carriers. The simulation results indicate that the swarm intelligence and Shapley value based cooperative decision model outperforms that of the independent decision model.

The integration role of governmental information disclosure platform

PurposeThe purpose of this paper is to provide a feasible scheme for local governments to regulate corporate environmental data fraud and to discuss whether the influence of the construction of online information disclosure platform on the environmental behavior of enterprises is better than the offline spot check.Design/methodology/approachUnder the background of changing environmental fees into taxes in China, this paper conducts evolutionary game analysis between local governments and enterprises in view of the existing problem of environmental data fraud. Furthermore, through the introduction of government information disclosure platform, this paper discusses the impact of the integration of direct government regulation and indirect public concern regulation on the evolution of environmental behavior of both sides. Finally, the evolutionary game is simulated by adopting system dynamics to analyses the implementation effect of different cases on the game process and game equilibrium.FindingsThe results showed that the introduction of information disclosure platform mechanism can effectively suppress the fluctuations existing in the game play and stabilize the game. Moreover, it is worth noting that the regulatory effect of local governments investing part of the monitoring cost in the construction of online information platform is proved to be better than that of putting all the monitoring cost into offline investigation. While optimizing the monitoring cost allocation, the local government still needs to attach great importance to organically combine the attention of the public and media with the governmental official platform.Practical implicationsThe obtained results confirm that the proposed model can assist local government in refining the effects of their environmental regulatory decisions, especially in the case of corporate data fraud under environmental tax enforcement.Originality/valuePrevious literature only suggested that local governments should reduce the cost of supervision to change the corporate behavior to a better direction, but no further in-depth study. Thus, this study fills the gap by discussing the positive transformation effect of local government cost allocation scheme on corporate environmental behavior.

Tri-Level Expansion Planning for Transmission Networks and Distributed Energy Resources Considering Transmission Cost Allocation

A well-designed transmission cost allocation (TCA) scheme should reveal energy users’ actual usage of transmission assets. By investing in distributed energy resources (DERs), a user should be allocated with a lower transmission price if devoted to reducing transmission capacity usage. To evaluate the impacts of TCA, a coplanning framework for centralized transmission networks and DERs is developed in this paper. Distinguished from the existing literature, we consider hourly transmission prices (TPs) that are updated by power flow tracing, together with locational marginal prices (LMPs) to incentivize users’ investments for DERs. This problem is formulated as a tri-level model. On the first level, transmission expansion planning is optimized to satisfy transmission capacity requirements. On the second level, given the hourly LMPs and TPs, users at different buses strategically invest in DERs. On the third level, the market is cleared, and LMPs and TPs are updated. The lower two levels form a market equilibrium problem that can be solved by a diagonalization method. An iterative algorithm is proposed to solve the tri-level model, and convergence analysis is conducted. Case studies based on a modified Garver's 6-bus system and the IEEE 118-bus system demonstrate that a well-designed TCA scheme can incentivize users to invest in DERs, which effectively defers transmission expansion and reduces the system-wide investment costs.

Optimal Planning Strategy for Distributed Energy Resources Considering Structural Transmission Cost Allocation

A well-designed transmission cost allocation (TCA) scheme is able to reflect each consumer’s contributions to the actual usage of transmission assets, thereby generating fair price signals to guide consumers in making investments. In this paper, an optimal planning strategy and a model for distributed energy resources (DERs) are formulated, considering structural TCA. In the proposed TCA scheme, the transmission costs are identified according to the actual usage of transmission assets under different conditions. The effects of DERs on TCA are considered. Because the DERs have a fast-response capability at each time slot, DERs are able to provide power support for the bulk power system in an emergency and thus can efficiently reduce the peak loads of the system and relieve the congestion of transmission assets. This grid-friendly manner allows lower transmission prices, thereby decreasing the DER operators’ transmission costs. Therefore, by strategically investing in and operating DERs, consumers can reduce their electricity energy cost and transmission cost and maximize long-term revenue. To address the uncertainties of load growth and distributed energy fluctuation, stochastic programming is adopted. Restricted by the non-analytical mapping between transmission prices and planning strategies, an algorithm is presented to iteratively solve the problem. Case studies based on a 3-bus test system and IEEE 118-bus system with various DERs verify the effectiveness of the proposed scheme.