Cooperative Game Theory Approach Research Articles

Evolutionary game theoretical approach for reducing carbon emissions in a complex supply chain organization

PurposeOur research explores the intricate behavior of low-carbon supply chain organizations in an ever-evolving landscape, emphasizing the profound implications of government-mandated low-carbon policies and the growing low-carbon market. Central to our exploration is applying a combined game theory model, merging Evolutionary Game Theory (EGT) with the Shapley Value Cooperative Game Theory Approach (SVCGTA).Design/methodology/approachWe establish a two-tier supply chain featuring retailers and manufacturers within this novel framework. We leverage an integrated approach, combining strategic Evolutionary Game Theory and Cooperative Game Theory, to conduct an in-depth analysis of four distinct low-carbon strategy combinations for retailers and manufacturers.FindingsThe implications of our findings transcend theoretical boundaries and resonate with a trinity of economic, environmental and societal interests. Our research goes beyond theoretical constructs to consider real-world impacts, including the influence of changes in government low-carbon policies, the dynamics of consumer sensitivities and the strategic calibration of retailer carbon financing incentives and subsidies on the identified ESS. Notably, our work highlights that governments can effectively incentivize organizations to reduce carbon emissions by adopting a more flexible approach, such as regulating carbon prices, rather than imposing rigid carbon caps.Originality/valueOur comprehensive analysis reveals the emergence of an Evolutionary Stability Strategy (ESS) that evolves in sync with the phases of low-carbon technology development. During the initial stages, our research suggests that manufacturers or retailers adopt low-carbon behavior as the optimal approach.

Cooperative game theory approach towards capital equipment procurement in construction projects: a concrete batch plant case study

This study explores how contractors can benefit from joint resource procurement in construction projects, particularly in case with capital-intensive ones. Capital equipment is costly and has a long beneficial life compared to other resources; hence, minimizing its long-term costs is pivotal. Also, the lack of open dialogue under a legal structure in projects may deprive project participants from many beneficial partnering opportunities. Given this background, a comprehensive framework based on cooperative game theory is introduced to form a systematic partnership among contracting parties to share their capital equipment. Cooperative game theory is suggested as the basis for increase the gains to all parties and fair and efficient allocation of the incremental benefits of cooperation. The proposed flowchart is then applied to a real-world project and various coalitional cooperative game theory solutions for dividing gains equitably, from sharing a site-located concrete batch plant among three contractors are evaluated and the most stable cost allocation is identified. Results reveal that with the application of this cooperative procurement, the three contractors can achieve an average of twenty three percent considerable savings through practical alliance that deliver vast improvements in project productivity and performance.

Optimizing healthcare workforce for effective patient care: a cooperative game theory approach

Optimizing healthcare workforce for effective patient care: a cooperative game theory approach

A cooperative game-theory approach for incentive systems in local energy communities

Prosumers have a central role in the context of smart grids, and in particular within local energy communities (LECs), as they are capable of being both energy producers and consumers. In a scenario where peer-to-peer (P2P) energy trading is allowed, prosumers can exchange the energy they produce with other prosumers: the primary outcome of this is the improvement of energy self-consumption across the grid, which leads to decreased transmission losses, as well as lower energy costs and diminished long-term damage to the grid itself. Previous work proposed a mechanism to achieve multiple objectives for a cooperative game theory perspective for small coalitions, but its behavior for coalitions of arbitrary size remains unexplored, and it does not consider the objective of peak shaving. This paper aims to (i) design an algorithm for calculating schedules for coalitions of arbitrary size, (ii) analyze the behavior of this mechanism for large coalitions, (iii) create a new incentive mechanism by proposing new selling functions that ensure that the resulting mechanism would optimize for the objective of peak shaving when all the prosumers work together in one large coalition, and (iv) demonstrate the performance of the existing mechanism in terms of peak shaving, by comparing against the mechanism specifically optimized for this objective. Simulations conducted on data from a grid in Cardiff, UK, reveal that the existing mechanism works particularly well for the non-cooperative game, achieving results for cost reduction and self-consumption almost identical to the cooperative game, no matter the size of the coalitions. More precisely, although all mechanisms achieve optimal peak shaving for the grand coalition, the existing mechanism achieves this objective even within the framework of the selfish game, resulting in a reduction of the peak by approximately 29% compared to alternative methods. Furthermore, the mechanism is proven to optimally achieve peak shaving in both cooperative and non-cooperative cases.

Vibration based single-objective finite element model updating using cooperative game theory approach

This paper introduces a novel approach that focuses on application of cooperative game theory model for automated finite element model updating. The approach involves transforming the conventional single-objective optimization finite element model updating problem into a game theory problem. Among the different alternatives, a cooperative game model is considered to solve the transformed updating problem. This new approach overcomes the limitations of the conventional single-objective optimization formulation, which include challenges related to selecting appropriate weighting factor of residuals and analysing their influence on optimization results. To demonstrate the effectiveness of the proposed approach, an ultralight fibre reinforced polymer footbridge is considered. The numerical model of the footbridge is updated using the experimentally determined inherent properties (natural frequencies and mode shapes) using the proposed approach. A comparison with the conventional sensitivity-based weighted single objective method reveals that the inclusion of the weighting factor in the optimization process under the cooperative game model method allows for more efficient solution of the single-objective updating optimization problem, while maintaining accuracy and quality in the updated model.

Additional Income Distribution between Small Hydropower Plant and Public Trader Using Shapley Value

The small rivers energy usage and regime optimization is considered in the paper. Possibility of additional income reception in case of cooperative behavior of the power plant owners is demonstrated. Cooperative game theory approach is used for the additional income distribution and the small-scale hydro power plants (SHPP) regime planning and management. The obtained results demonstrate the possibility of the current supporting schemes revision, in case if SHPP regime optimization is made taking into account the market conditions.

Design of a Green Supply Chain Based on the Kano Model Considering Pricing

Nowadays, the design of supply chain networks should be based on environmental issues as well as the needs of customers since the main driver of a supply chain network is customers. Continuous innovation of products requires understanding the features that are most important to customers, and product pricing should be carried out in a way that includes the satisfaction of both customers and manufacturers. This study uses the Kano model to classify product features into different categories. The design of the green supply chain network based on the Kano model has not been investigated in the literature so far. This study examines a green supply chain network including multiple manufacturers, product types, distributors, and carriers that is designed based on Kano’s conceptual model of multiple needs. In the proposed mathematical model of this paper, customer demand is a function of the selling price of the product, transportation pollution is minimized, and a solution based on the Cooperative Game Theory approach is used to solve the mathematical model using the GAMS software. One of the advantages of the proposed mathematical model in this research compared to other supply chain models is that the design needs of the supply chain network based on the Kano model (“must-be”, “one-dimensional”, “attractive” and “indifferent”) can be determined based on customer satisfaction. In addition, the price of the product can be determined according to the satisfaction of both customers and the manufacturers.

A cooperative three-player game theory approach for designing an ideal video steganography framework

ABSTRACT This paper presents a cooperative game theory approach to improve the video steganography framework. Wherein, the video steganography framework comprises the following steps: (1) Cover video devising, (2) Secret image pre-processing, and (3) Embedding process. In the first step, the cover video is segmented using scene change detection method. Once the scenes are segmented the motion vectors are identified by Block Matching Motion Estimation Algorithm (BMMEA). Based on these motion vectors, the Region of Interest (ROI) is selected. The selected ROI is grouped using their momentum. In order to add an additional layer of security, the secret image is scrambled using pixel-wise Arnold Transform in the next step. Finally, based on the ROI groups, the scrambled secret data is embedded into the ROI's locations. An ideal and perfect video steganography system should be able to maintain the quality of the video after embedding the secret data. The quality factors of the video steganography approach include perceptible invisibility, payload capacity, and robustness. However, these quality factors are conflicted with each other. In order to address this issue, having these three quality factors as players a cooperative 3-players game theory approach is proposed. This technique provides an optimal solution for the video steganography framework using strategy adaption. The optimal solution is acquired by applying the Iterative Elimination of Strictly Dominant Strategies (IESDS) method. The achieved optimal solution comes up with the best trade-off between the quality factors. Experimental results have deduced the best optimal solution from the proposed three-player game theoretic approach that helps the video steganography approach to resolve the quality and security issues. It is also observed that the optimized proposed method outperforms the contemporary methods by attaining significant outcomes.

Fair travel distances in tournament schedules: A cooperative game theory approach

The most studied problem in sports scheduling, so-called traveling tournament problem (TTP), aims at finding schedules minimizing the total distance traveled by the teams. While minimizing all the traveling between games is efficient from the overall perspective, it overlooks the distribution of the travel among the teams. Consequently, some teams may end up better than others with respect to their individual goals, an imbalance which may affect teams’ often-limited resources or preparedness for the games. This article adopts a cooperative game theory framework to obtain tournament schedules where the distances traveled by the teams are allocated according to fairness criteria. The approach consists of three steps. First, the scheduling problem is reformulated as a transferable utility game. Second, by means of well-established allocation methods, an ideal distance distribution among the teams is determined. Third, we introduce fairness measures to produce a schedule which approximately resembles the ideal distribution. We also discuss the case of not pursuing fairness, but rather a compromise between fairness and minimum total distance. We illustrate the approach by a numerical example in one of the classic TTP data instances.

Bargaining Power in Cooperative Resource Allocations Games

Cooperative game theory provides an appropriate framework to assess the likelihood of conflict resolution, encourage cooperation among parties, and determine each party’s share in resource sharing conflicts. In calculating the fair and efficient allocation of the incremental benefits of cooperation, cooperative game theory methods often do not consider the exogenous bargaining powers of the players based on factors, that are external to the game, such as their political, economic, and military powers. This study reformulates three well-known cooperative game theory methods, namely, Nash-Harsanyi, Shapley, and Nucleolus, to account for the exogenous bargaining powers of the players in cooperative games. Using the Caspian Sea international conflict as an example, this paper shows how the negotiators’ exogenous bargaining power can change the outcome of resource sharing games. The proposed weighted cooperative game theory approach can help determine practical resolutions for real-world conflicts in which the exogenous powers of players can have a significant influence on the outcome of negotiations.

Dynamic Radio Access Selection and Slice Allocation for Differentiated Traffic Management on Future Mobile Networks

The development of future wireless networks focuses on providing services with strict, dynamic, and diverse quality of service (QoS) requirements. In this sense, the network slicing paradigm arises as a critical piece on the efficient allocation and management of network resources, allowing for dividing the network into several logical networks with specific functionalities and performance. This paper aims at finding the best combination of access network and network slices over a heterogeneous environment to fulfill users’ requests and optimize network resources usage. We propose the Dynamic radio Access selection and Slice Allocation (DASA) algorithm, flexibly adapted to network conditions, user priorities, and mobility behavior. DASA is based on a multi-attribute decision making (MADM) and analytical hierarchy process (AHP) to face the complex problem of network selection. Moreover, it uses a cooperative game theory approach to handle load balancing during overload situations. This work presents an integral solution that combines software-defined network (SDN) and network function virtualization (NFV) technologies to improve network performance and user satisfaction. DASA algorithm is evaluated through network-level simulations, focusing on flexibility and the effective utilization of network resources during network selection and load balancing mechanisms.

Nucleolus based cost allocation methods for a class of constrained lane covering games

In truckload transportation procurement networks, shippers purchasing the services of truckload carriers seek to form multi-company tours which consist of regularly scheduled shipments with minimal empty truck movements with the aim of getting better rates from the carriers in return. Identifying the minimum cost collaborative solution and sharing the costs in a fair manner are two interrelated and critical tasks for the success of such networks. The restrictions on the tours which can be formed make both of these tasks very challenging. In this paper, we take a cooperative game theory approach and study the resulting constrained lane covering game. We first show the set of conditions which must be satisfied for the game to have a non-empty core. We propose nucleolus based cost allocation methods. We also develop column and row generation methods for solving these mathematical models. We compare our proposed cost allocation models with alternative cost allocation methods and each other using a set of core stability metrics through computational experiments on randomly generated instances from the literature.

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.

Cost-effective municipal unions formation within intermediate regions under prioritized waste energy recovery

Waste-to-Energy technology is a promising approach, that should help to mitigate risk of energy crisis and to overcome problem with increasing waste generation. The European union actively promotes energy recovery from waste in its policies and limits landfilling of utilizable waste. These legally induced changes substantially affect municipalities: waste treatment becomes more expensive and complex. To react to these up-coming complications, it is beneficial to create municipal unions, focused on the cooperation in waste management operations. Such municipal unions help to lower waste treatment costs and to optimize waste collection. This paper applies general cooperative game theory approach to study how municipalities cooperate within regions of intermediate urban-rural type. Novel distributed dynamic coalition formation algorithm is used to describe process of unions’ creation. Moreover, on the basis of sampling Shapley value, suitable financial transfers within unions are proposed. The paper also presents a review of current applications of game theory to energetics, validates proposed method using the Czech Republic interregional case study and discusses the conducted sensitivity analysis. Results suggest the most suitable municipal unions for adaptation to new waste treatment legislative and demonstrate that through cooperation municipalities are able to save up to 8% of waste treatment costs.

A cooperative robust human resource allocation problem for healthcare systems for disaster management

Similar to other human-made institutes, healthcare systems often experience post-disaster disruptions in performance, which can pose significant threats to the people’s lives in the affected zone. In this study, we develop a cooperative game theory approach to alleviate the negative impacts of such catastrophic events, minimize normal hospital service levels, and reduce undesired expenses. Hence, we propose a linear robust formulation to enable the observation of collaborative behaviors among medical centers, including transferring staff, beds, and patients between hospitals. In our proposed model, information uncertainty is considered the right-hand side parameter (i.e., as coefficients for the decision variables of the constraints). Moreover, the existence of a core in the developed game structure is investigated to demonstrate the stability of the developed cooperative structure. Finally, we generated many numerical examples to evaluate the performance of the model under various circumstances and presented a number of managerial insights.

A stochastic tri-layer optimization framework for day-ahead scheduling of microgrids using cooperative game theory approach in the presence of electric vehicles

This study provides a tri-layer optimization framework in which the microgrid strategy for day-ahead market participation is determined by considering the uncertainties of load, RESs and EVs. Scenario-based method is utilized to deal with uncertainties. Besides, distribution feeder reconfiguration (DFR) and an incentive-based demand response (DR) program are used to enhance scheduling flexibility. In the first layer of the proposed model, the buy/sell bids of the microgrids are determined and sent to the pool market. In the second layer, the market clearing price (MCP) is determined according to the buy/sell bids of microgrids, and in the third layer, the microgrids are scheduled by a cooperative game theory approach. The proposed model is implemented on a 118-bus distribution system consisting of 4 microgrids and the results show that the dynamic topology improves the scheduling flexibility and thus reduces the total operating cost by 9.2%. The simulation results also show that EVs participation in scheduling leads to a reduction in the MCP during peak hours. Finally, the results illustrate that considering energy storage systems (EESs) and DR program leads to a reduction in the MCP and thus a 9.33% reduction in the total operating cost.

Supply network design with uncertain demand: Computational cooperative game theory approach using distributed parallel programming

In this work we investigate a supply chain design problem with uncertain final demands for the end products produced by a set of manufacturers. This network of manufacturers has the organizational decisions made internally by pooling resources in a cooperative manner and externally by determining the dominant strategic actors that are characterized by the retailer. A non-linear production game (NLPG) is formulated as a mathematical programming problem to describe coalition formation among the manufacturers based on initial contractual agreements. We show that NLPG is a grand coalition game when demand distribution has an increasing generalized failure rate. The conditions that impact the profit allocation in the game including core set, fairness, stability, superadditivity, least-core and ∊-core are defined. A core allocation solution is generated by using an algorithmic approach. This algorithmic solution is tested in a distributed work station network and resulted in attaining strong computational results for the proposed mathematical programming problem; optimization results with 1000 players are determined in 6 min 40 s on a university computer network using parallel programming with 52 powerful work stations. The numerical results indicated O(log(-N|)) complexity of the algorithmic solution up to 800 players. To the best of our knowledge, this work is the first of its kind in which distributed parallel processing is implemented in a university work station network with distributed parallel programming and processing for solving a production cooperative game.

A cooperative game theory approach for location-routing-inventory decisions in humanitarian relief chain incorporating stochastic planning

This paper describes a new simulation based mathematical model for locating distribution centres, vehicle routing, and inventory problems under earthquake conditions. In this paper, the proposed network includes affected areas, suppliers, distribution centres, and hospitals. Additionally, the basic infrastructures of the city, which are very fragile at the time of the earthquake, are identified. Then, the demand of each relief commodity is calculated depending on the different earthquake scenarios using simulation. The estimated demand is incorporated into the mathematical model as an uncertain parameter. The proposed methodology is designed as a two-stage model so that in the first stage the location and inventory of distribution centres are addressed. Thereafter, in the second stage the routing decisions are taken for the distribution of relief commodities from the distribution centres and suppliers to the affected areas. Due to the NP-hardness of the second stage model, this model is solved using multi-objective stochastic fractal search. This algorithm is one of the population-based and stochastic optimization techniques and inspired by the natural phenomenon of fractal growth. It should be noted that in the second stage, a cooperative game theory of coalition type is considered, which resulted in synergies that minimize the relief golden time. The kind of cooperation is the use of potential of co-operators’ vehicle. In this stage, the possibility for the players to cooperate by sharing people and commodities transportation requests is considered. Also, to validate the model, a real case study is provided for a possible earthquake in Tehran. Finally, the comparison between the simulation results and the values obtained from the real system evaluates the performance of the implemented model. Considering normality and a 95% confidence interval, it can be concluded that the proposed model provides a precise representation of the real system's performance.

Redistribution of tax resources: a cooperative game theory approach

We consider the problem of how to distribute public expenditure among the different regions of an economic entity after all taxes have been collected. Typical examples are: the regions that make up a country, the states of a federal country, or the countries of a confederation of countries. We model the problem as a cooperative game in coalitional form, called the tax game. This game estimates the fiscal resources collected in each region, or coalition of regions, by differentiating between what comes from economic activity within each region and what comes from trade with the other regions. This methodology provides a measure of the disagreement within a region, or coalitions of regions, with respect to the budget received. Similarly, the stability of a budget allocation can be inferred by its situation within the core of the corresponding tax game. We consider the Spanish case as an example and show that the current regional financial system has a moderate degree of instability. We introduce two budget allocation rules, both borrowed from the cooperative games literature: the balanced allocation, which coincides with the nucleolus and with the Shapley value of the tax game, and the weighted balanced allocation, which coincides with the weighted Shapley value. We compare both budget allocation rules with the current Spanish financial system.

A multi-image hiding technique in dilated video regions based on cooperative game-theoretic approach

This paper presents a cooperative game theory approach to design an optimal video steganography framework. An ideal and perfect video steganography system should provide enough space to embed the secret data without degrading the visual quality. However, in a steganographic system, the payload capacity and visual quality are always inversely proportional to each other. This paper proposes a novel game-theoretic approach using strategy adaption to address this issue. This approach utilizes the Iterative Elimination of Strictly Dominant Strategies (IESDS) method to acquire the optimal solution. The obtained optimal solution comes up with the best trade-off between capacity and visual quality. Also, experimental results infer that the optimal solution outperforms the contemporary methods by attaining significant outcomes.