Comprehensive Utility Function Research Articles

A novel approach to optimize an integrated network design and pricing of a healthcare supply chain

Adopting various pricing policies has been highly regarded in recent years for setting prices and increasing firms' profits. One of the most common steps in pricing is to identify costs. Since a significant part of costs is related to the corresponding supply chain, many researchers in different fields have used decision-making for simultaneous pricing and network design. However, there is no such approach in the field of healthcare. This paper tries to fill this gap by formulating a mixed-integer nonlinear bi-level programming model examining the interaction of hospitals and their medicines suppliers. At the upper level, there is a competitive market where a new firm (entrant) intends to enter the market and faces the challenge of pricing medicines and making network design decisions. At the lower level, there is a private hospital competing with a public hospital, and it also struggles with healthcare services pricing and supplier selection. A comprehensive utility function that considers healthcare services prices, quality, waiting time, health insurance, readmission rate, and referral rate is extended at this level. Three novel meta-heuristic algorithms are recommended, including bi-level, improved fruit fly, jellyfish optimization, and forensic-based investigation optimization algorithms to solve the presented complex mathematical problem.

High-fidelity entanglement routing in quantum networks

Quantum networks, endowed with their distinct quantum properties, are poised to emerge as indispensable platforms for the implementation of quantum information technology applications in the near future. Within the realm of quantum networks, the transmission of remote quantum information presents a pivotal and intricate challenge, commonly referred to as the entanglement routing problem. While previous efforts have delved into the intricacies of entanglement routing, there has been a notable oversight concerning the crucial parameter of entanglement fidelity. Hence, this paper introduces a novel Purification-enabled Entanglement Routing Algorithm (PERA). Initially, PERA utilizes the network throughput as the routing metric and ensures fidelity guarantees on all nodes in the link through a hop-by-hop purification process. Subsequently, acknowledging the probabilistic nature of entanglement purification, a strategy involving multiple attempts at purification is employed. Finally, a comprehensive utility function is deployed to address the path selection problem for multiple source–destination pairs. Numerical simulations confirm that PERA establishes entanglement connections with outstanding fidelity guarantees. Concurrently, PERA outperforms the baseline algorithm in terms of network throughput and resource utilization.

A Deep Learning Approach for Mobility-Aware and Energy-Efficient Resource Allocation in MEC

Mobile Edge Computing (MEC) has emerged as an alternative to cloud computing to meet the latency and Quality-of-Service (QoS) requirements of mobile devices. In this paper, we address the problem of server resource allocation in MEC. Due to the dynamic load conditions on MEC servers, their resources need to be used intelligently to meet the QoS requirements of the users and to minimize server energy consumption. We present a novel resource allocation algorithm, called Power Migration Expand ( PowMigExpand ). Our algorithm assigns user requests to the optimal server and allocates optimal amount of resources to User Equipment (UE) based on our comprehensive utility function. PowMigExpand also migrates UE requests to new servers, when needed due to the mobility of users. We also present a low cost Energy Efficient Smart Allocator (EESA) algorithm that uses deep learning for energy efficient allocation of requests to optimal servers. The proposed algorithms consider varying load of incoming requests and their heterogeneous nature, energy efficient activation of servers, and Virtual Machine (VM) migration for smart resource allocation and, thus, is the first comprehensive approach to address the complex and multidimensional resource allocation problem using deep learning. We compare our proposed algorithms with other resource allocation approaches and show that our approach can handle the dynamic load conditions better. The proposed algorithms improve the service rate and the overall utility with minimum energy consumption. On average, it reduces 26% energy consumption of MESs and improves the service rate by 23%, compared with other algorithms. We also get more than 70% accuracy for EESA in allocating the resources of multiple servers to multiple users.

A Cross-Layer Routing Protocol Based on Quasi-Cooperative Multi-Agent Learning for Multi-Hop Cognitive Radio Networks.

Transmission latency minimization and energy efficiency improvement are two main challenges in multi-hop Cognitive Radio Networks (CRN), where the knowledge of topology and spectrum statistics are hard to obtain. For this reason, a cross-layer routing protocol based on quasi-cooperative multi-agent learning is proposed in this study. Firstly, to jointly consider the end-to-end delay and power efficiency, a comprehensive utility function is designed to form a reasonable tradeoff between the two measures. Then the joint design problem is modeled as a Stochastic Game (SG), and a quasi-cooperative multi-agent learning scheme is presented to solve the SG, which only needs information exchange with previous nodes. To further enhance performance, experience replay is applied to the update of conjecture belief to break the correlations and reduce the variance of updates. Simulation results demonstrate that the proposed scheme is superior to traditional algorithms leading to a shorter delay, lower packet loss ratio and higher energy efficiency, which is close to the performance of an optimum scheme.

Improving physical layer security and efficiency in D2D underlay communication

Device-to-device (D2D) communication is a core technology for expanding the next generation wireless cellular network. To deal with the security challenges and optimize the system communication quality, this paper investigates the security and efficiency problem of D2D underlay communication in a base station cell area with the presence of malicious eavesdroppers. Fairness and strategy space of both D2D User Equipment and Cellular User Equipment are taken into consideration under the control of Efficiency Functions. The optimization problems are formulated as a game model series of utility functions built on the unit price of jamming power and the amount of jamming service. We extracting the system into a price bargain game with a buyer and a seller both desiring maximum profits, a bargaining game approach is adopted to solve this problem by reaching an agreement of unit price. The step number of bargain process is also a restriction under consideration. For the non-steps model, an Evaluation Function and a Comprehensive Utility Function are demonstrated to analyze the bargain process. For steps-contained model, the step number of iteration is involved and an attenuation function is introduced to modify the bargaining game. The algorithms of two models are proposed to derive the equilibrium point for reaching an agreement. Finally, extensive simulations are illustrated for verifying proposed theory.

Service-Oriented Dynamic Connection Management for Software-Defined Internet of Vehicles

Internet of vehicles (IoV) is an emerging paradigm for accommodating the requirements of future intelligent transportation systems (ITSs) with the overwhelming trend of equipping vehicles with versatile sensors and communications modules, and facilitating drivers and passengers with a variety of innovative ITS applications. However, the implementation of IoV still faces many challenges, such as flexible and efficient connections, quality of service guarantee, and multiple concurrent support requests. To this end, in this paper we introduce the software-defined IoV (SD-IoV), which is able to tackle the above-mentioned issues by adopting the software-defined networking framework. We first present the architecture of SD-IoV and develop a centralized vehicular connection management approach. Then, we aim to allocate dedicated communications resources and underlying vehicular nodes to satisfy each service. We formulate the dynamic vehicular connection as an overlay vehicular network creation (OVNC) problem. A comprehensive utility function is also designed to serve as the optimization objective of OVNC. Finally, we solve the OVNC problem by developing a graph-based genetic algorithm and a heuristic algorithm, respectively. Extensive simulation results are provided to demonstrate the effectiveness of our proposed solution of dynamic vehicular connection management.

Analysis on Impact of Individual’s Bid Propensity on Optimal Bidding of “Naive-Rational” Bidders

This paper considers the emotional factors of the naive and rational bidder in the auctions, and introduces it in the bidder’s utility function. We study the impact of individual’s bid propensity on optimal bidding in the different case of “naive-rational” bidders’ value judgments by establishing the naive and rational bidder’s comprehensive utility function including monetary utility and emotional utility. Further, we analyze who is the last winner in the condition of difference of individual’s bid propensity.

Integration of a Capacity-Constrained Workplace Choice Model: Recent Developments and Applications with an Agent-Based Simulation in Singapore

Destination choice models can be embedded in transport and land use models to understand travel and location choice behavior and to forecast scenarios. Utility-maximizing destination choice models can account for individual behavior and make them suitable for agent-based models, while processing destination capacities is also in line with agent-based modeling. This paper addresses the possibility and impact of introducing capacity constraints, their effect on choice behavior, and the feasibility of applying an approach like this in agent-based microsimulations with individual characteristics for each agent. Here, a comprehensive workplace choice model and its application in a large-scale simulation case study for Singapore are described; one technical and one methodological achievement are highlighted. Technical achievement benefits from recent computational advances; the workplace choice model is estimated with a comprehensive utility function on a large data set with 103 destinations. Reasonable model fit and robust parameters are achieved while obviating sampling techniques; resulting parameters are efficiently applied to the entire 5.4 million Singapore population and validated with survey data. For methodological innovation, capacity limitations are introduced at workplaces to avoid oversaturation. A robust optimization method based on shadow prices is proposed to accommodate capacity limitations at all workplaces during the choice model application defined above. The proposed method efficiently assigns commuters to unused workplaces while respecting individual commuter preferences. Validation of the simulation results, by comparing travel time distributions for commuting trips reported in travel diary data, shows that the model fits well with observed data.

Conformity and Reciprocity in the 'Exclusion Game': An Experimental Investigation

Sacconi and Grimalda (2002, 2005a, 2005b) introduced a model in which two basic motives to action are understood as different type of preferences and represented by a comprehensive utility function: the first is consequentialist motivation, whereas the second is a conditional willingness to conform with an ideal, or a moral principle, which they call a conformist, or ideal, motive to action. A moral ideal is meant as a normative principle of evaluation for collective modes of behaviours which provides agents with a ranking of states of affairs resulting from strategic interaction expressing a greater or lesser consistency with the ideal. The principle moreover is seen as resulting from a (possibly hypothetical) contract between the agents involved in the interaction in an ex-ante phase. Thus, the normative principle boils down to a social welfare function that measures the consistency of outcomes with the normative prescriptions provided by the ideal. Hence, agents understand their own and any other agent’s degree of conformity in terms of their contribution to carrying out the ideal given the others’ expected action, and a person’s own motivation to act in conformity with the principle increases with others’ (expected) conformity. In other words, individual conformity with the principle is conditional on others’ conformity with it, as perceived by the agent. This peculiar feature of reciprocity over others’ behaviour calls for an extension of the usual equipment of decision theory, which is provided by the theory of Psychological Games (Geanakoplos et al., 1989). In this paper we design an experiment for preliminary exploration of the empirical validity of the conformist preferences model, applying it to a simple non cooperative game(the Exclusion Game) meant as the problem of dividing a sum between two active players and a third, dummy player (passive beneficiary). Results are encouraging. Behaviours dramatically change passing from the simple exclusion game to a three steps game, in which once the players have first played the typical non cooperative exclusion game, and before playing it again, they participate in a middle phase, where they anonymously agree on a principle of division. Having agreed on a principle, even though this agreement does not implies reputation effects nor is externally enforceable, induces a substantial part of players - who acted selfishly in the first step - to conform to the principle in the third phase. The additional condition being that they believe the other players will also conform to the agreed principle (what here does happen, as a mater of fact). These results strictly accord with the prediction of the conformist preferences model, but cannot be accounted for by alternative theories of reciprocity.

The Constitution of the Non-Profit Enterprise: Ideals, Conformism and Reciprocity

We provide an account of the non profit enterprise based on the motivations of the agents involved. Our main idea is that these are ex-post motivated by both self-interest and a conditional willingness to conform to their ex ante accepted constitutional ideology, which are weighed up in a comprehensive utility function. Ideology is shaped as the result of a hypothetical 'social' contract between the relevant figures participating in the venture, in particular an entrepreneur, a worker, and a consumer who acts as a dummy beneficiary in the ex-post stage. It can thus be defined as a normative principle of fairness that boils down to a distributive social welfare function defined over the outcomes of a game, which permits to order them according to their conformity to the constitutional ideology. For conformist preferences depend upon expectations of reciprocal conformity to a normative principle, defined on social states described in as much they conform to an ideal, then the agents' model of choice asks for the adoption of the psychological games approach, where payoff functions range over not only the players' strategies but also their beliefs. If the conformist prompt to action is sufficiently strong then the outcome in which both the active agents perform an action improving the quality of the good with respect to the free market standard, thus maximising the surplus of the consumers, results in a psychological Nash equilibrium of the game. We associate this outcome, and the corresponding norm of behaviour, with the constitution of the non profit enterprise. We also show that the structure of the interaction is a coordination game, thus calling for the necessity of devices such as codes of ethics to solve the coordination problem.