Preference Constraints Research Articles

Time preferences and energy consumption of rural household in China

Recent empirical evidence suggests that time preferences have significant effects on intertemporal investment, saving behavior and asset pricing. However, limited research has been conducted on the impact of time preferences on energy consumption. This study aims to investigate the relationship between time preferences and energy consumption among rural households in China, using survey data collected on energy consumption patterns. The results reveal that as time preferences increase, there is a reduction in the diversity of energy sources used by households, and this finding remains robust after rigorous testing. Moreover, our findings support the applicability of the energy ladder theory in rural China, indicating that higher household income can mitigate the inhibiting effect of time preferences on the transition and improvement of energy sources. Additionally, policies promoting energy accessibility, raising awareness about high-quality energy sources, and facilitating credit access significantly promote rural households' advancement on the energy ladder toward superior-quality sources. These policy measures, along with heightened awareness and credit access, can alleviate time preference constraints on the energy transition of rural households. Consequently, we recommend enhancing future policies focusing on efficiency and providing customized financial support for energy initiatives.

Community Preserving Social Recommendation with Cyclic Transfer Learning

Transfer learning-based recommendation mitigates the sparsity of user-item interactions by introducing auxiliary domains. Social influence extracted from direct connections between users typically serves as an auxiliary domain to improve prediction performance. However, direct social connections also face severe data sparsity problems that limit model performance. In contrast, users’ dependency on communities is another valuable social information that has not yet received sufficient attention. Although studies have incorporated community information into recommendation by aggregating users’ preferences within the same community, they seldom capture the structural discrepancies among communities and the influence of structural discrepancies on users’ preferences. To address these challenges, we propose a community-preserving recommendation framework with cyclic transfer learning, incorporating heterogeneous community influence into the rating domain. We analyze the characteristics of the community domain and its inter-influence on the rating domain, and construct link constraints and preference constraints in the community domain. The shared vectors that bridge the rating domain and the community domain are allowed to be more consistent with the characteristics of both domains. Extensive experiments are conducted on four real-world datasets. The results manifest the excellent performance of our approach in capturing real users’ preferences compared with other state-of-the-art methods.

Exploring the duality of disruptive innovation: a technology-utility model analysis of market encroachment

This study explores and explains how duality enables disruptive innovation to encroach on the market and redefine its boundaries under constraints of consumer preferences, purchasing power, technological performance, and complementary technologies. The findings indicate (1) disruptive innovation introduces a new value dimension into the market and enhances the heterogeneity of consumers’ demand, which creates prerequisites for its market encroachment while avoiding competing directly with incumbent enterprises; (2) when considering purchasing power constraints, the disadvantage of disruptive innovation in the preexisting value dimension becomes a price advantage of encroaching on the low-end market; (3) under the constraint of complementary technologies, disruptive innovation can open up new markets that incumbent enterprises have not yet touched by virtue of its advantages in the new value dimension; (4) disruptive innovation does not rely on technological performance to encroach on the market, indicating technological performance is not a necessity for identifying disruptive innovations.

Decision-Maker’s Preference-Driven Dynamic Multi-Objective Optimization

Dynamic multi-objective optimization problems (DMOPs) are optimization problems where elements of the problems, such as the objective functions and/or constraints, change with time. These problems are characterized by two or more objective functions, where at least two objective functions are in conflict with one another. When solving real-world problems, the incorporation of human decision-makers (DMs)’ preferences or expert knowledge into the optimization process and thereby restricting the search to a specific region of the Pareto-optimal Front (POF) may result in more preferred or suitable solutions. This study proposes approaches that enable DMs to influence the search process with their preferences by reformulating the optimization problems as constrained problems. The subsequent constrained problems are solved using various constraint handling approaches, such as the penalization of infeasible solutions and the restriction of the search to the feasible region of the search space. The proposed constraint handling approaches are compared by incorporating the approaches into a differential evolution (DE) algorithm and measuring the algorithm’s performance using both standard performance measures for dynamic multi-objective optimization (DMOO), as well as newly proposed measures for constrained DMOPs. The new measures indicate how well an algorithm was able to find solutions in the objective space that best reflect the DM’s preferences and the Pareto-optimality goal of dynamic multi-objective optimization algorithms (DMOAs). The results indicate that the constraint handling approaches are effective in finding Pareto-optimal solutions that satisfy the preference constraints of a DM.

Optimal Parking Slots Reservation and Allocation Problem for Periodic Parking Platforms with Preference Constraints

Various solutions, such as parking reservation systems, have been proposed to alleviate the difficulty in finding parking slots. In such systems, parking requests are submitted in advance by drivers, and the systems will reserve appropriate parking spots for drivers if their requests are accepted. However, the parking slots may be allocated unreasonably, which may lead to a waste of space and time resources. In addition, there is a game relationship between operator’s profit (OP) and users’ benefits (UB), which may affect the sustainable development of the system, if balanced improperly. Given the drivers’ arrival and departure time and their parking preference, the paper proposes a periodic reservation and allocation mode (PRAM) and establishes a dual-objective binary integer linear model to solve the reservation and allocation problem. The model aims to maximize the comprehensive benefits of the operator and users and to take full advantage of parking resources. We proposed a TOPSIS-SA algorithm (Technique for Order Preference by Similarity to an Ideal Solution and Simulated Annealing algorithm) to solve our model. Numerical experiments show that our model performs better than the baseline models on all performance metrics such as total operating profit, users’ average walking distance, acceptance rate, and utilization of parking slots.

Tolerance framework for robust group multiple criteria decision making

A tolerance framework is developed to address a group multiple criteria ranking problem with indirect preference information, which is referred to as the interaction, importance and tolerance of criteria as well as pairwise comparisons among alternatives and criteria. Choquet integral preference model is employed to capture the interaction, importance and tolerance of criteria, all of which are specific to decision makers (DMs). Some mandatory/sufficient requirements concerning criteria which are global or local, also called the tolerance attitudes of DMs, are quantified as tolerability constraints. Preference disaggregation analysis is extended to solve this type of tolerability constraints for preference elicitation. Confronted with the inconsistency issue (the feasibility of the whole preference constraints translated from indirect preference information), cause oriented strategy and consequence oriented strategy are established by regression-based mixed 0–1 integer linear programs with the objectives of the most credible minimal inconsistent preference constraints and the most credible maximal preference constraints in the context of group decision making. Considering a wide range of the minimal unsatisfied subsets of preference constraints responsible for the inconsistency and the maximum satisfied subsets of preference constraints as a consistent result, to reach a robust decision, stochastic multicriteria acceptability analysis (SMAA)-like simulation analysis is generated to examine the whole instances of compatible preference models in the modified feasible preference polyhedron and compute the result in a probabilistic form. Simulation experiment is conducted to investigate the influence of global tolerance attitudes of DMs on preference elicitation in conservative and radical scenarios. Finally, the application of the proposed approach to a credit ranking of small and medium-sized enterprises (SMEs) and the comparison analysis with objective methods are presented and discussed for the effectiveness of the proposed tolerance framework.

Approval-Based Committee Voting under Incomplete Information

We investigate approval-based committee voting with incomplete information about the approval preferences of voters. We consider several models of incompleteness where each voter partitions the set of candidates into approved, disapproved, and unknown candidates, possibly with ordinal preference constraints among candidates in the latter category. This captures scenarios where voters have not evaluated all candidates and/or it is unknown where voters draw the threshold between approved and disapproved candidates. We study the complexity of some fundamental computational problems for a number of classic approval-based committee voting rules including Proportional Approval Voting and Chamberlin-Courant. These problems include that of determining whether a given set of candidates is a possible or necessary winning committee and whether it forms a committee that possibly or necessarily satisfies representation axioms. We also consider the problem whether a given candidate is possibly or necessarily a member of the winning committee.

Fish Acquisition and Consumption in the African Great Lakes Region through a Food Environment Lens: A Scoping Review.

Effective actions for the fishery and aquaculture sectors to contribute toward improving nutrition rely on an understanding of the factors influencing fish intake, particularly amongst vulnerable populations. This scoping review synthesises evidence from 33 studies in the African Great Lakes Region to examine the influence of food environments on fish acquisition and consumption. We identified only two studies that explicitly applied a food environment framework and none that linked policy conditions with the contribution of fish to diets. Economic access to fish was represented in the largest number of included studies (21 studies), followed by preferences, acceptability and desirability of fish (17 studies) and availability and physical access (14 studies). Positive perceptions of taste and low cost, relative to other animal-source foods, were drivers of fish purchases in many settings; however, limited physical and economic access were frequently identified as preventing optimal intake. In lakeside communities, fish were increasingly directed toward external markets which reduced the availability and affordability of fish for local households. Few studies considered intra-household variations in fish access according to age, gender or physiological status, which represents an important knowledge gap. There is also scope for future research on seasonal influences on fish access and the design and rigorous evaluation of programmes and policies that address one or more constraints of availability, cost, convenience and preferences.

BiS4EV: A fast routing algorithm considering charging stations and preferences for electric vehicles

Electric Vehicles (EVs) have grown in recent years as they have become a promising alternative to traditional fossil fuel-driven vehicles. As a result, new routing algorithms that consider both the locations of charging stations and the charging preferences of users are necessary to maintain urban traffic efficiency. This paper proposes a Constrained Route Planning (CRP) approach, called BiS4EV, to provide efficient route planning for EVs in large urban road networks. CRP is an NP-hard problem. BiS4EV introduces a two-layered structure to solve the CRP problem for EVs. In the first layer, BiS4EV integrates the locations of charging stations and the battery consumption on each road arc into a road network graph and proposes a fast routing algorithm that considers the constraints of battery capacity and charging preferences to find the shortest feasible path for EVs. The second layer incorporates charging policies to the obtained path, which forms a complete route plan for EVs. Such a two-layered method breaks down the complexity of the problem and avoids directly solving the entire NP-hard problem. We have proved the correctness of the proposed approach in theory, and the presented experimental results verified the efficiency and the effectiveness of BiS4EV from the practical aspect.

Asking more than one question in one turn in oral examinations and its impact on examination quality

The assessment of oral skills is a key part of school examination systems around the world. Typically, examiners engage candidates in a conversational encounter to elicit assessable talk. However, we know little about how examiner's elicitations may impact, constrain, or create opportunities for subsequent talk by candidates. In this study, we analyse a Norwegian dataset comprising video-recorded disciplinary oral competence exams in secondary schools. Using conversation analysis, we focus on a specific phenomenon observed in the data, in which examiners' elicitations comprise more than one discrete question—what are termed “multi-unit questions” (MUQs). We found that MUQs within the same turn scaffolded the candidates' answers, provided hints and steered the candidates towards adequate answers. However, when the MUQs were separated by more talk across turns, candidates typically addressed only the final question. When this final question provided a pragmatic context for a specific answer, it constrained the candidate's opportunity to expand upon their overall answer. However, high-performing candidates overruled preference constraints to produce sequence-expanding answers. We conclude that MUQs both afford and constrain opportunities for candidates to display competence and discuss how the current imprecision in guidelines may impact examination quality.

Public acceptance of renewable electricity generation and transmission network developments: Insights from Ireland

This paper analyses how people’s attitudes towards onshore wind power and overhead transmission lines affect the cost-optimal development of electricity generation mixes, under a high renewable energy policy. A power systems generation and transmission expansion planning model is used for the analysis, combined with a novel additional modelling constraint incorporating public acceptance of energy infrastructure. In the scenarios examined the least cost solutions increase by as much as 33% compared to a base case where the constraint on public acceptance of energy infrastructure is excluded. In the most extreme public acceptance scenario considered, the greatest share of additional costs (>80%) is related to value of lost load, while additional investment and operational costs associated with public acceptance constraints for new energy infrastructure are between 5–6% of base case costs. The results are indicative of the cost that power systems face in reflecting the public’s preferences for new energy infrastructure in generation and grid expansion planning. Power system modelling that ignores the public’s acceptance of new energy infrastructure may offer generation or transmission pathways that are likely to be sub-optimal in practice. • Power system generation and transmission modelling with public preference constraints. • Public generally supportive of wind farms but less so when close to homes. • Power system cost up to 33% higher as public opposition to energy infrastructure increases. • Greatest share of additional costs associated with value of lost load.

Effects of Ad-hoc Data Truncation and Homogeneous Preferences on Recreational Demand and Values: An Application to the George Washington and Jefferson National Forests

AbstractWe estimate a travel cost model for the George Washington & Jefferson National Forests using an On-Site Latent Class Poisson Model. We show that the constraints of ad-hoc truncation and homogenous preferences significantly impact consumer surplus estimates derived from the on-site travel cost model. By relaxing the constraints, we show that more than one class of visitors with unique preferences exists in the population. The resulting demand functions, price responsive behaviors, and consumer surplus estimates reflect differences across these classes of visitors. With heterogeneous preferences, a group of ‘local residents’ exists with a probability of 8% and, on average take 113 visits.

Dial-a-Ride Problem with Users’ Accept/Reject Decisions Based on Service Utilities

The classic dial-a-ride problem (DARP) aims at designing the minimum-cost routing that accommodates all requests under a set of constraints. However, several modeling and computational challenges have hindered the successful deployment of dial-a-ride solutions. This work proposes incorporating user preference decisions within a rich DARP formulation. Specifically, it is considered that two travel modes are available: a shared mobility (DARP) service and a private travel option. Utility functions for each travel mode are integrated and it is assumed that the utility of the shared mobility service depends on the collective choice of travelers whereas the utility of private travel is fixed. Assuming that travelers are rational and seek to maximize their trip utility, extra variables and constraints are added to ensure that all requests are served by the mode with the higher utility. The behavior of the proposed integrated DARP with formulation of user preference constraints is examined by comparing the optimal solutions and computational time of this model with its classic DARP counterpart. Furthermore, the impact of various formulations of fare and the tolerance of the integrated DARP model is explored. Results show that user-personalized fare formulation improves model tolerance and profit margin, albeit it is rather computationally expensive.

DEVELOPING A PORTFOLIO OPTIMIZATION MODEL BASED ON LINEAR PROGRAMMING UNDER CERTAIN CONSTRAINTS: AN APPLICATION ON BORSA ISTANBUL 30 INDEX

The aims of this study are to lend assistance for the account owners who plan to make an investment in the financial markets to make the most accurate investments possible; accordingly, to develop a portfolio selection model and present it with its implementations. Instead of the L2 (standard deviation), risk function which is approached as a risk by Markowitz, the L1 (absolute deviation) risk function was used in the study and the optimal portfolios were trying to be attained. After the data acquired from the index of the Borsa Istanbul 30 index, the portfolio optimization model which is based on linear programming and was developed by Ching-Ter Chang (2005) was embraced in order to create an optimal portfolio. In this model, a new model was proposed by adding a limit on trading volume to reduce the systematic risk of the portfolio with the idea that it is one of the important indicators of the market and that it can create a decision-making risk perception. Thus, it was enabled for the portfolio to contain the equities from the industrial branch in desired numbers in accordance with the desire of the investors by adding the preference constraints on the Chang model. It can be said that this study will be useful for the investors and the finance executives who want to create a portfolio on specific risk and return level.

Thermal Performance of Double-Sided Metal Core PCBs

Thermal management in printed circuit boards is becoming increasingly more important as the use of LEDs is now widespread across all industries. Due to availability of the preferred electronic LED current drivers and system constraints for a machine-vision application, the design dictated the need for a double-sided metal core printed circuit board (MCPCB). However, design information for this relatively new MCPCB offering is sparse to non-existent. To fill-in this missing information in the literature, experiments were conducted where LEDs were arranged on a double-sided metal core printed circuit board (MCPCB), and their impact on the board temperature distribution was tested in a static fan-less configuration where the first condition was at room temperature, 23 °C, and the second configuration was for a heated environment, 40 °C. Two MCPCB orientations were tested (vertical and horizontal). Additionally, several LED arrangements on the MCPCB were configured, and temperatures were measured using a thermocouple as well as with a deep-infrared thermal imaging camera. Maximum temperatures were found to be 65.3 °C for the room temperature tests and 96.4 °C for the heated tests with high temperatures found in near proximity to the heat sources (LEDs), indicating less than ideal heat-conduction/dissipation by the MCPCB. The results indicate that the double-sided MCPCB topology is not efficient for high thermally loaded systems, especially when the target is a fan-less system. The results of testing indicate that for fan-less systems requiring high-performance heat-transfer, these new MCPCB are not a suitable design alternative, and instead, designers should stick with the more traditional single-sided metal-back PCB.

SmartStreamer: Preference-Aware Multipath Video Streaming Over MPTCP

Simultaneous access to multiple interfaces (e.g., WiFi and cellular networks) can significantly improve the users’ quality of experience (QoE) in video streaming. However, some interfaces could be more expensive to use and less energy efficient. Therefore, in this paper, we propose a preference-aware multipath video streaming algorithm over HTTP using multipath TCP (MPTCP). First, we formulate the quality decisions of the video chunks and the chunk's download policy subject to the chunk's deadlines, the available bandwidth of the different paths, and the link preferences as a non-convex optimization problem. The objective is to optimize a novel QoE metric that maintains a tradeoff between maximizing the quality of every video's chunk and ensuring quality fairness among all chunks for the minimum re-buffering (stall) duration, and without violating the link preference constraint. Second, we develop a polynomial time complexity algorithm to solve the proposed optimization problem, and provide guarantees for the proposed algorithm. We further propose a sliding window based online algorithm where several challenges including short bandwidth prediction with prediction errors are addressed. Extensive emulated experiments with real bandwidth traces of public datasets reveal the robustness of our scheme and demonstrate its significant performance improvement compared to the state-of-the-art multi-path streaming algorithms.

Primal-Dual Learning Based Risk-Averse Optimal Integrated Allocation of Hybrid Energy Generation Plants under Uncertainty

A groundswell of opinion in utilizing environmentally friendly energy technologies has been put forth worldwide. In this paper, we consider an energy generation plant distribution and allocation problem under uncertainty to get the utmost out of available developments, as well as to control costs and greenhouse emissions. Different clean and traditional energy technologies are considered in this paper. In particular, we present a risk-averse stochastic mixed-integer linear programming (MILP) model to minimize the total expected costs and control the risk of CO2 emissions exceeding a certain budget. We employ the conditional value-at-risk (CVaR) model to represent risk preference and risk constraint of emissions. We prove that our risk-averse model can be equivalent to the traditional risk-neutral model under certain conditions. Moreover, we suggest that the risk-averse model can provide solutions generating less CO2 than traditional models. To handle the computational difficulty in uncertain scenarios, we propose a Lagrange primal-dual learning algorithm to solve the model. We show that the algorithm allows the probability distribution of uncertainty to be unknown, and that desirable approximation can be achieved by utilizing historical data. Finally, an experiment is presented to demonstrate the performance of our method. The risk-averse model encourages the expansion of clean energy plants over traditional models for the reduction CO2 emissions.

Towards the optimality of QoS-aware web service composition with uncertainty

Quality of service (QoS)-aware web service composition (QWSC) has recently become one of the most challenging research issues. Although much work has been investigated, they mainly focus on certain QoS of web services, while QoS with uncertainty exposes the most important characteristic in real and highly dynamic environment. In this paper, with the consideration of uncertain service QoS and user's preferences, we model the issue of uncertain QoS-aware WSC via interval number and translate it into a multi-objective optimisation problem with global QoS constraints of user's preferences. The encoded optimisation problem is solved by an non-deterministic multi-objective evolutionary algorithm, which exploits new genetic encoding schema, the strategy of crossover and uncertain interval Pareto comparison. To validate the feasibility, large-scale experiments have been conducted on simulated datasets. The results demonstrate that our proposed approach can effectively and efficiently find optimum composite service solutions set with satisfactory convergence.

Towards the optimality of QoS-aware web service composition with uncertainty

Quality of service (QoS)-aware web service composition (QWSC) has recently become one of the most challenging research issues. Although much work has been investigated, they mainly focus on certain QoS of web services, while QoS with uncertainty exposes the most important characteristic in real and highly dynamic environment. In this paper, with the consideration of uncertain service QoS and user's preferences, we model the issue of uncertain QoS-aware WSC via interval number and translate it into a multi-objective optimisation problem with global QoS constraints of user's preferences. The encoded optimisation problem is solved by an non-deterministic multi-objective evolutionary algorithm, which exploits new genetic encoding schema, the strategy of crossover and uncertain interval Pareto comparison. To validate the feasibility, large-scale experiments have been conducted on simulated datasets. The results demonstrate that our proposed approach can effectively and efficiently find optimum composite service solutions set with satisfactory convergence.

Risk, Preference, Capital Structure and Incentives

Using the surplus theory of the firm, we examine capital and labor as inputs of a surplus generating firm, and study the capital structure of the firm. We derive two equilibrium models on: (a) the capital structure and ownership of the firm and (b) alternative incentive compensation structures. Within the framework of the firm as a cooperative surplus generating enterprise, we introduce the concepts of risk, preference and resource constraints, to provide a framework for analyzing more sophisticated methods of dividing the risks and rewards of the firm’s surplus.