An efficient algorithm for bi-objective combined heat and power production planning under the emission trading scheme

The growing environmental awareness and the apparent conflicts between economic and environmental objectives turn energy planning problems naturally into multi-objective optimization problems. In the current study, mixed fuel combustion is considered as an option to achieve tradeoff between economic objective (associated with fuel cost) and emission objective (measured in CO2 emission cost according to fuels and emission allowance price) because a fuel with higher emissions is usually cheaper than one with lower emissions. Combined heat and power (CHP) production is an important high-efficiency technology to promote under the emission trading scheme. In CHP production, the production planning of both commodities must be done in coordination. A long-term planning problem decomposes into thousands of hourly subproblems. In this paper, a bi-objective multi-period linear programming CHP planning model is presented first. Then, an efficient specialized merging algorithm for constructing the exact Pareto frontier (PF) of the problem is presented. The algorithm is theoretically and empirically compared against a modified dichotomic search algorithm. The efficiency and effectiveness of the algorithm is justified.Peer reviewe

Customers satisfaction in pediatric inpatient services: A multiple criteria satisfaction analysis

Objective: To assess customer satisfaction determinants in a public pediatric inpatient service and propose some strategies to enhance the consumer and customer experience. Methods: We applied a Multiple Criteria Customer Satisfaction Analysis to estimate the value functions associated with each satisfaction (sub)criterion and determine the corresponding weights. We characterized satisfaction criteria (according to the Kano’s model), estimated the customers’ demanding nature and the potential improvements, and proposed strategic priorities and opportunities to enhance customer satisfaction. Main findings: Strategies for satisfaction enhancement do not depend solely on the criteria with the lowest satisfaction levels and the estimated weights, each criterion’s nature, the customers’ demanding nature, and the technical margin for improvements. Conclusions: Areas deserving attention include clinical staff’s communication skills, the non-clinical professionals’ efficiency, availability, and kindness; food quality; visits’ scheduling and quantity; and facilities’ comfort.info:eu-repo/semantics/publishedVersio

A hybrid meta-heuristic for the generation of feasible large-scale course timetables using instance decomposition

This study introduces a hybrid meta-heuristic for generating feasible course timetables in large-scale scenarios. We conducted tests using our university's instances. The current commercial software often struggles to meet constraints and takes hours to find satisfactory solutions. Our methodology combines adaptive large neighbourhood search, guided local search, variable neighbourhood search, and an innovative instance decomposition technique. Constraint violations from various groups are treated as objective functions to minimize. The search focuses on time slots with the most violations, and if no improvements are observed after a certain number of iterations, the most challenging constraint groups receive new weights to guide the search towards non-dominated solutions, even if the total sum of violations increases. In cases where this approach fails, a shaking phase is employed. The decomposition mechanism works by iteratively introducing curricula to the problem and finding new feasible solutions while considering an expanding set of lectures. Assignments from each iteration can be adjusted in subsequent iterations. Our methodology is tested on real-world instances from our university and random subdivisions. For subdivisions with 400 curricula timetables, decomposition reduced solution times by up to 27%. In real-world instances with 1,288 curricula timetables, the reduction was 18%. Clustering curricula with more common lectures and professors during increments improved solution times by 18% compared to random increments. Using our methodology, viable solutions for real-world instances are found in an average of 21 minutes, whereas the commercial software takes several hours