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Surgical cases assignment problem (SCAP) is among the most investigated interests in operating room planning. The studies related to the SCAP are mainly focused on the single objective optimization. To concern multiple criteria in the management, this paper reformulates a multi-objective SCAP with minimizing total operating cost and maximizing scheduled surgeries number, simultaneously. Although dozens of multi-objective methods have been introduced in recent years, these cannot be applied into the multi-objective SCAP directly considering the characteristics of the problem. To tackle the multi-objective SCAP, a problem-specified multi-objective squirrel search algorithm (MOSSA) is presented. First, a targeted single-list scheme is devised to encode individuals and an efficient decoding scheme integrating a repair strategy is proposed to construct feasible scheduling plan. Second, two simple heuristics are incorporated into the algorithm to imitate the behaviors of squirrels and improve the search ability. Third, an addition archive is employed to store the non-dominated solutions. Moreover, a DOE method is performed to investigate the influence of parameter settings. The performance of the MOSSA is assessed by a representative benchmark dataset with three different evaluating indicators. Extensive experiments and analysis results demonstrate the effectiveness of the proposed algorithm and the superiority over compared approaches in addressing the multi-objective SCAP. [ABSTRACT FROM AUTHOR]

The potential of using computer vision to estimate the weight of pigs has been demonstrated in several studies. In this paper, we propose a Vision Analysis and Prediction (VAP) system, which consisted of two main parts: (1) computer vision – a vision algorithm was developed to segment the pigs and identify which pig area could be used to estimate the corresponding weight; (2) statistical analysis – a statistical method for predicting pig weight using the area was presented, including BLUP model and linear regression as well as a prediction function. The results showed that our approach has successfully estimated the weight of growing pigs (approximate ranges from 20 to 105 kg) with an accuracy of weight estimation of 97.76% on average for the predicted mean weight at pen level. The proposed system does not require modifications to the building and shows a strong potential to be utilized in pig weight estimation in slaughter pens. • A VAP system has great potential for pig weight estimation in slaughter pens. • The VAP includes two parts: a computer vision algorithm and a predicting model. • The VAP can accurately predict pig weights from 20 to 105 kg in a slaughter pen. • The VAP can help for optimal decision making to improve animal productivity. [ABSTRACT FROM AUTHOR]

• Squirrel Search algorithm is adapted to solve the portfolio optimization problem. • Different variants of the problem are considered. • Cardinality constrained mean-variance and Sharpe models are adopted. • Extensive computational experiments show the efficiency of the approach. Portfolio Optimization is a standard financial engineering problem. It aims for finding the best allocation of resources for a set of assets. This problem has been studied and different models have been proposed since the classical Mean-Variance model was introduced by Harry Markowitz in 1952 and the later modified version by William Sharpe. The inclusion of real-life constraints to the problem has led to the introduction of the extended Mean-Variance model. However, the successes of nature-inspired algorithms in hard computational optimization problems have encouraged researchers to design and apply these algorithms for a variety of optimization problems. In this paper, we design and adapt a Squirrel Search Algorithm (SSA) for the unconstrained and constrained portfolio optimization problems. SSA is a very recent swarm intelligence algorithm inspired by the dynamic foraging behavior of flying squirrels. The proposed SSA metaheuristic approach is compared with a variety of approaches presented in the literature such as classical single metaheuristics, hybrid metaheuristic approaches and multi-objective optimization approaches for portfolio optimization. Comparative analysis and computational results using different performance indicators show the superiority of the proposed approach for the unconstrained portfolio optimization using both extended Mean-Variance and Sharpe models. For the constrained version of the problem, the proposed approach has also achieved highly competitive results for the different models adopted. [ABSTRACT FROM AUTHOR]