Journal of the Operational Research Society

Lee, Yongjae; Kim, Min Jeong; Kim, Jang Ho; Jang, Ju Ri; Chang Kim, Woo

doi: 10.1080/01605682.2019.1581408pmid: N/A

AbstractIn investment management, especially for automated investment services, it is critical for portfolios to have a manageable number of assets and robust performance. First, portfolios should not contain too many assets in order to reduce the management fees, transaction costs, and taxes. Second, portfolios should be robust as investment environments change rapidly. In this study, therefore, we propose two convex portfolio selection models that provide portfolios that are sparse and robust. We first perform semi-definite relaxation to develop a sparse mean-variance portfolio selection model, and further extend the model by using -norm regularization and worst-case optimization to formulate two sparse and robust portfolio selection models. Empirical analyses with historical stock returns demonstrate the effectiveness of the proposed models in forming sparse and robust portfolios.

Tian, Zhang-Peng; Nie, Ru-Xin; Wang, Jian-Qiang

doi: 10.1080/01605682.2019.1632752pmid: N/A

AbstractThis study aims to develop an integrated approach for solving probabilistic linguistic multi-criteria decision-making (MCDM) problems. This study first reveals the limitations in the existing methods for probabilistic linguistic term sets (PLTSs). Subsequently, an improved aggregation method and a novel ranking method are developed for addressing PLTSs. The proposed aggregation method is based on the evidence reasoning algorithm and the proposed ranking method that integrates with a three-fold ranking method is based on optimism, neutralism and pessimism decision-making processes. Thus, the proposed approach can straightforwardly and robustly deal with probabilistic linguistic MCDM problems considering decision-makers’ psychological preferences. Moreover, to flexibly obtain criteria weights, several models are constructed to adapt to different decision-making situations, in which criteria weight information is incompletely, inconsistently or completely unknown. Finally, a case study on selecting the best investment objective(s) among the member counties of “One Belt, One road” is conducted to validate the feasibility and effectiveness of the proposed approach, followed by a comparative analysis between the existing methods and the proposed approach.

Yu, Ming-Miin; Chen, Li-Hsueh

doi: 10.1080/01605682.2019.1578625pmid: N/A

AbstractTourist hotels may face different production frontiers and bias of technology between the group frontier and meta-frontier due to technological heterogeneity. This paper develops a meta-frontier data envelopment analysis framework for evaluating the efficiency and technological bias of tourist hotels. By comparing the curvatures of the group frontier and meta-frontier, the relative technological bias between a specific group and the whole industry can be obtained. Furthermore, by investigating the relative technological bias, the direction of technological improvement needed for individual hotels can be ascertained. The proposed method is applied in an empirical example of Taiwanese tourist hotels. The results indicate that most hotels have technological bias and should adjust the curve of their production possibility frontier to match the meta-technology.

Gul, Muhammet; Fuat Guneri, Ali; Gunal, Murat M.

doi: 10.1080/01605682.2019.1582588pmid: N/A

AbstractEmergency departments (EDs) provide health care services to people in need of urgent care. Their role is remarkable when extraordinary events that affect the public, such as earthquakes, occur. In this paper, we present a hybrid framework to evaluate earthquake preparedness of EDs in cities. Our hybrid framework uses artificial neural networks (ANNs) to estimate number of casualties and discrete event simulation (DES) to analyse the effect of surge in patient demand in EDs, after an earthquake happens. At the core of our framework, Earthquake Time Emergency Department Network Simulation Model (ET-EDNETSIM) resides which can simulate patient movements in a network of multiple and coordinated EDs. With the design of simulation experiments, different resource levels and sharing rules between EDs can be evaluated. We demonstrated our framework in a network of five EDs located in a region of which is estimated to have the highest injury rate after an earthquake in Istanbul, Turkey. Results of our study contributed to the planning for expected earthquake in Istanbul. Simulating a network of EDs extends the individual ED studies in the literature and furthermore, our hybrid framework can help increase earthquake preparedness in cities around the world. On the methodological side, the use of ANN, which is a member of machine learning (ML) algorithms family, in our hybrid framework also shows the close links between ML and DES.

Liu, Ming; Xu, Xifen; Cao, Jie; Zhang, Ding

doi: 10.1080/01605682.2019.1582589pmid: N/A

AbstractInfectious disease outbreaks have occurred many times in the past decades and are more likely to occur in the future. Recently, Büyüktahtakin et al. (2018) proposed a new epidemics-logistics model to control the 2014 Ebola outbreak in West Africa. Considering that different diseases have dissimilar diffusion dynamics and can cause different public health emergencies, we modify the proposed model by changing capacity constraint, and then apply it to control the 2009 H1N1 outbreak in China. We formulate the problem to be a mixed-integer non-linear programming model (MINLP) and simultaneously determine when to open the new isolated wards and when to close the unused isolated wards. The test results reveal that our model could provide effective suggestions for controlling the H1N1 outbreak, including the appropriate capacity setting and the minimum budget required with different intervention start times.

Marcelino, Carolina; Baumann, Manuel; Carvalho, Leonel; Chibeles-Martins, Nelson; Weil, Marcel; Almeida, Paulo; Wanner, Elizabeth

doi: 10.1080/01605682.2019.1582590pmid: N/A

AbstractHybrid micro-grid systems (HMGS) are gaining increasing attention worldwide. The balance between electricity load and generation based on fluctuating renewable energy sources is a main challenge in the operation and design of HMGS. Battery energy storage systems are considered essential components for integrating high shares of renewable energy into a HMGS. Currently, there are very few studies in the field of mathematical optimisation and multi-criteria decision analysis that focus on the evaluation of different battery technologies and their impact on the HMGS design. The model proposed in this paper aims at optimising three different criteria: minimising electricity costs, reducing the loss of load probability, and maximising the use of locally available renewable energy. The model is applied in a case study in southern Germany. The optimisation is carried out using the C-DEEPSO algorithm. Its results are used as input for an AHP-TOPSIS model to identify the most suitable alternative out of five different battery technologies using expert weights. Lithium batteries are considered the best solution with regard to the given group preferences and the optimisation results.

Bakoben, Maha; Bellotti, Tony; Adams, Niall

doi: 10.1080/01605682.2019.1582586pmid: N/A

AbstractAssessment of risk levels for existing credit accounts is important to the implementation of bank policies and offering financial products. This article uses cluster analysis of behaviour of credit card accounts to help assess credit risk level. Account behaviour is modelled parametrically and we then implement the behavioural cluster analysis using a recently proposed dissimilarity measure of statistical model parameters. The advantage of this new measure is the explicit exploitation of uncertainty associated with parameters estimated from statistical models. Interesting clusters of real credit card behaviours data are obtained, in addition to superior prediction and forecasting of account default based on the clustering outcomes.

Ozkan, Omer; Ermis, Murat; Bekmezci, Ilker

doi: 10.1080/01605682.2019.1582587pmid: N/A

AbstractReliable communication network design (RCND) is a well-known optimisation problem to produce a network with maximum reliability. This paper addresses the minimum cost communication network design problem under the all-terminal reliability constraint. Due to the NP-hard nature of RCND, several different metaheuristic algorithms have been widely applied to solve this problem. The aim of this paper is to propose two new hybrid metaheuristic algorithms, namely, GABB and SABB, by integrating either a Genetic Algorithm (GA) with the Branch and Bound method (B&B) or Simulated Annealing (SA) with B&B. The GABB and SABB algorithms have the advantages of finding higher performance solutions produced from the GA or SA, along with the ability to repair infeasible solutions or improve solution quality by integrating the B&B method. To investigate the effectiveness of the proposed algorithms, extensive comparisons with individual application of the GA and SA (the basic forms of GABB and SABB), two different hybrid algorithms (GABB and SABB) and other two approaches (ACO_SA and STH) that give the best results in the literature for the design problems are carried out in a three-stage experimental study (ie, small-, medium-, and large-sized networks). The computational results show that hybridisation of metaheuristics with the B&B method is an effective approach to designing reliable networks and finding better solutions for existing problems in the literature.

Geramian, Arash; Shahin, Arash; Minaei, Behzad; Antony, Jiju

doi: 10.1080/01605682.2019.1606986pmid: N/A

AbstractThe aim of this study is to modify and enhance the quantitative/mathematical features of both computational and analytical aspects of the process failure modes and effects analysis (FMEA). For this purpose, a hybrid approach including the Fuzzy Logic-based FMEA (FFMEA) and collective process capability analysis (CPCA) has been developed in three phases. First, failure modes have been defined based on lack of quality in quality characteristics under investigation, and then, they have been prioritised using FFMEA. Second, the most critical failure has been selected for statistical analysis using CPCA, leading to the corrective actions in the third phase. The proposed approach was investigated in an electrical-equipment-manufacturing company. Findings indicated that the diameter deviation in Insulator A was the most critical failure effect caused by a rightward mean shift of 0.32 cm. In addition, Cpk has been improved from 0.41 to 1.12, and defective products have been reduced from 115,083.09 to 336.98 parts per million.

Zhen, Lu; Lv, Wenya; Wang, Kai; Ma, Chengle; Xu, Ziheng

doi: 10.1080/01605682.2019.1590134pmid: N/A

AbstractTo improve customer service in the reverse logistics, this article defines a new variant of the vehicle routing problem (VRP) by combining the consistent VRP (ConVRP) and the VRP with simultaneous distribution and collection (VRPSDC). This new variant is called the consistent vehicle routing problem with simultaneous distribution and collection, for which a mixed-integer programming model is formulated. To solve this problem, three heuristics are proposed on the basis of the record-to-record (RTR) travel algorithm, the local search with variable neighbourhood search (LSVNS), and the tabu search-based method. Numerical experiments are performed to validate the efficiency of our proposed solution methods and the effectiveness of the proposed model. The results show that the RTR-based heuristic has an advantage in small-scale instances. However, for medium-scale instances, the best option is the LSVNS-based heuristic, which can solve instances with 40 customers and 5 days within 10 s. Moreover, the LSVNS-based heuristic can solve large-scale instances with 200 customers and 5 days 3 hours.