Asia-Pacific Journal of Operational Research

According to the management by objectives (MBO) theory, performance evaluations should take into account the achievement of management objectives (MOs). The most commonly used performance evaluation method — the cross-efficiency evaluation method — seldom considers the role of MOs as a reference point. According to the prospect theory, decision-makers underestimate the benefits that exceed the reference point; they also exaggerate the losses that fall below the reference point. This irrational psychology is more obvious when evaluating the performance evaluation of peers. As such, this paper proposes a cross-efficiency evaluation method based on prospect theory, which takes MOs as a reference point from a peer perspective. First, taking MOs as reference points, a decision-making unit (DMU) chooses a set of weights for each peer, in order to maximize or minimize the prospects of the peer, according to the benevolent or aggressive attitude of the DMU. In order to improve the adaptability of the method, the precise number of MOs is further extended to be an interval number. Finally, the relationship between models, which are based on precise MOs and interval MOs, is illustrated by propositions. Finally, numerical examples are provided to illustrate the applications of the proposed cross-efficiency evaluation method.

Data envelopment analysis (DEA) is a method that finds the effectiveness of an existing system using a number of input and output variables. In this study, we obtained energy efficiencies of construction, industrial, power, and transportation sectors in OECD countries for 2011 using DEA. It is possible to achieve the efficiencies in different sectors. However, we aim to find joint energy efficiency scores for all sectors. One of the methods proposed in the literature to obtain joint efficiency is network data envelopment analysis (network DEA). Network DEA treats sectors as sub-processes and obtains system and process efficiencies through optimal weights. Alternatively, we used a novel copula-based approach to achieve common efficiency scores. In this approach, it is possible to demonstrate the dependency structure between the efficiency scores of similar qualities obtained with DEA by copula families. New efficiency scores are obtained with the help of joint probability distribution. Then, we obtained joint efficiency scores through the copula approach using these efficiency scores. Finally, we obtained the joint efficiency scores of the same sectors through network DEA. As a result, we compared network DEA with the copula approach and interpreted the efficiencies of each energy sector and joint efficiencies.

With the development of modern science and technology, it is easy to obtain a large number of high-dimensional datasets, which are related but different. Classical unimodel analysis is less likely to capture potential links between the different datasets. Recently, a collaborative regression model based on least square (LS) method for this problem has been proposed. In this paper, we propose a robust collaborative regression based on the least absolute deviation (LAD). We give the statistical interpretation of the LS-collaborative regression and LAD-collaborative regression. Then we design an efficient symmetric Gauss–Seidel-based alternating direction method of multipliers algorithm to solve the two models, which has the global convergence and the Q-linear rate of convergence. Finally we report numerical experiments to illustrate the efficiency of the proposed methods.

Stochastic second-order cone programming (SSOCP) is an extension of deterministic second-order cone programming, which demonstrates underlying uncertainties in practical problems arising in economics engineering and operations management. In this paper, asymptotic analysis of sample average approximation estimator for SSOCP is established. Conditions ensuring the asymptotic normality of sample average approximation estimators for SSOCP are obtained and the corresponding covariance matrix is described in a closed form. Based on the analysis, the method to estimate the confidence region of a stationary point of SSOCP is provided and three examples are illustrated to show the applications of the method.

In this paper, we present a refined convergence analysis for a simple yet powerful method for computing a symmetric low-rank orthogonal approximation of a symmetric tensor proposed in the literature. The significance is that the assumption guaranteeing the global convergence is vastly relaxed to only on an input parameter of this algorithm.

This work investigates the problem of vessel fleet deployment for liner shipping. The objective is to minimize the total cost, i.e., the sum of vessel chartering cost and vessel-route operating cost. In the considered problem, the shipment demand for each route is uncertain and its distribution is unknown. Due to lacking historical data, we use the moment-based ambiguity set to characterize the unknown distributions of demands. We then introduce a distributionally robust model and propose a new approximation approach to solve this problem. Finally, numerical experiments are conducted to demonstrate the performance of our approximation approach.

This paper studies the slack due-window assignment scheduling problem with deterioration effects and a deterioration maintenance activity on a single-machine. The machine deteriorates during the machining process, and at a certain moment performs a deterioration maintenance activity, that is, the duration time of the maintenance activity is a linear function of the maintenance starting time. It is needed to make a decision on when to schedule the deteriorating maintenance activity, the optimal common flow allowances and the sequence of jobs to minimize the weighted penalties for the sum of earliness and tardiness, weighted number of early and delayed, and weighted due-window starting time and size. This paper proposes a polynomial time algorithm to solve this problem.

This study aims to formulate the least-distance range adjusted measure (LRAM) in data envelopment analysis (DEA) and apply it to evaluate the relative efficiency and provide the benchmarking information for Japanese banks. In DEA, the conventional range adjusted measure (RAM) acts as a well-defined model that satisfies a set of desirable properties. However, because of the practicality of the least-distance measure, we formulate the LRAM and propose the use of an effective mixed integer programming (MIP) approach to compute it in this study. The formulated LRAM (1) satisfies the same desirable properties as the conventional RAM, (2) provides the least-distance benchmarking information for inefficient decision-making units (DMUs), and (3) can be computed easily by using the proposed MIP approach. Here, we apply the LRAM to a Japanese banking data set corresponding to the period 2017–2019. Based on the results, the LRAM generates higher efficiency scores and allows inefficient banks to improve their efficiency with a smaller extent of input–output modification than that required by the RAM, thereby indicating that the LRAM can provide more easy-to-achieve benchmarking information for inefficient banks. Therefore, from the perspective of the managers of DMUs, this study provides a valuable LRAM for efficiency evaluation and benchmarking analysis.

The need for enterprises to manage project portfolio risks over the life cycle has become increasingly prominent. It is essential to evaluate and manage them to achieve project portfolios and organizations’ success. Unlike project risk, project portfolio risk is more complex and uncertain due to risk interactions. Risk management is unsatisfactory in project portfolios due to the lack of awareness of risk interactions and the life cycle. The purpose of this paper is to identify the critical risks of project portfolios over the life cycle considering risk interactions. We primarily verified 20 identified risks through a questionnaire survey and an expert interview method and evaluated the interactions among them using the Delphi method. Furthermore, risk interactions were analyzed using the social network analysis (SNA) methodology to determine the important risks. Finally, a comprehensive evaluation of important risks was carried out to identify critical risks according to the evaluation principles. The results identified six critical portfolio risks, two key risk contagion paths and revealed risk characteristics of different life cycle phases. This research considerably contributes to the body of knowledge pertaining to project portfolio management that will enable organizations that implement project portfolios and similar multi projects to emphasize critical risks.

This paper proposes a composite indicator methodology to rank National Olympic Committees (NOCs) by considering only the medals won at both Summer and Winter Olympic games. The lexicographic and sum ranking systems at both Summer and Winter Olympics are comprehensively investigated to create the preliminary decision matrix for constructing composite indicators. Then we investigate the preferential difference and preferential priority for better reflecting the preference structures of the sub-indicators, and thereby obtain a revised decision matrix. The SMAA-2 is employed to implement the composite indicator construction based upon the revised decision matrix. An empirical study is performed to demonstrate the applicability of our methodology, using the data set that is composed of 28 NOCs who have participated in both the Rio 2016 Summer Olympic Games and the PyeongChang 2018 Winter Olympics. The Spearman’s rank correlation coefficient and the satisfaction index are calculated to justify the merits of the proposed methodology.

In this paper, we consider the scheduling of high multiplicity jobs on parallel multi-purpose machines with setup times and machine available times, with the objective of minimizing makespan. High multiplicity means that jobs are partitioned into several groups and in each group all jobs are identical. Whenever there is a switch from processing a job of one group to a job of another group, a setup time is needed. Multi-purpose machine implies that each job can only be processed by a specific subset of all the machines, called processing set. A mixed integer programming is formulated for this NP-hard problem. A heuristic is proposed to solve the problem. Lower bounds are developed to evaluate the heuristic algorithm. Extensive numerical computations are performed and the results show that the heuristic generates solutions with makespan within 2% above the lower bounds in average, and outperforms CPLEX 12.6 for large scale and complex problems.

The field of Submodular Maximization subject to a Knapsack constraint has recently expanded to a variety of application domains, which is facing some challenges such as data explosions or additional conditions. There exist plenty of objective functions that cannot be evaluated exactly in many real cases unless they are estimated with errors. It leads to solving the problem under noise models. Somewhat surprisingly, Submodular Maximization subject to a Knapsack constraint under Noise models ($SMKN$) has never been discussed a lot before. Hence, in this paper, we consider the problem with two kinds of noise models which are addition and multiplication. Inspired by the traditional Greedy algorithm, we first propose a Greedy algorithm under Noises with provable theoretical bounds. In order to find the solution when input data are extremely large, we then devise an efficient streaming algorithm that scans only a single pass over the data and guarantees theoretical approximations. Finally, we conduct some experiments on Influence Maximization problem under knapsack constraint, an instance of $SMKN$ to show the performances of the proposed algorithms.

Diversification of investment is a well-established practice for reducing the total risk of investing. Portfolio optimization is an effective way for investors to disperse investment risk and increase portfolio return. Under the assumption of no short selling, a bi-objective minimizing portfolio optimization model, in which the first objective is a semi-absolute deviation mean function used to measure the portfolio risk, and the second objective is a maximum entropy smooth function used to measure the portfolio return, is given in this paper. Also, a maximum entropy multi-objective evolutionary algorithm is designed to solve the bi-objective portfolio optimization model. In order to obtain a sufficient number of uniformly distributed portfolio Pareto optimal solutions located on the true Pareto frontier and fully exploit the useful asset combination modes which can lead the search process toward the frontier direction quickly in the objective space, a subspace multi-parent uniform crossover operator and a subspace decomposition mutation operator are given. Furthermore, a normalization method to deal with the tight constraint and the convergence analysis of the proposed algorithm are also discussed. Finally, the performance of the proposed algorithm is verified by five benchmark investment optimization problems. The performance evaluations and results analyses illustrate that the proposed algorithm is capable of identifying good Pareto solutions and maintaining adequate diversity of the evolution population. Also, the proposed algorithm can obtain faster and better convergence to the true portfolio Pareto frontier compared with the three state-of-the-art multi-objective evolutionary algorithms. The result can also provide optimal portfolio plan and investment strategy for investors to allocate and manage asset effectively.