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Publisher: Springer-Verlag (Total: 2351 journals)

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 Annals of Operations ResearchJournal Prestige (SJR): 0.943 Citation Impact (citeScore): 2Number of Followers: 10      Hybrid journal (It can contain Open Access articles) ISSN (Print) 1572-9338 - ISSN (Online) 0254-5330 Published by Springer-Verlag  [2351 journals]
• Dutch book rationality conditions for conditional preferences under
ambiguity
• Abstract: We study preference relations on conditional gambles of a decision maker acting under ambiguity. Dutch book rationality conditions are provided under a linear utility scale, encoding either an optimistic or a pessimistic attitude towards uncertainty. These conditions characterize possibly incomplete preferences representable by totally alternating or monotone conditional functionals. In general, the uniqueness of the representation is not guaranteed, but it can be obtained by adding the hypothesis of existence of a conditional fair price for every conditional gamble. The given rationality conditions have a betting scheme interpretation relying on “penalty fees” for betting on strict preference comparisons.
PubDate: 2019-06-21

• A bi-criteria optimization model for medical device sterilization
• Abstract: This paper proposes a scheduling model for the washing step of medical device sterilization. After use in a surgery, medical devices pass through several steps where the washing is usually a bottleneck. We study the cases of external and internal sterilization services to minimize two objectives: makespan and flow time of washing operations. First, we study internal sterilization services considering jobs can have different or unit sizes. For these two cases, we provide a mixed integer linear model and a dynamic programming model, and integrate these methods in an $$\epsilon$$ -constraint model. For the case of external services, first we develop a simultaneous (2, 2) approximation algorithm and then derive an algorithmic scheme to generate a partial 2-approximation of the Pareto set. Several numerical experiments are conducted to demonstrate the strength of proposed solution methods.
PubDate: 2019-06-20

heterogeneous agents and uncertain carbon price
PubDate: 2019-06-17

• Evidence regarding external financing in manufacturing MSEs using partial
least squares regression
• Abstract: The purpose of this study is to explore and explain the relationship between networking, external financing by banks and equity investors, and export effort in medium sized enterprises. Our research model was empirically tested on a sample of 143 MSEs in the manufacturing sector and our PLS results do not confirm the existence of a direct association between networking and export effort by MSEs. They show, however, that networking indirectly enhances MSEs’ export effort through facilitating their access to equity financing and reducing managerial risks. Whilst networking are not found to be associated with a higher access to bank financing by MSEs, this type of external financing seems to have a greater impact on export effort than equity financing. This research it is one of the first studies to explore the role of networking with stakeholders in facilitating MSEs’ access to external financing and to international markets and to merge these variables in a single model which consider simultaneously the MSE access to bank financing and to equity financing.
PubDate: 2019-06-12

• Factors affecting bike-sharing behaviour in Beijing: price, traffic
congestion, and supply chain
• Abstract: The bike-sharing boom is receiving growing attention with societies becoming more aware of active non-motorized traffic modes and their importance. However, the usage of this transport mode remains low in China, raising several concerns. The development of the bike-sharing behaviour can help to achieve sustainable development goals. Thus, this study aims to explore the impact of price, traffic congestion, and supply chain on bike-sharing selection behaviour. We employ big data technology to accurately research and evaluate usage behaviour towards bike-sharing apps in Beijing over a 4-day period. First, we administer a preliminary questionnaire survey across the country to explore factors influencing the choice of bike-sharing. Second, we perform a panel model regression to analyse the degree of impact by major factors on bike-sharing usage. The results revealed that price, traffic congestion, and supply chain affect the choice of bike-sharing and the degree of each factor’s impact differs by time of day. Drawing on these findings, we offer the following suggestions to increase the usage amount of bike-sharing and improve the operating efficiency of bike-sharing companies: (1) design time-based charging strategies (2) reasonably limit the number of shared bikes as per time of day (3) establish effective layouts for bicycle sites (4) account for maintenance time, and (5) use methods other than price wars to increase usage.
PubDate: 2019-06-11

• Coordination of social welfare, collecting, recycling and pricing
decisions in a competitive sustainable closed-loop supply chain: a case
• Abstract: In order to attain competitive advantage, it is of high importance for firms to move towards sustainability. In practice, an efficient sustainable closed-loop supply chain (SCLSC) can reduce the negative effects of hazardous wastes and consequently improve the environmental dimension of sustainability. Besides the environmental dimension, the social aspect of sustainability can be achieved through initiating corporate social responsibility and enhancing social welfare of customers. Different from the existing literature, this paper proposes an analytical coordination model to not only cover all three dimensions of sustainability in a SCLSC but also to align different decisions made in competitive forward and reverse logistics. The proposed SCLSC is modeled under decentralized, centralized, and coordinated decision-making structures considering different game behaviors in the forward and reverse links. The results reveal that the proposed two-way two-part tariff (TWTPT) contract is of high benefit to the sustainable CLSC as it is able to simultaneously enhance the environmental, economic, and social performances. To be more precise, the proposed model improves the collection rate, consumer surplus, social welfare, and profits of all CLSC members. In addition, our findings demonstrate the applicability and efficiency of the proposed TWTPT contract in motivating the agents of both competitive forward and reverse chains to participate in the coordination scheme.
PubDate: 2019-06-10

• Better to stay apart: asset commonality, bipartite network centrality, and
investment strategies
• Abstract: By exploiting a bipartite network representation of the relationships between mutual funds and portfolio holdings, we propose an indicator that we derive from the analysis of the network, labelled the Average Commonality Coefficient (ACC), which measures how frequently the assets in the fund portfolio are present in the portfolios of the other funds of the market. This indicator reflects the investment behavior of funds’ managers as a function of the popularity of the assets they held. We show that ACC provides useful information to discriminate between funds investing in niche markets and those investing in more popular assets. More importantly, we find that ACC is able to provide indication on the performance of the funds. In particular, we find that funds investing in less popular assets generally outperform those investing in more popular financial instruments, even when correcting for standard factors. Moreover, funds with a low ACC have been less affected by the 2007–2008 global financial crisis, likely because less exposed to fire sales spillovers.
PubDate: 2019-06-10

• Fused Lasso approach in portfolio selection
• Abstract: In this work we present a new model based on a fused Lasso approach for the multi-period portfolio selection problem in a Markowitz framework. In a multi-period setting, the investment period is partitioned into sub-periods, delimited by the rebalancing dates at which decisions are taken. The model leads to a constrained optimization problem. Two $$l_1$$ penalty terms are introduced into the objective function to reduce the costs of the investment strategy. The former is applied to portfolio weights, encouraging sparse solutions. The latter is a penalization on the difference of wealth allocated across the assets between rebalancing dates, thus it preserves the pattern of active positions with the effect of limiting the number of transactions. We solve the problem by means of the Split Bregman iteration. We show results of numerical tests performed on real data to validate our model.
PubDate: 2019-06-08

• Modelling history in nurse rostering
• Abstract: Standard data formats for problems and solutions are significant enablers of progress in fields that deal with complex real-world data. This paper makes a contribution to standardizing the nurse rostering problem in the area of history: how solutions to previous instances influence the current instance. Several issues are addressed, including avoiding double counting of penalties, constraining consecutive busy times, and completeness. The work is implemented within the XESTT model of nurse rostering.
PubDate: 2019-06-06

• The stochastic opportunistic replacement problem, part III: improved
bounding procedures
• Abstract: We consider the problem to find a schedule for component replacement in a multi-component system, whose components possess stochastic lives and economic dependencies, such that the expected costs for maintenance during a pre-defined time period are minimized. The problem was considered in Patriksson et al. (Ann Oper Res 224:51–75, 2015b), in which a two-stage approximation of the problem was optimized through decomposition (denoted the optimization policy). The current paper improves the effectiveness of the decomposition approach by establishing a tighter bound on the value of the recourse function (i.e., the second stage in the approximation). A general lower bound on the expected maintenance cost is also established. Numerical experiments with 100 simulation scenarios for each of four test instances show that the tighter bound yields a decomposition generating fewer optimality cuts. They also illustrate the quality of the lower bound. Contrary to results presented earlier, an age-based policy performs on par with the optimization policy, although most simple policies perform worse than the optimization policy.
PubDate: 2019-06-05

• Labor inspectorates’ efficiency and effectiveness assessment as a
learning path to improve work-related accident prevention
• Abstract: Labor inspectorates, by monitoring and enforcing labor legislation, giving information and technical advice to employers and employees, promoting awareness-raising campaigns and implementing occupational risk prevention policies, play a fundamental role in promoting safer and healthier working conditions and, ultimately, in reducing the number of work-related accidents. Even so, every year, hundreds of thousands of people die from accidents at work and around 2 million die from occupational diseases, highlighting that considerable room for improvement remains. The identification and sharing of best practices in labor inspection can be a useful first step in this direction. The main purpose of this research is to propose Data Envelopment Analysis as a suitable technique for assessing the performance of labor inspectorates and identifying best practices. The potential of the proposed methodology is tested by using it to assess the efficiency and the effectiveness of the 32 local branches of the Portuguese Authority for Working Conditions, a key player of the Portuguese system of occupational risk prevention, and by exploring its value in identifying best practices and possible paths for performance improvement.
PubDate: 2019-06-04

• A Benders decomposition approach to product location in carousel storage
systems
• Abstract: In this paper we discuss the problem of locating items within carousel bins in order to minimize the average carousel rotational distance per retrieval. We consider two cases: (1) a single two-dimensional carousel and (2) a group of two one-dimensional carousels. The corresponding problems are formulated as mixed-integer programs. In the second problem we apply concepts from Markovian performance evaluation methods to write the objective functions in a simple linear form. We also define a set of uniqueness constraints that significantly reduces the size of the solution feasibility space. We then apply Benders decomposition algorithm to solve both problems. We develop a closed form solution for the dual subprobem and present numerical results to show the efficiency of the proposed solution methodology.
PubDate: 2019-06-04

• Comprehensive analysis of gradient-based hyperparameter optimization
algorithms
• Abstract: The paper investigates hyperparameter optimization problem. Hyperparameters are the parameters of model parameter distribution. The adequate choice of hyperparameter values prevents model overfit and allows it to obtain higher predictive performance. Neural network models with large amount of hyperparameters are analyzed. The hyperparameter optimization for models is computationally expensive. The paper proposes modifications of various gradient-based methods to simultaneously optimize many hyperparameters. The paper compares the experiment results with the random search. The main impact of the paper is hyperparameter optimization algorithms analysis for the models with high amount of parameters. To select precise and stable models the authors suggest to use two model selection criteria: cross-validation and evidence lower bound. The experiments show that the models optimized using the evidence lower bound give higher error rate than the models obtained using cross-validation. These models also show greater stability when data is noisy. The evidence lower bound usage is preferable when the model tends to overfit or when the cross-validation is computationally expensive. The algorithms are evaluated on regression and classification datasets.
PubDate: 2019-06-03

• Preface: Queueing theory and network applications
• PubDate: 2019-06-01

• Pseudo conservation for partially fluid, partially lossy queueing systems
• Abstract: We consider a queueing system with heterogeneous customers. One class of customers is eager; these customers are impatient and leave the system if service does not commence immediately upon arrival. Customers of the second class are tolerant; these customers have larger service requirements and can wait for service. In this paper, we establish pseudo-conservation laws relating the performance of the eager class (measured in terms of the long run fraction of customers blocked) and the tolerant class (measured in terms of the steady state performance, e.g., sojourn time, number in the system, workload) under a certain partial fluid limit. This fluid limit involves scaling the arrival rate as well as the service rate of the eager class proportionately to infinity, such that the offered load corresponding to the eager class remains constant. The workload of the tolerant class remains unscaled. Interestingly, our pseudo-conservation laws hold for a broad class of admission control and scheduling policies. This means that under the aforementioned fluid limit, the performance of the tolerant class depends only on the blocking probability of the eager class, and not on the specific admission control policy that produced that blocking probability. Our psuedo-conservation laws also characterize the achievable region for our system, which captures the space of feasible tradeoffs between the performance experienced by the two classes. We also provide two families of complete scheduling policies, which span the achievable region over their parameter space. Finally, we show that our pseudo-conservation laws also apply in several scenarios where eager customers have a limited waiting area and exhibit balking and/or reneging behaviour.
PubDate: 2019-06-01

• Analysis of a Markovian feedback queue with multi-class customers and its
application to the weighted round-robin queue
• Abstract: We consider an M/G/1 Markovian feedback queue with multi-class customers. We derive functional equations for the stationary distribution of the queue size and the total response time. A system of linear equations is also derived for the moments of the queue size and the total response time distributions. The mean and the variance of the queue size and the total response time can be computed by solving the system of linear equations. By using the Markovian feedback queue with multi-class customers, we also investigate the M/PH/1 weighted round-robin queue. Numerical examples are given to show that moments of the queue sizes and of the total response times can be easily computed for the weighted round-robin queue. As the service quantum shrinks to zero, the moments of the queue sizes and the total response times converge to some limits which should be the moments of the corresponding discriminatory processor sharing queue.
PubDate: 2019-06-01

• Analysis of the waiting time distribution for polling systems with
retrials and glue periods
• Abstract: We consider a single-server multi-station polling system with retrials and glue periods. Just before the server arrives at a station, there is a deterministic glue period. During a glue period, arriving customers (either newly arriving customers or retrying customers) at the station stick in the queue of that station and will be served during the following service period of that station. Whereas during any other period, arriving customers at the station join the orbit of that station and will retry after an exponentially distributed time. In this paper, we derive the Laplace–Stieltjes transform of the waiting time distribution of an arbitrary customer. This transform allows us to obtain the mean and variance of the waiting time.
PubDate: 2019-06-01

• Preface: reliability and quality management in stochastic systems
• Abstract: This special issue of the Annals of Operations Research is related to the Asia–Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM 2016), held during 24–26 August 2016, at the Hanyang University, Seoul, South Korea. It focuses on new international research in theoretical and related applications to solve quality management problems. Reliability and quality management are quite important in many practical stochastic systems, such as computer systems, logistic systems, production systems, and the like. Developing and applying operations research methods to measure the robustness and to solve the engineering problem of a stochastic system is indeed a crucial task, enabling the system manager to carry out improvements after measuring the stochastic system.
PubDate: 2019-06-01

• Coherent quality management for big data systems: a dynamic approach for
stochastic time consistency
• Abstract: Big data systems for reinforcement learning have often exhibited problems (e.g., failures or errors) when their components involve stochastic nature with the continuous control actions of reliability and quality. The complexity of big data systems and their stochastic features raise the challenge of uncertainty. This article proposes a dynamic coherent quality measure focusing on an axiomatic framework by characterizing the probability of critical errors that can be used to evaluate if the conveyed information of big data interacts efficiently with the integrated system (i.e., system of systems) to achieve desired performance. Herein, we consider two new measures that compute the higher-than-expected error,—that is, the tail error and its conditional expectation of the excessive error (conditional tail error)—as a quality measure of a big data system. We illustrate several properties (that suffice stochastic time-invariance) of the proposed dynamic coherent quality measure for a big data system. We apply the proposed measures in an empirical study with three wavelet-based big data systems in monitoring and forecasting electricity demand to conduct the reliability and quality management in terms of minimizing decision-making errors. Performance of using our approach in the assessment illustrates its superiority and confirms the efficiency and robustness of the proposed method.
PubDate: 2019-06-01

• Reliability evaluation of a multi-state air transportation network meeting
multiple travel demands
• Abstract: In last decades, air transportation plays an important role in global economy. Several scholars have studied optimizing air transportation system or proposed reliability evaluation algorithms from airline management viewpoints. This work evaluates the reliability of an air transportation system from the perspective of travel agency instead. An air transportation system can be modeled as a multi-state air transportation network (MATN) wherein each node represents an airport and each arc denotes a flight carrying passengers between a pair of airports from scheduled departure time to scheduled arrival time. Significantly, this study focuses on investigating the reliability of multiple travel demands. Therefore, the reliability of an MATN is defined as the probability that a set of demands can be carried successfully under constraints of time and number of stopovers. This study employs the concept of minimal paths in reliability evaluation. Subsequently, a searching procedure is added to the proposed algorithm. In addition, an illustrative example and a case study are utilized to demonstrate the proposed algorithm and discuss the implications of reliability evaluation for the management of travel agency.
PubDate: 2019-06-01

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