This article evaluates 186 papers, published between 1978 and 2013 in 16 representative scientific journals, related to maintenance and reliability problems that were tackled from a multi-criteria (MC) perspective. An overview and insights are presented. This study may be useful to researchers and others concerned with maintenance and reliability who seek not only to understand the potential of MC and multi-objective models but also to develop and apply an MC decision model to help solve a real problem in these areas. There are some discussions on some principles for the application of MC in maintenance and reliability and some guidance on how to choose a suitable MC method is given, based on previous applications.
Mergers and acquisitions are important operations that happen nowadays. The goal of such processes is to "conquer" new markets and benefit from their resources (natural or human), or to lower competition (by acquiring a competitor or merging with it). More and more studies are written on this subject, thing that makes people interested in it have a difficult job in staying up to date. That is why the present research had as a goal to evaluate and summarize the latest trends in the study of this subject. Based on our goal we have conducted an extended analysis on the studies published in 2014 in this field. Additionally, we have also descriptively analyzed the period 2010-2014. For this, we have presumed that the most important research is to be found in the ISI-Thomson Web of Knowledge. We point out the lack on such literature on the developing countries, as most of these studies are related to the developed ones, such as the USA, the UK, China or Germany. The major part of them is published in the Journal of Corporate Finance. The second part of the article comes to emphasize the most important ideas that are to be found in the 2014 field's literature. Many of the studies are related to the banking sector. Additionally, we found new indexes created to evaluate the M&A performance or the concentration degree of the market due to and after M&A operations. There are papers that assess different theories, such as the merger waves theory, the concentration-fragility hypothesis, the too-big-to-merge, too-big-to-succeed or, too-big-to-fail theories and so on.
Organizations need to better design, manage and improve their supply chains as these become global and more complex. To do this, they need to learn from other organizations and sectors, preempt problems before they occur, and understand the future challenges they may face. Although over 40,000 articles and books have been published on supply chain management since the term was coined in 1982, a clear understanding of the emerging trends, current knowledge gaps and potential areas for future development is only now emerging. Our bibliometric analysis of the existing literature suggests we still need to better understand how to manage security, insourcing, sustainability, competition, risk and disruption, and human behaviour within supply chains. Equally, there is still a lack of research within healthcare, disaster and humanitarian supply chains, as well as within small and medium enterprises.
Operating theatre scheduling is a critical task that directly impacts the efficient delivery of surgical care. In this context, we propose a comprehensive stochastic programming modelling framework which handles the inherent uncertainty characterizing the arrival of emergency patients and the duration of surgery. In particular, three recourse strategies are presented with the aim of modelling different reactive scheduling policies actually adopted by hospital managers. In order to solve realistic-sized instances in a reasonable amount of time, we develop tailored heuristic solution strategies that exploit the problem structure. Computational results obtained on a set of randomly generated problems show the effective impact of the stochastic programming approach and the efficiency of the proposed heuristics.
Lack of knowledge or epistemic uncertainty in technical systems can be treated with so-called Solution Spaces. They are sets of good designs that reach by definition all design goals. Considering sets rather than one single design allows for unintended variations of component properties that are typical in the early stages of systems design. Box-shaped Solution Spaces can be expressed as the Cartesian product of permissible intervals for design variables. These intervals serve as independent target regions and can be interpreted as component requirements. Existing algorithms optimize the size of box-shaped Solution Spaces. Unfortunately, the size of the permissible intervals for crucial design variables is often not large enough to encompass all uncertainty and to ensure feasibility. A new approach is introduced where the design variables are divided into a set of early- and a set of late-decision variables. Early-decision variables are associated with permissible intervals on which they may assume any value to encompass uncertainty due to limited controllability. Late-decision variables are controllable and therefore associated with intervals where they can be adjusted to any specific value. The Cartesian product of these intervals is called a Solution-Compensation Space. It has the property that for all values of early-decision variables from their permissible intervals there exists at least one set of late-decision variable values from their intervals such that the resulting design reaches all design goals. The approach is applied to a design problem from vehicle driving dynamics. It is shown that the permissible intervals for the early-design variables can be increased significantly.
The transportation problem is one of the most popular problems in linear programming. Over the course of time a multitude of exact solution methods and heuristics have been proposed. Due to substantial progress of exact solvers since the mid of the past century, the interest in heuristics for the transportation problem over the past few decades has been reduced to their potential as starting methods for exact algorithms. However, in the context of ever increasing problem dimensions, a thorough cost-benefit analysis of exact methods versus heuristics is asked for. For this reason, this paper contributes an in-depth study of heuristics with respect to their performance in terms of computation time and objective value. Furthermore, we consider-to the best of our knowledge for the first time-simple efficient dual heuristics to obtain performance certificates based on weak duality without the need to solve the problem exactly. We test these heuristics in conjunction with state-of-the-art solvers on a variety of test problems. Thus, we especially close the gap to rather outdated comparative studies from the past century. For specific random test problems we extend previous approaches to provide a consistent and flexible problem generator for transportation problems with known solutions. Based on our numerical results, it can be concluded that primal and dual heuristics are able to rapidly generate good approximations for specific randomly generated problem instances but-as expected-are not able to yield satisfactory performance in realistic instances.
Order sets are a critical component in hospital information systems, designed to substantially reduce clinician workload and improve patient safety and health outcomes. Order sets represent clusters of order items, such as medications prescribed at hospital admission, that are administered to patients during their hospital stay. In prior research, we constructed order sets for defined time intervals during inpatient stay based on historical data on items ordered by clinicians across a large number of patients. In this study, we build on our prior work to formulate a mathematical program for optimizing order sets that are applicable across the entire duration of inpatient stay and are independent of the time intervals. Furthermore, due to the intractability of the problem, we develop a Greedy algorithm to tackle real-world test instances. We extract data sets for three clinical scenarios and conduct both cognitive and physical workload (PW) analyses. Finally, we extend a software application to facilitate the comparison of order sets by practitioners. Our computational results reveal that the optimization-based physical and cognitive workload (CW) models can solve small test instances to optimality. However, for real-world instances, the Greedy heuristic is more competitive, in particular, when PW instead of CW is the optimization objective. Overall, the Greedy heuristic can solve the test instances within one minute and outperforms the mathematical program in 2/3 of the test instances within a time limit of 10 min, demonstrating a feasible and promising approach to develop inpatient order sets that can subsequently be validated by clinical experts.
When randomness in demand affects the sales of a product, retailers use dynamic pricing strategies to maximize their profits. In this article, we formulate the pricing problem as a continuous-time stochastic optimal control problem and find the optimal policy by solving the associated Hamilton-Jacobi-Bellman (HJB) equation. We propose a new approach to modelling the randomness in the dynamics of sales based on diffusion processes. The model assumes a continuum approximation to the stock levels of the retailer which should scale much better to large-inventory problems than the existing Poisson process models in the revenue management literature. The diffusion process approach also enables modelling of the demand volatility, whereas Poisson process models do not. We present closed-form solutions to the HJB equation when there is no randomness in the system. It turns out that the deterministic pricing policy is near-optimal for systems with demand uncertainty. Numerical errors in calculating the optimal pricing policy may, in fact, result in a lower profit on average than with the heuristic pricing policy.
In the present article, we study an optimal control problem for a general stochastic factor model under the existence of private information. More precisely, we consider a portfolio manager who has the possibility to invest part of her wealth in a financial market consisting of a risk-free asset and a risky one, whose coefficients depend on some external stochastic factor. Moreover, we assume that the manager, from the beginning of the trading interval, observes an information signal associated with the future evolution of the risky asset. This information is not clear but is subject to some observation noise. Within a very general framework, by resorting to a mixture of dynamic programming and initial enlargement of filtrations techniques, we characterize the optimal value function and the feedback control law, by solving an expected utility maximization problem under the enlarged information set of the economic agent. In the case of the exponential utility function and considering a specific form for the market parameters and the information signal, we provide closed form solutions for the optimal investment decision and the optimal value function. Additionally, by employing an Euler-Maruyama scheme followed by a Monte-Carlo approach, we numerically study the impact of the private information on the optimal investment strategy for this concrete example. The article has Supplementary Material, which provides the extension of our model to other classes of utility functions (logarithmic and power) and also presents the general case of multiple assets and factors.
This article proposes a non-convex meta-frontier Malmquist index for measuring productivity over time, where the panel data comprise groups of decision making units (DMUs) operating under the influence of different local technologies. The suggested approach overcomes a weakness of the conventional meta-frontier Malmquist index, which implicitly neglects that the technology under which each group of DMUs operates can change over time. This negligence may lead to a poor approximation of the meta-frontier and accordingly to misleading results and wrong managerial conclusions. The new Malmquist index and its properties will be illustrated by means of an empirical application to KONE Corporation, which is recognized as one of the global leaders in the elevator and escalator industry.
The deterministic traveling purchaser problem (TPP) aims to select a subset of suppliers, offering products at different prices and quantities, to satisfy demand while minimizing travelling and purchasing costs. In this paper, we study a variant of the TPP in which both the available quantities and the purchasing prices are uncertain. This more challenging version of the problem, TPP under uncertainty, allows a purchaser to protect himself against risks of insufficient demand fulfilment and to exploit the benefits of procurement at lower prices. We introduce a two-stage stochastic programming formulation of the problem and we present a tailored solution approach based on a Branch-and-Cut method and on a heuristic approach to find good initial solutions. Extensive computational experiments show the efficiency of the proposed approach in finding the optimal solution of the deterministic equivalent problem for instances with up to 75 suppliers, 50 products and 200 scenarios in less than 2 h.
Deriving accurate fuzzy priorities is very important in multi-criteria decision making with vague information. In this paper, appropriate formulas for obtaining fuzzy priorities from additive fuzzy pairwise comparison matrices are introduced. The formulas are based on the proper fuzzy extension of the formulas for obtaining priorities from additive pairwise comparison matrices proposed by Fedrizzi & Brunelli (2010, Soft Comput., 14, 639-645) satisfying Tanino's characterization. Moreover, a new normalization condition for priorities reachable (unlike other normalization conditions formerly proposed in the literature) also for inconsistent additive pairwise comparison matrices is proposed and extended properly to additive fuzzy pairwise comparison matrices. Furthermore, a new definition of a consistent additive fuzzy pairwise comparison matrix independent of the ordering of objects in the matrix is given, and the consistency requirement is also employed directly into the formulas for obtaining fuzzy priorities. Triangular fuzzy numbers are used for the fuzzy extension in the paper, and a brief discussion on how to easily modify the formulas and the definitions presented in the paper in order to apply on intervals, trapezoidal fuzzy numbers or any other type of fuzzy numbers is provided. The theory is illustrated on numerical examples throughout the paper.