Operations Research Perspectives最新文献

The need for clean water is expected to substantially increase while further reductions of water availability in sufficient quantity and quality are projected owing to climate change and anthropogenic activities. Accordingly, the debate on water security has recently intensified and reached the intergovernmental arena. Industry is, in particular, one of the largest (non-consumptive) water users, accountable for massive toxic wastewater discharges and facing stringent and costly environmental oversight. However, the management of reservoirs is intricate and operational research must be further expanded to design tools that enhance water security while improving operators’ profitability.

We therefore consider a game-theoretic framework to study the strategies adopted by a large group of similar producers sharing a water reservoir for their manufacturing activities. Each operator faces random demand for its outputs and chooses the optimal time to invest in a technology that ends its reliance on the reservoir. This technology introduces cost saving opportunities for the operator and benefits for the environment. Each producer therefore solves a so-called optimal stopping problem, and all problems are coupled through the reservoir level. We formulate the problem of finding a Nash equilibrium as a mean-field game (MFG) of optimal stopping. We then apply the model to the paper milling industry, an extensive water user facing a tightening of environmental regulations. This paper provides fresh insights into how to rethink the problem of technological change and water management, by offering an innovative application of operational research that builds on recent mathematical developments made in MFG theory.

A large international energy corporation owning power operations in several countries, including several generation plants as well as transmission and distribution units in Brazil's energy matrix, adopts for quite some time now the policy that performance monitoring should be left to each individual operation's management. This decentralized approach is viewed as undoubtedly having a number of managerial advantages, so much, so that it is currently in force and performance indicators are being established locally. This approach, however, has the basic disadvantage that quantitative comparisons are hard to make between operations, i.e., adequate tactical comparative assessment at corporate level, with immediate and potential unfavorable implications. Therefore, the present work undertakes to investigate the possibility of consolidating a comprehensive set of indicators capable of accommodating more possibilities of comparison between different power generation plants. The research also employs an effective ranking method in order to take as much managerial advantage as possible of the new body of identified indicators. An application example involving a set of selected indicators is presented.

In the perfect rectangle packing problem, a set of rectangular items have to be placed inside a rectangular container without overlap or empty space. In this paper, we generate a large number of random instances and decide them all with an exact solving algorithm. Both an instance’s solution probability and its hardness measured in recursions or system time, seems to critically depend on $t_{max}$, a parameter in the generation procedure that assigns the maximally choosable random side lengths of items in the instance. We numerically characterize the solvability across instance sizes, and derive a rule for generating (un)solvable problem instances of arbitrary size.

Geographical considerations such as contiguity and compactness are necessary elements of political districting in practice. Yet an analysis of the problem without such constraints yields mathematical insights that can inform real-world model construction. In particular, it clarifies the sharp contrast between proportionality and competitiveness and how it might be overcome in a properly formulated objective function. It also reveals serious weaknesses of the much-discussed efficiency gap as a criterion for gerrymandering.

Purpose

The aim of the paper is to discuss the current knowledge about Health Lean Management (LM) implementations extending the focus toward the unexplored setting of Local Health Networks (HNs). In particular, the study analyses the fundamental variables of LM implementations: context, project characteristics, the process of implementation, outcomes, and enablers.

Methodology

A systematic literature review has been conducted according to the PRISMA statement, selecting 70 relevant papers for the descriptive and content analysis.

Findings

The conducted analysis allowed to formulate a theoretical framework. It points out the hallmarks of HLM projects in LHNs, structuring the existing knowledge and highlighting the gaps.

Originality

This paper offers the first systematic overview of LM implementations in HNs. Addressing this gap, the study provides meaningful insights about LM projects in a new complex context, suggesting future research opportunities. New synergies between LM and care integration strategies have been investigated, with the purpose of improving the quality of care. From a managerial perspective, the formulated theoretical framework could support health providers with a synthesis of the best managerial practices for further implementations.

This paper studies a duopolistic market consisting of a conventional offline retailer and an online retailer, each of whom offers a competing benefit (attribute): Specifically, the conventional retailer is superior in terms of social aspects of the shopping experience, such as helpfulness of salespeople, whereas the online retailer offers the possibility of rapid and convenient procurement. Importantly, customers are assumed to be heterogeneous in the extent to which they value each attribute. We first study a scenario of a fully-covered market, in which consumers’ valuations of the attributes are high enough that every consumer purchases a product from one of the channels. In this case, we derive closed-form expressions for the retailers’ pricing strategies, their expected profits, and their market shares. We find that, surprisingly, under some conditions, the online retailer's price is higher than the conventional retailer's, despite the lower costs incurred by the former. Next, we study the realistic scenario in which the market is not fully covered (i.e., consumers have the option to avoid purchasing altogether), and compare it with the fully-covered market scenario. Specifically, we identify conditions under which, at equilibrium, each consumer buys a product, regardless of consumers’ valuations of the attributes.

Effective management of perishable inventory systems is often strewn with challenges, especially when a strong trade-off relationship exists between shortage and wastage of perishables: A smaller inventory increases the chance to lose sales (leading to higher expected shortage cost), while a larger inventory increases the chance to waste perishables (leading to higher expected wastage cost). The root cause of this strong trade-off relationship is high product demand variability. To mitigate the issue and reduce the cost of operating perishable inventory systems, some grocery stores utilize an opaque selling scheme: selling an anonymous product whose brand or exact specification is shielded from customers at the time of sale. The use of opaque products has now become a popular means to reduce shortage/wastage at grocery stores. However, there has been little discussion of the effectiveness of opaque schemes applied to perishable inventory systems.

In this paper, we propose an opaque scheme based on the balancing policy on demand, which tries to average out orders for products. We confirm both analytically and numerically that this opaque scheme effectively reduces product demand variability, thereby reducing both shortage and wastage for perishable inventory systems under a base-stock policy. We also present an analytical formula that reveals insights into the opaque scheme: The ratio between the opaque proportion and the coefficient of variation of product demands plays a key role to determine the effectiveness of our opaque scheme. Furthermore, we provide a rule of thumb to find the threshold opaque scheme parameters needed to achieve the maximum total cost savings for perishable inventory systems. We hope that many retailers selling perishables (e.g., fresh produce and baked goods) find the opaque scheme useful, implement it, and contribute to the reduction of the food wastage.

Seismic History Matching (SHM) is a key problem in the geosciences community, requiring optimal parameters of a subsurface model that match the observed data from multiple in-situ measurements. Therefore, the SHM problems are usually solved with Multi-Objective Evolutionary Algorithms (MOEAs). This group of algorithms optimize multiple objectives simultaneously, considering the trade-off between objectives. However, SHM requires the solutions that are good on all objectives rather than a trade-off. In this study, we propose a Differential Evolution algorithm using Lexicase Selection to solve the SHM problems. Unlike the MOEAs, this selection method pushes the solutions to perform well on all objectives. We compared this method with two MOEAs, namely Non-dominated Sorting Genetic Algorithm II and Reference Vector-guided Evolutionary Algorithm, on two SHM problems. The results show that this method generates more solutions near the ground truth.

This paper develops an approximate closed-form optimal portfolio allocation formula for a spot asset whose variance follows a GARCH(1,1) process. We consider an investor with constant relative risk aversion (CRRA) utility who wants to maximize the expected utility from terminal wealth under a Heston and Nandi (2000) GARCH (HN-GARCH) model. Based on an approximation of the log returns from Campbell and Viceira (1999), we obtain closed formulas for the optimal investment strategy, the value function and the optimal terminal wealth. We find the optimal strategy is independent of the development of the risky asset, and the solution converges to that of a continuous-time Heston stochastic volatility model (Kraft, 2005), albeit under additional conditions. For a daily trading scenario, the optimal solutions are quite robust to variations in the parameters, while the numerical wealth equivalent loss (WEL) analysis shows good performance of the Heston solution, with a quite inferior performance of the Merton solution.The solution is extended to two dimensions under the multivariate affine GARCH in Escobar-Anel et al. (2020).

In many of our real life problems, we often come across situations where there is no information about the priority weights which make it difficult to analyze the objects under consideration. Instead of employing simple fuzzy sets, “interval type-2 trapezoidal pythagorean fuzzy preference relations (IT2TrPFPRs)” can be used which have better representational power and ability to cope with uncertain situations. The approach discussed in this article is an effective tool for managing multiple criteria group decision-making situations with completely unknown priority weights modeled as IT2TrPFPRs. To aggregate the opinion of multiple decision-makers, a hybrid averaging operation based on weighted averaging and ordered weighted averaging (OWA) operations is employed for a collective decision environment. To calculate the fuzzy priority weight vectors in case of completely unknown environment, we construct a non-linear optimization model. An integrated optimization model based on a new signed distance-based closeness coefficients approach is employed to determine the priority ranking of alternatives. Feasibility of the proposed technique is discussed with an implementation of patient centered medicine system for choosing the appropriate treatment method. Moreover, a comparative investigation with previous approaches is conducted to demonstrate the effectiveness of the given approach.