Operations Research Perspectives最新文献

Coronavirus Disease 2019 (COVID-19), a new illness caused by a novel coronavirus, a member of the corona family of viruses, is currently posing a threat to all people, and it has become a significant challenge for healthcare organizations. Robotics are used among other strategies, to lower COVID’s fatality and spread rates globally. The robot resembles the human body in shape and is a programmable mechanical device. As COVID is a highly contagious disease, the treatment for the critical stage COVID patients is decided to regulate through medication service robots (MSR). The use of service robots diminishes the spread of infection and human error and prevents frontline healthcare workers from exposing themselves to direct contact with the COVID illness. The selection of the most appropriate robot among different alternatives may be complex. So, there is a need for some mathematical tools for proper selection. Therefore, this study design the MAUT-BW Delphi method to analyze the selection of MSR for treating COVID patients using integrated fuzzy MCDM methods, and these alternatives are ranked by influencing criteria. The trapezoidal intuitionistic fuzzy numbers are beneficial and efficient for expressing vague information and are defuzzified using a novel algorithm called converting trapezoidal intuitionistic fuzzy numbers into crisp scores (CTrIFCS). The most suitable criteria are selected through the fuzzy Delphi method (FDM), and the selected criteria are weighted using the simplified best–worst method (SBWM). The performance between the alternatives and criteria is scrutinized under the multi-attribute utility theory (MAUT) method. Moreover, to assess the effectiveness of the proposed method, sensitivity and comparative analyses are conducted with the existing defuzzification techniques and distance measures. This study also adopt the idea of a correlation test to compare the performance of different defuzzification methods.

The exact analysis of optimal ordering policies for periodic-review perishable inventory systems is challenging because of their high-dimensional state space arising from multiple interrelated ordering decisions over many periods and age distributions of on-hand products. We develop a new marginal analysis framework to approximate this complex multiple-decision model into a single-decision model with an externality term, which internalizes the long-term impact of ordering decisions. Our externality-based approximation utilizes a constant base-stock policy; it is fast and easy to apply. Numerical experiments show that our approach provides state-dependent ordering amounts almost identical to the optimal dynamic programming-based policy.

This article focuses on the Mixed-Model Assembly Line Balancing single-target problem of type 2 with single-sided linear assembly line configurations, which is common in the industrial environment of small and medium-sized enterprises (SMEs). The main objective is to achieve Algorithmic Transparency (AT) when using Genetic Algorithms for the resolution of balancing operation times. This is done by means of a new matrix methodology that requires working with product functionalities instead of product references.

The achieved AT makes it easier for process engineers to interpret the obtained solutions using Genetic Algorithms and the factors that influence decisions made by algorithms, thereby helping in the later decision-making process. Additionally, through the proposed new matrix methodology, the computational cost is reduced with respect to the stand-alone use of Genetic Algorithms.

The AT produced using the new matrix methodology is validated through its application in an industry-based paradigmatic example.

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.