Operations Research Perspectives最新文献

The job shop scheduling problem is one of the most studied optimization problems to this day and it becomes more and more important in the light of the fourth industrial revolution (Industry 4.0) that aims at fully automated production processes. For a long time exact methods like constraint programming had problems to solve real large-scale problem instances and methods of choice were to be found in the area of (meta-) heuristics. However, developments during the last decade improved the performance of state-of-the-art constraint solvers dramatically, to the point that they can be applied also on large-scale instances. The presented work’s main target is to elaborate the performance of state-of-the-art constraint solvers with respect to industrial-size job shop scheduling problem instances. To this end, we analyze and compare the performance of two cutting-edge constraint solvers: OR-Tools, an open-source solver developed by Google and recurrent winner of the MiniZinc Challenge, and CP Optimizer, a proprietary constraint solver from IBM targeted at industrial optimization problems. In order to reflect real-world industrial scenarios with heavy workloads like found in the semi-conductor domain, we use novel benchmarks that comprise up to one million operations to be scheduled on up to one thousand machines. The comparison is based on the best makespan (i.e. completion time) achieved and the time required to solve the problem instances. We test the solvers on single-core and quad-core configurations.

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.

The paper intends to understand the research trends in “Covid-19 and SME” through a Systematic Literature Review (SLR) and extract themes to explore the most affected areas of SMEs during the Covid-19 pandemic. Subsequently, the study attempts to know the struggles of SME during Covid-19 crisis in a developing country . Furthermore, the study provides a critical dynamic resilience strategy framework to manage the SMEs in the crisis period. The authors extracted data from Scopus and Web of science to conduct a Systematic Literature Review (SLR), Extracted data from both databases were merged using R programming get the same tag in R programming. The study adopts a bibliometric analysis to present the research corpus in the domain of “Covid-19 and SMEs”. The cluster method of R programming has been used to usher the significantly affected areas of SMEs. Based on the cluster theme, an open-ended questionnaire was developed and used to interview 23 SMEs in Bangladesh for the case study. NVIVO-13.00 was used to extract the topic from the transcriptions of the interviews. The study reveals that Cash flow shortages and Supply Chain Disruptions are the critical constraints of SMEs. On the contrary, Digital transformation has gained momentum during this crisis. Enterprises that made the best use of digital platforms through technology, digital marketing, and innovations secured the peak of success and profitability. The study also recommends a critical dynamic, resilient strategy model to adopt in the "new normal" for successful navigation of SME business in the future. The study is the first of its kind that integrates SLR and a case study on the hurdles of SME owners during the Covid 19 crisis. Thus, it helps advance the understanding of the subject matter and enables the formulation of resilient strategies by policymakers and SME owners to navigate the business in any potential crisis in the future. The study has significant methodological contribution as it presents how to merge both Scopus and web of science data to conduct bibliometric analysis through R programming. Besides, it also contributes to using R’s clustering method to extract themes for “SME and Covid -19” domain. Finally, the study presents an overview of SMEs in crisis such as Covid-19 and a case study of a rising economy and its response measures. The Case study has been designed to concentrate on Bangladesh’s SME owners and practical implications potentially limited to Emerging Asian Economies.

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.