Operations Research Perspectives最新文献

Considering a temporal dimension allows for the delivery of rolling solutions to complex real-world problems. Moving forward in time brings uncertainty, and large margins for potential error in solutions. For the multi-year crop planning problem, the largest uncertainty is how the climate will change over coming decades. The innovation this paper presents are novel methods that allow the solver to produce feasible solutions under all climate models tested, simultaneously. Three new measures of robustness are introduced and evaluated. The highly robust solutions are shown to vary little across different climate change projections, maintaining consistent net revenue and environmental flow deficits.

In this paper, we develop a supply chain optimization model for the preparation, provision, transportation, and execution of swab tests during COVID-19 pandemic. The proposed approach is based on a multi-tiered network consisting of manufacturing companies of reagents, processing laboratories (where the swab kits are prepared and some swab tests are analyzed), landing stations for UAVs and test centers. As innovations in the supply chain, the sharing of reagents between processing laboratories and the use of UAVs, using 5G technology, are contemplated in the management of the COVID-19 Pandemic. To obtain the optimal solutions of the underlying optimization problem, we provide a variational formulation problem for which results of existence and uniqueness will be provided. Finally, some numerical simulations are examined to validate the effectiveness of our approach.

The challenges of implementing circular supply chains (CSCs) are well documented, but their characterization in public sector supply chains is unavailable. Thus, this paper closes such a gap by viewing it as a multiple criteria sorting (MCS) problem. Following a systematic literature review of identifying CSC challenges, they are analytically mapped to public sector characteristics through a novel integration of Criteria Importance Through Intercriteria Correlation (CRITIC) and Combinative Distance-based Assessment (CODAS)-SORT (CODAS-SORT) methods under a Fermatean fuzzy environment. Unlike other comparable methods, the proposed Fermatean fuzzy CRITIC-CODAS-SORT is a computationally efficient MCS tool that only requires minimal cognitive workload from expert decision-makers. The Fermatean fuzzy CRITIC generates the weights of domain characteristics, while the Fermatean fuzzy CODAS-SORT assigns the CSC challenges to predefined classes. Findings reveal that inadequate top management support, inadequate government policies, lack of interest in the concept of circularity, lack of incentives to adopt CSC practices, restrictive collection policies, inadequate knowledge on CSC, and size and complexity of supply chains are the most relevant CSC challenges in the public sector. These findings would contribute to the design of effective initiatives and resource allocation decisions in advancing the circularity of supply chains in the public sector. Various policy insights are proposed, along with some future research agenda.

The tourism sector represents an opportunity for economic growth in countries with tourism potential. However, new trends in global tourism require efficiency in tourism supply chain management. One of the main changes is the trend of personalized tourism, which includes the management of itineraries by the tourist. The tourist trip design problem is the problem associated with personalized trip design. This problem has been modeled in the field of Operations Research in different ways, being the Orienteering Problem the most widely applied in the specialized literature. The objective of this paper is to carry out a systematic review of the contributions made to the tourist trip design problem. A review taxonomy is proposed to analyze the main variants, objectives, and solution techniques. Also, future research lines are analyzed and proposed to enrich the literature and provide new solutions to real problems.

This paper presents a literature review and analysis of fuzzy linear programming models and methods. This review traces back to the linear programming with fuzzy linear constraints models proposed by Hans Jürgen Zimmermann during 1974–76 which have become the seminal works for scholars and practitioners in this field due to its ease to implement and understand. To do so, we analyze the timeline of publication of the most important works in the field, evaluate the influence of relevant papers using a time-weighted index based on the amount of citations in order to provide a perspective about the future directions in fuzzy linear programming and to recommend selected books, journals and some recent contributions to readers.

Supply Chain Management (SCM) is increasingly complex and dynamic. Radio frequency identification (RFID) and the Internet of Things (IoT) are expected to play a significant role in fulfilling customer requirements in the supply chain. In this work, the integration of RFID with IoT is called RFID-IoT. RFID-IoT strives to develop automated sensing, seamless, interoperable and highly secure systems by connecting IoT devices through the internet. In this paper, the authors have systematically reviewed the selected literature on the application of RFID-IoT in supply chain management. The contribution of this paper is to review the current state-of-the-art literature and potential trend on the application of RFID-IoT in SCM. There is a need for an in-depth, comprehensive analysis of up-to-date literature to help enhance the management system efficiency, maximize productivity, and minimize cost. This work also explores the current challenges of the reviewed papers in RFID-IoT implementation in the supply chain. The conceptual framework model has been conducted from four major critical SCM perspectives: product manufacturing, shipping and distribution, inventory and retail shop. In the future, this review's highlighted insights and recommendations will hopefully lead to increased efforts toward developing RFID-IoT technologies.

Natural disasters (e.g., hurricanes) can cause widespread power outages within distribution networks and interrupted power supply to critical loads (e.g., grocery stores, hospitals, gas, fire, and police stations) that provide utility services. Microgrids are localized power grids that can incorporate solar/photovoltaic (PV) distributed generators (PV-DGs) and energy storage systems (ESSs) for stand-alone system operations independent of the main grid, known as the island mode. This study investigates a microgrid design problem using PV-DGs and ESSs when facing prolonged power outages in the main grid. We propose a multi-stage stochastic program that holistically considers the techno-economics of microgrid investment and daily operations by optimizing the reliability and resilience of the microgrid during a week-long power outage. The model is designed from a utility perspective that includes budget constraints for investment. Due to the large model size, we develop a nested L-shaped algorithm that solves the problem exactly and analyzes the microgrid’s reliability across different weather scenarios in the entire decision-making horizon. Results from a case study using real-world data show that an islanded utility-scale microgrid can effectively provide uninterrupted power supply to a network of 5 and 10 critical loads, covering 100% and 97% of the demand in all possible future scenarios, with potential investments of $8 million and $15 million, respectively.

The COVID-19 pandemic has disrupted supply chains globally with a major shortfall being that of labor shortages from production through distribution activities. In this paper, we construct a new supply chain network optimization model that includes both domestic labor and international migrant labor from multiple countries, with the latter made possible through investments in attracting labor subject to a budget constraint. We allow for different wage settings for domestic versus migrant labor and also have the flexibility of providing true information as to the wages of migrants or not. We derive variational inequality formulations of the model, along with qualitative properties, and present an algorithm that yields closed form expressions for the underlying problem variables at each iteration. The model is one of the very few variational inequality operations research models with nonlinear constraints. Three series of algorithmically solved numerical examples, motivated by a high value agricultural product — that of truffles, demonstrate the insights in terms of profits, prices, product path flows, and investments, with variations in the data including that of truthful and untruthful wages being used to attract migrant labor.

The purpose of this article is twofold: to identify the critical risk factors (RFs) that impact supply chains (SC) in the engineering, procurement, and construction of large-scale projects (EPC-LSP) of the oil and gas industry (OGI) and to apply these RFs in a mathematical model developed, based on multiple-criteria decision-making (MCDM) methods in an expert group. The mathematical model was developed in MATLAB and was based on the Preference Ranking Organization Method for Enrichment Evaluations (PROMÉTHÉE) II and (PROMÉTHÉE GDSS) Group Decision Support System methods. The model's criteria were defined with the RF mapping identified using 33 years of literature and the application of questionnaires to specialists. The evaluation process of the alternatives concerning the defined criteria was conducted through questionnaires to specialists. Finally, the functionality and results of the model were validated by the specialists in the field through interviews. As a contribution, managers, companies, and industry could adopt this solution as a practical and dynamic tool to support decision-making. This fact especially holds true in possible critical supply scenarios, where it is necessary to direct resources to minimize risks and other impacts to EPC-LSP SC. Another novelty refers to the critical risk factors identified, originating from an extensive literature mapping covering the three pillars of sustainability. Moreover, this research was to fill the literature gap, given the lack of studies that propose clear, practical, and specific tools for SCRM in EPC-LSP.

The Saaty analytic network process (Saaty-ANP) is a generalization of the analytic hierarchy process. The Markov chain-based ANP (MC-ANP) is another decision-making approach suitable for general structures. Both ANPs use a relative measurement (paired comparisons with ratio scales) to estimate tangible and intangible factors, use a stochastic matrix (SM) to solve feedback problems and obtain the same results under some conditions. The Saaty-ANP does not define the basic concepts, nor does it check the rationality of the structure, which may lead to meaningless solutions and ignore a subclass of feedback decision problems. MC-ANP separates the alternatives from the criteria and defines the attributes, criteria, criterion dominated relations (CDRs) and reasonable constraints of the CDRs by means of digraphs; it also represents CDRs as Markov chain transition diagrams and corresponding (stochastic) adjacency matrices and obtains solutions from a system of linear equations. With the MC-ANP, for the real alternative problems (Class I), the solutions are priorities of the alternatives obtained by the parametric positive left eigenvectors of the SM, and for the nominal alternative problems (Class II), the solutions are priorities of the criteria obtained by the nonnegative right eigenvector of the SM. We analyze the conditions and causes of rank reversal; note that rank reversal does not appear in Class II problems; the study offers a rank reversal ANP example (with feedback) and presents a rank-preserving method for Class I problems. We discuss the contribution of MC-ANP, how to compensate for the defects of Saaty-AHP/ANP, and present issues that need further consideration.