Operations Research Perspectives最新文献

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 COVID-19 pandemic is unleashing crises of humanity, economy, and finance. Portfolio selection is widely recognized as the foundation of modern financial economics. Therefore, it is naturally crucial and inviting to utilize portfolio selection in order to counter COVID-19 in stock markets. We originate a counter-COVID measure for stocks, extend portfolio selection, and construct multiple-objective portfolio selection. Because of the uncertainty in measuring counter-COVID, we perform robust optimization. Specifically, we analytically compute the optimal solutions as a trail of an optimal portfolio due to the change of counter-COVID. We call the trail as mean-parameterized nondominated path. Moreover, the path is a continuous function of the change, so the portfolio relatively mildly varies for the change. In contrast, researchers typically still focus on 2-objective robust illustrations and infrequently explicitly compute the optimal solutions for multiple-objective portfolio optimization.

To the best of our knowledge, there is limited research for multiple-objective portfolio selection of COVID and for the robust optimization of multiple-objective portfolio selection. In such an area, this paper contributes to the literature. The implications to fight COVID are that investors minimize risk, maximize return, and maximize counter-COVID in stock markets and that investors ascertain the multiple-objective portfolio selection as relatively robust.

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.

COVID-19 vaccinations have been shown to be safe, efficacious, and life-saving. They, like other vaccines, do not entirely protect everyone who receives them, and no one knows how effectively they can prevent people from spreading the virus to others or whether the booster dosage is dangerous to some vulnerable people. So, in addition to getting vaccinated, we must continue with additional efforts to combat the pandemic. Quantitatively, the pragmatic, appropriate, and phenomenal mechanism of the complex spherical fuzzy set enhances the decision-making efficacy and the ordering quality of the ELECTRE I method to include a profitable and optimal approach for MAGDM. In the CSF environment, critically ill patients are investigated systematically using a pairwise comparison based ELECTRE-I technique. In this paper, we improve the precision of the CSF-based ELECTRE-I approach to an unique score function. The suggested approach’s comparability is examined with techniques that should provide equal importance to the alternatives, and the presented score function’s reliability is validated using the existing score function with the two cases.

In this paper we explore the application of the minimum total opportunity cost (MTOC) model of Fuller and Celebi (2017) to multicommodity market planning models containing binary variables and price sensitive demands, with or without substitution among commodities. We present a greatly simplified derivation of the MTOC approximation of Fuller and Celebi (2017), here called the near equilibrium (NE) model, a mixed integer program with nonlinearities only in the objective function. For some models, the NE solution achieves the MTOC solution exactly, as in an example. We provide a simple example of capacity expansion in gas and electricity markets that are linked through substitution in demand and in the possibility of using gas to produce electricity. In several cases, we compare the NE solution to the social welfare (SW) maximization solution calculated by a sequential optimization algorithm. In one case, the sequential optimization algorithm fails to converge, due to the binary variables. For the other cases, the NE model has smaller producer opportunity costs – in particular, in most cases, smaller make whole payments that bring negative producer profits up to zero – at some sacrifice of social welfare. We suggest that the NE model could be useful to government regulators as a supplementary tool along with SW models, as the NE solution usually reduces subsidies needed for make whole payments, and sometimes benefits consumers compared to the SW solution.

Food production systems are complex industrial operations that often involve multiple parties. This study proposes inventory management strategies for a multi-echelon perishable food supply chain with growing and deteriorating items. The upstream end of the proposed food supply chain is the farming echelon where newborn growing items are reared to maturity. Following this, the items are sent to the processing echelon for processing, a term that collectively describes activities such as slaughtering, cutting and packaging. The aim of the processing echelon is to transform live growing items into processed food products that are suitable for human consumption. The downstream end of the supply chain is the retail echelon where consumer demand for processed food products is met. Once the items are processed, they are subject to deterioration at both the processing and retail echelons. In light of this, an integrated inventory model aimed at optimising the performance of the entire food supply chain is formulated. The impact of investing in preservation technologies is also investigated due to the perishable nature of food products. To do this, a secondary model that incorporates an investment in preservation technologies is formulated. The model, representing a simplified industrial food production system, is aimed at jointly optimising the lot-size, number of shipments, growing cycle duration, processing cycle duration and the preservation technology investment amount. The results from the numerical example demonstrate that the preservation technology investment is worthwhile because it results in reduced inventory management costs across the supply chain.

This paper presents an approach to select and plan the optimal execution of potential investment activities. The model is composed by a computational part, in the form of a combinatorial optimization problem, coupled with a preference elicitation module used to capture subjective judgments. In particular, the structure of the elicitation module draws from portfolio decision analysis and Multi-Attribute Value Theory and shows how their use can be integrated with a multi-period optimization problem with activities durations and constraints on their overlaps. The problem formulation was inspired by a real-world infrastructure management case in the energy distribution sector and tested on a dataset of more than three hundred activities of improvement of infrastructure conditions. Finally, the approach proposed in this paper is validated by analyzing its results and its robustness concerning the input data of the real-world case study.

This study compiled a Bayesian inspection game as a branch in game theory to deal with non-performing loans (NPLs). Three types of games are analyzed, which are false alarm (FA), non-detection (ND), and bull's eye (BE). A Bayesian Nash equilibrium calculation process took place to formulate the player's strategy proportion. The equilibrium solution indicates the causative factors and develops the strategies to anticipate NPLs. The identified factors causing NPLs include customers' utility and disutility, inspection error in the form of false alarm and non-detection, operational costs to conduct an inspection, and bank utility related to inspection. The results showed that some examinations of type I and II errors to the game model could provide more comprehensive and interesting insights in managing NPL problems.

Purpose

– Based on the fact that punishment and subsidy mechanisms affect the anti-epidemic incentives of major participants in a society, the issue of this paper is how the penalty and subsidy mechanisms affect the decisions of governments, businesses, and consumers during Corona Virus Disease 2019 (COVID-19).

Design/Methodology/approach

- This paper proposes a tripartite evolutionary game theory, involving governments, businesses, and consumers, to analyze the evolutionary stable strategies and the impact of penalty and subsidy mechanism on their strategy selection during COVID-19. We then uses numerical analysis to simulate the strategy formation process of governments, businesses, and consumers for the results of tripartite evolutionary game theory.

Findings

– This paper suggests that there are four evolutionary stable strategies corresponding to the actual anti-epidemic situations. We find that different subsidy and penalty mechanisms lead to different evolutionary stable strategies. High penalties for businesses and consumers can prompt them to choose active prevention strategies no matter what the subsidy mechanism is. For the government, the penalty mechanism is better than the subsidy mechanism, because the excessive subsidy mechanism will raise the government expenditure. The punishment mechanism is more effective than the subsidy mechanism in realizing the tripartite joint prevention of the COVID-19. Therefore, the implementation of strict punishment mechanism should be a major government measure under COVID-19.

Originality/value

- Our paper extends the existing theoretical work. We use political economy to make the preference hypothesis, and we explicitly state the effect of subsidy and penalty mechanisms on the decision making of participants and compare their applicability. This is the work that the existing literature did not complete before. Our findings can provide an important theoretical and decision-making basis for COVID-19 prevention and control.

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.