Operations Research Perspectives最新文献

Worldwide, chemotherapy centers that provide outpatient services face significant challenges owing to increased demand and limited resources. Therefore, outpatient chemotherapy process (OCP) optimization has attracted the attention of operations management scholars. This review seeks to provide a comprehensive analysis of existing quantitative optimization-oriented research that addresses OCP problems and identifies departure points for future research.

Various scientific databases were searched to collect the maximum number of OCP optimization-oriented publications. Bibliometric data mining tools were used to provide descriptive analyses of the publications. The OCP optimization-oriented research framework was obtained through social network analysis of the formulation narratives of the models. Content analysis was performed to classify the literature based on several optimization-oriented perspectives. From 1500 publications, 45 studies were screened and included in the review. The current literature lacks a holistic solution to OCP challenges, as most publications are pure optimization studies that consider narrow scopes and idealized problems. This review proposes future research opportunities based on the gaps discovered, which may lead to more insightful results for real-life OCP problems.

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.

Product returns is a widespread phenomenon associated with online retailing, where both consumer behavior and retailers’ operation decisions are inextricable topics. To improve the returns experience, retailers have forayed into omnichannel return operations and consumers are sophisticated enough to make strategic channel decisions.

Taking into account consumer disappointment aversion triggered by value uncertainty online, three return configurations were explored: non-return, same-channel return and omnichannel return and the impacts of disappointment aversion on the retailer's optimal pricing, return penalty, and profitability are investigated. The most important conclusion is that the effect of disappointment aversion on return strategy is influenced by distinct product return rate. We identify conditions under which the retailer should choose omnichannel return strategy and the other key findings show that the optimal pricing under a same-channel strategy should be no lower than the other two strategies, and depending on the level of unit travel cost, the optimal pricing is symmetric on different disappointment aversion intervals and the correlations are opposite. The return penalty keeps down with disappointment aversion. The retailer will implement a non-return policy only when return rate is relatively high. When both the return rate and the disappointment aversion is intermediate, the same-channel return is the optimal strategy. However, it is only profitable for retailers to implement an omnichannel return strategy when disappointment aversion is low, which supports for the retailer's choice of return strategies.

This research intends to synthesize and analyze the existing research published with respect to “extent of supply chain integration” and impact of Multiple extents of supply chain integration on supply chain performance. Three fundamental questions, including; How the extent of supply chain integration has been defined and measured in the exiting SC literature? What are the bases of differences/inconsistencies in defining various extents of integration, and What is the impact of certain contingencies (i.e. moderators & situational factors) on a relationship between, supply chain integration (extents), and supply chain performance?. In desire of sketching the available extents of supply chain integration, and their impact on chains performance, an organized technique is made use of, to observe 293 (Ultimately trimmed to 113) research studies, within the time frame of Jan 1^st, 2015 till April 2020, gathered through multiple platforms, including, Emerald, Taylor & Francis, Wiley & Science Direct. Results of the study, generate the notable list of dimensions, performance outcomes, contingent variables, theoretical lenses and list of Industries which have been considered for studies in the field of SCI within the considered time frame.

This paper mainly studies two topics: linear complementarity problems for modeling electricity market equilibria and optimization under uncertainty. We consider both perfectly competitive and Nash–Cournot models of electricity markets and study their robustifications using strict robustness and the $\Gamma$-approach. For three out of the four combinations of economic competition and robustification, we derive algorithmically tractable convex optimization counterparts that have a clear-cut economic interpretation. In the case of perfect competition, this result corresponds to the two classic welfare theorems, which also apply in both considered robust cases that again yield convex robustified problems. Using the mentioned counterparts, we can also prove the existence and, in some cases, uniqueness of robust equilibria. Surprisingly, it turns out that there is no such economic sensible counterpart for the case of $\Gamma$-robustifications of Nash–Cournot models. Thus, an analog of the welfare theorems does not hold in this case. Finally, we provide a computational case study that illustrates the different effects of the combination of economic competition and uncertainty modeling.

Data Envelopment Analysis (DEA) has been widely applied in measuring the efficiency of Decision-Making Units (DMUs). The conventional DEA has three major drawbacks: a) it does not consider Decision Makers’ (DMs) preferences in the evaluation process, b) DMUs in this model are flexible in weighting the criteria to reach the maximum possible efficiency, and c) it ignores the uncertainty in data. However, in many real-world applications, data are uncertain as well as imprecise and managers want to impose their opinions in decision-making procedure. To address these problems, this paper develops a novel multi-objective Best Worst Method (BWM)-Robust DEA (RDEA) for incorporating DMs’ preferences into DEA model in an uncertain environment. The proposed model tries to provide a new efficiency score which is more reliable and compatible with real problems by taking the advantages of the BWM to apply experts’ opinions and RDEA to model the uncertainty This bi-objective BWM-RDEA model is solved utilizing amin-max technique and so as to illustrate its usefulness, this model is implemented for assessing Iranian airlines.

Rank reversal in Analytic Hierarchy Process (AHP) due to introduction or removal of a new alternative is well documented in literature. However, rank reversal due to the aggregation of benefit and cost criteria has not been addressed with requisite rigour. This paper demonstrates a new type of rank reversal in AHP which can arise due to the method (ratio or difference) and approach (sum 1 or sum 2) used to aggregate the benefit and cost criteria. Numerical examples, mathematical analyses and computer simulations have been used for demonstration of results. It is found that considering the benefit and cost criteria separately (sum 2 approach) while aggregating them can yield irrational ranking. It is also demonstrated that ratio method of aggregation is untenable in additive variants of AHP as it yields identical ranking in sum 1 and sum 2 approaches. Difference method of aggregation considering the benefit and cost criteria together (sum 1 approach) is most logical and sound in additive variants of AHP. The results also counter the notion that multiplicative AHP is immune to rank reversal between ratio and difference methods of aggregation. Besides, sum 1 and sum 2 approaches produce different raking of alternatives in multiplicative AHP irrespective of the method of aggregation. Outcome of this research will be helpful while choosing the appropriate method and approach for aggregation of benefit and cost criteria in different variants of AHP.

Public-private partnership (PPP)-based infrastructure projects generally face many risks and uncertainties at all stages of the project, including initial studies, design, construction, and operation. This causes many challenges such as increased costs, delays in the project and loss of materials and equipment, and so on. Given the need to use the PPP method in the development of infrastructure projects and its broad dimensions, it is important to accurately identify and evaluate the risks involved in these projects. In the present paper, the most important risks in these projects are identified by case study in PPP-based freeway projects in Iran and using the six step-by-step process of risk management based on PMBOK standard, a model for risk assessment it is developed. Also, for quantitative risk analysis, an attempt was made to determine the importance of risks and their priority in the studied projects by using fuzzy multi-criteria decision making techniques (FAHP and FTOPSIS). The results of quantitative risk analysis by FAHP method showed that first level risks in seven different categories including economic and financing risks, construction, operational, legal, political, other risks and government risks, respectively, have the greatest impact on PPP-based freeway projects. They are dedicated to themselves. Also, the results of the general ranking of the sub-criteria showed that high risk financing costs, quality of performance and standards, lack of support infrastructure have the greatest impact on these projects. Finally, the results of FTOPSIS similarity index showed that according to project experts, projects A (Isfahan-Shiraz Freeway), B (Salafchegan-Arak Freeway) and C (Khorramabad-Arak Freeway) with scores of 0.433, 0.3369 and 0.283, respectively, had a greater impact on risks, respectively. Also, the remaining risks that were jointly or at least one of the studied projects had a high impact were identified as final risks and were allocated among the various pillars of the project. The results of this research can be used as a management model in the process of risk assessment and management of PPP projects.

We consider the problem of maximizing the worst-case return of a portfolio when the manager can invest in stocks as well as European options on those stocks, and the stock returns are modeled using an uncertainty set approach. Specifically, the manager knows a range forecast for each factor driving the returns and a budget of uncertainty limiting the scaled deviations of these factors from their nominal values. Our goal is to understand the impact of options on the optimal portfolio allocation. We present theoretical results regarding the structure of that optimal allocation, in particular with respect to portfolio diversification. Specifically, we show that the presence of options only leads to limited diversification across the financial instruments available. We compare our robust portfolio to several benchmarks in numerical experiments and analyze how the optimal allocation varies with the budget of uncertainty. Our results indicate that our approach performs very well in practice.