Abstract: Data Envelopment Analysis is a powerful tool for evaluating the efficiency of decision-making units for the purpose of ranking, comparing, and differentiating efficient and inefficient units. Classical Data Envelopment Analysis methods operate by measuring the efficiency of each DMU compared to similar units without considering their internal workings and structures, which make them unsuitable for cases where DMUs are multistaged processes with intermediate products or when inputs and outputs are ambiguous or nonconfigurable. In problems that involve uncertainty, intuitionistic fuzzy sets can offer a better representation and interpretation of information than classic sets. In this paper, the noncooperative network data envelopment analysis model of Liang et al. (2008), which is based on Stackelberg game theory and efficiency decomposition, is expanded using the concepts of best and worst relative returns Data Envelopment Analysis model of Azizi et al. (2013) into an interval efficiency estimation model with α-β cuts for two-stage DMUs with trapezoidal intuitionistic fuzzy data. Furthermore, the method of Yue (2011) is used to rank these DMUs in terms of their intuitionistic fuzzy interval efficiency. A numerical example is also provided to illustrate the application of the proposed bounded two-stage intuitionistic Data Envelopment Analysis model. PubDate: Tue, 17 May 2022 02:35:00 +000
Abstract: This paper studies the impact of Value at Risk (VaR) constraints on investors with hyperbolic absolute risk aversion (HARA) risk preferences. We derive closed-form representations for the “triplet”: optimal investment, terminal wealth, and value function, via extending the Bellman-based methodology from constant relative risk aversion (CRRA) utilities to HARA utilities. In the numerical part, we compare our solution (HARA-VaR) to three critical embedded cases, namely, CRRA, CRRA-VaR, and HARA, assessing the influence of key parameters like the VaR probability and floor on the optimal wealth distribution and allocations. The comparison highlights a stronger impact of VaR on a CRRA-VaR investor compared to a HARA-VaR (HV). This is in terms of not only lower Sharpe ratios but also higher tail risk and lower returns on wealth. The HV analysis demonstrates that combining both, capital guarantee and VaR, may lead to a correction of the partially adverse effects of the VaR constraint on the risk appetite. Moreover, the HV portfolio strategy also does not show the high kurtosis observed for the PV strategy. A wealth-equivalent loss (WEL) analysis is also implemented demonstrating that, for a HV investor, losses would be more serious if adopting a CRRA-VaR strategy as compared to a HARA strategy. PubDate: Mon, 09 May 2022 11:20:01 +000
Abstract: The aim of this article is to analyze the scientific developments in public sector decision making during the period 2010–2020, to identify which decision-making methods are preferred in different sectors of the public sector, and to determine which integrated methods are applied in this sector. In total, 468 scholarly articles were selected covering a near comprehensive review of the literature, as described below in the search process. We found that 271studies utilized a single method, whereas 180 studies utilized integrated methods. Data envelopment analysis (DEA) was the most common, used by 97 studies. However, an analytic hierarchy process (AHP) was utilized by 178 studies when counting both simple and integrated methods. It was shown that single methods were more commonly used in education, environment, health, and public services, and integrated methods were relatively favored in economics/finance, energy, site selection, and waste management. We conclude that multiple decision-making methods are used in the public sector, and during2010–2020, there has been a tendency to use unified methods in decision-making processes. PubDate: Mon, 11 Apr 2022 06:35:00 +000
Abstract: Achieving a high-resolution approximation and hitting the Pareto optimal set with some if not all members of the population is the goal for multi- and many-objective optimization problems, and more so in real-world applications where there is also the desire to extract knowledge about the problem from this set. The task requires not only to reach the Pareto optimal set but also to be able to continue discovering new solutions, even if the population is filled with them. Particularly in many-objective problems where the population may not be able to accommodate the full Pareto optimal set. In this work, our goal is to investigate some tools to understand the behavior of algorithms once they converge and how their population size and particularities of their selection mechanism aid or hinder their ability to keep finding optimal solutions. Through the use of features that look into the population composition during the search process, we will look into the algorithm’s behavior and dynamics and extract some insights. Features are defined in terms of dominance status, membership to the Pareto optimal set, recentness of discovery, and replacement of optimal solutions. Complementing the study with features, we also look at the approximation through the accumulated number of Pareto optimal solutions found and its relationship to a common metric, the hypervolume. To generate the data for analysis, the chosen problem is MNK-landscapes with settings that make it easy to converge, enumerable for instances with 3 to 6 objectives. Studied algorithms were selected from representative multi- and many-objective optimization approaches such as Pareto dominance, relaxation of Pareto dominance, indicator-based, and decomposition. PubDate: Thu, 30 Dec 2021 07:35:01 +000
Abstract: Oil industry in India has entered the competitive world, and each organization used probing strategies to reduce cost. India is a non-oil-producing country, and the scope for this lies in reducing supply chain cost in downstream logistics. This research provides an integrated model of key enablers for transporter’s performance in downstream logistics excellence of Indian oil sector to provide oil marketing companies’ a direction for design of future strategies to reduce downstream logistics cost. The sequential mixed-methods design is adopted. It identifies the enablers through literature review and interviews with transporters, working managers, and logistics experts (qualitative), and then, interpretive structural modeling (ISM) and MICMAC analysis (quantitative) are used to develop the diagraph and matrix to establish the contextual relationship and find their role and influence on each other. This readymade, unique, and unified model provides enablers for transporters’ performance in different individual categories, namely, dependent, independent, and autonomous enablers, and link them based on their driving power and dependence power along with their influencing behavior to enable transporters, working managers, and top management to focus on for reducing the logistics cost and shall add value for the ultimate customers. The academicians shall be benefited by appreciating practical aspects of this business. PubDate: Mon, 20 Dec 2021 03:35:00 +000
Abstract: Overcrowding of emergency departments (EDs) is a problem that affected many hospitals especially during the response to emergency situations such as pandemics or disasters. Transferring nonemergency patients is one approach that can be utilized to address ED overcrowding. We propose a novel mixed-integer nonlinear programming (MINLP) model that explicitly considers queueing effects to address overcrowding in a network of EDs, via a combination of two decisions: modifying service capacity to EDs and transferring patients between EDs. Computational testing is performed using a Design of Experiments to determine the sensitivity of the MINLP solutions to changes in the various input parameters. Additional computational testing examines the effect of ED size on the number of transfers occurring in the system, identifying an efficient frontier for the tradeoff between system cost (measured as a function of the service capacity and the number of patient transfers) and the systemwide average expected waiting time. Taken together, these results suggest that our optimization model can identify a range of efficient alternatives for healthcare systems designing a network of EDs across multiple hospitals. PubDate: Mon, 23 Aug 2021 09:50:00 +000
Abstract: The multiple objective simplex algorithm and its variants work in the decision variable space to find the set of all efficient extreme points of multiple objective linear programming (MOLP). Other approaches to the problem find either the entire set of all efficient solutions or a subset of them and also return the corresponding objective values (nondominated points). This paper presents an extension of the multiobjective simplex algorithm (MSA) to generate the set of all nondominated points and no redundant ones. This extended version is compared to Benson’s outer approximation (BOA) algorithm that also computes the set of all nondominated points of the problem. Numerical results on nontrivial MOLP problems show that the total number of nondominated points returned by the extended MSA is the same as that returned by BOA for most of the problems considered. PubDate: Mon, 05 Jul 2021 08:20:00 +000
Abstract: The main objective of this study was to assess customers’ relationship management practices of Oromia Credit and Saving Share Company, Bule Hora city branch in Bule Hora, Ethiopia. Customer relationship management (CRM) as a strategy has gained tremendous interest among researchers and practitioners in recent times. Thus, this study tried to assess the status and ways CRM has been put in for practice by Oromia Credit and Saving Share Company (OCSSCO). In addition, this study considers different CRM dimensions such as empathy, bonding and satisfaction, and responsiveness. To achieve the objective of the study, primary data were collected through a questionnaire from a sample of 246 Oromia Credit and Saving Share Company customers of Bule Hora city branch, Bule Hora, Ethiopia, by using simple random sampling technique. The data collected through the questionnaire were analyzed using descriptive statistical analysis method and inferential statistics by using SPSS version 20 as a tool of data analysis. The study clearly revealed that the four CRM dimensions are strongly related. Thus, from the perspective of customers as well as management bodies of the Oromia Credit and Saving Share Company, CRM has a significant influence on customer retention and loyalty of the organization. Generally speaking, microfinance institutions are in need of doing a lot of CRM-based customer-focused practices. PubDate: Thu, 24 Jun 2021 11:05:01 +000
Abstract: In the classical inventory systems, the retailer had to settle the accounts of the purchased items at the time they were received. But in practice, the supplier applies some strategic tools, such as trade credit contract, to enhance his sales channel and offers delay period to his customers to settle the account. Any member of the supply chain may offer full or partial trade credit contract to his downstream level. Full trade credit is the case that the latter is allowed to defer the whole payment to the end of the credit period. In partial trade credit, however, the downstream supply chain member must pay for a proportion of the purchased goods at first and can delay paying for the rest until the end of the credit period. This paper considers a two-level trade credit, where the supplier offers order-quantity-dependent partial trade credit to a retailer, who suggests full trade credit to his customers. An economic order quantity (EOQ) inventory model of a deteriorating item is formulated here, and the Branch and Reduce Optimization Navigator is applied to find the optimal replenishment policy. The sensitivity of the variables on different parameters has been analyzed by applying some numerical examples. The data reveal that increasing the credit periods of the retailer and the customers can decrease and increase the retailer’s total cost, respectively. PubDate: Tue, 13 Apr 2021 08:05:00 +000
Abstract: Workforce scheduling process consists of three major phases: workload prediction, shift generation, and staff rostering. Shift generation is the process of transforming the determined workload into shifts as accurately as possible. The Shift Minimization Personnel Task Scheduling Problem (SMPTSP) is a problem in which a set of tasks with fixed start and finish times must be allocated to a heterogeneous workforce. We show that the presented three-phase metaheuristic can successfully solve the most challenging SMPTSP benchmark instances. The metaheuristic was able to solve 44 of the 47 instances to optimality. The metaheuristic produced the best overall results compared to the previously published methods. The results were generated as a by-product when solving a more complicated General Task-based Shift Generation Problem. The metaheuristic generated comparable results to the methods using commercial MILP solvers as part of the solution process. The presented method is suitable for application in large real-world scenarios. Application areas include cleaning, home care, guarding, manufacturing, and delivery of goods. PubDate: Fri, 29 Jan 2021 06:50:00 +000
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