Annals of Operations Research最新文献

Strategic customer behavior regarding the join-or-balk dilemma in queueing systems has been studied intensively under various kinds of information structures. The majority of these studies focus on the observable and the unobservable cases, where an arriving customer observes or does not observe, respectively, the number of present customers before making her decision. An important finding is that more information does not always improve customers’ and/or the administrator’s benefits and may result to a deterioration of a system. Therefore, intermediate information structures have been proposed that bridge the two extreme cases: partially observable models, models with delayed observations, alternating observable models etc. All these structures revolve around the idea that the administrator of a service system should control somehow the information about the state of the system, which is usually the number of present customers. In this paper we consider a new mechanism which consists in informing customers about other customers’ decisions. Such a mechanism helps customers to coordinate themselves and possibly leads to better outcomes. To present this idea in the simplest possible framework we consider the M/M/1 queue with strategic customers that face the join-or-balk dilemma and assume that each arriving customer is informed about the decision of the most recent arrival. We show that this system outperforms the observable and unobservable systems for certain ranges of the parameters. Moreover, the effective arrival process is more regular, a fact that improves several performance measures of the system.

The minimum distance models have undoubtedly represented a significant advance for the establishment of targets in Data Envelopment Analysis (DEA). These models may help in defining improvement plans that require the least overall effort from the inefficient Decision Making Units (DMUs). Despite the advantages that come with Closest Targets, in some cases unsatisfactory results may be given, since improvement plans, even in that context, differ considerably from the actual performances. This generally occurs because all the effort employed to reach the efficient DEA frontier is channeled into just a few variables. In certain contexts these exorbitant efforts in some inputs/outputs become unapproachable. In fact, proposals for sequential improvement plans can be found in the literature. It could happen that the sequential improvement plans continue to be so demanding in some variable that it would be difficult to achieve such targets. We propose an alternative approach where the improvement plans require similar efforts in the different variables that participate in the analysis. In the absence of information about the limitations of improvement in the different inputs/outputs, we consider that a plausible and conservative solution would be the one where an equitable redistribution of efforts would be possible. In this paper, we propose different approaches with the aim of reaching an impartial distribution of efforts to achieve optimal operating levels without neglecting the overall effort required. Therefore, we offer different alternatives for planning improvements directed towards DEA efficient targets, where the decision-maker can choose the one that best suits their circumstances. Moreover, and as something new in the benchmarking DEA context, we will study which properties satisfy the targets generated by the different models proposed. Finally, an empirical example used in the literature serves to illustrate the methodology proposed.

Dial-a-Ride Problem (DARP) is one of the classic routing problems with pairing and precedence constraints. Due to these types of constraints, it is quite challenging to design an efficient evolutionary algorithm for solving this problem. In this paper, a genetic algorithm in combination with a variable neighborhood descent procedure is suggested to solve the DARP. This algorithm, which is called Hybrid Genetic Algorithm (HGA), is independent of any repairing procedure or user-defined penalty factors. Instead, it uses the constraint dominance principle with respect to the number of unserved requests. Our algorithm employs an adaptive population management technique which takes into account not only the quality of solutions but also their contribution in the diversity level. To do so efficiently, this population management technique utilizes a simple arc-based representation for the DARP solutions. A route-based crossover procedure known as Route Exchange Crossover is used in the HGA. This crossover method is thoroughly compared with five other crossover techniques including a new one called Block Exchange Crossover. The HGA produces competitive solutions in comparison with the state-of-the-art methods for tackling the DARP and Heterogeneous DARP (H-DARP). It obtains the optimal solutions of all the small and medium size standard instances of the DARP and finds new best results for two large ones with unknown optimal solutions. Moreover, for 12 out of 24 new instances of the H-DARP, the best known solutions are improved using the HGA.

In this paper we introduce new procedures, based on generating functions, for calculating some power measures for weighted majority games. In particular, we present methods for computing the Johnston index and the Colomer–Martínez measure. Besides, we introduce a new power measure that combines the principles underlying the Johnston index and Colomer–Martínez measure as well as a procedure for computing it using generating functions. Finally, we introduce the new R package powerindexR and describe its capabilities to compute some power measures by means of generating functions. We illustrate its performance with a real example.

We propose a new methodology to simulate the discounted penalty applied to a wind-farm operator by violating ramp-rate limitation policies. It is assumed that the operator manages a wind turbine plugged into a battery, which either provides or stores energy on demand to avoid ramp-up and ramp-down events. The battery stages, namely charging, discharging, or neutral, are modeled as a semi-Markov process. During each charging/discharging period, the energy stored/supplied is assumed to follow a modified Brownian bridge that depends on three parameters. We prove the validity of our methodology by testing the model on 10 years of real wind-power data and comparing real versus simulated results.

This paper studies the evolution of an oligopoly market where two types of companies, brown and green, are present. Green firms adopt a less polluting technology that allows a reduction in emissions. We want to investigate the possibility of an environmental-friendly transition where insurance can give its support to cover the (endogenous) climate change loss. The model is composed of two parts. We analyze a two-stages game in which the companies maximize their profits by choosing output in the first stage and insurance coverage in the second one. Then we develop an evolutionary game to endogenize the selection of being brown or green, according to the expected random profits. We derive analytically the dynamic regimes may arise and we perform a sensitivity analysis at the stable inner steady state, where firms coexist, changing the main key parameters to understand which ones may be strategic for an ecological transition.

Owing to its unique property of being both coherent and elicitable, expectile has recently been studied as an alternative risk measure to value-at-risk (({{,textrm{VaR},}})) and conditional value-at-risk (({{,textrm{CVaR},}})). Analogously, as a risk measure, it is defined as expectile value-at-risk (({{,textrm{EVaR},}})). This study proposes to enhance the Mean-({{,textrm{EVaR},}}) portfolio optimization model to incorporate short selling strategy. To assimilate different practical arrangements of a short-sale transaction, we analyze constraints such as proportional bounds, (l_1)-norm constraint, bounded budget, and turnover constraints. We conduct extensive in-sample and out-of-sample analyses using historical data of stocks from the CNX NIFTY 50 (India), Hang Seng (Hong Kong), FTSE 100 (UK), and DAX 100 (Germany) indices over 10 years using a rolling window strategy. While the (l_1)-norm constraint and the bounded budget help to restrict the total short-sale budget, the turnover constraint helps in tuning the portfolio turnover, thereby reducing the overall transaction cost. The empirical results highlight the benefits of choosing specific constraints to assist practical decision-making for the short-selling strategy in the proposed model. We further perform a comparative study of Mean-({{,textrm{EVaR},}}) model with the 1/n portfolio strategy and two popular portfolio optimization models, Mean-Variance and Mean-({{,textrm{CVaR},}}) under a similar setting and observe the financial benefit of the proposed model indicating its importance in investment practices.