Pessimistic Criterion Research Articles

Co-Evolutionary Algorithm for Two-Stage Hybrid Flow Shop Scheduling Problem with Suspension Shifts

Demand fluctuates in actual production. When manufacturers face demand under their maximum capacity, suspension shifts are crucial for cost reduction and on-time delivery. In this case, suspension shifts are needed to minimize idle time and prevent inventory buildup. Thus, it is essential to integrate suspension shifts with scheduling under an uncertain production environment. This paper addresses the two-stage hybrid flow shop scheduling problem (THFSP) with suspension shifts under uncertain processing times, aiming to minimize the weighted sum of earliness and tardiness. We develop a stochastic integer programming model and validate it using the Gurobi solver. Additionally, we propose a dual-space co-evolutionary biased random key genetic algorithm (DCE-BRKGA) with parallel evolution of solutions and scenarios. Considering decision-makers’ risk preferences, we use both average and pessimistic criteria for fitness evaluation, generating two types of solutions and scenario populations. Testing with 28 datasets, we use the value of the stochastic solution (VSS) and the expected value of perfect information (EVPI) to quantify benefits. Compared to the average scenario, the VSS shows that the proposed algorithm achieves additional value gains of 0.9% to 69.9%. Furthermore, the EVPI indicates that after eliminating uncertainty, the algorithm yields potential improvements of 2.4% to 20.3%. These findings indicate that DCE-BRKGA effectively supports varying decision-making risk preferences, providing robust solutions even without known processing time distributions.

Group decision-making with multiplicative probabilistic linguistic preference relations based on consistency improvement and upgraded multiplicative DEA cross-efficiency

Multiplicative probabilistic linguistic preference relation (MPLPR) has been widely used by decision-makers (DMs) to tackle group decision-making (GDM) problems. However, due to the complexity of the decision-making circumstance and individual subjectivity of DMs, they often provide inconsistent MPLPRs which often lead to unreasonable decision results. To solve this problem, this paper investigates a novel approach to GDM with MPLPRs based on consistency improvement and upgraded multiplicative data envelopment analysis (DEA) cross-efficiency. First, the concept of sequential consistency of MPLPR is defined. Then, a consistency improvement algorithm is proposed, which can convert any unacceptable consistent MPLPR into an acceptable one. Furthermore, we use geometric averages to transform MPLPR into multiplicative preference relation (MPR). Meanwhile, considering the conservative psychology of DMs, an upgraded multiplicative DEA cross-efficiency model based on the pessimistic criterion is constructed, which can derive the priority vector of MPLPR. Therefore, we can obtain the rational ranking results for all alternatives. Finally, a case analysis of emergency logistics under COVID-19 is provided to illustrate the validity and applicability of the proposed approach.

A new D numbers’ integration rule based on pessimistic criterion

For D numbers theory, there are some drawbacks in the D numbers’ integration rule. For example, the missing information is ignored in the final decision judgment for multi-attribute decision (MADM). For this problem, some researchers have improved the D numbers’ integration rules based on optimistic criterion for overcoming the shortcoming of D numbers’ integration rule. However, optimistic and pessimistic criterion are two sides of the coin for fuzzy environment. Therefore, in this article, a new D numbers’ integration rules based on pessimistic criterion is proposed. We improve the D numbers’ integration rules to redefine the missing information distribution rules based on pessimistic criterion. The missing information is distributed in inverse proportion to each D number according to the size of the original evidence credibility. Two examples of MADM is applied by the proposed method, the results show that the proposed method can be applied to MADM.

OPTIMIST AND PESSIMIST MOON-SIGHTING: THE STUDY OF ISLAMIC CALENDAR DETERMINATION IN INDONESIA

The Islamic calendar decision has always been a public discourse among Muslims at the beginning of Ramadan, Shawwal, and Dhulhijjah. It is often the case that different decisions made among Muslim groups in determining the first of Ramadhan, Syawal, and Dhulhijjah which will affect the public holiday in Muslim majority countries. This study incorporates the theory of limit proposed by Muhammad Syahrour to analyze the ongoing contestation of Islamic calendar determination. Based on the in-depth analysis, this study suggests that the height criteria of the crescent can be categorized as optimistic and pessimistic criteria. Crescent's altitude of 3 degrees is the lower limit of optimism in crescent observations. Pessimism can be seen from astronomical calculations in which hilal altitude is below 3 degrees. These two criteria could be supported by the existing scientific proof through the image of the hilal altitude below 3 degrees.

Optimal design and scheduling of carbon capture power plant based on uncertainty decision-making methods

Post-combustion capture is one of the most mature carbon reduction technologies which plays an important role in pushing forward the process of "carbon neutrality". However, most previous efforts focused merely on the fixed operation of power plants and carbon capture devices, without taking into account the effects of fluctuating electricity prices and the subsequent variations in energy penalty of solvent regeneration process. As a result, the ability of carbon capture power plants (CCPP) to withstand fluctuations has deteriorated, leading to a decrease in robustness. In order to take advantage of the flexible operation characteristics of the power plant and the carbon capture device, the deterministic hourly electricity price and power load demand, as well as uncertainty of power load demand in future are considered in the study to determine the optimal network configuration and the flexible scheduling of carbon capture power plants based on decision-making theories. The uncertain parameters are presented by scenarios, and through MINLP-based optimization, the optimal system configuration under each scenario is obtained. Then these configurations are applied to other scenarios to obtain the scheduling and economic performance of the system. Five uncertainty decision-making theories (optimistic method, pessimistic method, optimistic coefficient method, equal probability method, minimum maximum regret value method) are adopted to select the most appropriate system scheme. Finally, a case study is investigated to demonstrate the proposed method, and the effectiveness and the feasibility are proved by the obtained results. Two network configurations with daily profit from $30,000 to $240,000 by scenarios are obtained and investigated. Results show that the turbine layout in the configuration chosen by optimistic method is radical, while by pessimistic criterion the turbine system shows margin in operation. Configuration selected by the equal possible value method and minimax regret method can achieve good economic performance operating in various scenarios. This research can help decision makers to make scientific decisions and reduce operating risks under uncertainties.

Mitigation strategies for expiration in perishable emergency inventory system

Considering uncertainties in emergency demand quantity and occurrence time, allowance cooperation strategy and prosocial sale strategy are designed to mitigate expiration wastages for emergency perishable inventory system. Distribution-free newsboy models are developed for relief supply chain. For computational tractability, we derive upper bound of the expected total cost, then determine the applicable conditions of proposed strategies with pessimistic criterion. We offer a proof that the controversial sale strategy may be effective in both expiration mitigation and cost reduction, and elaborate an application framework and managerial insights of proposed strategies based on the type of emergency supplies and type of disasters.

Study on mechanism and application of water resources system adaptability under changing environment

Abstract The water resources system is in a vulnerable situation because of the influence of climate change and the changing environment. The adaptation mechanism was discussed through the analysis of the process of passive response, adaptation and even active adjustment of water resources system under changing environment. The adaptability of the water resources system can be described as natural resilience (NR) of natural system and artificial adaptation (AA) social (artificial) system. The natural resilience indexes were identified and analyzed from the aspects of water quantity, water quality and water ecology. The artificial adaptation indexes were identified and analyzed from the aspects of resource, eco-environment, socio-economic and technical factors. On this basis, the index system was constructed in accord with process mechanism of water resources system adaptability. Besides, to address the two-dimension factors of water resources system adaptability, a method of system analysis based on connection numbers–fuzzy risk matrix was proposed based on the theory of risk matrix. The synthesis interval [Apess, Aopt] of water resources system adaptability is obtained, by defining the pessimistic criterion when two-dimension factors meet the evaluation standard at the same time, and the optimistic criterion when either of the two-dimension factors meets evaluation standard. Finally, the case study in the Huaihe River basin in China was carried out. The results show that the adaptability level of water resources system in the Huaihe River basin expressed fluctuating uprising tendency in 2006–2015. The adaptability level is the lowest [1.856, 2.625] in 2009, the highest [2.500, 3.536] in 2015.

Hurwicz model of uncertain optimal control with jump

How to choose the optimization criterion of the objective function is an important issue for uncertain optimal control. The Hurwicz criterion is a flexible optimization criterion attempting to find the intermediate area between the extremes posed by the optimistic and pessimistic criteria. Based on uncertainty theory, in this paper, we establish a new uncertain optimal control model with jump by making use of Hurwicz criterion to optimize an uncertain objective function. By applying Bellman's principle of optimality, the principle of optimality for the proposed model is presented and then the equation of optimality is derived. Finally, an example is given to show the the effectiveness of the results obtained.

Sequential decision making under ordinal uncertainty: A qualitative alternative to the Hurwicz criterion

This paper focuses on sequential qualitative decision problems, where no probability distribution on the states that may follow an action is available. New qualitative criteria that are based on ordinal uninorms and namely R⁎ and R⁎ are proposed. Like the Hurwicz criterion, the R⁎ and R⁎ uninorms arbitrate between pure pessimism and pure optimism, and generalize the Maximin and Maximax criteria. But contrarily to the Hurwicz criterion they are associative, purely ordinal and compatible with Dynamic Consistency and Consequentialism. These important properties allow the construction of an optimal strategy in polytime, following an algorithm of Dynamic Programming. Making a step further, we then generalize R⁎ to qualitative decision under possibilistic uncertainty, proposing an alternative to the classical optimistic and pessimistic criteria used for the computation of optimal strategies in possibilistic decision trees.

Accounting for multiplicity in calculating eta Earth

ABSTRACT Using the updated exoplanet population parameters of our previous study, which includes the planetary radius updates from Gaia DR2 and an inferred multiplicity distribution, we provide a revised η⊕ calculation. This is achieved by sampling planets from our derived population model and determining which planets meet our criterion for habitability. To ensure robust results, we provide probabilities calculated over a range of upper radius limits. Our most optimistic criterion for habitability provides an η⊕ value of $0.34\pm 0.02 \frac{\rm planets}{\rm star}$. We also consider the effects of multiplicity and the number of habitable planets each system may contain. Our calculation indicates that $6.4\pm 0.5{{\ \rm per\ cent}}$ of GK dwarfs have more than one planet within their habitable zone. This optimistic habitability criterion also suggests that $0.036\pm 0.009{{\ \rm per\ cent}}$ of solar-like stars will harbour five or more habitable planets. These tightly packed highly habitable systems should be extremely rare, but still possible. Even with our most pessimistic criterion, we still expect that $1.8\pm 0.2{{\ \rm per\ cent}}$ of solar-like stars harbour more than one habitable planet.

Optimal design of distributed energy resource systems under large-scale uncertainties in energy demands based on decision-making theory

This study focuses on the optimal design of distributed energy resource systems with consideration of large-scale uncertainty of energy demands based on decision- making theory. Five integrated modeling and optimization frameworks are developed through the combined use of mixed integer linear programming and uncertainty decision-making criteria (including optimistic criterion, pessimistic criterion, Hurwicz criterion, Laplace criterion, and minimax regret criterion). Superstructure-based mixed integer linear programming models are used for the optimal design and optimal operation of the system where the objective function is to minimize the annual cost. The uncertainty of energy demands is represented by assuming a set of possible scenarios. The proposed methods are applied to the planning of a distributed energy resource system for a hotel in city of Guangzhou, China and their validity and effectiveness are verified. Results show that each method has its specific feature. Optimistic method is risky and recommends a relative small-scale system, while pessimistic method is conservative presenting a relative large-scale system. Hurwicz method is with great subjectivity, making different decisions at different values of optimism coefficient. Both Laplace method and minimax regret method identify a moderate-scale system as the best alternative. Sensitivity analyses on the energy demand scenarios are conducted and results show that the five methods have high sensitivity to the choice of scenarios.

Optimal investment decision making on the model of production enterprise with limited resources

Investments are among the most important factors of national economic growth. Selection of optimal investment project is the first priority for any enterprise with limited financial resources. This study is dedicated to a choice among mutually exclusive projects, which are impossible to complete partially, so, one project must be chosen and all others must be rejected. An investor must find among all possible projects the one that allows to better achieve all investor’s aims. A mathematical model of multi-purpose multi-criteria investor decision making is proposed for investment project selection problem. Efficiency and riskiness of studied projects are evaluated using such indicators as profit, rate of return, payback period, marginal cost of capital, also taking into account subjective characteristics, namely the investor’s attitude towards financial risks, importance assessment of decision making criteria, etc. Decision making assessment methods for the situations of risk and uncertainty are applied to resolve the problem of optimal project selection, such as Wald’s pessimistic criterion, maximax optimistic criterion, as well as Hurwicz’s, Laplace’s, Bayes- Laplace, Hodges-Lehmann criteria, and Savage’s minimax risk criterion. Calculations carried out and results obtained indicate that the best investment project chosen that way will provide the highest absolute profit, despite certain disadvantages such as lower rate of return, longer payback period and higher risk than other projects.

Pessimistic Determination of Mechanical Conditions and Micro/macroeconomic Evaluation of Mine Pillar Replacement

Numerous pillars are left after mining of underground mineral resources using the open stope method or after the first step of the partial filling method. The mineral recovery rate can, however, be improved by replacement recovery of pillars. In the present study, the relationships among the pillar type, minimum pillar width, and micro/macroeconomic factors were investigated from two perspectives, namely mechanical stability and micro/macroeconomic benefit. Based on the mechanical stability formulas for ore and artificial pillars, the minimum width for a specific pillar type was determined using a pessimistic criterion. The microeconomic benefit c of setting an ore pillar, the microeconomic benefit w of artificial pillar replacement, and the economic net present value (ENPV) of the replacement process were calculated. The values of c and w were compared with respect to ENPV, based on which the appropriate pillar type and economical benefit were determined.

Two-stage credibility-constrained programming with Hurwicz criterion (TCP-CH) for planning water resources management

In water-resources management problems, uncertainties exist in a number of impact factors and water-allocation processes, which can bring about enormous difficulties and challenges in generating desired decision alternatives under such complexities. In this study, a two-stage credibility-constrained programming with Hurwicz criterion (TCP-CH) approach is developed for water resources management and planning under uncertainty. TCP-CH can tackle uncertainties presented as probability distributions and fuzzy sets that exist in left- and right-hand sides of constraints and in objective function; it can also permit in-depth analyses of various policy scenarios based on confidence degrees that are associated with different levels of economic penalties when the promised targets are violated. The TCP-CH method is applied to a real case of planning water resources in Kaidu-qongque River basin, which is one of the aridest regions of China. Support-vector-regression (SVR) technique is introduced into TCP-CH for predicting the water demand in the future, which takes advantage of uncertainty reflection and dynamic analysis. Results of water allocation, water shortage, system benefit, and economic penalty are obtained. The results discover that water deficit has brought negative effects on regional economic development, particularly for agricultural production activities. Moreover, tradeoffs between economic benefit and system-failure risk are also examined under different risk preferences of decision makers (i.e., optimistic and pessimistic criteria), which support generating an increased robustness in risk control for water resources allocation under uncertainties. These findings can facilitate the local decision makers in adjusting the current water-allocation pattern based on risk preference option (Hurwicz criterion) to satisfy the increasing water demand.

EOR POTENTIAL WITHIN IRAN

As the application of enhanced oil recovery (EOR) mechanisms expands globally, a vast amount of information is reported annually and many statistical works are carried out aiming mostly at the feasibility study of employing EOR methods for candidate reservoirs. In the last decades, researchers have gathered huge databases and used various techniques to identify screening criteria ranges for each EOR method. Screening algorithms are then developed to desirably benefit from the criteria ranges for candidate selection. The numerous screening criteria ranges reported for each EOR technique and the variety of screening algorithms raise the problem of choice, especially for petroleum engineers who plan to initiate EOR projects. This paper summarizes important screening criteria and screening algorithms and discusses and elaborates on subjects such as the importance of the following: accuracy in the EOR screening, comparing the parameters of the candidate reservoir with the criteria ranges, establishing a quantitative basis for comparison, dealing with a large number of candidate reservoirs, ranking the importance of parameters for each EOR method, assigning weights for parameters, and defining cutoffs. Based on a series of optimistic and pessimistic criteria extracted from the collected criteria, a case study is performed involving 11 candidate Iranian reservoirs and the EOR options for each reservoir are ranked accordingly. Then, a predictive correlation is used to identify reservoirs with the highest recovery factors among the candidates of the CO2 miscible flooding. The results show that miscible CO2 and hydrocarbon gas injections are two promising mechanisms for the reservoirs under study.

Revising option status in argument-based decision systems1

Decision making is usually based on the comparative evaluation of different options by means of a decision criterion. Recently, the qualitative pessimistic criterion was articulated in terms of a four-step argumentation process: (i) to build arguments in favour/against each option, (ii) to compare and evaluate those arguments, (iii) to assign a status for each option, and (iv) to rank order the options on the basis of their status. Thus, the argumentative counter-part of the pessimistic criterion provides not only the ‘best’ option to the user but also the reasons justifying this recommendation. The aim of this article is to study the dynamics of this argumentation model. The idea is to study how the ordering on options changes in light of a new argument. For this purpose, we study under which conditions an option may change its status, and under which conditions the new argument has no impact on the status of options, and consequently, on the ordering. This amounts to study how the acceptability of arguments evolves when the decision system is extended by new arguments. In the article, we focus on two acceptability semantics the skeptical grounded semantics and the credulous preferred semantics.

K-Balanced games and capacities

In this paper, we present a generalization of the concept of balanced game for finite games. Balanced games are those having a nonempty core, and this core is usually considered as the solution of the game. Based on the concept of k-additivity, we define the so-called k-balanced games and the corresponding generalization of core, the k-additive core, whose elements are not directly imputations but k-additive games. We show that any game is k-balanced for a suitable choice of k, so that the corresponding k-additive core is not empty. For the games in the k-additive core, we propose a sharing procedure to get an imputation and a representative value for the expectations of the players based on the pessimistic criterion. Moreover, we look for necessary and sufficient conditions for a game to be k-balanced. For the general case, it is shown that any game is either balanced or 2-balanced. Finally, we treat the special case of capacities.

Inherent biases in decision support systems: the influence of optimistic and pessimistic DSS on choice, affect, and attitudes

AbstractThis study investigates the influence of a decision aid on decision makers' model‐based choices, emotions during the use of the model, and attitudes towards the model. A time allocation decision model was biased to purposefully provide optimistic or pessimistic criterion levels, on which subjects based their allocations. The results of our experiment indicate that the degree of “optimism” and “pessimism” inherent in the decision model had a significant impact on the decision maker's choices of criterion values, with optimism leading to higher criterion level choices and pessimism to lower levels. Furthermore, compared to pessimistic models, optimistic models significantly improved the decision makers' emotional states and, to some degree, their attitudes towards the decision aid. The implications of these conscious and sub‐conscious influences on decision makers' choices, emotions, and attitudes are discussed and the need for model‐builders and users to be aware of them is highlighted. Copyright © 2007 John Wiley & Sons, Ltd.

Bounded-parameter Markov decision processes

In this paper, we introduce the notion of a bounded-parameter Markov decision process (BMDP) as a generalization of the familiar exact MDP. A bounded-parameter MDP is a set of exact MDPs specified by giving upper and lower bounds on transition probabilities and rewards (all the MDPs in the set share the same state and action space). BMDPs form an efficiently solvable special case of the already known class of MDPs with imprecise parameters (MDPIPs). Bounded-parameter MDPs can be used to represent variation or uncertainty concerning the parameters of sequential decision problems in cases where no prior probabilities on the parameter values are available. Bounded-parameter MDPs can also be used in aggregation schemes to represent the variation in the transition probabilities for different base states aggregated together in the same aggregate state. We introduce interval value functions as a natural extension of traditional value functions. An interval value function assigns a closed real interval to each state, representing the assertion that the value of that state falls within that interval. An interval value function can be used to bound the performance of a policy over the set of exact MDPs associated with a given bounded-parameter MDP. We describe an iterative dynamic programming algorithm called interval policy evaluation that computes an interval value function for a given BMDP and specified policy. Interval policy evaluation on a policy π computes the most restrictive interval value function that is sound, i.e., that bounds the value function for π in every exact MDP in the set defined by the bounded-parameter MDP. We define optimistic and pessimistic criteria for optimality, and provide a variant of value iteration (Bellman, 1957) that we call interval value iteration that computes policies for a BMDP that are optimal with respect to these criteria. We show that each algorithm we present converges to the desired values in a polynomial number of iterations given a fixed discount factor.

Concerning the fracture of graphite under different test conditions

Results from different fracture tests on near-isotropic polycrystalline graphites of the type used in nuclear reactors are presented in terms of the maximum elastic tensile stress at failure in uniform tensile, bend, internal pressure and diametral compression tests. These show that the calculated stress at failure exceeds the tensile strength in uniform tension and increases with the degree of severity in the stress gradient. Various theoretical approaches offering qualitative explanations are explored quantitatively in an attempt to inter-relate the results from the different laboratory tests, viz. the effect of the non-linear stress-strain curve for graphite, the statistical theory of Weibull, and empirical correlations in terms of energy density and stress gradient. The limitations of these approaches are discussed, and it is concluded that in a general case none of them offers a consistent method of treating failure under different geometric conditions and improving providing on a pessimistic criterion of equating the maximum calculated elastic stress to the tensile strength. It is possible that specific cases in service may be related to a particular laboratory test with some better definitions of the failure conditions.