Optimal preventive strike strategy vs. optimal attack strategy in a defense-attack game

This paper analyzes an attack-defense game between one defender and one attacker. Among, the defender moves first and allocates its resources to three different methods: employing a preventive strike, founding false targets, and protecting its genuine object. The preventive strike may expose the genuine object, and different from previous literature, a false target may also be detected to be false. The attacker, observing the actions taken by the defender and allocating its resources to three methods: protecting its own base from the preventive strike, founding false bases, and attacking the defender's genuine object. Similarly, a false base may be correctly identified. Different from previous methods in evaluating the potential outcome, for each of the defender's given strategies, the attacker tries to maximize its cumulative prospect value considering different possible outcomes. Similarly, the defender maximizes its cumulative prospect value, assuming that the attacker chooses the strategy to maximize the attacker's cumulative prospect value. Numerical examples are presented to illustrate the optimal number of bases to attack by preventive strike, and the optimal number of targets to attack by attacker

Group Decision Algorithm for Aged Healthcare Product Purchase Under q-Rung Picture Normal Fuzzy Environment Using Heronian Mean Operator

With the intensification of the aging, the health issue of the elderly is arousing public concern increasingly. Various healthcare products for the elderly are emerging from the market, thus how to select suitable aged healthcare product is critical to the well-being of the elderly. In the literature, nonetheless, a comprehensive and standardized evaluation framework to support healthcare product purchase decision for the aged is currently lacking. This paper proposes a novel group decision-making method to aid the decision-making of aged healthcare product purchase based on q-rung picture normal fuzzy Heronian mean (q-RPtNoFHM) operators. In it, firstly, a new fuzzy variable called the q-rung picture normal fuzzy set (q-RPtNoFS) is defined to reasonably describe different responses to healthcare product evaluation, for which, some definitions including operational laws, a score function, and an accuracy function of q-RPtNoFSs are introduced. Then, two q-RPtNoFHM operators are presented to aggregate group decision information. In addition, some properties of q-RPtNoFHM operators, such as monotonicity, commutativity, and idempotency, are discussed. Finally, an example on antihypertensive drugs purchase is gave to illustrate the practicality of the proposed method, and conduct sensitivity analysis to analyze the effectiveness and flexibility of proposed methods

Considering greenhouse gas emissions in maintenance optimisation

Greenhouse gases (GHG) from human activities are the main contributor to climate change since the mid-20th century. Reducing the release of GHG emissions is becoming a thematic research topic in many research disciplines. In the reliability research community, there are research papers relating to reliability and maintenance for systems in power generation farms such as offshore farms. Nevertheless, there is sparse research that aims to optimise maintenance policies for reducing the GHG emissions from systems such as automotive vehicles or building service systems. To fill up this gap, this paper optimises replacement policies for systems that age and degrade and that produce GHG emissions (i.e., exhaust emissions) including the initial manufacturing GHG emissions produced during the manufacturing stage and the emissions generated during the operational stage. Both the exhaust emissions process and the failure process are considered as functions of two time scales (i.e., age and accumulated usage), respectively. Other factors that may affect the two processes such as ambient temperature and road conditions are depicted as random effects. Under these settings, the decision problem is a nonlinear programming problem subject to several constraints. Replacement policies are then developed. Numerical examples are provided to illustrate the proposed methods

Guest Editorial: Reliability analysis for infrastructure systems

Reliability analysis is a process aiming to analyze the reliability of a technical system. It provides basic information to asset managers in their planning resources to ensure the system operating at a desired level of performance. Infrastructure systems are an essential asset in modern businesses and its reliable operation is important to the countries as their incapacity will have a debilitating impact on security, national economy, national public health or safety, or any combination of these matters. Thus, ensuring the availability and safety of these infrastructure systems are vital for business operation. As such, research on reliability analysis of infrastructure systems is needed in modern businesses

Green Synthesis of Carbon Nanospheres for Enhanced Electrochemical Sensing of Dopamine

The detection of dopamine (DA) has received enormous attention since it is widely recognized as an important neurotransmitter associated with nerve signaling and some diseases. In this work, glucose-derived carbon nanospheres (CNs) are synthesized by the green hydrothermal approach and are served to modify electrodes for the detection of DA. The CNs were successfully synthesized and were investigated in detail by various characterization technologies. The CNs modified glassy carbon electrode (CNs/GCE) exhibits better electrochemical sensing performance with a wide linear range of 0.05–1600 μM and a low limit of 8.3 nM for determination of DA, as compared with the modified electrodes reported previously. The CNs/GCE was successfully applied to detect DA in human serum samples, which makes it promising for a variety of biomedical applications. More importantly, this work shows a novel green and simple strategy for the development of cost-effective and high-performance sensing materials, which provides more opportunities for design of electrochemical sensors with future capabilities of mass production in practical applications

Optimal defence-attack strategies between one defender and two attackers

This paper analyses the optimal strategies for one defender and two attackers in a defence-attack game, where a) the defender allocates its resource into defending against and attacking the two attackers, and b) the two attackers, after observing the action of the defender, allocate their resources into attacking and defending against the defender, on either a cooperative or non-cooperative basis. On a cooperative basis, for each of the defender’s given strategies, the two attackers work together to maximise the sum of their cumulative prospect values while anticipating the eight possible game outcomes. On a non-cooperative basis, for each of the defender’s given strategies, each attacker simultaneously yet independently tries to maximise their own cumulative prospect value. In both cases, the defender maximises its cumulative prospect value while anticipating the attackers’ actions. Backward induction is employed to obtain the optimal defence and attack strategies for all scenarios. Numerical examples are performed to illustrate the applications of the strategies. In general, we find two opposing effects considering the attackers’ strategies and analyse the alteration of strategies for the participants under two different risk preferences: risk-averse and risk seeking. The reasons for the alteration are also performed to illustrate the practical applications

Risk-attitude-based defense strategy considering proactive strike, preventive strike and imperfect false targets

This paper analyzes the optimal strategies for the attacker and the defender in an attack–defense game, considering the risk attitudes of both parties. The defender moves first, allocating its limited resources to three different measures: launching a proactive strike or preventive strike, building false targets, and protecting its genuine object. It is assumed that (a) launching a proactive strike has limited effectiveness on its rival and does not expose the genuine object itself, (b) a false target might be correctly identified as false, and (c) launching a preventive strike consumes less resources than a proactive strike and might expose the genuine object. The attacker moves after observing the defender's movements, allocating its limited resources to three measures: protecting its own base from a proactive strike or preventive strike, building false bases, and attacking the defender's genuine object. For each of the defender's given strategies, the attacker chooses the attack strategy that maximizes its cumulative prospect value, which accounts for the players’ risk attitudes. Similarly, the defender maximizes its cumulative prospect value by anticipating that the attacker will always choose the strategy combination that maximizes its own cumulative prospect value. Backward induction is used to obtain the optimal defense, attack strategies, and their corresponding cumulative prospect values. Our results show that the introduction of risk attitudes leads the game to a lose-lose situation under some circumstances and benefits one party in other cases

An effective approach for the dual-resource flexible job shop scheduling problem considering loading and unloading

Many manufacturing systems need more than one type of resource to co-work with. Commonly studied flexible job shop scheduling problems merely consider the main resource such as machines and ignore the impact of other types of resource. As a result, scheduling solutions may not put into practice. This paper therefore studies the dual resource constrained flexible job shop scheduling problem when loading and unloading time (DRFJSP-LU) of the fixtures is considered. It formulates a multi-objective mathematical model to jointly minimize the makespan and the total setup time. Considering the influence of resource requirement similarity among different operations, we propose a similarity-based scheduling algorithm for setup-time reduction (SSA4STR) and then an improved non-dominated sorting genetic algorithm II (NSGA-II) to optimize the DRFJSP-LU. Experimental results show that the SSA4STR can effectively reduce the loading and unloading time of fixtures while ensuring a level of makespan. The experiments also verify that the scheduling solution with multiple resources has a greater guiding effect on production than the scheduling result with a single resource

Linking component importance to optimisation of preventive maintenance policy

In reliability engineering, time on performing preventive maintenance (PM) on a component in a system may affect system availability if system operation needs stopping for PM. To avoid such an availability reduction, one may adopt the following method: if a component fails, PM is carried out on a number of the other components while the failed component is being repaired. This ensures PM does not take system’s operating time. However, this raises a question: Which components should be selected for PM? This paper introduces an importance measure, called Component Maintenance Priority (CMP), which is used to select components for PM. The paper then compares the CMP with other importance measures and studies the properties of the CMP. Numerical examples are given to show the validity of the CMP