A system dynamics model for determining the waste disposal charging fee in construction

The waste disposal charging fee (WDCF) has long been adopted for stimulating major project stakeholders’ (particularly project clients and contractors) incentives to minimize solid waste and increase the recovery of wasted materials in the construction industry. However, the present WDCFs applied in many regions of China are mostly determined based on a rule of thumb. Consequently the effectiveness of implementing these WDCFs is very limited. This study aims at addressing this research gap through developing a system dynamics based model to determine an appropriate WDCF in the construction sector. The data used to test and validate the model was collected from Shenzhen of south China. By using the model established, two types of simulations were carried out. One is the base run simulation to investigate the status quo of waste generation in Shenzhen; the other is policy analysis simulation, with which an appropriate WDCF could be determined to reduce waste generation and landfilling, maximize waste recycling, and minimize the waste dumped inappropriately. The model developed can function as a tool to effectively determine an appropriate WDCF in Shenzhen. Further, it can also be used by other regions intending to stimulate construction waste minimization and recycling through implementing an optimal WDCF.     

Systemic risk elicitation: Using causal maps to engage stakeholders and build a comprehensive view of risks

As evidenced through both a historical and contemporary number of reported over-runs, managing projects can be a risky business. Managers are faced with the need to effectively work with a multitude of parties and deal with a wealth of interlocking uncertainties. This paper describes a modelling process developed to assist managers facing such situations. The process helps managers to develop a comprehensive appreciation of risks and gain an understanding of the impact of the interactions between these risks through explicitly engaging a wide stakeholder base using a group support system and causal mapping process. Using a real case the paper describes the modelling process and outcomes along with its implications, before reflecting on the insights, limitations and future research.     

Using possibilistic logic for modeling qualitative decision: Answer Set Programming algorithms

A qualitative approach to decision making under uncertainty has been proposed in the setting of possibility theory, which is based on the assumption that levels of certainty and levels of priority (for expressing preferences) are commensurate. In this setting, pessimistic and optimistic decision criteria have been formally justified. This approach has been transposed into possibilistic logic in which the available knowledge is described by formulas which are more or less certainly true and the goals are described in a separate prioritized base. This paper adapts the possibilistic logic handling of qualitative decision making under uncertainty in the Answer Set Programming (ASP) setting. We show how weighted beliefs and prioritized preferences belonging to two separate knowledge bases can be handled in ASP by modeling qualitative decision making in terms of abductive logic programming where (uncertain) knowledge about the world and prioritized preferences are encoded as possibilistic definite logic programs and possibilistic literals respectively. We provide ASP-based and possibilistic ASP-based algorithms for calculating optimal decisions and utility values according to the possibilistic decision criteria. We describe a prototype implementing the algorithms proposed on top of different ASP solvers and we discuss the complexity of the different implementations.     

Multi-confidence rule acquisition and confidence-preserved attribute reduction in interval-valued decision systems

Rule acquisition is one of the most important objectives in the analysis of decision systems. Because of the interference of errors, a real-world decision system is generally inconsistent, which can lead to the consequence that some rules extracted from the system are not certain but possible rules. In practice, however, the possible rules with high confidence are also useful in making decision. With this consideration, we study how to extract from an interval-valued decision system the compact decision rules whose confidences are not less than a pre-specified threshold. Specifically, by properly defining a binary relation on an interval-valued information system, the concept of interval-valued granular rules is presented for the interval-valued decision system. Then, an index is introduced to measure the confidence of an interval-valued granular rule and an implication relationship is defined between the interval-valued granular rules whose confidences are not less than the threshold. Based on the implication relationship, a confidence-preserved attribute reduction approach is proposed to extract compact decision rules and a combinatorial optimization-based algorithm is developed to compute all the reducts of an interval-valued decision system. Finally, some numerical experiments are conducted to evaluate the performance of the reduction approach and the gain of using the possible rules in making decision.     

Robust optimal strategies in Markov decision problems

An optimal strategy in a Markov decision problem is robust if it is optimal in every decision problem (not necessarily stationary) that is close to the original problem. We prove that when the state and action spaces are finite, an optimal strategy is robust if and only if it is the unique optimal strategy.     

Superiority–inferiority modeling coupled minimax-regret analysis for energy management systems

In this study, a superiority–inferiority-based minimax-regret analysis (SI-MRA) model is developed for supporting the energy management systems (EMS) planning under uncertainty. In SI-MRA model, techniques of fuzzy mathematical programming (FMP) with the superiority and inferiority measures and minimax regret analysis (MMR) are incorporated within a general framework. The SI-MRA improves upon conventional FMP methods by directly reflecting the relationships among fuzzy coefficients in both the objective function and constraints with a high computational efficiency. It can not only address uncertainties expressed as fuzzy sets in both of the objective function and system constraints but also can adopt a list of scenarios to reflect the uncertainties of random variables without making assumptions on their possibilistic distributions. The developed SI-MRA model is applied to a case study of long-term EMS planning, where fuzziness and randomness exist in the costs for electricity generation and demand. A number of scenarios associated with various alternatives and outcomes under different electricity demand levels are examined. The results can help decision makers identify an optimal strategy of planning electricity generation and capacity expansion based on a minimax regret level under uncertainty.     

不完备序区间值决策系统中的可信规则获取

以不完备序区间值决策系统为研究对象,其中不仅包含遗漏型未知区间值,而且属性值域为全序集.给出了未知区间值的三种形式及其填充式区间值的定义,引入灰的白化方法用以构建一个新的填充式不完备序白化值决策系统,并讨论其在优势和弱势关系下的可信规则获取.进一步研究了优势和弱势对象的约简以及其决策类的相对约简问题,给出了相应的判定定理与区分函数,为最终从不完备序区间值决策系统中获取最优可信决策规则提供了新的理论基础与操作手段.、     

危险品运输安全-效益优化决策研究

对危险品运输安全-效益优化问题进行了研究,基于委托-代理理论,建立了在委托人对运输安全具有完全明辨性的进度激励-安全控制决策的博弈模型,分析了双方实现各自利益最大化的最优策略,即委托人确定合理的危险品提前运达效益的分配系数、运输安全保证率水平及委托人对运输各环节安全的检测水平,以及承运人在上述条件下,确定合理的进度加快投入的努力与安全控制的水平,进而可为危险品运输委托人与承运人之间的最优运费确定提供依据;文章的最后进行实例应用分析.     

基于遗传算法和满意度函数的武器系统维修性分配规划问题研究

武器系统维修性分配是一个复杂的多目标规划问题.常规求解方法得到的结果难以满足其约束条件,并且难以体现不同目标间重要性的差别.据此,通过引入遗传算法(GA)和满意度函数解决了上述问题.首先利用GA求解单目标的最优解,建立各目标的满意度函数并综合为一个总满意度函数,最后利用GA求总满意度的最大值,即可获得模型的满意解.仿真算例表明,GA算法可适用于解决武器系统维修性分配问题,同时多目标规划中采用满意度函数法比线性加权法更可行.     

直觉语言Hamacher加权集结算子及其应用

研究带参数的直觉语言Hamacher集结算子。基于Hamacher t-模与s-模定义直觉语言Hamacher运算法则。而后,利用所提Hamacher运算法则提出含参数的直觉语言Hamacher加权平均算子与几何算子,研究其基本性质。进一步地,剖析算子与参数之间的关系,如算子关于参数的单调性、算子关于参数的退化情况等。最后,基于所提出的集结算子构建出多属性决策方法,通过算例分析表明所提决策方法的可行性和实用性。