Perceived Risks of Radioactive Waste Transport Through Oregon: Results of a Statewide Survey

Transportation of hazardous materials, and particularly radioactive wastes, on public highways has become an important risk management issue. The unfavorability of public attitudes regarding hazardous and nuclear wastes signals the potential for strong public opposition to programs for transporting these materials. This paper presents the results of a survey conducted to assess public reactions to a long-term nuclear waste transport program planned to follow a route through a portion of rural Oregon. The survey assessed a number of key risk perception issues, including perceived health and safety risks of nuclear waste transport, relative risks of transport vs. storage at an existing site, trust in state officials, and satisfaction with life in communities along the transport route. The survey identified a number of attitudes and concerns that need to be understood and considered by those in charge of designing and implementing the waste-transportation program.     

A zero-one goal programming approach for information system project selection

This paper presents a multi-objective model that can be used to help information systems (IS) managers decide which IS projects should be selected for development and implementation in a constrained resource environment. Developed to overcome the problems observed in currently used IS selection procedures, the model presented in this paper can be used to make decisions when IS selection goals are conflicting and measured in incommensurable units. To illustrate the use of this multi-objective approach to model IS project selection, a corporate resource allocation problem is developed and solved. Comparative results with currently used scoring and ranking methods reveals the superiority of the proposed multi-objective model.     

基于模糊权重的多目标订单分配模型

随着竞争的日益加剧,采购成了影响企业生存和发展的关键因素。在这种情况下,正确的订单分配是成功实行采购的关键。实际上,影响订单分配的许多信息并不是确定的已知条件,例如目标、约束条件和影响权重等。本文通过考虑上述信息的不确定性和模糊性,构建一个包含模糊目标、模糊权重和随机约束的订单分配模型。然后,利用数值算例证明了模型的可解性。最后,指出了未来可能的几个研究方向。     

An efficient spread-based evolutionary algorithm for solving dynamic multi-objective optimization problems

Dynamic multi-objective optimization algorithms are used as powerful methods for solving many problems worldwide. Diversity, convergence, and adaptation to environment changes are three of the most important factors that dynamic multi-objective optimization algorithms try to improve. These factors are functions of exploration, exploitation, selection and adaptation operators. Thus, effective operators should be employed to achieve a robust dynamic optimization algorithm. The algorithm presented in this study is known as spread-based dynamic multi-objective algorithm (SBDMOA) that uses bi-directional mutation and convex crossover operators to exploit and explore the search space. The selection operator of the proposed algorithm is inspired by the spread metric to maximize diversity. When the environment changed, the proposed algorithm removes the dominated solutions and mutated all the non-dominated solutions for adaptation to the new environment. Then the selection operator is used to select desirable solutions from the population of non-dominated and mutated solutions. Generational distance, spread, and hypervolume metrics are employed to evaluate the convergence and diversity of solutions. The overall performance of the proposed algorithm is evaluated and investigated on FDA, DMOP, JY, and the heating optimization problem, by comparing it with the DNSGAII, MOEA/D-SV, DBOEA, KPEA, D-MOPSO, KT-DMOEA, Tr-DMOEA and PBDMO algorithms. Empirical results demonstrate the superiority of the proposed algorithm in comparison to other state-of-the-art algorithms.

     

Are Nuclear-Weapon States not Parties to the Non-proliferation Treaty Ready to Renounce to the Possession of Nuclear Weapons?

The situation of nuclear-weapon states not parties to the NPT is different from the situation of the nuclear-weapon states parties to that treaty. This last group of countries has signed and ratified a treaty that encourage them to begin negotiations in good faith for the discussion of concrete measures that lead them to nuclear disarmament, while the first group of states have made no commitment to do that. In other words, nuclear-weapon states not parties to the NPT have made no commitment to nuclear disarmament and are not obliged to report anything related to the possession of nuclear weapons to the any specific body or organ or to the international community. Taking into account the position of nuclear-weapon states parties to the NPT regarding the possession of nuclear weapons, there is no possibility to convince nuclear-weapon states not parties to this treaty to renounce to the possession of this type of weapons under the present circumstances.     

A simulation-based multi-objective optimization framework: A case study on inventory management

We propose a simulation-based solution framework for tackling the multi-objective inventory optimization problem. The goal is to find appropriate settings of reorder point and order quantity to minimize three objective functions simultaneously, which are the expected values of the total inventory cost, the average inventory level, and the frequency of inventory shortage. We develop new algorithms that can exploit statistically valid ranking and selection (R&S) procedures and the desirable mechanics of conventional multi-objective optimization techniques. Two simulation algorithms are proposed to be applied in different scenarios depending on the preference information that is revealed either during or after the actual optimization process. Experimental results are provided to evaluate the efficiency of the developed algorithms and other existing solution frameworks.     

Risk Perception, Federal Spending, and the Savannah River Site: Attitudes of Hunters and Fishermen

This paper examines the attitudes of 285 hunters and fishermen from South Carolina about hunting and fishing, risk, environmental issues, and future land use of the Savannah River Site. We test the null hypothesis that there is no difference in hunting and fishing rates, attitudes toward the safety of fish and deer obtained from SRS, attitudes toward future land use at SRS, and perceptions of the severity of environmental problems as a function of how far respondents lived from the site. Respondents hunted or fished an average of over 40 days a year, and only half felt that the fish and deer from SRS were safe to eat. Willingness to expend federal funds was correlated with perceptions of the severity of the problem. Preferences for future land use at SRS fell into three categories: high (environmental research park, hunting, fishing, camping), medium (nuclear production, factories, preserve only), and low (nuclear waste storage, residential). There were no differences in hunting and fishing rates, ranking of the severity of environmental problems, and willingness to expend federal funds as a function of distance of residence from SRS, but attitudes toward future land use differed significantly as a function of location of residence. Those living close to SRS were more willing to have the site used for factories, residential, nuclear material production and to store nuclear wastes than those living farther from the site. Our data on recreational rates, attitudes toward future land use, and willingness to expend federal funds to solve environmental problems reiterate the importance of assessing stakeholder attitudes toward decisions regarding future land use at DOE sites     

GIS‐Based Integration of Social Vulnerability and Level 3 Probabilistic Risk Assessment to Advance Emergency Preparedness,Planning, and Response for Severe Nuclear Power Plant Accidents

In the nuclear power industry, Level 3 probabilistic risk assessment (PRA) is used to estimate damage to public health and the environment if a severe accident leads to large radiological release. Current Level 3 PRA does not have an explicit inclusion of social factors and, therefore, it is not possible to perform importance ranking of social factors for risk‐informing emergency preparedness, planning, and response (EPPR). This article offers a methodology for adapting the concept of social vulnerability, commonly used in natural hazard research, in the context of a severe nuclear power plant accident. The methodology has four steps: (1) calculating a hazard‐independent social vulnerability index for the local population; (2) developing a location‐specific representation of the maximum radiological hazard estimated from current Level 3 PRA, in a geographic information system (GIS) environment; (3) developing a GIS‐based socio‐technical risk map by combining the social vulnerability index and the location‐specific radiological hazard; and (4) conducting a risk importance measure analysis to rank the criticality of social factors based on their contribution to the socio‐technical risk. The methodology is applied using results from the 2012 Surry Power Station state‐of‐the‐art reactor consequence analysis. A radiological hazard model is generated from MELCOR accident consequence code system, translated into a GIS environment, and combined with the Center for Disease Control social vulnerability index (SVI). This research creates an opportunity to explicitly consider and rank the criticality of location‐specific SVI themes based on their influence on risk, providing input for EPPR.     

Robustness analysis methodology for multi-objective combinatorial optimization problems and application to project selection

Multi-objective combinatorial optimization (MOCO) problems, apart from being notoriously difficult and complex to solve in reasonable computational time, they also exhibit high levels of instability in their results in case of uncertainty, which often deviate far from optimality. In this work we propose an integrated methodology to measure and analyze the robustness of MOCO problems, and more specifically multi-objective integer programming ones, given the imperfect knowledge of their parameters. We propose measures to assess the robustness of each specific Pareto optimal solution (POS), as well as the robustness of the entire Pareto set (PS) as a whole. The approach builds upon a synergy of Monte Carlo simulation and multi-objective optimization, using the augmented ε-constraint method to generate the exact PS for the MOCO problems under examination. The usability of the proposed framework is justified through the identification of the most robust areas of the Pareto front, and the characterization of every POS with a robustness index. This index indicates a degree of certainty that a specific POS sustains its efficiency. The proposed methodology communicates in an illustrative way the robustness information to managers/decision makers and provides them with an additional supplement/tool to guide and support their final decision. Numerical examples focusing on a multi-objective knapsack problem and an application to academic capital budgeting problem for project selection, are provided to verify the efficacy and added value of the methodology.     

A heuristic approach to long-haul freight transportation with multiple objective functions

This paper studies a long-haul freight transportation problem stimulated by a real-life application, whose underlying vehicle routing problem is a multi-objective one, where travel time and route cost are to be minimized together with the maximization of a transportation mean sharing index, related to the capability of the transportation system of generating economy scale solutions. In terms of constraints, besides vehicle capacity and time windows, transportation jobs have to obey additional constraints related to mandatory nodes (e.g., logistic platform nearest to the origin or the destination) and forbidden nodes (e.g., logistic platforms not compatible with the operations required). Based on the network definition, routes can be multimodal. To solve this problem, we propose a heuristic algorithm that can be applied in the tactical and the operational planning phase, and present the results of an extensive experimentation.     

基于多目标加权灰靶决策模型的节能服务公司选择研究

用能方对节能服务公司（Energy Service Company，ESCO）的选择关系到合同能源管理（Energy Performance Contracting，EPC）能否顺利实施。从用能方的角度，采用灰色系统理论中的多目标加权灰靶决策模型，对存在多决策目标的ESCO选择问题进行研究。通过层次分析法确定ESCO的11个决策目标的决策权数，根据综合效果测度值的比较，最终实现最优对策的选择。本文为用能方的ESCO选择问题提供了一种新的思路。     

Supplier selection with multiple criteria in volume discount environments

Supplier selection is a multi-criteria decision making problem which includes both qualitative and quantitative factors. In order to select the best suppliers it is necessary to make a trade-off between these tangible and intangible factors some of which may conflict. When business volume discounts exist, this problem becomes more complicated as, in these circumstances, buyer should decide about two problems: which suppliers are the best and how much should be purchased from each selected supplier. In this article an integrated approach of analytical hierarchy process improved by rough sets theory and multi-objective mixed integer programming is proposed to simultaneously determine the number of suppliers to employ and the order quantity allocated to these suppliers in the case of multiple sourcing, multiple products, with multiple criteria and with supplier's capacity constraints. In this context, suppliers offer price discounts on total business volume, not on the quantity or variety of products purchased from them. A solution methodology is presented to solve the multi-objective model, and the model is illustrated using two numerical examples.     

一种能力外包且费用时变批量问题的启发式算法

当企业自身能力不能满足需求时考虑外包,可以有效提高企业的竞争力。一种带能力外包的多产品动态经济批量问题得以提出,并设定外包能力使用价格较高且费用时变。建立混合整数规划模型,通过约束松弛与模型分解,设计出一个基于拉格朗日松弛理论的启发式算法进行模型求解。大量随机实验表明,无论解的质量还是求解时间都表现较好。     

碳交易政策下多式联运路径选择问题的鲁棒优化研究

针对碳交易政策下的多式联运路径选择问题,考虑运输时间和单位运费率不确定且其概率分布未知的情况,引入鲁棒优化建模方法对其进行研究。首先利用box不确定集合刻画分布未知的运输时间和运费率,然后在碳交易政策下确定模型的基础上,构建鲁棒性可调节的多式联运路径选择模型,并通过对偶转化得到相对易求解的鲁棒等价模型。实例分析表明,鲁棒模型能较好地处理参数概率分布未知的多式联运路径选择问题,方便决策者根据偏好调整不确定预算水平进行决策。运输时间和单位运费率的不确定性都会影响多式联运路径决策,但是作用机理有所不同。将上述碳交易政策下的模型拓展到其他低碳政策,结果表明多种低碳政策的组合能更好实现多式联运减排。     

多目标物流网络优化模型的研究

针对物流网络规划中需要考虑多个目标的问题,以配流中心存储容量及使用率为约束,建立了基于总费用及最大单程距离(费用)最小的双目标数学模型,给出了优化模型的求解方法,为决策者提供多种可供选择的优化方案。     

Multiattribute Risk Analysis in Nuclear Emergency Management

Radiation protection authorities have seen a potential for applying multiattribute risk analysis in nuclear emergency management and planning to deal with conflicting objectives, different parties involved, and uncertainties. This type of approach is expected to help in the following areas: to ensure that all relevant attributes are considered in decision making; to enhance communication between the concerned parties, including the public; and to provide a method for explicitly including risk analysis in the process. A multiattribute utility theory analysis was used to select a strategy for protecting the population after a simulated nuclear accident. The value-focused approach and the use of a neutral facilitator were identified as being useful.     

An integrated multi-objective decision-making process for multi-period lot-sizing with supplier selection

Supplier selection is a multi-criteria problem which includes both tangible and intangible factors. In these problems if suppliers have capacity or other different constraints two problems will exist: which suppliers are the best and how much should be purchased from each selected supplier? In this paper an integrated approach of analytic network process (ANP) and multi-objective mixed integer linear programming (MOMILP) is proposed. This integrated approach considers both tangible and intangible factors in choosing the best suppliers and defines the optimum quantities among selected suppliers to maximize the total value of purchasing (TVP), and to minimize the total cost and total defect rate and to balance the total cost among periods. The priorities are calculated for each supplier by using ANP. Four different plastic molding firms working with a refrigerator plant are evaluated according to 14 criteria that are involved in the four clusters: benefits, opportunities, costs and risks (BOCR). The priorities of suppliers will also be used as the parameters of the first objective function. This multi-objective and multi-period real-life problem is solved by using previous techniques and a reservation level driven Tchebycheff procedure (RLTP). Finally the most preferred nondominated solutions are determined by considering the decision maker's (DM's) preferences and the results obtained by these techniques are compared.     

AMBIVALENT INVESTMENT AND THE HOLD‐UP PROBLEM

In this paper we add to the foundations of incomplete contracting literature. We study the hold‐up problem with ambivalent investment, where investment benefits the investing party if ex post the right decision is undertaken but harms the investing party if the wrong decision is made. In this context, we show that the power of contracts to provide investment incentives depends on three factors: the commitment value of contracts, the amount of quasirents that the investing party can expect in the case of out‐of‐contract renegotiation, and the degree of ambivalence of investment. First, contracts provide first‐best investment incentives when parties can commit to a contract regardless of the type of investment. Second, with sufficiently ambivalent investment, if parties cannot commit not to renegotiate a contract and if the investing party's bargaining power is intermediate, contracts cannot improve investment incentives above those provided by no contract. In contrast, a simple buyer or seller option contract is optimal when the investing party's bargaining power is extreme. (JEL: D23, K12, L22)     

Single vs. multiple objective supplier selection in a make to order environment

The problem of allocation of orders for custom parts among suppliers in make to order manufacturing is formulated as a single- or multi-objective mixed integer program. Given a set of customer orders for products, the decision maker needs to decide from which supplier to purchase custom parts required for each customer order. The selection of suppliers is based on price and quality of purchased parts and reliability of on time delivery. The risk of defective or unreliable supplies is controlled by the maximum number of delivery patterns (combinations of suppliers delivery dates) for which the average defect rate or late delivery rate can be unacceptable. Furthermore, the quantity or business volume discounts offered by the suppliers are considered. Numerical examples are presented and some computational results are reported.     

The Nevada Initiative: A Risk Communication Fiasco

The U.S. Congress has designated Yucca Mountain, Nevada as the only potential site to be studied for the nation's first high-level nuclear waste repository. People in Nevada strongly oppose the program, managed by the U.S. Department of Energy. Survey research shows that the public believes there are great risks from a repository program, in contrast to a majority of scientists who feel the risks are acceptably small. Delays in the repository program resulting in part from public opposition in Nevada have concerned the nuclear power industry, which collects the fees for the federal repository program and believes it needs the repository as a final disposal facility for its high-level nuclear wastes. To assist the repository program, the American Nuclear Energy Council (ANEC), an industry group, sponsored a massive advertising campaign in Nevada. The campaign attempted to assure people that the risks of a repository were small and that the repository studies should proceed. The campaign failed because its managers misunderstood the issues underlying the controversy, attempted a covert manipulation of public opinion that was revealed, and most importantly, lacked the public trust that was necessary to communicate credibly about the risks of a nuclear waste facility.