Collective foraging in heterogeneous landscapes

Animals foraging alone are hypothesized to optimize the encounter rates with resources through Lévy walks. However, the issue of how the interactions between multiple foragers influence their search efficiency is still not completely understood. To address this, we consider a model to study the optimal strategy for a group of foragers searching for targets distributed heterogeneously. In our model, foragers move on a square lattice containing immobile but regenerative targets. At any instant, a forager is able to detect only those targets that happen to be in the same site. However, we allow the foragers to have information about the state of other foragers. A forager who has not detected any target walks towards the nearest location, where another forager has detected a target, with a probability exp(−αd), where d is the distance between the foragers and α is a parameter characterizing the propensity of the foragers to aggregate. The model reveals that neither overcrowding (α → 0) nor independent searching (α → ∞) is beneficial for the foragers. For a patchy distribution of targets, the efficiency is maximum for intermediate values of α. In addition, in the limit α → 0, the length of the walks can become scale-free.     

测量不确定度的灰评定

提出以灰色系统理论为基础评定测量不确定度的一种新方法。通过理论分析与测量实例的评定表明,它不仅对测量的样本量和分布类型没有要求,而且具有相当的计算精度,对统计成非统计测量不确定度的评定均为适用。     

Reorientation patterns in central-place foraging: internal clocks and klinokinesis

We study central-place foraging patterns of Aphaenogaster senilis ants at a population level by video framing individual ant trajectories in a circular arena with a nest connected to its centre. The ants naturally leave and enter the nest and forage generating non-trivial movement patterns around the nest. Our data analysis indicated that the trajectories observed can be classified into two strategies: the risk-averse strategy, which involves wandering around the nest without departing far from it and the risk-prone strategy, which involves long exploration paths with periodic returns to the central region, nearby the nest. We found that both risk-prone and risk-averse strategies exhibit qualitatively the same reorientation patterns, with the time between consecutive reorientations covering a wide range of scales, and fitting a stretched exponential function. Nevertheless, differences in the temporal scales and the time variability of such reorientation events differ, together with other aspects of motion, such as average speed and turns. Our results give experimental evidence that the internal mechanisms driving reorientations in ants tend to favour frequently long relocations, as theory predicts for efficient exploration in patchy landscapes, but ants engaged in central-place foraging can modulate such behaviour to control distances from the nest. Previous works on the species support the idea that risk-prone and risk-averse strategies may reflect actual differences between individuals age and experience; these factors (age and experience) should be then relevant in modulating the internal reorientation clocks. To support the validity of our findings, we develop a random-walk model combining stretched exponential reorientation clocks with klinokinesis that fits the time length and the travelled distance distributions of the observed trajectories.     

The evolution of continuous learning of the structure of the environment

Continuous, ‘always on’, learning of structure from a stream of data is studied mainly in the fields of machine learning or language acquisition, but its evolutionary roots may go back to the first organisms that were internally motivated to learn and represent their environment. Here, we study under what conditions such continuous learning (CL) may be more adaptive than simple reinforcement learning and examine how it could have evolved from the same basic associative elements. We use agent-based computer simulations to compare three learning strategies: simple reinforcement learning; reinforcement learning with chaining (RL-chain) and CL that applies the same associative mechanisms used by the other strategies, but also seeks statistical regularities in the relations among all items in the environment, regardless of the initial association with food. We show that a sufficiently structured environment favours the evolution of both RL-chain and CL and that CL outperforms the other strategies when food is relatively rare and the time for learning is limited. This advantage of internally motivated CL stems from its ability to capture statistical patterns in the environment even before they are associated with food, at which point they immediately become useful for planning.     

基于证据理论的测量不确定度评定与分析

针对GUM推荐的基于概率统计理论的不确定度表达方法存在的局限性,提出了一种更为通用的基于证据理论的随机模糊变量（RFV）方法,实现对测量结果及其不确定度的评定。根据有效测量信息构建了RFV隶属函数以及相应的数学理论框架,通过简单的例子给出了如何运用RFV进行不确定度评定的流程。实验结果表明,该方法既可以提供测量结果的置信区间也可以给出不确定度分量的贡献程度。通过与GUM方法的比较,当存在不可忽略的非随机分量影响时,该方法比概率统计方法能更客观地描述测量结果离散程度。     

总承包工程PDMs决策模型研究与应用

针对传统模型未考虑决策者在决策问题上的不确定性因素而可能导致方案排序不稳定的问题,应用不确定多属性(UMA)理论对总承包工程交易模式(PDMs)决策进行了研究。根据总承包工程交易模式的不确定性因素提出了模型假设条件,选择了模型构建的不确定多属性理论工具,应用UEOWA算子和ULHA算子构建了基于可能度矩阵排序向量的决策支持模型;建立了我国总承包工程交易模式决策问题的属性评价体系,通过工程实例说明了模型的决策步骤和方法。     

Solving cell formation and task scheduling in cellular manufacturing system by discrete bacteria foraging algorithm

We consider a joint decision model of cell formation and task scheduling in cellular manufacturing system under dual-resource constrained (DRC) setting. On one hand, machines and workers are multi-functional and/or multi-skilled, and they are grouped into workstations and cells. On the other hand, there is a processing sequence among operations of the parts which needs to be dispatched to the desirable workstations for processing. Inter-cell movements of parts can reduce the processing times and the makespan but will increase the inter-cell material handling costs. The objective of the problem is to minimise the material handling costs as well as the fixed and operating costs of machines and workers. Due to the NP-hardness of the problem, we propose an efficient discrete bacteria foraging algorithm (DBFA) with elaborately designed solution representation and bacteria evolution operators to solve the proposed problem. We tested our algorithm using randomly generated instances with different sizes and settings by comparing with the original bacteria foraging algorithm and a genetic algorithm. Our results show that the proposed DBFA has better performance than the two compared algorithms with the same running time.     

Uncertainty management in optimal disassembly planning through learning-based strategies

Currently there is increasing consensus that one of the main issues differentiating remanufacturing from more traditional manufacturing processes is the need to effectively model and manage the high levels of uncertainty inherent in these new processes. Hence, the work presented in this paper concerns the issue of uncertainty modeling and management as it arises in the context of the optimal disassembly planning problem, one of the key problems to be addressed by remanufacturing processes. More specifically, the presented results formally establish that the theory of reinforcement learning, currently one of the most actively researched paradigms in the area of machine learning, constitutes a rigorous, efficient, and effectively implementable modeling framework for providing (near-)optimal solutions to the optimal disassembly problem, in the face of the aforementioned uncertainties. In addition, the proposed approach is exemplified and elucidated by application on a case study borrowed from the relevant literature.     

疫情演变不确定情境下考虑多参考点的应急医疗用品生产决策模型

针对疫情演变不确定情境下考虑多种决策信息和企业社会责任的应急医疗用品生产决策问题,提出一种基于累积前景理论的考虑多个参考点的应急医疗用品生产决策模型。综合考虑企业经济目标和社会责任对生产决策的影响,基于决策者的风险规避、参考依赖和损失厌恶等心理特征,建立多个参考点以表征疫情演变方向不确定时决策者期望的不确定性。将含有多种评价信息的决策矩阵归一化处理,利用不同演变方向的参考点,建立益损决策矩阵。根据决策者面对收益和损失的态度,建立主观价值矩阵,依据主观概率函数得到扭曲后演变方向的概率。计算各个生产方案的累积前景值并对其进行优先排序。通过算例验证模型的有效性,进行对比分析和敏感性分析证明模型的优越性。     

Evolution of reproductive strategies in incipient multicellularity

Multicellular organisms potentially show a large degree of diversity in reproductive strategies, producing offspring with varying sizes and compositions compared to their unicellular ancestors. In reality, only a few of these reproductive strategies are prevalent. To understand why this could be the case, we develop a stage-structured population model to probe the evolutionary growth advantages of reproductive strategies in incipient multicellular organisms. The performance of reproductive strategies is evaluated by the growth rates of the corresponding populations. We identify the optimal reproductive strategy, leading to the largest growth rate for a population. Considering the effects of organism size and cellular interaction, we found that distinct reproductive strategies could perform uniquely or equally well under different conditions. If a single reproductive strategy is optimal, it is binary splitting, dividing into two parts. Our results show that organism size and cellular interaction can play crucial roles in shaping reproductive strategies in nascent multicellularity. Our model sheds light on understanding the mechanism driving the evolution of reproductive strategies in incipient multicellularity. Beyond multicellularity, our results imply that a crucial factor in the evolution of unicellular species’ reproductive strategies is organism size.     

改进证据理论及其在电力系统故障诊断中的应用

 针对D-S证据理论中高度冲突的证据难以融合的问题,提出一种基于海明距离的证据合成规则.首先根据冲突系数的大小判断证据冲突程度,利用海明距离确定的相似度区分每条证据,并赋予权重,然后对原始基本信度分配函数进行重新分配,最终得到更加合理的合成结果.将该数据融合方法用于电力系统的故障诊断实例中,表明该方法合理有效,计算结果更符合实际.     

Joint sensitivity in bayesian decision theory

Research in Bayesian robustnes has mainly concentrated on sensitivity to the prior, although it is well-known that joint changes in both the prior and the utility (and likelihood, as well) may be very influential. We provide some tools to detect changes in the ranking of decisions under perturbations of the prior and the utility, as well as relevant changes in expected utility. The methods allow us to detect also the most critical judgements in determining choices and they may guide additional modeling efforts. Partially supported by projects from Junta de Extremadura, CAM and CICYT Started while the three authors were visiting ISDS at Duke Univesity. We are grateful to discussions with Katja Ickstadt and comments from the referres and the associate editor.     

Uncertainty quantification using evidence theory in multidisciplinary design optimization

Advances in computational performance have led to the development of large-scale simulation tools for design. Systems generated using such simulation tools can fail in service if the uncertainty of the simulation tool's performance predictions is not accounted for. In this research an investigation of how uncertainty can be quantified in multidisciplinary systems analysis subject to epistemic uncertainty associated with the disciplinary design tools and input parameters is undertaken. Evidence theory is used to quantify uncertainty in terms of the uncertain measures of belief and plausibility. To illustrate the methodology, multidisciplinary analysis problems are introduced as an extension to the epistemic uncertainty challenge problems identified by Sandia National Laboratories.After uncertainty has been characterized mathematically the designer seeks the optimum design under uncertainty. The measures of uncertainty provided by evidence theory are discontinuous functions. Such non-smooth functions cannot be used in traditional gradient-based optimizers because the sensitivities of the uncertain measures are not properly defined. In this research surrogate models are used to represent the uncertain measures as continuous functions. A sequential approximate optimization approach is used to drive the optimization process. The methodology is illustrated in application to multidisciplinary example problems.     

Integrating production-transportation planning decision with fuzzy multiple goals in supply chains

This work presents a novel fuzzy multi-objective linear programming (f-MOLP) model for solving integrated production-transportation planning decision (PTPD) problems in supply chains in a fuzzy environment. The proposed model attempts to simultaneously minimise total production and transportation costs, total number of rejected items, and total delivery time with reference to available capacities, labor level, quota flexibility, and budget constraints at each source, as well as forecast demand and warehouse space at each destination. An industrial case demonstrates that the proposed f-MOLP model achieves an efficient compromise solution and overall decision maker satisfaction with determined goal values. Additionally, the proposed model provides a systematic framework that facilitates decision makers to interactively modify the fuzzy data and parameters until a satisfactory solution is obtained. Overall, the f-MOLP model offers a practical method for solving PTPD problems with fuzzy multiple goals, and can effectively improve producer–distributor relationships within a supply chain.     

How high-tech entrepreneurial ventures cope with the global crisis: changes in product innovation and internationalization strategies

The global crisis that began in the second half of 2008 abruptly changed the business context, inducing firms to react by modifying their strategies. This paper examines changes in innovation and internationalization strategies that high-tech entrepreneurial ventures implemented to react to the crisis. Relying on insights from the behavioural theory of the firm and threat-rigidity theory, we explore the antecedents of firms’ investments in development of new products and in expansion in international markets and the consequences of these changes on firms’ growth performance. Econometric results from a sample of 140 Italian high-tech entrepreneurial ventures support the view that the stock of resources accumulated by larger firms, firms’ innovation and internationalization investments in the pre-crisis period and firms’ cash flow determine the extent of the two changes. The effects of these changes on firms’ short-term growth performance are positive only for investments in development of new products.     

Uncertainty quantification in non-linear dynamic response of functionally graded materials plate

This study deals with the stochastic non-linear dynamic response of functionally graded materials (FGMs) plate with uncertain system properties subjected to time-dependent uniformly distributed transverse load in thermal environments. System properties, such as material properties of each constituent's material, volume fraction index, and transverse load, are taken as uncorrelated random input variables. Material properties are assumed as temperature dependent (TD). The formulation is based on higher-order shear deformation theory (HSDT) with von-Karman non-linear strain kinematics using modified C° continuity. A Newton–Raphson-based non-linear finite element method along with a first-order perturbation technique (FOPT) and Monte Carlo sampling (MCS) is outlined to examine the second-order statistics (mean, standard deviation (SD), and probability density function (PDF)) of the non-linear dynamic response of the FGM plate. The governing dynamic equation is solved by Newmark's time integration scheme. The effects of volume fraction index, load parameters, plate thickness ratios, and temperature changes with random system properties are examined through parametric studies. The present outlined approach is validated with the results available in the literature and by MCS.     

Product variety strategies for vertically differentiated products in a two-stage supply chain

In this paper, we study how the presence of coordination, competition and cost structures affect the product variety and pricing decisions in a two-stage supply chain. We consider vertically differentiated industries. Products reach the end customers via assembly companies positioned in the middle-tier between the producers and end customers. The assembly operations incur bill-of-materials (BOM) cost, which includes the cost of raw materials and components as well as the cost of producing, managing and synchronising supply, storage and distribution of these materials and components. We consider three coordination scenarios in the supply chain, and compare an uncoordinated supply chain with a horizontal and a vertical coordination scenario using the Nash equilibria of a multi-leader Stackelberg game between the producers and assembly companies. For exogenously given quality levels, we show that producers and assembly companies should either differentiate their product offering, or offer the high-end product only depending on the BOM cost. In addition, the uncoordinated scenario helps reduce the intensity of competition in the supply chain. For endogenous quality levels, partial product-differentiation equilibrium can emerge. Numerical experiments are performed to illustrate the impacts of BOM cost and quality levels on the equilibrium.     

A framework to engineer production control strategies and its application in electronics manufacturing

One of the most challenging tasks of companies with complex and discrete production systems is to determine an appropriate production control strategy (PCS). The PCS is crucial, as it influences inventory, costs, and service. We propose a framework to address the most important questions of PCS engineering: limiting work in process, positioning the order penetration point (OPP), and coping with demand uncertainty upstream the OPP. Thereby, uncertain advance demand information in the form of forecasts is considered and desirable characteristics of push and pull strategies are combined. A serial manufacturing system with multiple products, variable processing times, and uncertain advance demand information is examined. The model is formulated as a queuing network model and solved numerically by discrete-event simulation. The framework and a resulting hybrid control strategy are applied to a case study from electronics manufacturing, where a significant improvement potential is identified.     

A new belief Markov chain model and its application in inventory prediction

The Markov chain model is widely applied in many fields, especially the field of prediction. The discrete-time Markov chain (DTMC) is a common method for prediction. However, the classical DTMC model has some limitations when the system is complex with uncertain information or state space is not discrete. To address it, a new belief Markov chain (BMC) model combining Dempster-Shafer evidence theory and the DTMC is proposed. In our model, the uncertain data are allowed to be handled in the form of interval number, and the basic probability assignment is generated by an optimisation method based on the distance between interval numbers. The shortcoming of classical DTMC is overcome in the BMC model. Also, it has an efficient ability of dealing with uncertain information, including both the uncertainty of collected data and discerning states. Our model is applied to do the prediction of inventory demand and the result is close to the practical. Also, sensitivity analysis and some comparisons are accomplished to show the effectiveness and rationality of our proposed model.     

Order timing strategies in a single-supplier,multi-retailer system

The timing of retailer order placement is an important factor in supply chain performance in systems with uncertain end-customer demands. Retailers often prefer short order lead times, which permits the resolution of demand uncertainty prior to order placement, and reduces the risks associated with excess inventory. Suppliers, in contrast, prefer long lead times, in order to match supply output with retailer demand. These conflicting preferences create tension between a supplier and retailers regarding order timing preferences. This paper considers order timing preferences within a strategic framework involving a supplier and one of its retailers in a multiple-retailer system. We identify and explore several mechanisms a supplier can use within this framework to induce early retailer order placement and improve expected cost performance.