Efficient formation of storage classes for warehouse storage location assignment: A simulated annealing approach

Class-based storage policy distributes products among a number of classes and for each class it reserves a region within the storage area. The procedures reported in the literature for formation of storage classes primarily consider order-picking cost ignoring storage-space cost. Moreover, in these procedures items are ordered on the basis of their cube per order index (COI), and items are then partitioned into classes maintaining this ordering. This excludes many possible product combinations in forming classes which may result in inferior solutions. In this paper, a simulated annealing algorithm (SAA) is developed to solve an integer programming model for class formation and storage assignment that considers all possible product combinations, storage-space cost and order-picking cost. Computational experience on randomly generated data sets and an industrial case shows that SAA gives superior results than the benchmark dynamic programming algorithm for class formation with COI ordering restriction.     

等待时间受限的置换流水车间调度启发式算法

针对等待时间受限的置换流水车间调度问题,分析了其可行解与流水车间调度最优解的关系,给出了计算最大完工时间的有向图,证明了等待时间受限的置换流水车间调度问题的可逆性,并以此为基础提出了一种启发式算法.算法首先根据等待时间受限约束与无等待(no-wait)约束的相似特征,生成初始工件序列集;然后利用问题可逆性给出了复杂度为O(n2m)的插入优化机制,进一步优化初始解.数据实验的结果验证了启发式算法的可行性和有效性.     

Identifying Equilibrium Models of Labor Market Sorting

We assess the empirical content of equilibrium models of labor market sorting based on unobserved (to economists) characteristics. In particular, we show theoretically that all parameters of the classic model of sorting based on absolute advantage in Becker, 1973 with search frictions can be nonparametrically identified using only matched employer–employee data on wages and labor market transitions. In particular, these data are sufficient to nonparametrically estimate the output of any individual worker with any given firm. Our identification proof is constructive and we provide computational algorithms that implement our identification strategy given the limitations of the available data sets. Finally, we add on‐the‐job search to the model, extend the identification strategy, and apply it to a large German matched employer–employee data set to describe detailed patterns of sorting and properties of the production function.     

基于HTFWGBM算子的供应商选择模型研究

由于供应商选择中的的复杂性与不确定性以及人类认知的有限性，从而导致信息融合失真和决策结果不准确的情况。针对决策属性以犹豫三角模糊数（HTFN）给出的供应商的选择问题，本文提出一种基于HTFGWBM算子的决策算法。首先，针对犹豫三角模糊数和几何Bonferroni平均算子理论，分别定义了犹豫三角模糊几何Bonferroni平均（HTFGBM）算子和犹豫三角模糊几何加权Bonferroni平均（HTFGWBM）算子，同时分别研究了算子的幂等性，置换不变性，单调性和有界性等性质。其次，基于HTFGWBM算子构建新型犹豫多属性决策模型，结合HTFN排序方法进行备选供应商排序。最后通过算南水北调中线工程中的供应商选择实例证明了决策模型的可行性与有效性。结果表明，通过调整模型参数，模型具有一定的延展性和容错能力，能够很好的进行科学决策。     

Inferring decision strategies from clickstreams in decision support systems: a new process-tracing approach using state machines

Webstores can easily gather large amounts of consumer data, including clicks on single elements of the user interface, navigation patterns, user profile data, and search texts. Such clickstream data are both interesting to merchandisers as well as to researchers in the field of decision-making behavior, because they describe consumer decision-behavior on websites. This paper introduces an approach that infers decision-behavior from clickstream data. The approach observes clicks on elements of a decision-support-system and triggers a set of state-machines for each click. Each state-machine represents a particular decision-strategy which a user can follow. The approach returns a set of decision strategies that best explain the observed click-behavior of a user. Results of two experiments show that the algorithm infers strategies accurately. In the first experiment, the approach correctly infers most of the pre-defined decision-strategies. The second study analyzes the behavior of thirty-eight respondents and finds that the inferred mix of decision-strategies fits well the behavior described in the literature to date. Results show that using decision-support-systems on a web site and observing the user’s click-behavior make it possible to infer a specific decision strategy. The proposed method is general enough to be easily applied to both research and real-world settings, along with other decision-support-systems and strategies.     

On the Tightness of the Alternating-Cycle Lower Bound for Sorting by Reversals

We give a theoretical answer to a natural question arising from a few years of computational experiments on the problem of sorting a permutation by the minimum number of reversals, which has relevant applications in computational molecular biology. The experiments carried out on the problem showed that the so-called alternating-cycle lower bound is equal to the optimal solution value in almost all cases, and this is the main reason why the state-of-the-art algorithms for the problem are quite effective in practice. Since worst-case analysis cannot give an adequate justification for this observation, we focus our attention on estimating the probability that, for a random permutation of n elements, the above lower bound is not tight. We show that this probability is low even for small n, and asymptotically (1/n⁵), i.e., O(1/n⁵) and (1/n⁵). This gives a satisfactory explanation to empirical observations and shows that the problem of sorting by reversals and its alternating-cycle relaxation are essentially the same problem, with the exception of a small fraction of pathological instances, justifying the use of algorithms which are heavily based on this relaxation. From our analysis we obtain convenient sufficient conditions to test if the alternating-cycle lower bound is tight for a given instance. We also consider the case of signed permutations, for which the analysis is much simpler, and show that the probability that the alternating-cycle lower bound is not tight for a random signed permutation of m elements is asymptotically (1/m²).     

A multi-objective particle swarm for a flow shop scheduling problem

Flow shop problems as a typical manufacturing challenge have gained wide attention in academic fields. In this paper, we consider a bi-criteria permutation flow shop scheduling problem, where weighted mean completion time and weighted mean tardiness are to be minimized simultaneously. Since a flow shop scheduling problem has been proved to be NP-hard in strong sense, an effective multi-objective particle swarm (MOPS), exploiting a new concept of the Ideal Point and a new approach to specify the superior particle's position vector in the swarm, is designed and used for finding locally Pareto-optimal frontier of the problem. To prove the efficiency of the proposed algorithm, various test problems are solved and the reliability of the proposed algorithm, based on some comparison metrics, is compared with a distinguished multi-objective genetic algorithm, i.e. SPEA-II. The computational results show that the proposed MOPS performs better than the genetic algorithm, especially for the large-sized problems.     

The evolution of industrial environmental performance metrics: Trends and challenges

Environmental performance metrics are measuring sticks against which companies can gauge their progress toward environmental goals and related business strategies. Good metrics represent information that has been purposefully distilled into manageable bits that help focus attention on key issues. For example, a particular compliance goal may require tracking the mass or concentration of regulated substances that are released to the environment. Progress toward meeting an internal goal of reducing materials or eliminating targeted substances from the waste stream may be tracked by measuring the mass of material inputs and outputs. These metrics, when compared to an appropriate benchmark, such as a regulatory standard or an industry average, give a company some means of assessing its performance. A comprehensive set of metrics can assist management to direct corporate resources toward the most processing problems and identify the most cost efficient manner of addressing them. They can also be used by companies to communicate the details of their environmental performance to customers, investors and other external audiences.     

SERVICE PROCESS CONFIGURATIONS IN ELECTRONIC RETAILING: A TAXONOMIC ANALYSIS OF ELECTRONIC FOOD RETAILERS

We developed a taxonomy of service processes in electronic retailing using data from 255 electronic food retailers. The taxonomy is comprised of eight service process configurations. Analysis of publicly available data on on‐line customer ratings for 52 retailers in the study sample shows that the ordering of the configurations in the taxonomy on a continuum of low to high flexibility is associated positively with (i) customer satisfaction with web site aesthetics, web site navigation, product selection, product information, customer support, and ease of return, and (ii) customer loyalty.     

Establishing dominance between strategies with interval judgments of state probabilities

In decision-making under uncertainty, a decision-maker is required to specify, possibly with the help of decision analysts, point estimates of the probabilities of uncertain events. In this setting, it is often difficult to obtain very precise measurements of the decision-maker׳s probabilities on the states of nature particularly when little information is available to evaluate probabilities, available information is not specific enough, or we have to model the conflict case where several information sources are available.In this paper, imprecise probabilities are considered for representing the decision-maker׳s perception or past experience about the states of nature, to be specific, interval probabilities, which can be further categorized as (a) intervals of individual probabilities, (b) intervals of probability differences, and (c) intervals of ratio probabilities. We present a heuristic approach to modeling a wider range of types of probabilities as well as three types of interval probabilities. In particular, the intervals of ratio probabilities, which are widely used in the uncertain AHP context, are analyzed to find extreme points by the use of change of variables, not to mention the first two types of interval probabilities. Finally, we examine how these extreme points can be used to determine an ordering or partial ordering of the expected values of strategies.     

Efficient Algorithms for Similarity Search

The problem of our interest takes as input a database of m sequences from an alphabet and an integer k. The goal is to report all the pairs of sequences that have a matching subsequence of length at least k. We employ two algorithms to solve this problem. The first algorithm is based on sorting and the second is based on generalized suffix trees. We provide experimental data comparing the performances of these algorithms. The generalized suffix tree based algorithm performs better than the sorting based algorithm.     

Optimal Ordering Policies for Inventory Problems With Dynamic Information Delays

Information delays exist when the most recent inventory information available to the Inventory Manager (IM) is dated. In other words, the IM observes only the inventory level that belongs to an earlier period. Such situations are not uncommon, and they arise when it takes a while to process the demand data and pass the results to the IM. We introduce dynamic information delays as a Markov process into the standard multiperiod stochastic inventory problem with backorders. We develop the concept of a reference inventory position. We show that this position along with the magnitude of the latest observed delay and the age of this observation are sufficient statistics for finding the optimal order quantities. Furthermore, we establish that the optimal ordering policy is of state‐dependent base‐stock type with respect to the reference inventory position (or state‐dependent (s, S) type if there is a fixed ordering cost). The optimal base stock and (s, S) levels depend on the magnitude of the latest observed delay and the age of this observation. Finally, we study the sensitivity of the optimal base stock and the optimal cost with respect to the sufficient statistics.     

Exploring uncertainties in a marketplace for cloud computing: a revelatory case study

Today, highly standardized information technology (IT) resources such as storage and processing power are becoming available and affordable to all. Therefore, IT managers evermore focus on reducing the costs and risks that these resources entail rather than on the benefits or competitive edge they provide. A marketplace for cloud computing represents a promising alternative for obtaining standardized IT resources in a highly flexible and scalable manner. Based on a revelatory case study of a cloud market project, we show that uncertainties are prevalent in cloud markets and that the principal-agent theory is an adequate perspective to study and explain these uncertainties. By triangulating data from formal and informal interviews, documentations and project meetings, we develop a second-level contextual understanding of uncertainties in the relationships between market operator, cloud providers and users. Our analysis reveals that while cloud market operators have capabilities to mitigate uncertainties between cloud provider and user, they also cause new uncertainties from the cloud provider and user perspective. As an outcome, we present a framework that sheds light on the uncertainty trade-offs involved in the decision to adopt a cloud market by cloud providers and users.     

Procurement Contracts in the Presence of Endogenous Disruption Risk

We consider a manufacturer facing an unreliable supplier with high or low type on initial reliability. The private reliability can be enhanced through process improvement initiated by either manufacturer (manufacturer‐initiated improvement, MI) or supplier (supplier‐initiated improvement, SI). We derive optimal procurement contracts for both mechanisms and find that the moral hazard does not necessarily generate more profit for high‐type supplier. Furthermore, information asymmetry causes a greater possibility of not ordering from low type in SI than MI. For low type, when an upward effort distortion appears in both mechanisms, a decreased (increased) unit penalty should be imposed in MI (SI) compared with symmetric information case. Although possibly efficient effort from the supplier could yield greater channel profit in SI, several scenarios violate this expectation. However, the manufacturer's expected profit in MI is no less than that in SI. When MI is extended to MSI where both manufacturer and supplier can exert effort, the expected profits of two parties are equal to those in SI. We further extend SI to SID, where both process improvement and dual‐sourcing are available. The manufacturer considers the trade‐off between the benefit from diversification and the loss from dual information rent to decide to choose SID or MI. By comparing SID with pure dual‐sourcing, we find that supplier's process improvement could either accelerate or retard the exercise of dual‐sourcing.     

Dynamic Financial Constraints: Distinguishing Mechanism Design From Exogenously Incomplete Regimes

We formulate and solve a range of dynamic models of constrained credit/insurance that allow for moral hazard and limited commitment. We compare them to full insurance and exogenously incomplete financial regimes (autarky, saving only, borrowing and lending in a single asset). We develop computational methods based on mechanism design, linear programming, and maximum likelihood to estimate, compare, and statistically test these alternative dynamic models with financial/information constraints. Our methods can use both cross‐sectional and panel data and allow for measurement error and unobserved heterogeneity. We estimate the models using data on Thai households running small businesses from two separate samples. We find that in the rural sample, the exogenously incomplete saving only and borrowing regimes provide the best fit using data on consumption, business assets, investment, and income. Family and other networks help consumption smoothing there, as in a moral hazard constrained regime. In contrast, in urban areas, we find mechanism design financial/information regimes that are decidedly less constrained, with the moral hazard model fitting best combined business and consumption data. We perform numerous robustness checks in both the Thai data and in Monte Carlo simulations and compare our maximum likelihood criterion with results from other metrics and data not used in the estimation. A prototypical counterfactual policy evaluation exercise using the estimation results is also featured.     

需求信息扭曲条件下供应链成员运营决策研究

本文在供应商和零售商需求信息不对称的背景下,考虑了由一个供应商和两个具有不平等地位零售商组成的供应链,运用信号博弈理论和完美贝叶斯纳什均衡理论,研究批发价格合同下,供应链中存在的信息泄露和信息扭曲对供应链成员运营决策的影响。在批发价格合同下,供应商总会泄露市场地位高的零售商订单信息,两个零售商存在斯塔克伯格竞争。本文进一步研究分析,得到存在信息泄露的两种均衡策略——分离均衡和混同均衡,给出零售商在不同市场需求条件下的订购决策。本文还指出在一定条件下,供应商可能在信息泄露过程中扭曲信息,误导市场地位低的零售商做出错误决策,供应商和市场地位高的零售商获得更大利润。当供应商扭曲信息时,尽管市场地位低的零售商利润降低,但其仍将继续留在市场中,零售商之间仍然保持水平竞争。最后,通过算例分析,对研究结论进行直观验证和说明。本文研究了供应链存在的信息泄露和信息扭曲问题,为现实中供应链各个主体行为提供理论依据。     

The Institutionalization of Third Stream Activities in UK Higher Education: The Role of Discourse and Metrics

In this paper we examine the institutionalization of a new organizational practice around third stream activities in UK higher education from 1994 to 2008. Employing a longitudinal research design, involving archival, survey and contemporary interview data, we show how a new organizational practice diffused through the use of discourse and metrics around commercialization but over time was threatened on pure economic criteria. In response to the threat we explore how actors worked to institutionalize the new practice, reshaping both discourse and metrics to ensure that they were robust so that they could align with the interests and values of major stakeholders. In addition, actors worked to align discourse and metrics, as any misalignment between the two would have undermined the institutionalization of the new organizational practice.     

Newsvendor Problems with Demand Shocks and Unknown Demand Distributions

In today's competitive market, demand volume and even the underlying demand distribution can change quickly for a newsvendor seller. We refer to sudden changes in demand distribution as demand shocks. When a newsvendor seller has limited demand distribution information and also experiences underlying demand shocks, the majority of existing methods for newsvendor problems may not work well since they either require demand distribution information or assume stationary demand distribution. We present a new, robust, and effective machine learning algorithm for newsvendor problems with demand shocks but without any demand distribution information. The algorithm needs only an approximate estimate of the lower and upper bounds of demand range; no other knowledge such as demand mean, variance, or distribution type is necessary. We establish the theoretical bounds that determine this machine learning algorithm's performance in handling demand shocks. Computational experiments show that this algorithm outperforms the traditional approaches in a variety of situations including large and frequent shocks of the demand mean. The method can also be used as a meta‐algorithm by incorporating other traditional approaches as experts. Working together, the original algorithm and the extended meta‐algorithm can help manufacturers and retailers better adapt their production and inventory control decisions in dynamic environments where demand information is limited and demand shocks are frequent      

Modeling Uncertainties in Workforce Disruptions from Influenza Pandemics Using Dynamic Input‐Output Analysis

Influenza pandemic is a serious disaster that can pose significant disruptions to the workforce and associated economic sectors. This article examines the impact of influenza pandemic on workforce availability within an interdependent set of economic sectors. We introduce a simulation model based on the dynamic input‐output model to capture the propagation of pandemic consequences through the National Capital Region (NCR). The analysis conducted in this article is based on the 2009 H1N1 pandemic data. Two metrics were used to assess the impacts of the influenza pandemic on the economic sectors: (i) inoperability, which measures the percentage gap between the as‐planned output and the actual output of a sector, and (ii) economic loss, which quantifies the associated monetary value of the degraded output. The inoperability and economic loss metrics generate two different rankings of the critical economic sectors. Results show that most of the critical sectors in terms of inoperability are sectors that are related to hospitals and health‐care providers. On the other hand, most of the sectors that are critically ranked in terms of economic loss are sectors with significant total production outputs in the NCR such as federal government agencies. Therefore, policy recommendations relating to potential mitigation and recovery strategies should take into account the balance between the inoperability and economic loss metrics.     

基于状态空间模型的动态基金业绩评价研究

本文在基金整体业绩评价研究领域对以往经典基金业绩评价指标詹森alpha指数以及基金资产投资的系统风险指标beta的估计方法进行了修正和改良。以往的詹森指数和beta值的估计是将其视为常系数,然而实际中基金的詹森指数和系统风险beta具备时变性。在对常系数下詹森alpha和系统风险beta值的分解式中,本文证明了传统估计值由詹森alpha和系统风险beta的期望值与一系列协方差项组成。随后本文构建了反映动态指标变化的SSM模型,并利用Particle EM算法来估计动态詹森alpha和系统风险beta在各期的估计值,并以此来计算基金在评价时期内平均詹森指数水平和系统风险水平。此外由于获取了各期的系统风险beta,根据择时能力的定义本文构建了反映基金在时期内的择时能力指标。