The nearest neighbor Spearman footrule distance for bucket, interval, and partial orders

Comparing and ranking information is an important topic in social and information sciences, and in particular on the web. Its objective is to measure the difference of the preferences of voters on a set of candidates and to compute a consensus ranking. Commonly, each voter provides a total order of all candidates. Recently, this approach was generalized to bucket orders, which allow ties. In this work we further generalize and consider total, bucket, interval and partial orders. The disagreement between two orders is measured by the nearest neighbor Spearman footrule distance, which has not been studied so far. For two bucket orders and for a total and an interval order the nearest neighbor Spearman footrule distance is shown to be computable in linear time, whereas for a total and a partial order the computation is NP-hard, 4-approximable and fixed-parameter tractable. Moreover, in contrast to the well-known efficient solution of the rank aggregation problem for total orders, we prove the NP-completeness for bucket orders and establish a 4-approximation.     

Dual Objective Segmentation to Improve Targetability: An Evolutionary Algorithm Approach**

Cluster‐based segmentation usually involves two sets of variables: (i) the needs‐based variables (referred to as the bases variables), which are used in developing the original segments to identify the value, and (ii) the classification or background variables, which are used to profile or target the customers. The managers’ goal is to utilize these two sets of variables in the most efficient manner. Pragmatic managerial interests recognize the underlying need to start shifting from methodologies that obtain highly precise value‐based segments but may be of limited practical use as they provide less targetable segments. Consequently, the imperative is to shift toward newer segmentation approaches that provide greater focus on targetable segments while maintaining homogeneity. This requires dual objective segmentation, which is a combinatorially difficult problem. Hence, we propose and examine a new evolutionary methodology based on genetic algorithms to address this problem. We show, based on a large‐scale Monte Carlo simulation and a case study, that the proposed approach consistently outperforms the existing methods for a wide variety of problem instances. We are able to obtain statistically significant and managerially important improvements in targetability with little diminution in the identifiability of value‐based segments. Moreover, the proposed methodology provides a set of good solutions, unlike existing methodologies that provide a single solution. We also show how these good solutions can be used to plot an efficient Pareto frontier. Finally, we present useful insights that would help managers in implementing the proposed solution approach effectively.     

Combining analytical hierarchy process and Choquet integral within non-additive robust ordinal regression

We consider multiple criteria decision aiding in the case of interaction between criteria. In this case the usual weighted sum cannot be used to aggregate evaluations on different criteria and other value functions with a more complex formulation have to be considered. The Choquet integral is the most used technique and also the most widespread in the literature. However, the application of the Choquet integral presents two main problems being the necessity to determine the capacity, which is the function that assigns a weight not only to all single criteria but also to all subset of criteria, and the necessity to express on the same scale evaluations on different criteria. While with respect to the first problem we adopt the recently introduced Non-Additive Robust Ordinal Regression (NAROR) taking into account all the capacities compatible with the preference information provided by the DM, with respect to the second one we build the common scale for the considered criteria using the Analytic Hierarchy Process (AHP). We propose to use AHP on a set of reference points in the scale of each criterion and to use an interpolation to obtain the other values. This permits to reduce considerably the number of pairwise comparisons usually required by the DM when applying AHP. An illustrative example details the application of the proposed methodology.     

A Flexible Evaluative Framework for Order Picking Systems

This paper develops a novel framework to evaluate the integral performance of order picking systems with different combinations of storage and order picking policies. The warehousing literature on order picking mostly considers minimizing either elapsed time or distance as the sole objective, whereas warehouse managers in a supply chain have to look beyond single‐dimensional performance and consider trade‐offs among different criteria. Thus managers still need a unified and efficient framework to select a portfolio of appropriate order picking policies from a multi‐criteria and contextual perspective. Our framework—combining data envelopment analysis, ranking and selection, and multiple comparisons—provides an efficient methodology to simultaneously analyze several interrelated problems in order picking systems with multiple performance attributes, such as service levels and operational costs. We demonstrate our approach through comprehensive evaluations of order picking policies in three low‐level, picker‐to‐parts rectangular warehouses facing demand variations.     

Keyword Auctions,Unit‐Price Contracts,and the Role of Commitment

Motivated by the enormous growth of keyword advertising, this paper explores the design of performance‐based unit‐price contract auctions, in which bidders bid their unit prices and the winner is chosen based on both their bids and performance levels. The previous literature on unit‐price contract auctions usually considers a static case where bidders' performance levels are fixed. This paper studies a dynamic setting in which bidders with a low performance level can improve their performance at a certain cost. We examine the effect of the performance‐based allocation on overall bidder performance, auction efficiency, and the auctioneer's revenue, and derive the revenue‐maximizing and efficient policies accordingly. Moreover, the possible upgrade in bidders' performance level gives the auctioneer an incentive to modify the auction rules over time, as is confirmed by the practice of Yahoo! and Google. We thus compare the auctioneer's revenue‐maximizing policies when she is fully committed to the auction rule and when she is not, and show that the auctioneer should give less preferential treatment to low‐performance bidders when she is fully committed.     

Efficient Competition Through Cheap Talk: The Case of Competing Auctions

We consider a large market where auctioneers with private reservation values compete for bidders by announcing cheap‐talk messages. If auctioneers run efficient first‐price auctions, then there always exists an equilibrium in which each auctioneer truthfully reveals her type. The equilibrium is constrained efficient, assigning more bidders to auctioneers with larger gains from trade. The choice of the trading mechanism is crucial for the result. Most notably, the use of second‐price auctions (equivalently, ex post bidding) leads to the nonexistence of any informative equilibrium. We examine the robustness of our finding in various dimensions, including finite markets and equilibrium selection.     

On Broadening Failure Rate Distributions in PRA Uncertainty Analyses

Several recent nuclear power plant probabilistic risk assessments (PRAs) have utilized broadened Reactor Safety Study (RSS) component failure rate population variability curves to compensate for such things as expert "overvaluation bias" in the estimates upon which the curves are based.
A simple two-components of variation empirical Bayes model is proposed for use in estimating the between-expert variability curve in the presence of such biases. Under certain conditions this curve is a population variability curve. Comparisons are made with the existing method.
The popular procedure appears to be generally much more conservative than the empirical Bayes method in removing such biases. In one case the broadened curve based on the popular method is more than two orders of magnitude broader than the empirical Bayes curve. In another case it is found that the maximum justifiable degree of broadening of the RSS curve is to increase α from 5% to 12%, which is significantly less than the 20% value recommended in the popular approach.     

Pushing frontiers in auction-based transport collaborations

In collaborative logistics, carriers form coalitions in order to perform parts of their operational business jointly. Combinatorial auctions can be used to exchange transportation requests without having to reveal critical information. Collaborators submit requests for exchange to a common pool. The requests in the pool are then grouped into bundles either by the auctioneer or by the carriers themselves, and offered for auction. While in the literature it is mostly assumed that both bundling and bidding is done by the carriers, our study shows that bundles built by the auctioneer under incomplete information can yield significantly higher collaboration profits, and allow for a lower number of bundles to be generated. However, even if the number of traded bundles is decreased by only offering some promising auctioneer bundles, the carriers have to present their bids for all the offered bundles. Typically, a bid is based on the carrier’s marginal profit. Hence, for each bid, an NP-hard routing problem has to be solved. To decrease this enormous computational effort, we develop different approximate bidding strategies. We show that for the single vehicle case, very simple bidding strategies are sufficient: they allow to reduce the computational time of the auction process by an order of magnitude, with no significant loss in collaboration profit. For multiple vehicles, however, our research shows that more sophisticated bidding strategies are recommended. Finally, we introduce a new profit sharing method, which is computationally tractable, guarantees individual rationality and does not require that critical information is revealed.     

拍卖中的串通出价

本文在扩展的独立私有价值模型下,考虑第二价格和第一价格拍卖机制下的串通出价行为和卖方对串通出价的反映。第二价格串通出价机制和第一价格串通出价机制分别为在这两种拍卖机制下的激励相容、有效的直接显示串通出价机制。我们发现卖方若知道有串通行为,可以提高保留价来减少损失。由于卖方一般情况下不太可能确定是否存在串通行为及参与串通的人数k,但可以确定给定保留价时串通人数的'最优反应',在第二价格串通出价机制下,给定串通人数,增加保留价可以提高卖方的期望收益,对于确定的保留价,串通人数的增加会减少卖方的期望收益。从而存在这样的纳什均衡:所有的投标人串通,即,k=N,而卖方宣布与之相应的保留价r (N)。当然,并不是所有的串通出价对投标人都是有利可图的,在第一价格拍卖机制下,若卖方可以调整保留价,只有当参与竞投的人很多时,串通出价才有利可图。     

An online algorithm for continuous slab caster scheduling

Lee et al. (Lee, K., Chang, S.Y., and Hong, Y., 2004. Continuous slab caster scheduling and interval graphs. Production Planning & Control, 13 (5), 495–501) have introduced a slab caster scheduling problem and developed an optimal algorithm. Their algorithm is efficient but an offline algorithm that we need the information on all the customer orders a priori to implement. In this article, we propose an online algorithm that we can implement without knowledge of the orders yet to arrive. We show that the offline version of our new algorithm also provides an optimal solution and the online version has the worst case performance ratio of 3. We also give a short proof on the correctness of Lee et al.'s algorithm.     

Non-convex value efficiency analysis and its application to bank branch sales evaluation

We have observed when applying Value Efficiency Analysis [21] that decision makers wish to provide preference information related to existing rather than virtual (efficient) units. This observation motivated us to develop an approach based on the preference comparisons of existing units. The Free Disposal Hull model provides the requisite framework. We assume that a Decision Maker compares units using an implicitly known value function that reaches its maximum at his/her most preferred (efficient) unit. The unknown value function is assumed to be quasi-concave in outputs and quasi-convex in inputs. The main purpose – as in the original Value Efficiency Analysis – is to approximate the distance of each unit from the contour of the value function passing through the most preferred unit. We use examples to illustrate the approach. Finally, we describe a real application in which Value Efficiency Analysis was used to produce information for bank managers wishing to evaluate the performance of bank branches.     

A Comparison of Methods of Benchmark-Dose Estimation for Continuous Response Data

Methods of quantitative risk assessment for toxic responses that are measured on a continuous scale are not well established. Although risk-assessment procedures that attempt to utilize the quantitative information in such data have been proposed, there is no general agreement that these procedures are appreciably more efficient than common quantal dose–response procedures that operate on dichotomized continuous data. This paper points out an equivalence between the dose–response models of the nonquantal approach of Kodell and West⁽¹⁾ and a quantal probit procedure, and provides results from a Monte Carlo simulation study to compare coverage probabilities of statistical lower confidence limits on dose corresponding to specified additional risk based on applying the two procedures to continuous data from a dose–response experiment. The nonquantal approach is shown to be superior, in terms of both statistical validity and statistical efficiency.     

Optimising correlated QCHs in robust design using principal components analysis and DEA techniques

Taguchi method is found efficient for optimising process performance with a single quality characteristic (QCH) of a product or process. In practice, however, customers are concerned about multiple QCHs, which are usually correlated. This research proposes and implements an approach using principal components analysis (PCA) and two data envelopment analysis (DEA) models, including CCR and super efficiency, for optimising multiple correlated QCHs in robust design. The PCA is first utilised to obtain multiple uncorrelated linear combinations of principal components, which are the same number of QCHs and hence avoid the loss of information by ignoring some principal components. Then, these components are utilised in two DEA models to decide optimal factor levels. Three real case studies are provided for illustration; in all of which the proposed approach is found more efficient than some other techniques in literature, including engineering judgement, PCA, PCA and grey analysis, and utility concept. In conclusion, the proposed approach shall provide a great assistance to process/product engineers for obtaining robust design with multiple correlated QCHs.     

Bidding under auctioneer default risk

In a financially turbulent economy, participants of a procurement auction should consider in their bids the event of default of the auctioneer, which may result to substantial damages for the winning bidder. We examine a sealed bid auction, with private cost values and interdependence among the beliefs of the bidders about the auctioneer׳s default risk. The probability of payment of the bid price by the auctioneer is estimated by each bidder. For a first and a second price auction, we derive equilibrium bidding strategies, which address the risk of default and optimally adjust the bid price, introducing a risk premium in the form of an additional mark-up. A numerical illustration of the proposed strategies is provided. The effect of auctioneer׳s risk of default on the procurement project cost is examined. Financial arrangements that may be used to relax or eliminate the effect of the risk of default, such as early payment methods, third party guarantees or insurance programs are discussed and evaluated in comparison with the approach of risk premium on bid price.     

Estimating the Probability of Rare Events Occurring Using a Local Model Averaging

In statistical applications, logistic regression is a popular method for analyzing binary data accompanied by explanatory variables. But when one of the two outcomes is rare, the estimation of model parameters has been shown to be severely biased and hence estimating the probability of rare events occurring based on a logistic regression model would be inaccurate. In this article, we focus on estimating the probability of rare events occurring based on logistic regression models. Instead of selecting a best model, we propose a local model averaging procedure based on a data perturbation technique applied to different information criteria to obtain different probability estimates of rare events occurring. Then an approximately unbiased estimator of Kullback‐Leibler loss is used to choose the best one among them. We design complete simulations to show the effectiveness of our approach. For illustration, a necrotizing enterocolitis (NEC) data set is analyzed.     

Robust Ordinal Regression and Stochastic Multiobjective Acceptability Analysis in multiple criteria hierarchy process for the Choquet integral preference model

The paper deals with two important issues of Multiple Criteria Decision Aiding: interaction between criteria and hierarchical structure of criteria. To handle interactions, we apply the Choquet integral as a preference model, and to handle the hierarchy of criteria, we apply the recently proposed methodology called Multiple Criteria Hierarchy Process. In addition to dealing with the above issues, we suppose that the preference information provided by the Decision Maker is indirect and has the form of pairwise comparisons of criteria with respect to their importance and pairwise preference comparisons of some pairs of alternatives with respect to some criteria. In consequence, many instances of the Choquet integral are usually compatible with this preference information. These instances are identified and exploited by Robust Ordinal Regression and Stochastic Multiobjective Acceptability Analysis. To illustrate the whole approach, we show its application to a real world decision problem concerning the ranking of universities for a hypothetical Decision Maker.     

A Double‐Track Adjustment Process for Discrete Markets With Substitutes and Complements

We propose a new Walrasian tâtonnement process called a double‐track procedure for efficiently allocating multiple heterogeneous indivisible items in two distinct sets to many buyers who view items in the same set as substitutes but items across the two sets as complements. In each round of the process, a Walrasian auctioneer first announces the current prices for all items, buyers respond by reporting their demands at these prices, and then the auctioneer adjusts simultaneously the prices of items in one set upward but those of items in the other set downward. It is shown that this procedure converges globally to a Walrasian equilibrium in finitely many rounds.     

A GENERAL APPROACH FOR COORDINATING PRODUCTION IN MULTIPLE-CELL MANUFACTURING SYSTEMS

We introduce a general approach to the coordination and control of material and information flow in multiple cell manufacturing systems. This approach uses production authorization cards (called PAC system) and generalizes such well-known approaches as MRP (material requirements planning), KANBAN (japanese card system), OPT (optimized production technology), BSS (base. stock system), IC (integral control), CONWIP (constant work-in-process), and others. It provides a framework for developing coordination and control mechanisms that combine the desirable features of more than one of these traditional approaches. The coordination is achieved by the rules that determine when and how material and information flow through the system and by the appropriate choice of the parameters of these rules. We also discuss various models of the PAC system that can be used to gain insight into the impact of the choices of the parameters. Directions for future research in this area is also outlined.     

Reduced costs propagation in an efficient implicit enumeration for the 01 multidimensional knapsack problem

In a previous work we proposed a variable fixing heuristics for the 0-1 Multidimensional knapsack problem (01MDK). This approach uses fractional optima calculated in hyperplanes which contain the binary optimum. This algorithm obtained best lower bounds on the OR-Library benchmarks. Although it is very attractive in terms of results, this method does not prove the optimality of the solutions found and may fix variables to a non-optimal value. In this paper, we propose an implicit enumeration based on a reduced costs analysis which tends to fix non-basic variables to their exact values. The combination of two specific constraint propagations based on reduced costs and an efficient enumeration framework enable us to fix variables on the one hand and to prune significantly the search tree on the other hand. Experimentally, our work provides two main contributions: (1) we obtain several new optimal solutions on hard instances of the OR-Library and (2) we reduce the bounds of the number of items at the optimum on several harder instances.     

不完全确定信息的群体语言指派问题的求解方法

针对决策者权重和准则权重为不完全确定信息且评价语言值确定或位于二个标准语言值之间甚至缺失的多准则指派问题,提出了一种求解方法。首先利用证据推理算法计算得到各候选人完成各任务的优劣程度属于各个语言评价等级的信任度,并据此利用二元语义的Δ函数及其函数Δ-1将其集成为群体在所有准则下的综合评价矩阵,然后结合决策者权重和准则权重的不完全确定信息等构建非线性混合整数规则模型,并利用粒子群算法与匈牙利算法联合进行求解。最后实例说明该方法的可行性和有效性。