Economic approaches to environmental decision-making

There are several important economic reasons why government has a role in environmental management. However there are also substantial informational problems that must be solved if government intervention is to be adequately directed. Traditional cost-benefit techniques are subject to some serious conceptual and empirical problems. However there are less powerful approaches which provide more feasible alternatives to environmental measurement which the authors discuss.     

Exact and heuristic methods for a class of selective newsvendor problems with normally distributed demands

In this paper we study a class of selective newsvendor problems, where a decision maker has a set of raw materials each of which can be customized shortly before satisfying demand. The goal is then to select which subset of customizations maximizes expected profit. We show that certain multi-period and multi-product selective newsvendor problems fall within our problem class. Under the assumption that the demands are independent and normally, but not necessarily identically, distributed we show that some problem instances from our class can be solved efficiently using an attractive sorting property that was also established in the literature for some related problems. For our general model we use the KKT conditions to develop an exact algorithm that is efficient in the number of raw materials. In addition, we develop a class of heuristic algorithms. In a numerical study, we compare the performance of the algorithms, and the heuristics are shown to have excellent performance and running times as compared to available commercial solvers.     

On the Testability of Identification in Some Nonparametric Models With Endogeneity

This paper examines three distinct hypothesis testing problems that arise in the context of identification of some nonparametric models with endogeneity. The first hypothesis testing problem we study concerns testing necessary conditions for identification in some nonparametric models with endogeneity involving mean independence restrictions. These conditions are typically referred to as completeness conditions. The second and third hypothesis testing problems we examine concern testing for identification directly in some nonparametric models with endogeneity involving quantile independence restrictions. For each of these hypothesis testing problems, we provide conditions under which any test will have power no greater than size against any alternative. In this sense, we conclude that no nontrivial tests for these hypothesis testing problems exist.     

Characterizing the Structure of Optimal Stopping Policies

This paper studies a stochastic model of optimal stopping processes, which arise frequently in operational problems (e.g., when a manager needs to determine an optimal epoch to stop a process). For such problems, we propose an effective method of characterizing the structure of the optimal stopping policy for the class of discrete‐time optimal stopping problems. Using this method, we also derive a set of metatheorems that can help identify when a threshold or control‐band type stopping policy is optimal. We show that our proposed method can determine the structure of the optimal policy for some stopping problems that conventional methods fail to do so. In some cases, our method also simplifies the analysis of some existing results. Moreover, the metatheorems we propose help identify sufficient conditions that yield simple optimal policies when such policies are not generally optimal. We demonstrate these benefits by applying our method to several optimal stopping problems frequently encountered in, for example, the operations, marketing, finance, and economics literatures. We note that with structural results, optimal‐stopping policies are easier to follow, describe, and compute and hence implement. They also help determine how a stopping policy should be adjusted in response to changes in the operational environment. In addition, as structural results are critical for the development of efficient algorithms to solve optimal stopping problems numerically, we hope that the method and results provided in the study will contribute to that effort.     

Trim loss and related problems

Because of the diversity of the structures of real-world trim loss (or cutting stock) problems there exist no general standard methods for solving these problems. Hence many solution methods for trim loss problems have been developed in practice, especially heuristics. Since these methods are strongly based on the particular problems to which they are applied, the study of papers on problems with a comparable structure may prove helpful. In this paper a detailed catalogue of criteria for characterization of real-world trim loss problems is developed in a differentiated and systematic manner. The main criteria are: dimension, planning situation, goal, restrictions and solution approach. The proposed system of classification is based on 34 case studies found in the relevant literature. The classification of these case studies is represented by means of a ‘classification schedule’. In view of existing formal similarities the classification system can also be of importance for (bin) packing and loading as well as some assortment problems.     

The application of dynamic programming in United Kingdom companies

Dynamic Programming (or DP as it is commonly known) is a mathematical programming method which would appear to be a very powerful technique for use in management decision problems. A great number of theoretical texts have been written on the mathematics of DP and a few articles have been published on the more practical aspects, but DP has remained very much on the theoretical shelf as far as practising management has been concerned. This paper gives the results of a survey carried out at the beginning of 1972 and is intended to provide some insight into the use of DP in real management problems in U.K. companies and to show what sort of problems are apparently restricting its use. The findings demonstrate that several firms have used DP in various applications with considerable success. There are also some enlightening comments on the difficulties involved, and on the future potential of DP in industry.     

A bicriteria approach to scheduling a single machine with job rejection and positional penalties

Single machine scheduling problems have been extensively studied in the literature under the assumption that all jobs have to be processed. However, in many practical cases, one may wish to reject the processing of some jobs in the shop, which results in a rejection cost. A solution for a scheduling problem with rejection is given by partitioning the jobs into a set of accepted and a set of rejected jobs, and by scheduling the set of accepted jobs among the machines. The quality of a solution is measured by two criteria: a scheduling criterion, F1, which is dependent on the completion times of the accepted jobs, and the total rejection cost, F2. Problems of scheduling with rejection have been previously studied, but usually within a narrow framework—focusing on one scheduling criterion at a time. This paper provides a robust unified bicriteria analysis of a large set of single machine problems sharing a common property, namely, all problems can be represented by or reduced to a scheduling problem with a scheduling criterion which includes positional penalties. Among these problems are the minimization of the makespan, the sum of completion times, the sum and variation of completion times, and the total earliness plus tardiness costs where the due dates are assignable. Four different problem variations for dealing with the two criteria are studied. The variation of minimizing F1+F2 is shown to be solvable in polynomial time, while all other three variations are shown to be $\mathcal{NP}$ -hard. For those hard problems we provide a pseudo polynomial time algorithm. An FPTAS for obtaining an approximate efficient schedule is provided as well. In addition, we present some interesting special cases which are solvable in polynomial time.     

LEARNING WITH DETERMINISTIC DECISION RULES

While many problems of uncertainty are commonly analyzed by means of stochastic models, under certain circumstances this may not be an appropriate approach. The latter situation arises when the decision maker knows that the uncertain variables are not generated by a stochastic process, or when he is unwilling, or unable, to compute subjective probabilities. One of the nonstochastic approaches to uncertainty is the expectational approach in which the decision maker forms deterministic expectations about the uncertain aspects of his environment. This paper is concerned with some criteria for selecting among available expectations, or anticipations functions, and the possibility of ordering them according to these criteria. This study focuses especially on the learning criterion. The discussion brings out conceptual problems in connection with the definition of learning, as well as some technical difficulties that one encounters when attempting to compare different anticipations functions from the point of view of the learning criterion. As an illustration of the issues discussed, the paper reports on the results of some simulated decision rules. These show that decision rules in which no learning takes place, and in which some information is ignored, may perform better than more sophisticated rules.     

Industrial relations problems—OR's contribution

This paper considers the contribution which OR (Operational Research) can make to solving some of the problems in the industrial relations field. It first discusses the characteristics of industrial relations problems, categorising them according to the degree of conflict involved. The kinds of contribution OR can make are then discussed, first in a general way and then for each category in turn. The paper draws particular attention to the need for OR work in much of the industrial relations field to take account of the existence of conflict and of the game-like nature of the negotiating process. Some of the problems this presents to the OR worker are mentioned. Directions for future developments in the field are proposed. Finally there is a brief discussion about whether the issues raised in the paper are relevant to OR's contribution in fields other than industrial relations.     

Assessing uncertain ventures using experts' judgments in the form of subjective probabilities

The paper deals with a procedure known as ASPRO which has been successfull applied at Shell Oil Company. Section II addresses general problems encountered in using subjective assessment procedures. Section III introduces ASPRO's decomposition process which is designed to alleviate some of the problems. The decomposition process produces a questionnaire, the answers to which are the inputs to the ASPRO assessment procedure. Examples of the questionnaire for assessing political risks to oil investment ventures and analyzing new product sectors are given. Section IV explains how the respondents assess the component parts, and Section V outlines the mechanical procedures which are used to combine the assessments. Examples of how ASPRO's output can be used to communicate the respondents' quantified judgments are given in Section VI. Conclusions are in Section VII.     

关联方关系及其交易的信息披露问题

上市公司关联方关系及其交易一直是证券市场关注的热点问题,对这一事项的信息披露是财务报表附注中的一项重要内容.因为关联方关系及其交易不同于-般的公司间关系及交易,有其自身独具的特点,因而成为投资者决策的一条主要依据.从中报和年报的总体来看,大部分上市公司能够比较客观、清楚地披露该项事宜,但仍然有一些上市公司不能按照准则要求去做,在信息披露中暴露出一些问题.因此,不论关联方关系及其交易的信息披露还是关联交易本身都有待于进一步规范.     

Evaluation of heuristics for scheduling in a flowshop: a case study

The problem of finding the best permutation schedule for a flowshop has engaged the attention of researchers for almost four decades. In view of its NP-completeness, the problem is not amenable to the development of efficient optimizing algorithms. A number of heuristics have been proposed, most of which have been evaluated by using randomly generated problems for a single measure of performance. However, real-life problems often have more than one objective. This paper discusses a live flowshop problem that has the twin objectives of minimizing the production run-time as well as the total flowtime of jobs. Five heuristics are evaluated in this study and some interesting findings are reported.     

车间作业计划编制问题的统一描述与应用——介绍车间作业计划编制体系的概念

提出车间作业计划编制体系的概念以统一描述车间作业计划问题,车间计划问题在内容上由计划类型、计划期、期量标准和编制方法四方面组成,在形式上与生产类型、生产组织形式和资源负荷能力相关,内容与形式的统一揭示了车间作业计划编制问题的一般规律,特别地,在一个飞机装配车间结合计划编制体系的概念,迅速确定了装配作业计划问题的界限,并提出用Petri网求解计划问题的新方法,它与各种经验方法相比,便于计算机模拟与实现。     

Some debates and challenges in the literature on action learning: the state of the art since Revans

Action learning has travelled in some new directions and become an evolving practice since Revans first articulated his great idea. This paper focuses on some key challenges in the literature, some of which relate to these more recent directions in theory and practice. In particular, we consider the persistent problem of defining action learning and the varieties in practice which are in evidence, the nature of ‘action’ in action learning and the developing theory and practice of critical action learning as contrasted with the ‘classical’ approach to action learning. These debates are chosen for consideration here because they appear to strike at the heart of what action learning is for and because they are in evidence across a range of action learning literature. Some implications of these debates for human resource development (HRD) are also considered, including the potential action learning has for making a contribution to organizational learning, especially in treating ‘wicked’ problems and ill-structured challenges.     

Change-making problems revisited: a parameterized point of view

The change-making problem is the problem of representing a given amount of money with the fewest number of coins possible from a given set of coin denominations. In the general version of the problem, an upper bound for the availability of every coin value is given. Even the special case, where for each value an unlimited number of coins is available, is NP-hard. Since in the original problem some amounts can not be represented, especially if no coin of value one exists, we introduce generalized problems that look for approximations of the given amount such that a cost function is minimized. We recall algorithms for the change-making problem and present new algorithms for the generalized version of the problem. Motivated by the NP-hardness we study fixed-parameter tractability of all these problems. We show that some of these problems are fixed-parameter tractable and that some are \(\hbox {W}[1]\)-hard. In order to show the existence of polynomial and constant-size kernels we prove some general results and apply them to several parameterizations of the change-making problems.     

TWO‐ AND THREE‐STAGE FLOWSHOP SCHEDULING WITH NO‐WAIT IN PROCESS

In this paper we study the time complexities of some two‐ and three‐stage no‐wait flowshop makespan scheduling problems where, in some stage, all the jobs require a constant processing time and the stage may consist of parallel identical machines. Polynomial time algorithms are presented for certain problems, while several others are proved to be strongly NP‐complete.     

Organizational identification: A conceptual and operational review

There is a growing body of literature presenting the argument that processes of organizational identification (OI) are extremely important in helping to ensure that staff work towards the interests of the organization. There are, however, a number of problems with the way that the notion of OI has been conceptualized and operationalized in the extant literature. This paper examines how OI has been defined and measured over a number of decades. A number of problems are identified with how OI has been conceptualized by researchers, including, for example, issues about whether there is an affective element to identification and how the construct relates to organizational commitment. The paper also includes a review of previous approaches to measuring the concept of OI and raises some key problems with existing research tools. The paper concludes by arguing for a particular conceptualization of OI which helps to clarify the complex relationship between identification and organizational commitment, while at the same time accommodating previous definitions of the construct.     

Boundary graph classes for some maximum induced subgraph problems

The notion of a boundary graph class was recently introduced for a classification of hereditary graph classes according to the complexity of a considered problem. Two concrete graph classes are known to be boundary for several graph problems. We formulate a criterion to determine whether these classes are boundary for a given graph problem or not. We also demonstrate that the classes are simultaneously boundary for some continuous set of graph problems and they are not simultaneously boundary for another set of the same cardinality. Both families of problems are constituted by variants of the maximum induced subgraph problem.     

Consistent Moment Selection Procedures for Generalized Method of Moments Estimation

This paper considers a generalized method of moments (GMM) estimation problem in which one has a vector of moment conditions, some of which are correct and some incorrect. The paper introduces several procedures for consistently selecting the correct moment conditions. The procedures also can consistently determine whether there is a sufficient number of correct moment conditions to identify the unknown parameters of interest. The paper specifies moment selection criteria that are GMM analogues of the widely used BIC and AIC model selection criteria. (The latter is not consistent.) The paper also considers downward and upward testing procedures. All of the moment selection procedures discussed in this paper are based on the minimized values of the GMM criterion function for different vectors of moment conditions. The procedures are applicable in time-series and cross-sectional contexts. Application of the results of the paper to instrumental variables estimation problems yields consistent procedures for selecting instrumental variables.     

A general space-time model for combinatorial optimization problems (and not only)

We consider the problem of defining a strategy consisting of a set of facilities taking into account also the location where they have to be assigned and the time in which they have to be activated. The facilities are evaluated with respect to a set of criteria. The plan has to be devised respecting some constraints related to different aspects of the problem such as precedence restrictions due to the nature of the facilities. Among the constraints, there are some related to the available budget. We consider also the uncertainty related to the performances of the facilities with respect to considered criteria and plurality of stakeholders participating to the decision. The considered problem can be seen as the combination of some prototypical operations research problems: knapsack problem, location problem and project scheduling. Indeed, the basic brick of our model is a variable x_ilt which takes value 1 if facility i is activated in location l at time t, and 0 otherwise. Due to the conjoint consideration of a location and a time in the decision variables, what we propose can be seen as a general space-time model for operations research problems. We discuss how such a model permits to handle complex problems using several methodologies including multiple attribute value theory and multiobjective optimization. With respect to the latter point, without any loss of the generality, we consider the compromise programming and an interactive methodology based on the Dominance-based Rough Set Approach. We illustrate the application of our model with a simple didactic example.