Integrated manufacturing/distribution planning decisions with multiple imprecise goals in an uncertain environment

This work presents a possibilistic linear programming (PLP) method for solving the integrated manufacturing/distribution planning decision (MDPD) problems with multiple imprecise goals in supply chains under an uncertain environment. The imprecise PLP model designed here aims to simultaneously minimize total net costs and total delivery time with reference to available supply, capacities, labor levels, quota flexibility and cost budget constraints at each source, as well as forecast demand and warehouse space at each destination. The proposed method achieves greater computational efficiency by employing the simplified triangular distribution to represent imprecise numbers. An industrial case is used to demonstrate the feasibility of applying the proposed method to a real MDPD problem. Overall, the proposed PLP method provides a practical means of solving the multi-objective MDPD problems in an uncertain environment, and can effectively improve manufacturer/ distributor relationships in a supply chain.     

Neyman–Pearson Lemma for Fuzzy Hypotheses Testing with Vague Data

In hypotheses testing, such as other statistical problems, we may confront imprecise concepts. One case is a situation in which both hypotheses and observations are imprecise. This paper tries to develop a new approach for testing fuzzy hypothesis when the available data are fuzzy, too. First, some definitions are provided, such as: fuzzy sample space, fuzzy-valued random sample, and fuzzy-valued random variable. Then, the problem of fuzzy hypothesis testing with vague data is formulated. Finally, we state and prove a generalized Neyman–Pearson Lemma for such problem. The proposed approach is illustrated by some numerical examples.     

Decision making framework for tender evaluation and contractor selection in public organizations with risk considerations

Selecting the best construction contractor plays a critical role in the success of any construction project. Moreover, in the public sector, the traditional lowest bid method is still broadly used and contracts are often awarded based on the lowest price. However, this method has been criticized by many researchers because even if it might guarantee the lowest cost for a project, it does not guarantee the maximum value in terms of time and quality. More particularly, the risk exposure during the tendering process is usually very high and the success of the construction project is strongly related to managing this risk in an appropriate way. Therefore, the selection of the most appropriate contractor should be based on a set of criteria such as technical capability, financial stability, risk, safety, etc., in addition to cost in order to avoid problems that may happen after the contract is awarded. This study aims therefore to develop a decision making framework (DMF) to assist the public organizations in selecting the most appropriate construction contractor(s). The proposed DMF uses a combination of multi-criteria decision making (MCDM) tools and fuzzy logic theory and consists of three stages. In the first stage, data envelopment analysis (DEA) is used to reduce the number of contractors, based on multiple criteria, such that only efficient contractors are considered. In the second stage, the risk factors that are related to each efficient contractor are identified and assessed using a fuzzy logic based approach. Finally, in the third stage, a bi-objective mixed integer linear programming (MILP) model is developed to select the best contractor(s) based on cost and risk and to determine the optimal quantity of work to be assigned to each selected contractor. Indeed, the proposed approach considers the multiple award contract case in which more than one contractor can be selected to share the amount of work to be performed. We assume that prior to using the proposed DMF, a pre-qualification study is conducted using multiple criteria chosen by the public organization, and only pre-qualified contractors are included in the first stage of the proposed DMF. Moreover, a case study inspired from the contractor selection process in a public organization in the United Arab Emirates (UAE) is used to show the effectiveness of the proposed approach.     

A fuzzy group decision-making approach in quality function deployment

Quality function deployment (QFD) is comprised of two major group decision-making processes. One is to collect the customer requirements from a group of customers, whereas the other is to determine the relationship between customer requirements and technical measures by a cross-functional team. Generally, different and/or even subjective opinions are quite often in a group decision-making process due to the limitations of experience and impreciseness. Obviously, the importance of each customer requirement and the relationship between customer requirements and technical measures are determined by a group of people with imprecision and vagueness. Under such circumstances, a fuzzy group decision-making approach can be applied in QFD to deal with a group decision-making process when the information is imprecise and fuzzy. Moreover, an example is provided as well as the computational steps to show this fuzzy group decision-making approach can be effectively used in QFD to make decisions with imprecision and vagueness.     

Residual value forecasting using asymmetric cost functions

Leasing is a popular channel for marketing new cars. However, the pricing of leases is complicated because the leasing rate must embody an expectation of the car’s residual value after contract expiration. This paper develops resale price forecasting models in order to aid pricing decisions. One feature of the leasing business is that different forecast errors entail different costs. The primary objective of this paper is to identify effective ways of addressing cost asymmetry. Specifically, this paper contributes to the literature by (i) consolidating prior work in forecasting on asymmetric functions of the cost of errors; (ii) systematically evaluating previous approaches and comparing them to a new approach; and (iii) demonstrating that forecasting using asymmetric cost of error functions improves the quality of decision support in car leasing. For example, if the costs of overestimating resale prices are twice those of underestimating them, incorporating cost asymmetry into forecast model development reduces costs by about 8%.     

工程造价人员如何面对未来的挑战

文章对工程造价人员的素质要求、我国工程造价存在的问题、如何有效地控制工程造价、学习借鉴国外经验等几方面进行了探讨,指出工程造价人员必须把造价、工期、质量三者结合在一起综合考虑来开展工程造价工作。     

Staffing and equipment decisions for services: An experimental analysis

Many service-oriented firms face demand/work load variations that occur both within and between days, and seasonally during the year. These service firms tend to develop staffing plans based on peak periods, resulting in substantial idle time during low demand periods. To counter this, some firms hire part-time employees to work peak periods to improve productivity. In this way, the cost per customer/transaction (unit) can be reduced.However, the staffing decision is more complex than just determining the number and timing of the work force. The development of good staffing plans must consider the impact of available work stations like telephone consoles and data entry terminals. To use effectively more personnel during peak periods, the firm must have sufficient work stations to assign this work force. Effective staff scheduling must consider simultaneously the work load variations, employ capabilities, and equipment requirements. This article presents a model that incorporates these factors into the decision-making process and examines their interaction.The model analysis illustrates how uncertainty in work load forecasts and staffing flexibility influence expected cost performance and equipment requirements. A set of computer simulation experiments are conducted using operating data from the proof and encoding departments for Ohio National Bank and Chemical Bank. The results suggest that increased staffing flexibility reduces the needed equipment investment, since part time staff can be scheduled more conveniently to meet varying work loads. Also, basing capital equipment decisions on peak work load levels can substantially increase total operating costs. With these costs explicitly estimated, managers can weigh them against desired service level goals to determine the appropriate balance.     

Developing a multivariate approach to monitor fuzzy quality profiles

Referring to several applications in which the response quality characteristic is fuzzy, this paper studies how the profile functional relationship between a fuzzy response variable and a predictor variable can be monitored by using a fuzzy regression model which is referred to as profile. The purpose of this paper is to develop a multivariate approach for monitoring process/product fuzzy quality profiles in phase I for applications where the quality characteristic is linguistic, imprecise, vague or deficient. The multivariate approach includes three fuzzy multivariate control charts which are developed by using fuzzy set theory to monitor fuzzy profiles in order to achieve an in-control process. The performance of developed approach is investigated on the basis of signal probability in various out-of-control scenarios through a simulation study. Compared with univariate approach, the results indicate a good performance of our multivariate approach in detecting all sized shifts in process profiles. A real case in tourism industry is utilized to show the applicability of the proposed approach.     

Optimizing the credit term decisions in supply chain finance

We study a credit term determination problem in the context of a supplier-buyer supply chain. The supplier's credit term decision is simultaneously made with its production and inventory decisions, and most importantly, it is impacted by the buyer's order quantity. We present a new game-theoretic framework to model this problem, which captures the interaction between the supplier's credit term decision and the buyer's order decision in a multi-period setting. An exact method based on nonlinear programming is implemented to obtain the optimal solutions. We apply our methodologies on a real world case. The computational results show that our approach significantly outperforms the heuristics with fixed credit terms, and either a short or a long credit term can be sub-optimal for the supplier in profitability. Our work offers the first data-driven model and solution approach that assists purchasing and supply managers to make optimal dynamic credit term decision in conjunction with production, ordering and inventory decisions in a game-theoretic setting.     

Using decision modeling to examine management consensus: A study of a maintenance management control system

Using a decision modeling approach, this research examined differences across levels of management in a large construction company concerning the criteria for a maintenance management control system. The six criteria were cost efficiency, productivity improvement, materials management, unification of work control, planning and scheduling, and information and retrieval. A decision-making exercise was constructed that asked the respondent to make recommendations concerning the approval or disapproval of 32 hypothetical maintenance management systems. Among the 60 managers who completed the exercise, there was evidence of significant differences across levels of management concerning the criteria for a maintenance management control system. Additionally, distortions were observed between what the managers said was important and what their decisions indicated. These data demonstrate that the decision modeling approach presented can be effectively used to examine the degree of consensus within an organization.     

Assessing and predicting small industrial enterprises’ credit ratings: A fuzzy decision-making approach

Corporate credit-rating assessment plays a crucial role in helping financial institutions make their lending decisions and in reducing the financial constraints of small enterprises. This paper presents a new approach for small industrial enterprises’ credit-rating assessment using fuzzy decision-making methods and then tests this novel approach using real bank loan data from 1820 small industrial enterprises in China. The procedure of the proposed rating approach includes (1) using triangular fuzzy numbers to quantify the qualitative evaluation indicators; (2) adopting a correlation analysis, univariate analysis, and stepping backward feature selection method to select the input features; (3) employing the best-worst method (BWM) combined with the entropy weight method (EWM), the fuzzy c-means algorithm and the technique for order of preference by similarity to ideal solution (TOPSIS) to classify small enterprises into different rating classes; and (4) applying the lattice degree of nearness to predict a new loan applicant’s rating. We also conduct 10-fold cross-validation to evaluate the predictive performance of our proposed approach. The predictive results demonstrate that our proposed data-processing and feature selection approaches have better accuracy than the alternative approaches in predicting default, offering bankers a new valuable rating system to assist their decision making.     

Testing hypotheses with fuzzy data: The fuzzy <Emphasis Type="Bold">p</Emphasis>-value

Statistical hypothesis testing is very important for finding decisions in practical problems. Usually, the underlying data are assumed to be precise numbers, but it is much more realistic in general to consider fuzzy values which are non-precise numbers. In this case the test statistic will also yield a non-precise number. This article presents an approach for statistical testing at the basis of fuzzy values by introducing the fuzzy p-value. It turns out that clear decisions can be made outside a certain interval which is determined by the characterizing function of the fuzzy p-values.     

Efficient big data model selection with applications to fraud detection

As the volume and complexity of data continues to grow, more attention is being focused on solving so-called big data problems. One field where this focus is pertinent is credit card fraud detection. Model selection approaches can identify key predictors for preventing fraud. Stagewise Selection is a classic model selection technique that has experienced a revitalized interest due to its computational simplicity and flexibility. Over a sequence of simple learning steps, stagewise techniques build a sequence of candidate models that is less greedy than the stepwise approach.This paper introduces a new stochastic stagewise technique that integrates a sub-sampling approach into the stagewise framework, yielding a simple tool for model selection when working with big data. Simulation studies demonstrate the proposed technique offers a reasonable trade off between computational cost and predictive performance. We apply the proposed approach to synthetic credit card fraud data to demonstrate the technique’s application.     

Federal investment impact: An empirical benefit-cost evaluation

This paper attempts to evaluate the impacts of a Corps of Engineers multimillion dollar channelization project authorized in 1948 upon a regional economy in an economic depressed area in the western part of Tennessee.Using a cost/benefit approach, this paper analyzes some major benefits and costs of the project such as flood control, land utilization, regional economic development and recreational losses. On a reach-by-reach basis this paper illustrates how tangible benefits and costs are measured empirically with available hydrological, historical, economic and other statistics. Alternatives such as reservoir building and channel clean-out are considered together with the proposed channelization work. Problems on intangibles and externalities are also delineated.The most interesting findings of this study are not those pertaining to the successes and failures of the benefit-cost approach. Rather, they are the insights into which differences between social and private considerations, joint and separated projects, authorized and not proposed alternatives, current and future choices, etc., can be enlightened. The importance of this empirical study hinges upon its results highlighting the conflicts between efficiency in resource allocation and the equality in income distribution.     

投资项目可行性方案研究论证的新构想

投资项目往往会发生决策失误,究其原因,主要在于决策者的意志和论证者的权威性服从人格。要解决这一系关全局的重大决策问题,除了对投资项目正常事项的研究论证外,还必须对投资项目进行不可行性的研究论证、生态管理成本及安全管理成本的研究论证。使投资项目可行性决策方案真正可行而完整。     

A hybrid multi-attribute decision-making procedure for ranking project proposals: A historical data perspective

This paper develops a hybrid multiattribute decision-making methodology for ranking project proposals (PPs) through a judicious usage of historical data of completed projects to determine attribute weights, enabling elimination of problems associated with projected data such as cost and schedule overruns of real-world projects. The weights generated from data envelopment analysis are explicitly utilized for ranking PPs while allowing subjectivity to be ingeniously incorporated into the decision process. The new approach is implemented for ranking 25 PPs, and the rankings it yields are found indifferent to the decision maker's attitude, which ascertains the robustness of the ranking methodology.     

Low cost country sourcing and its effects on the total cost of ownership structure for a medical devices manufacturer

In this paper, we describe a total cost of ownership (TCO) method as an activity-based costing (ABC) application along the value chain to measure and analyse the costs of international sourcing activities. We further describe the method's implementation for a manufacturer of high-tech medical devices. For this case study, we analyse the effects of shifting purchasing volume from traditional procurement market suppliers to low cost country suppliers on the cost structure of purchasing projects. We find that particularly costs at component and supplier level gain importance over traditionally dominating unit level costs. Furthermore, we identify low cost country sourcing as a decision with extensive impact on value chain entities other than purchasing. Finally, a considerable part of costs in low cost country sourcing accrue at the beginning of a purchasing project due to problems stemming from unsatisfactory initial quality, language barriers and intercultural communication.     

工程勘察项目目标成本分析及有效控制

工程勘察项目自然环境条件复杂,影响其成本的因素众多。实际工作中对项目成本控制存在一些不足,针对问题,通过采取提高预算质量、加强定额管理、合同管理与采购控制等措施,实施对项目成本的有效控制。     

基于WBS的项目成本预算研究

以现行A建筑公司的X廉租房项目为例,运用WBS方法对X廉租房项目进行工作结构分解,对其成本预算进行深入分析,剖析了A建筑公司在该项目成本预算方面存在的不足之处,提出了X廉租房项目成本预算完善的成本管理方案。本文提出的项目成本管理工程的思维模式,可以作为建筑企业成本控制的参考。     

加强施工签证管理 提高政府投资效益

文章针对目前在政府投资项目签证工作中存在的问题,提出了加强工程签证管理的措施和办法,以期达到合理控制工程造价、提高政府投资效益的目标。