Fuzzy optimization of units products in mix-product selection problem using fuzzy linear programming approach

In this paper, the modified S-curve membership function methodology is used in a real life industrial problem of mix product selection. This problem occurs in the production planning management where by a decision maker plays important role in making decision in an uncertain environment. As analysts, we try to find a good enough solution for the decision maker to make a final decision. An industrial application of fuzzy linear programming (FLP) through the S-curve membership function has been investigated using a set of real life data collected from a Chocolate Manufacturing Company. The problem of fuzzy product mix selection has been defined. The objective of this paper is to find an optimal units of products with higher level of satisfaction with vagueness as a key factor. Since there are several decisions that were to be taken, a table for optimal units of products respect to vagueness and degree of satisfaction has been defined to identify the solution with higher level of units of products and with a higher degree of satisfaction. The fuzzy outcome shows that higher units of products need not lead to higher degree of satisfaction. The findings of this work indicates that the optimal decision is depend on vagueness factor in the fuzzy system of mix product selection problem. Further more the high level of units of products obtained when the vagueness is low.     

基于消费者时间偏好和决策者风险偏好的定价与订货策略*

在供应链管理过程中, 消费者时间偏好和决策者风险偏好是影响产品定价与订货决策的两个重要因素。本文以累积前景理论为框架, 将消费者时间偏好与价格依赖等影响产品市场需求的因素和决策者风险偏好与参考依赖等影响决策的因素共同纳入模型考量范围, 建立了基于消费者的时间偏好和决策者风险偏好的定价与订货模型。研究表明：随着消费者时间偏好的增大, 产品的最优定价和订货量都将减少;随着决策者心理参考点的增大, 产品的最优定价降低,最优订货量增加;随着决策者损失规避程度的增大, 产品最优定价增加, 而最优订货量减少。考虑消费者时间偏好和决策者的风险偏好的定价与订货模型能够提高供应链中决策者的最大累积前景效用。     

Data envelopment analysis of reservoir system performance

In long-term performance analyses of water systems with surface reservoirs for different operating scenarios, the analyst (or decision maker) is faced with two connected problems: (1) how to handle the extensive output of the simulation model and derive information on the scenarios scores for a prescribed set of performance criteria, and (2) how to compare scenarios in a multi-criterial sense while identifying the most desired. The data sets may overburden the analyst, while an evaluating procedure may be subjective due to personal preferences, attitudes, knowledge and miscellaneous factors. The data envelopment analysis (DEA) approach proposed here seems to be reliable in treating these situations, and sufficiently objective in evaluating and ranking the scenarios. Certain performance indices are defined as evaluating criteria in a standard multi-criterial sense, and then virtually divided into scenarios' output and input measures. By considering scenarios as product units, the DEA optimizes the weights of inputs and outputs, computes productivity efficiency for each unit, and rank them appropriately. Omitting the analyst's personal judgment on the technical parameters that describe system's performance restricts, in this way, the influence of the decision maker. A case study application on the reservoir system in Brazil proved that a methodological connection for solving decision problems with discrete alternatives really exists between the DEA and standard multi-criteria methods.     

A feasibility evaluation approach for time-evolving multi-item production–distribution networks

Time-dependent multi-item problems arise frequently in management applications, communication systems, and production–distribution systems. Our problem belongs to the last category, where we wish to address the feasibility of such systems when all network parameters change over time and product. The objective is to determine whether it is possible to have a dynamic production–shipment circuit within a finite planning horizon. And, if there is no such a flow, the goal is to determine where and when the infeasibility occurs and the approximate magnitude of the infeasibility. This information may help the decision maker in their efforts to resolve the infeasibility of the system. The problem in the discrete-time settings is investigated and a hybrid of scaling approach and penalty function method together with network optimality condition is utilized to develop a network-based algorithm. This algorithm is analysed from theoretical and practical perspectives by means of instances corresponding to some electricity transmission-distribution networks and many random instances. Computational results illustrate the performance of the algorithm.     

Pricing for seasonal deteriorating products with price- and ramp-type time-dependent demand

A production–inventory problem for a seasonal deteriorating product is considered. It is assumed that the demand is price- and ramp-type time-dependent. The selling season for the deteriorating product is fixed. The decision maker needs to set up the price and the production schedule at the beginning of the season. Although the profit function is not concave in general, the optimal price can be determined efficiently through a simple algorithm.     

A mathematical framework to optimize ATR systems with non-declarations and sensor fusion

Combat identification is one example where incorrect automatic target recognition (ATR) output labels may have substantial decision costs. For example, the incorrect labeling of hostile targets vs. friendly non-targets may have high costs; yet, these costs are difficult to quantify. One way to increase decision confidence is through fusion of data from multiple sources or from multiple looks through time. Numerous methods have been published to determine a Bayes’ optimal fusion decision if decision costs are well known. This research presents a novel mathematical programming ATR evaluation framework. A new objective function inclusive of time is introduced to optimize and compare ATR systems. Constraints are developed to enforce both decision maker preferences and traditional engineering measures of performance. This research merges rejection and receiver operating characteristic (ROC) analysis by incorporating rejection and ROC thresholds as decision variables. The rejection thresholds specify non-declaration regions, while the ROC thresholds explore viable true positive and false positive tradeoffs for output target labels. This methodology yields an optimal ATR system subject to decision maker constraints without using explicit costs for each type of output decision. A sample application is included for the fusion of two channels of collected polarized radar data for 10 different ground targets. A Boolean logic and probabilistic neural network fusion method are optimized and compared. Sensitivity analysis of significant performance parameters then reveals preferred regions for each of the fusion algorithms.     

Modeling and analysis of single item multi-period procurement lot-sizing problem considering rejections and late deliveries

Integer linear programming approach has been used to solve a multi-period procurement lot-sizing problem for a single product that is procured from a single supplier considering rejections and late deliveries under all-unit quantity discount environment. The intent of proposed model is two fold. First, we aim to establish tradeoffs among cost objectives and determine appropriate lot-size and its timing to minimize total cost over the decision horizon considering quantity discount, economies of scale in transactions and inventory management. Second, the optimization model has been used to analyze the effect of variations in problem parameters such as rejection rate, demand, storage capacity and inventory holding cost for a multi-period procurement lot-sizing problem. This analysis helps the decision maker to figure out opportunities to significantly reduce cost. An illustration is included to demonstrate the effectiveness of the proposed model. The proposed approach provides flexibility to decision maker in multi-period procurement lot-sizing decisions through tradeoff curves and sensitivity analysis.     

The foundations of modeling management processes for innovation projects in production-economics systems

A possible approach to constructing models for managing innovation projects in production–economics systems of enterprises and providing them with information support is described. The conceptual foundations for constructing models for innovation projects are presented, as well as the technique of transition from conceptual modeling to managing such projects. The obtained model and the set of parameters for project evaluation may be unique and depend on the specific features of the project, the preferences of the decision maker, and the production-economics system in which the project is carried out.     

利用MLC++实现数据挖掘

数据挖掘是从大量原始数据中抽取隐藏知识的过程。大部分数据挖掘工具采用规则发现和决策树分类技术来发现数据模式和规则，其核心是归纳算法。与传统统计方法相比，基于机器学习技术得到的分类结果具有较好的可解释性。在针对特定的数据集进行数据挖掘时，如果缺乏相应的领域知识，用户或决策者就很难确定选择何种归纳算法。因此，需要尝试各种算法。借助MLC＋＋，决策者能够轻而易举地比较不同分类算法对特定数据集的有效性，从而选择合适的分类算法。同时，系统开发人员也可以利用MLC＋＋设计各种混合算法。     

An economical cognitive approach for bi-objective optimization using bliss points, visualization, and interaction

When an optimization problem encompasses multiple objectives, it is usually difficult to define a single optimal solution. The decision maker plays an important role when choosing the final single decision. Pareto-based evolutionary multiobjective optimization (EMO) methods are very informative for the decision making process since they provide the decision maker with a set of efficient solutions to choose from. Despite that the set of efficient solutions may not be the global efficient set, we show in this paper that the set can still be informative when used in an interactive session with the decision maker. We use a combination of EMO and single objective optimization methods to guide the decision maker in interactive sessions.     

基于VB技术的电脑销售管理系统设计与开发

随着计算机技术的迅速发展,计算机信息管理系统在各行各业都得到了广泛的应用。不同的单位或同一单位的不同部门都有满足各自需要的管理系统,比如人事管理系统、档案管理系统、设备管理系统等。通过使用这些管理系统可以提高工作效率,创造更多的经济效益。此次开发的电脑销售管理系统,实现了利用计算机来管理销售工作。该系统运用VB技术,以MicrosoftSQL Server 2000为后台数据库,开发了一套为企业服务的销售管理系统。利用该系统可以为决策者和管理者提供全面的销售信息和商品信息,便于决策者及时掌握市场动态和本企业的生产状况,有效的对企业经营策略进行调整。     

Decision support for moving from a single product to a product portfolio in evolving software systems

Successful software systems continuously evolve to accommodate ever-changing needs of customers. Accommodating the feature requests of all the customers in a single product increases the risks and costs of software maintenance. A possible approach to mitigate these risks is to transition the evolving software system (ESS) from a single system to a portfolio of related product variants, each addressing a specific customers’ segment. This evolution should be conducted such that the extent of modifications required in ESS's structure is reduced. The proposed method COPE+ uses preferences of customers on product features to generate multiple product portfolios each containing one product variant per segment of customers. Recommendations are given to the decision maker to update the product portfolios based on structural analysis of ESS. Product portfolios are compared with the ESS using statechart representations to identify the level of similarity in their behaviors. A proof of concept is presented by application to an open-source text editing system. Structural and behavioral analysis of candidate portfolios helped the decision maker to select one portfolio out of three candidates.     

An interactive game for production and financial planning

This paper describes a computerized interactive game for use by students of business administration. The problem situation involves the need to determine an aggregate production plan for a small manufacturing firm which faces highly seasonal demand for its product. The student user is required to make decisions regarding planned manpower and production levels in future periods. In order to assist him in making these decisions, functional relationships describing pertinent production costs (wages, cost of hiring and firing, cost of carrying inventory) and financial costs (the cost of borrowing money to support production) are made available to the student decision maker.     

Optimization of testability parameters

The main objective of this study was to investigate performance models of automatic diagnostic systems taking into consideration its imperfections such as incorrect isolation and false alarms. This was accomplished by developing an optimization model to assist the decision maker in determining the optimal values of testability parameters which maximize his/her utility function. This will provide the decision maker with a tool to evaluate the parameters set forth by the designer and to assess the real capability of the diagnostic system. This tool also will help the decision maker to check if the correct detection and isolation capability of the system, as well as the imperfections of the system, e.g., incorrect isolation and false alarms are acceptable and satisfactory.

An interactive program was developed and help to implement the optimization modedl. This program provides the decision maker with enough flexibility to avaluate different different strategies and to repeat the decision process after changing one or more of the model's parameter. An example will be presented to show the application of this optimization model.     

Integrated evidential reasoning approach in the presence of cardinal and ordinal preferences and its applications in software selection

A combination of cardinal and ordinal preferences in multiple-attribute decision making (MADM) demonstrates more reliability and flexibility compared with sole cardinal or ordinal preferences derived from a decision maker. This situation occurs particularly when the knowledge and experience of the decision maker, as well as the data regarding specific alternatives on certain attributes, are insufficient or incomplete. This paper proposes an integrated evidential reasoning (IER) approach to analyze uncertain MADM problems in the presence of cardinal and ordinal preferences. The decision maker provides complete or incomplete cardinal and ordinal preferences of each alternative on each attribute. Ordinal preferences are expressed as unknown distributed assessment vectors and integrated with cardinal preferences to form aggregated preferences of alternatives. Three optimization models considering cardinal and ordinal preferences are constructed to determine the minimum and maximum minimal satisfaction of alternatives, simultaneous maximum minimal satisfaction of alternatives, and simultaneous minimum minimal satisfaction of alternatives. The minimax regret rule, the maximax rule, and the maximin rule are employed respectively in the three models to generate three kinds of value functions of alternatives, which are aggregated to find solutions. The attribute weights in the three models can be precise or imprecise (i.e., characterized by six types of constraints). The IER approach is used to select the optimum software for product lifecycle management of a famous Chinese automobile manufacturing enterprise.     

A genetic algorithm based approach to the mixed-model assembly line balancing problem of type II

Mixed-model assembly lines allow for the simultaneous assembly of a set of similar models of a product, which may be launched in the assembly line in any order and mix. As current markets are characterized by a growing trend for higher product variability, mixed-model assembly lines are preferred over the traditional single-model assembly lines.

This paper presents a mathematical programming model and an iterative genetic algorithm-based procedure for the mixed-model assembly line balancing problem (MALBP) with parallel workstations, in which the goal is to maximise the production rate of the line for a pre-determined number of operators.

The addressed problem accounts for some relevant issues that reflect the operating conditions of real-world assembly lines, like zoning constraints and workload balancing and also allows the decision maker to control the generation of parallel workstations.     

In-process regressions and adaptive multicriteria neural networks for monitoring and supervising machining operations

The authors develop a monitoring and supervising system for machining operations using in-process regressions (for monitoring) and adaptive feedforward artificial neural networks (for supervising). The system is designed for: (1) in-process tool life measurement and prediction; (2) supervision of machining operations in terms of the best machining setup; and (3) catastrophic tool failure monitoring. The monitoring system predicts tool life by using different sensors for gathering information based on a regression model that allows for the variations between tools and different machine setups. The regression model makes its prediction by using the history of other tools and combining it with the information obtained about the tool under consideration. The supervision system identifies the best parameters for the machine setup problem within the framework of multiple criteria decision making. The decision maker (operator) considers several criteria, such as cutting quality, production rate and tool life. To make the optimal decision with several criteria, an adaptive feedforward artificial neural network is used to assess the decision maker's preferences. The authors' neural network approach learns from the decision maker's complex behavior and hence, in automatic mode, can make decisions for the decision maker. The approach is not computationally demanding, and experiments demonstrate that its predictions are accurate.     

A comparative study of different approaches using an outranking relation in a multi-objective evolutionary algorithm

This paper presents a comparative analysis of three versions of an evolutionary algorithm in which the decision maker's preferences are incorporated using an outranking relation and preference parameters associated with the ELECTRE TRI method. The aim is using the preference information supplied by the decision maker to guide the search process to the regions where solutions more in accordance with his/her preferences are located, thus narrowing the scope of the search and reducing the computational effort. An example dealing with a pertinent problem in electrical distribution network is used to compare the different versions of the algorithm and illustrate how meaningful information can be elicited from a decision maker and used in the operational framework of an evolutionary algorithm to provide decision support in real-world problems.     

Multiproduct aggregate production planning with fuzzy demands and fuzzy capacities

Given the uncertain market demands and capacities in production environment, this paper discusses some practical approaches to modeling multiproduct aggregate production planning problems with fuzzy demands, fuzzy capacities, and financial constraints. By formulating the fuzzy demand, fuzzy equation, and fuzzy capacities, a fuzzy production-inventory balance equation for single period and a dynamic balance equation are formulated as fuzzy/soft equations and they represent the possibility levels of meeting the market demands. Using this formulation and interpretation, a fuzzy multiproduct aggregate production planning model is developed, and its solutions using parametric programming, best balance and interactive techniques are introduced to cater to different scenarios under various decision making preferences. Using the proposed models and techniques, first, the decision maker can select a preferred production plan with a common satisfaction level or different combinations of preferred possibility level and satisfaction levels, according to the market demands and available production capacities, and second, the obtained structure of the optimal solution can help decision maker in aggregate production planning. The decision maker can also make a preferred and reasonable production plan corresponding to one's most concerned criteria. Hence, decision makers not only can come up with a reasonable aggregate production plan with minimum efforts, but also have more choices of making a preferred aggregate plan based on his most concerned criteria. These models can effectively enhance the capability of an aggregate plan to give feasible family disaggregation plans under different scenarios with fuzzy demands and capacities. Simulation and the results of analysis on the proposed techniques are also given in detail in this paper.     

A practical measure of the information in a logical theory

The information in data depends on the subjective value system that the receiver of the data uses to interpret them. This paper looks at the information in a theory of first order logic (a knowledge base) from the perspective of a decision maker for whom the validation of formulae (facts and rules) have varying importance. The decision maker's preferences and prior knowledge are both incorporated into the information measure. The value of information is determined by what it conveys about the formulae of importance to the decision maker. The information measure is applied as a heuristic in commonsense reasoning; in relevance assessment ; and as a preference function in belief revision.