Disassembly to order system under uncertainty

This paper presents a multi-criteria optimization model of a disassembly-to-order (DTO) system under uncertainty. The goal of the proposed model is to determine the best combination of the number of each product type to be taken back from the last user and/or collectors. The EOL products are then disassembled for the retrieval of reusable components and materials and resold in order to meet a certain level of demand under a variety of physical, financial and environmental constraints. The surplus components are recycled, stored for usage in subsequent periods or properly disposed. The problem is modeled as a multi-criteria decision-making problem under uncertainty, where the aspiration levels for various goals are more likely to be in the “approximately more (less) than” and/or “more (less) is better” form. We employ fuzzy goal programming technique to solve the problem. When solved, the model provides the number of EOL products to be taken back as well as the number of items reused, recycled, stored and disposed. The values of a host of other performance measures are also obtained, including total profit, materials and items sales revenues, take back cost, transportation costs as well as costs of preparation of EOL products, destructive disassembly, non-destructive disassembly, recycling, storage and disposal. A case example is presented to illustrate the model's implementation.     

Stochastic simulation based genetic algorithm for chance constrained data envelopment analysis problems

Genetic algorithm (GA) approach is developed for solving the P-model of chance constrained data envelopment analysis (CCDEA) problems, which include the concept of “Satisficing”. Problems here include cases in which inputs and outputs are stochastic, as well as cases in which only the outputs are stochastic. The basic solution technique for the above has so far been deriving “deterministic equivalents”, which is difficult for all stochastic parameters as there are no compact methods available. In the proposed approach, the stochastic objective function and chance constraints are directly used within the genetic process. The feasibility of chance constraints are checked by stochastic simulation techniques. A case of Indian banking sector has been presented to illustrate the above approach.     

A unifying framework for duality and modeling in robust linear programs

In this paper, our major theme is a unifying framework for duality in robust linear programming. We show that there are two pair of dual programs allied with a robust linear program; one in which the primal is constructed to be “ultra-conservative” and one in which the primal is constructed to be “ultra-optimistic.” Furthermore, as one would expect, if the uncertainly in the primal is row-based, the corresponding uncertainty in the dual is column-based, and vice-versa. Several examples are provided that illustrate the properties of these primal and dual models.     

Upper Bounds for the SPOT 5 Daily Photograph Scheduling Problem

This paper introduces tight upper bounds for the daily photograph scheduling problem of earth observation satellites. These bounds, which were unavailable until now, allow us to assess the quality of the heuristic solutions obtained previously. These bounds are obtained with a partition-based approach following the divide and pas conquer principle. Dynamic programming and tabu search are conjointly used in this approach. We present also simplex-based linear programming relaxation and a relaxed knapsack approach for the problem.     

Interactive fuzzy goal programming for multi-objective transportation problems

This paper presents an interactive fuzzy goal programming approach to determine the preferred compromise solution for the multi-objective transportation problem. The proposed approach considers the imprecise nature of the input data by implementing the minimum operator and also assumes that each objective function has a fuzzy goal. The approach focuses on minimizing the worst upper bound to obtain an efficient solution which is close to the best lower bound of each objective function. The solution procedure controls the search direction via updating both the membership values and the aspiration levels. An important characteristic of the approach is that the decision maker's role is concentrated only in evaluating the efficient solution to limit the influences of his/her incomplete knowledge about the problem domain. In addition, the proposed approach can be applied to solve other multi-objective decision making problems. The performance of this solution approach is evaluated by comparing its results with that of the two existing methods in the literature.     

Using partially observed Markov processes to select optimal termination time of TV shows

This paper presents a method for optimal control of a running television show. The problem is formulated as a partially observed Markov decision process (POMDP). A show can be in a “good” state, i.e., it should be continued, or it can be in a “bad” state and therefore it should be changed. The ratings of a show are modeled as a stochastic process that depends on the show's state. An optimal rule for a continue/change decision, which maximizes the expected present value of profits from selling advertising time, is expressed in terms of the prior probability of the show being in the good state. The optimal rule depends on the size of the investment in changing a show, the difference in revenues between a “good” and a “bad” show and the number of time periods remaining until the end of the planning horizon. The application of the method is illustrated with simulated ratings as well as real data.     

Application of fuzzy goal programming technique to land use planning in agricultural system

This paper presents how fuzzy goal programming can be efficiently used for modelling and solving land-use planning problems in agricultural systems for optimal production of several seasonal crops in a planning year. In the model formulation of the problem, utilization of total cultivable land, supply of productive resources, aspiration levels of various production of crops as well as the total expected profit from the farm are fuzzily described. In the decision-making situation, minimization of the under-deviational variables of the membership goals with highest membership value (unity) as their achievement levels defined for the membership functions of the fuzzy goals of the problem on the basis of the priorities of importance of achieving the aspired levels of the fuzzy goals to the extent possible is considered. As a study region, the District Nadia, West Bengal, India is taken into account. To expound the potential use of the approach, the model solution is compared with the existing cropping plan of the District as well as a solution of the problem obtained by using the additive fuzzy goal programming model studied by Tiwari et al. (Fuzzy sets and systems 24(1987)27.) previously.     

An interactive decision support system for an aggregate production planning model based on multiple criteria mixed integer linear programming

In this paper, we present an aggregate production planning (APP) model applied to a Portuguese firm that produces construction materials. A multiple criteria mixed integer linear programming (MCMILP) model is developed with the following performance criteria: (1) maximize profit, (2) minimize late orders, and (3) minimize work force level changes. It includes certain operational features such as partial inflexibility of the work force, legal restrictions on workload, work force size (workers to be hired and downsized), workers in training, and production and inventory capacity. The purpose is to determine the number of workers for each worker type, the number of overtime hours, the inventory level for each product category, and the level of subcontracting in order to meet the forecasted demand for a planning period of 12 months. Additionally, a decision support system (DSS) based on the MCMILP model is proposed. It will help practitioners find the “best” solution for an APP problem without having to familiarize themselves with the mathematical complexities associated with the model. An example to illustrate the use of the DSS is also included.     

A real option theoretic fuzzy evaluation model for enterprise resource planning investment

The high failure rate of ERP implementation is due to a common pitfall that ERP projects are often enacted as merely investment into installation of IT infrastructure, rather than systematic planning of operation changes, business process re-engineering and a paradigm shift for the operation and management. To manage ERP investment in a changing environment for high payoff, this paper adopts a real option theoretic method. Fuzzy payoff valuation is introduced to deal with uncertainties in order to minimize the risk of failure. The proposed ERP evaluation model is geared towards small and medium enterprises. A case study is presented to validate the proposed fuzzy real options. The results indicate the potential of modeling ERP investment as “Expand”, “Contain” and “Abandon” options in different scenarios. The fuzzy real option model bestows a novel ex-ante cost analysis for justifying ERP investment in the implementation cycle.     

R&D project evaluation: An integrated DEA and balanced scorecard approach

We present and demonstrate a multi-criteria approach for evaluating R&D projects in different stages of their life cycle. Our approach integrates the balanced scorecard (BSC) and data envelopment analysis (DEA) and develops an extended DEA model. The input and output measures for the integrated DEA–BSC model are grouped in “cards” which are associated with a “BSC for R&D projects”. The BSC is embedded in the DEA model through a hierarchical structure of constraints that reflect the BSC balance considerations. We illustrate the proposed approach with a case study involving an industrial research laboratory that selects and executes dozens of R&D projects every year.     

A Decision Theory Perspective on the Disposal of High-Level Radioactive Waste

In this paper the problem of high-level nuclear waste disposal is viewed as a five-stage, cascaded decision problem. The first four of these decisions having essentially been made, the work of recent years has been focused on the fifth stage, which concerns specifics of the repository design. The probabilistic performance assessment (PPA) work is viewed as the outcome prediction for this stage, and the site characterization work as the information gathering option. This brief examination of the proposed Yucca Mountain repository through a decision analysis framework resulted in three conclusions: (1) A decision theory approach to the process of selecting and characterizing Yucca Mountain would enhance public understanding of the issues and solutions to high-level waste management; (2) engineered systems are an attractive alternative to offset uncertainties in the containment capability of the natural setting and should receive greater emphasis in the design of the repository; and (3) a strategy of waste management should be adopted, as opposed to waste disposal, as it allows for incremental confirmation and confidence building of a permanent solution to the high-level waste problem.     

A technical note on “A fuzzy set approach for R&D portfolio selection using a real options valuation model” by Wang and Hwang (2007)

In this paper, three critical issues with the paper “A fuzzy set approach for R&D portfolio selection using a real options valuation model”, coauthored by Wang and Hwang and published in Omega 2007 are addressed. Shortcomings of the original work are highlighted and corrective measures to improve the approach are proposed.     

Challenges and solutions for marketing in a digital era

Internet usage continues to explode across the world with digital becoming an increasingly important source of competitive advantage in both B2C and B2B marketing. A great deal of attention has been focused on the tremendous opportunities digital marketing presents, with little attention on the real challenges companies are facing going digital. In this study, we present these challenges based on results of a survey among a convenience sample of 777 marketing executives around the globe. The results reveal that filling “talent gaps”, adjusting the “organizational design”, and implementing “actionable metrics” are the biggest improvement opportunities for companies across sectors.     

Decision making in the newsvendor problem: A cross-national laboratory study

In this paper, we conduct a laboratory experiment using the classic newsvendor problem to examine cross-national differences in inventory ordering patterns between Chinese and American decision makers based on a theoretical examination of the role of the Doctrine of the Mean in Chinese decision making. Drawing on the theory of context-dependent preferences (specifically extremeness aversion), we also revisit the flat-maximum hypothesis of Bolton and Katok [12], i.e., “thinning the set of order options leads to newsvendor decisions that achieve a higher proportion of maximum expected profit.” The results show that the “pull-to-center” effect is more prominent for Chinese than Americans, i.e., average order quantities of Chinese subjects are closer to the anchor of mean demand than those of American subjects. Furthermore, we find that thinning the set of order options such that the optimal order quantity is a middle option, not an extreme option in the choice set, leads to better performance in newsvendor decisions, which complements the flat-maximum hypothesis.     

Stock screening with use of multiple criteria decision making and optimization

A new method for the stock ranking based on the multiple criterion decision making and optimization is proposed. Two general criteria are used in the analysis. The first of them is based on the financial indices and may be treated as the criterion of firm's “health” or its financial performance. The second one is the two-criteria performance of firm based on the stock prices. It represents the firm's market success. The method rests on the selection of the stocks with a great correlation of the firm's financial performance and its market success. The local criteria are built in the form of the membership function of corresponding fuzzy subsets. Two different strategies for stock ranking and three most popular methods for local criteria aggregation are compared. As the example the values of financial rations and prices from database comprising the data of 162 firms from subsector of the biotechnology of US economy were used. It is shown that the proposed method makes it possible to select a small group of “good” stocks characterized by a great coincidence of firm's financial performance and its market success. The method rejects from the consideration all the “unsafe” firms, i.e., such ones that their market success is based rather on the public relations, rumors and other rather unreliable information. The method is addressed to those who prefer to select for a portfolio only the firms which demonstrate the closeness of their overall financial performance in the past year and success in the Stock Exchange in the following year.     

Parenting in Complex Structures

In complex, interdependent corporate structures, there is overlap and sharing of responsibilities between the operating units and the corporate parent. As a result, the distinction between “business” and “parent” is blurred and a focus on the role and added-value of the parent is less obviously relevant. This article explores the nature of corporate parenting in complex structures and draws out the particular demands placed on parent managers in these structures. It also examines the role of the parent’s functional support staff and the complications that result from having more than one level of parenting. It concludes that there are some special parenting challenges in complex structures, but that the quest for “parenting advantage” should remain a fundamental driver of corporate strategy and structure.     

Eight Motivational Forces and Voluntary Turnover: A Theoretical Synthesis with Implications for Research

In this study, we synthesize, from the attitude and turnover literatures, a framework of eight (8) distinctive motives, or “Forces”. We then illustrate how the “8 Forces” framework can be utilized by turnover researchers as clarification of reported reasons for turnover, as causal mediators of turnover predictors, and as factors related to the type of turnover decision process. Finally, we discuss further implications of this framework.     

Ethical Futures and Public-Private Partnerships: Peering Far Down the Track

This article employs an institutional perspective in formulating predictions about the ethical futures of privatization partnerships. Although this paper focuses on ethical concerns in the U.S. public sector, it incorporates a multinational dimension in (a) comparing the meaning of privatization among societies and (b) probing privatization financing in the global economy. Five assumptions that flow from institutional reasoning are made explicit as supports for subsequent predictions. The institutional logic shifts privatization conversation away from conventional debate about competition and efficiency toward centralizing forces in both sectors in response to globalization. In that regard, this study identifies the systemic erosion of (local) community integrity as the key privatization problem of the future.     

Supplies from developing countries: Optimal order quantities under loss risks

When raw material suppliers of a global supply chain are situated in developing countries, not only will there be long lead times but there could also be a possibility of material losses in transit. The magnitude of the losses will be uncertain and can be significant. We consider the optimization of order quantity decisions in such situations. The long lead times imply that we need to take into account the uncertainty in finished goods demands for which the raw materials are to be ordered. The order quantities have to be optimized carefully as they determine the “real options” that become available later in production plans. Using data from the plywood industry and using stochastic programming we demonstrate a method for solving such problems. The method combines simulation and optimization. An interesting observation we make is that the optimal order quantity of a material need not be monotonic in expected loss of that material. In addition, we offer explanations as to why the expected loss need not be monotonic.