A framework for evaluating remote diagnostics investment decisions for semiconductor equipment suppliers

With advances in information technology, service activities for expensive equipment used in semiconductor manufacturing can be performed from a remote location. This capability is called remote diagnostics (RD). Currently, there are intense development efforts in the semiconductor industry for implementing RD in wafer fabrication facilities to reduce maintenance and capital costs and improve productivity. In this paper, we develop a queueing-location model to analyze the capacity and location problem of after sales service providers, considering the effects of RD technology. Our model optimizes the location, capacity and the type of service centers while taking congestion effects into consideration. We solve this model using a simulation optimization approach in which we use a genetic algorithm to search the solution space. We demonstrate how our methodology can be used in strategic investment planning regarding the adoption of RD technology and service center siting through a realistic case study.     

Optimal systems for equipment maintenance and replacement under Markovian deterioration

Deterioration of equipment is modeled as a multistate discrete time controlled Markov process. The states are classified according to the degree of deterioration. The problem of design of optimal systems for equipment maintenance and replacement is considered when the decision-maker may take, in each stage, one of many available maintenance actions, classified according to their “stochastic effectiveness”; no action and replacement are included as alternatives. It is assumed that the transition probabilities satisfy two conditions which effectively describe a trend for monotonically increasing expected deterioration and rate of deterioration. Under these assumptions it is proved in the paper that the optimal (cost minimizing) decision system in an infinite horizon is of the control limit rule type, rapidly obtained by policy improvement algorithms. A numerical example is presented for a specific practical application; detailed data are available from the authors on request.     

Optimal maintenance service contract negotiation with aging equipment

In recent years, there has been a growing trend to out-source service operations in which the equipment maintenance is carried out by an external agent rather than in-house. Often, the agent (service provider) offers more than one option and the owners of equipment (customers) are faced to the problem of selecting the optimal option, under the terms of a contract. In the current work, we develop a model and report results to determine the agent’s optimal strategy for a given type of contract. The model derives in a non-cooperative game formulation in which the decisions are taken by maximizing expected profits. This work extends previous models by considering the realistic case of equipments having an increasing failure intensity due to imperfect maintenance, instead of the standard assumption that considers failure times are exponentially distributed (constant failure intensity). We develop a model using a linear function of time to characterize the failure intensity. The main goal, for the agent, is to determine the pricing structure in the contract and the number of customers to service. On the other hand, for the clients, the main goal is to define the period between planned actions for preventive maintenance and the time to replace equipments. In order to give a complete characterization of the results, we also carry out a sensitivity analysis over some of the factors that would influence over the failure intensity.     

Efficient multi-objective tabu search for emergency equipment maintenance scheduling in disaster rescue

The paper describes a mathematical model of the emergency equipment maintenance scheduling problem particularly in disaster rescue operations, which aims to achieve a good balance between operational capability achieved by maintenance, cost-effectiveness, maintenance risks, and reserved maintenance capability for sustainable operations. We design a compact solution encoding that greatly facilitates the search process, and develop an efficient multi-objective tabu search algorithm that evolves a set of solutions towards the Pareto optimal frontier, using a weighted function based on the decision-maker’s preference to guide the search procedures. Simulation experiments and real-world application results demonstrate the effectiveness of our approach.     

A prototype cost model of functional check decisions in reliability-centred maintenance

In this paper, the functional check task specified in reliability-centred maintenance (RCM) is discussed and a general cost model under the assumption of a non-decreasing degradation process is established to jointly optimise the threshold of potential failure and inspection intervals to minimise the expected operating cost per unit time. A gamma process is used to describe a random wear degradation process and illustrate the model.     

A new MIP model for mine equipment scheduling by minimizing maintenance cost

Mining investment has been recognized as capital intensive due mainly to the cost of large equipment. Equipment capital costs for a given operation are usually within the order of hundreds of million dollars but may reach to billion dollars for large companies operating multiple mines. Such large investments require the optimum usage of equipment in a manner that the operating costs are minimized and the utilization of equipment is maximized through optimal scheduling. This optimum usage is required to ensure that the business remains sustainable and financially stable. Most mining operations utilize trucks to haul the mined material. Maintenance is one of the major operating cost items for these fleets as it can reach approximately one hundred million dollars yearly. There is no method or application in the literature that optimizes the utilization for truck fleet over the life of mine. A new approach based on mixed integer programming (MIP) techniques is used for annually scheduling a fixed fleet of mining trucks in a given operation, over a multi-year time horizon to minimize maintenance cost. The model uses the truck age (total hours of usage), maintenance cost and required operating hours to achieve annual production targets to produce an optimum truck schedule. While this paper focuses on scheduling trucks for mining operation, concept can be used in most businesses using equipment with significant maintenance costs. A case study for a large scale gold mine showed an annual discounted (10% rate) maintenance cost saving of over $2M and more than 16% ($21M) of overall maintenance cost reduction over 10 years of mine life, compared with the spreadsheet based approach used currently at the operation.     

Optimal preventive maintenance for equipment with two quality states and general failure time distributions

We present an economic model for the optimization of preventive maintenance in a production process with two quality states. The equipment starts its operation in the in-control state but it may shift to the out-of-control state before failure or scheduled preventive maintenance. The time of shift and the time of failure are generally distributed random variables. The two states are characterized by different failure rates and revenues. We first derive the structure of the optimal maintenance policy, which is defined by two critical values of the equipment age that determine when to perform preventive maintenance depending on the actual (observable) state of the process. We then provide properties of the optimal solution and show how to determine the optimal values of the two critical maintenance times accurately and efficiently. The proposed model and, in particular, the behavior of the optimal solution as the model parameters and the shift and failure time distributions change are illustrated through numerical examples.     

The effect of testing equipment shift on optimal decisions in a repetitive testing process

Repetitive testing process is commonly used in the final testing stage of semiconductor manufacturing to ensure high outgoing product quality and to reduce testing errors. The decision on testing lot size and the number of testing repetitions ultimately determines the effectiveness of the testing process. Setting the retest rule is often difficult in practice due to uncertainties in the incoming product quality and testing equipment condition. In this paper, we study a repetitive testing process where the testing equipment may shift randomly to an inferior state. We develop a cost model that helps us to make optimal decisions on retesting rule. Through numerical analysis, we provide practical insights about the effects of testing equipment shift rate, testing errors, and different costs such as cost of testing and cost of rejecting conforming products on the optimal decision and the system performance. We find that significant penalty may result if the potential testing equipment shift is ignored.     

Optimal healthcare decisions: Comparing medical treatments on a cost-effectiveness basis

This paper deals with medical treatments comparison from the cost-effectiveness viewpoint. A decision theory scheme is considered, where the decision space is the set of treatments involved, the space of states of nature consists of the respective net benefits of the treatments, and the utility function is one of two possible candidates. A first candidate is the one typically used in the literature on cost-effectiveness analysis, for which the utility of a decision is proportional to the net benefit gain, and a second one is of the type 0–1, which penalizes the wrong decisions with a fixed quantity. Their associated optimal decision rules, both frequentist and Bayesian, are analyzed and compared via frequentist evaluation of their performance, and the conclusion is that the latter beats the former in the sense of choosing the optimal treatment more often than any other, thus minimizing the proportion of wrong decisions. Illustrations with simulated and real data are provided.     

Comparison of Prioritization Techniques Using Interhierarchy Mappings

This paper discusses a method of comparing different prioritization techniques that entails mapping the input data from one system to another. The ranking vectors output by the methods should then be comparable. As an illustration, a method that uses pairwise comparison, e.g. the Analytic Hierarchy Process (AHP) of Saaty, itself viewed as a mapping, is compared with a method using cross impact analysis, the Edinburgh Approach. The problem centres on an appropriate inverse of the process mapping. Although the discussion is restricted to simple bilevel hierarchies, useful insight is provided into the methodologies in general and, in particular, the problem of inverse inconsistency associated with rank reversals. A way in which the theory of AHP might be incorporated into a Differential Hierarchy Process is suggested.     

Prioritization of e-learning forms: a multicriteria methodology

Strategic planning of e-learning implementation includes decision making about the most suitable form of implementing e-learning on different levels in an institution. Decision making about e-learning implementation has been covered as consisting of four phases: (1) intelligence, (2) design, (3) choice and (4) implementation. During the Intelligence phase we have precisely identified our central decision problem and have conducted situation analysis. In the Design phase we have developed alternatives and established criteria and subcriteria. The questionnaire about the importance of the advantages and goals of e-learning implementation and about criteria and subcriteria significant for decision making was created. Essential for the survey was use of Croatian e-learning experts that are familiar with higher education (HE) environment. Further, we connected these findings with the results of the factor analysis which was performed on the survey. The results of the factor analysis have served as input in the multicriteria decision model (AHP) that we have developed in the Choice phase. In the implementation phase we have solved the problem of prioritisation of e-learning options with the help of multi-criteria modelling in the process of group decision making. In this article, firstly we will present and analyze the results of the survey. Secondly, the outputs of factor analysis will be stated and compared with the model used in the questionnaire. Finally, the structure of AHP model will be given and the results of the quantitative evaluation of the model will be presented.     

Prioritization of organizational capital measurement indicators using fuzzy AHP

Organizational capital is a sub-dimension of the intellectual capital which is the sum of all assets that make the creative ability of the organization possible. To control and manage such an important force, the companies must measure it first. This study aims at defining a methodology to improve the quality of prioritization of organizational capital measurement indicators under uncertain conditions. To do so, a methodology based on the extent fuzzy analytic hierarchy process (AHP) is applied. Within the model, three main attributes; deployment of the strategic values, investment to the technology and flexibility of the structure; their sub-attributes and 10 indicators are defined. To define the priority of each indicator, preferences of experts are gathered using a pair-wise comparison based questionnaire. The results of the study show that “deployment of the strategic values” is the most important attribute of the organizational capital.     

Discrimination decisions for 100,000-dimensional spaces

Discrimination decisions arise in many natural language processing tasks. Three classical tasks are discriminating texts by their authors (author identification), discriminating documents by their relevance to some query (information retrieval), and discriminating multi-meaning words by their meanings (sense discrimination). Many other discrimination tasks arise regularly, such as determining whether a particular proper noun represents a person or a place, or whether a given work from some teletype text would be capitalized if both cases had been used.We [9] introduced a method designed for the sense discrimination problems mentioned.We also discuss areas for research based on observed shortcomings of the method. In particular, an example in the author identification task shows the need for a robust version of the method. Also, the method makes an assumption of independence which is demonstrably false, yet there has been no careful study of the results of this assumption.     

Advertising decisions for a segmented market

We consider the problem of choosing the levels of a set of advertising media in order to maximize the firm profit when the market is heterogeneous. Advertising efforts affect the demand of the different segments variably and we assume that the advertising effects on demand over time are mediated by a vector goodwill variable. A first general advertising decision problem is stated and solved in the non-linear programming framework. A preference index is then obtained for the medium selection problem when each segment demand function is linear in goodwill and each medium advertising cost function is quadratic in its level. Finally the theoretical case of disjoint advertising media is discussed.     

The effects of asset specificity on maintenance financial performance: An empirical application of Transaction Cost Theory to the medical device maintenance field

This study uses multivariate regression analysis to examine the effects of asset specificity on the financial performance of both external and internal governance structures for medical device maintenance, and investigates how the financial performance of external governance structures differs depending on whether a hospital is private or public. The hypotheses were tested using information on 764 medical devices and 62 maintenance service providers, resulting in 1403 maintenance transactions. As such, our data sample is significantly larger than those used in previous studies in this area. The results empirically support our core theoretical argument that governance financial performance is influenced by assets specificity.     

Incentive and production decisions for remanufacturing operations

We consider a manufacturer producing original products using virgin materials and remanufactured products using returns from the market where the amount of returns depend on the incentive offered by the manufacturer. We determine the optimal value of this incentive and the optimal production quantities in a stochastic demand setting with partial substitution. We analyze 3 different models in centralized and decentralized settings where the collection process of the returns is managed by a collection agency in the decentralized setting. We also analyze contracts to coordinate the decentralized systems and determine the optimal contract parameters. Finally, we present our computational study to observe the effect of different parameters on the system performance.     

Capacity decisions for high-tech products with obsolescence

Modern high-tech products experience rapid obsolescence. Capacity investments must be recouped during the brief product lifecycle, during which prices fall continuously. We employ a multiplicative demand model that incorporates price declines due to both market heterogeneity and product obsolescence, and study a monopolistic firm’s capacity decision. We investigate profit concavity, and characterize the structure of the optimal capacity solution. Moreover, for products with negligible variable costs, we identify two distinct strategies for capacity choice demarcated by an obsolescence rate threshold that relates both to market factors and capacity costs. Finally, we empirically test the demand model by analyzing shipping and pricing data from the PC microprocessor market.