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Describes a procedure for modelling the costs of production and distribution between several production facilities with economies of scale and many customers who are widely dispersed. The problem takes the form of a large transportation problem on which is superimposed a cost minimization problem involving variable production quantities. These costs involve fixed costs for initiating production and variable costs with diminishing returns to scale. Models the problem as a non‐linear integer programming problem and then solves it using a recently developed non‐linear integer algorithm. Describes two applications in Australia and New Zealand and illustrates how comparison with a mixed‐integer linear programming formulation shows a significant improvement.

Laboratory calibration methods are time-consuming and require accurate devices to find the error coefficients of the low-cost microelectromechanical system (MEMS) accelerometer. Besides, low-cost MEMS sensors highly depend on temperature because of their silicon property and the effect of temperature on error coefficients should also be considered for compensation. This paper aims to present a field calibration method in which the accelerometer is placed in different positions without any accurate equipment in a few minutes and its temperature is changed by a simple device like a hairdryer.

In this paper, a non-linear cost function is defined based on this rule that the magnitude of the acceleration measured by the accelerometer in static mode is equal to the gravity plus error factors. Also, the dependency of error coefficients of the accelerometer is presented as a second-order polynomial in this cost function. By minimizing the cost function, the accelerometer error coefficients include bias, scale factor and non-orthogonality and their temperature dependency are obtained simultaneously.

Simulation results in MATLAB and empirical results of a MPU6050 accelerometer verify the good performance of the proposed calibration method.

Finding a fast and simple field calibration method to calibrate a low-cost MEMS accelerometer and compensate for the temperature dependency without using accurate laboratory equipment can help a wide range of industries that use advanced and expensive sensors or use expensive laboratory equipment to calibrate their sensors, to decrease their costs.

Prior work has focused on the impact of using alternative bases for allocating costs to products but there has been little work that evaluates the use of alternative allocation bases for allocating costs to departments. In particular, if different departments of a multi‐national firm are located in settings with different reporting requirements, exchange rate risks, and costs of capital, then the choice of cost allocation base can be important. This paper examines the economic impact of alternative service department allocation bases in a decentralised setting. A non‐linear programming (NLP) approach is used to model the problem. A review of prior literature identifies a method, based on the NLP approach, for determining the economic impact of alternative allocation bases in a multi‐product setting. The method is adapted in this paper for the multi‐divisional context. The study finds that centralised production volume decision‐making is superior to decentralised decision‐making using either revenue or volume‐based cost allocation bases. Under certain conditions, revenue‐based allocation bases are also found to be superior to volume bases. Under the assumptions of the model no distinction can be made between the centralised solution and a profit‐based allocation regime. A practical implication of this study is that designers of cost allocation systems need to consider not only the direct income‐shifting effect of different cost allocation bases but also the indirect economic effect of consequential changes in the operating decisions of the firm.

Develops practical models for preventive maintenance policies using Bayesian methods of statistical inference. Considers the analysis of a delayed renewal process and a delayed alternating renewal process with exponential times to failure. This approach has the advantage of generating predictive distributions for numbers of failures and downtimes rather than relying on estimated renewal functions. Demonstrates the superiority of this approach in analysing situations with non‐linear cost functions, which arise in reality, by means of an example.

The purpose of the paper is to solve hydrothermal scheduling (HTS) problem for energy-efficient management by allocating the optimal real power outputs for thermal and hydroelectric generators.

HTS can be formulated as a complex and non-linear optimization problem which minimizes the total fuel cost and emissions of thermal generators subject to various physical and operational constraints. As the artificial bee colony algorithm has proven its ability to solve various engineering optimization problems, it has been used as a main optimization tool to solve the fixed-head HTS problem.

A meta-heuristic search technique-based algorithm has been implemented for hydrothermal energy management, and the simulation results show that this approach can provide trade-off between conflict objectives and keep a rapid convergence speed.

The proposed methodology is implemented on the standard test system, and the numerical results comparison indicates a considerable saving in total fuel cost and reduction in emission.

A detailed description will be provided of all the classification algorithms that have been widely used in the domain of medical science. The foundation will be laid by giving a comprehensive overview pertaining to the background and history of the classification algorithms. This will be followed by an extensive discussion regarding various techniques of classification algorithm in machine learning (ML) hence concluding with their relevant applications in data analysis in medical science and health care. To begin with, the initials of this chapter will deal with the basic fundamentals required for a profound understanding of the classification techniques in ML which will comprise of the underlying differences between Unsupervised and Supervised Learning followed by the basic terminologies of classification and its history. Further, it will include the types of classification algorithms ranging from linear classifiers like Logistic Regression, Naïve Bayes to Nearest Neighbour, Support Vector Machine, Tree-based Classifiers, and Neural Networks, and their respective mathematics. Ensemble algorithms such as Majority Voting, Boosting, Bagging, Stacking will also be discussed at great length along with their relevant applications. Furthermore, this chapter will also incorporate comprehensive elucidation regarding the areas of application of such classification algorithms in the field of biomedicine and health care and their contribution to decision-making systems and predictive analysis. To conclude, this chapter will devote highly in the field of research and development as it will provide a thorough insight to the classification algorithms and their relevant applications used in the cases of the healthcare development sector.

A promising approach for the solution of the electrocardiographic inverse problem is the calculation of the cardiac activation sequence from body surface potential (BSP) mapping data. Here, a two‐fold regularization scheme is applied in order to stabilize the inverse solution of this intrinsically ill‐posed problem. The solution of the inverse problem is defined by the minimum of a non‐linear cost function. The L‐curve method can be applied for regularization parameter determination. Solving the optimization problem by a Newton‐like method, the L‐curve may be of pronged shape. Then a numerically unique determination of the optimal regularization parameter will become difficult. This problem can be avoided applying an iterative linearized algorithm. It is shown that activation time imaging due to temporal and spatial regularization is stable with respect to large model errors. Even neglecting cardiac anisotropy in activation time imaging results in an acceptable inverse solution.

Regarding the immense real estate divestitures that have taken place over the last couple of years, some stakeholders have begun to wonder if these short‐term activities may affect the long‐term competitive advantage of a company. While it appears reasonable that property divestitures enhance the financial situation of a company from a so‐called owner perspective, there is no equivalent quantitative evaluation for the loss in space utilisation and flexibility from a user perspective. Consequently, real estate decision making is based upon an insufficient information basis and is dominated by the investment perspective. In order to better align corporate real estate and real estate investment functions better, this paper introduces a formal decision model which describes the situation of corporate real estate decision makers. They have to trade off entrepreneurial flexibility gained by real estate holdings against the financial opportunity cost of freeing up capital. Making use of a prototype decision situation, the paper demonstrates how the decision maker can improve the underlying information basis for property divestment decisions, using a real option approach. Hence, real estate decisions gain in two respects: they are more transparent and, more importantly, their design is more suitable if the company wants to employ real estate holdings to increase the overall value of the company.

In the last two decades, different policy initiatives have been set up to increase the share of intermodal freight transport through a modal shift. In the design of these policies, often critical break-even distances are set, showing the cost or price competitiveness of intermodal transport to delineate transport routes that qualify for such a modal shift. In this chapter, we discuss to which extent such break-even distances can be generalized on a larger scale and how they are calculated.

We use two price-based models to calculate break-even distances for an intermodal rail and an intermodal barge transport case. General break-even values do not show the price variation in the transport market and vagueness in the calculation of these values adds to this problem.

We find that for the inland waterway case, intermodal barge transport shows potential on shorter distances as well. In addition, different ways to lower the break-even distance are discussed and a framework for calculating break-even distances is suggested.

The research elaborates on break-even distances in a European context using price data which are fluctuating over time, location specific and often not publicly available.

Policy initiatives promoting intermodal transport should not focus solely on long distance transport. Moreover, evaluating the competitiveness of the intermodal sector solely on a price comparison dishonours its true potential.

This chapter challenges the current European policy on intermodal transport by showing the price competitiveness of intermodal transport in two cases.