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The present work seeks to determine the optimal delivery schedule of equipment at a project site in the backdrop of limited storage space, at a minimum cost, and without disturbing the overall project schedule. In addition, the optimized delivery schedule helps in minimizing the fluctuating requirements of space at the project site across the entire project lifespan.

The study is carried out at a Steel plant operating in a constrained space but undergoing a production capacity expansion. The problem motivated us to explore the possibility of postponing the delivery dates of certain equipment closer to the erection dates without compromising on the project schedule. Given the versatility of linear programming models in dealing with such schedule optimization problems, the authors formulated the above problem as a Zero-One Integer Linear Programming problem.

The model is implemented for all the new equipment arriving for two major units – the Hot Strip Mill (HSM) and the Blast Furnace (BF). It generates an optimized delivery schedule by delaying the delivery of some equipment by a certain number of periods, without compromising the overall project schedule and at a minimum storage cost. The average space utilization increases by 25.85 and 14.79% in HSM and BF units respectively. The fluctuations in space requirements are reduced substantially in both units.

The study shows a timeline in the form of a Gantt chart for the delivery of equipment, storage of equipment across different periods, and the number of periods for which the delivery of certain equipment needs to be postponed. The study uses linearly increasing storage costs with the increase in the number of periods for storage of the equipment in the temporary shed.

1. Determined the optimal delivery schedule of the equipment in a project environment in the backdrop of limited storage space in the project site.

2. Formulated the above problem as a Zero-One Integer Linear Programming (ILP) problem.

3. The average space utilization has increased by 25.85 and 14.79% in HSM and BF units respectively.

4. The optimized delivery schedule helps in reducing the fluctuations in space requirements substantially across the entire lifespan of the project.

5. The timeline of delivery of equipment, storage of equipment across different periods and periods of postponement of the equipment are shown in the form of a Gantt Chart.

Determined the optimal delivery schedule of the equipment in a project environment in the backdrop of limited storage space in the project site.

Formulated the above problem as a Zero-One Integer Linear Programming (ILP) problem.

The average space utilization has increased by 25.85 and 14.79% in HSM and BF units respectively.

The optimized delivery schedule helps in reducing the fluctuations in space requirements substantially across the entire lifespan of the project.

The timeline of delivery of equipment, storage of equipment across different periods and periods of postponement of the equipment are shown in the form of a Gantt Chart.

The purpose of this paper is to describe a method that has been set up to schedule preventive maintenance (PM) tasks for power and water plants with all constraints such as production and maintenance.

The proposed methodology relies on the zero-one integer programming model that finds the maximum number of power and water units available in separate generating units. To verify this, the model was implemented and tested as a case study in Kuwait for the Cogeneration Station.

An effective solution can be achieved for scheduling the PM tasks and production at the power and water cogeneration plant.

The proposed model offers a practical method to schedule PM of power and water units, which are expensive equipment.

This proposed model is an effective decision-making tool that provides an ideal solution for preventive maintenance scheduling problems for power and water units in a cogeneration plant, effectively and complies with all constraints.

One of the major components of an audit plan is risk assessment. Risk factors vary among organizations, and internal auditors must review their own organizational cultures to determine audit exposure. In order to minimize the risk, the internal audit department must develop an audit plan that ensures a maximum coverage of the areas to be audited. Presents the second phase of a study which aims to identify the relevant risk factors and rank the relevant auditable activities in terms of their riskiness. Develops a mathematical model that will ensure maximum audit coverage, using data from a local hospital. Suggests that the results of this research offer a practical alternative for audit managers.

Quality function deployment (QFD) is a structured approach to seek out customers, understand their needs, and ensure that their needs are met. QFD is probably the most important management tool developed to assure quality in new or improved products and services. As with any other tool, the quantum of benefits obtained from the use of QFD is proportional to the effectiveness of its use. To enhance the effectiveness and efficiency of QFD as a means to transfer the “voice of the customer” into design and production, a new comprehensive hierarchical framework for QFD planning process and a zero‐one goal programming model for the selection of design requirements are proposed. The hierarchical framework contributes to the strategic guidance and provides clear direction for QFD teams during the construction of the house of quality. The decision model assists in determining a set of design requirements that most effectively meet customer needs subject to limited resources and other organizational restrictions. An illustrative example is also provided to demonstrate the practical usage of the design selection model.

In a companion paper (1) a general mathematical model for the allocation of touristic investments was developed. In this paper a solution methodology for the model is developed based on the principles of dynamic programming. At each stage of the dynamic program an integer program is solved to limit the range of values of the state variable which must explicitly be considered. The algorithm is illustrated through an example, and the advantages of the solution procedure are explained by considering the solution as a base for the strategic decision making in the touristic sector.

This chapter sets out to survey the traditional plane boarding strategies employed by major airlines in the commercial aviation industry. It then reviews the simulation-based and analytical approaches to getting passengers on an aircraft in the existing literature before zeroing in on the latter. A new integer program is proposed to minimize the time required to eliminate the interferences among all passengers when boarding a flight so that the plane can take off as soon as possible. The mathematical model is subsequently put to test in an example involving a hypothetical small aircraft, and the results show that it is more efficient as well as more flexible than those proposed in other studies. Other ways to shorten the enplaning time are also discussed. The contributions of the present work and several directions for future research are described in the conclusions.

The purpose of this paper is to develop a virtual reality (VR)-based and user-oriented decision support system for interior design and decoration. The four-phase decision-making process of the system is verified through a case study of an office building.

Different “spatial layouts” are presented by VR for users to decide their preference (Phase 1). According to the selected spatial layout, a “spatial scene” is constructed by VR and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is used to determine the spatial scene preference (Phase 2). Based on the binary integer programming method, the system provides the optimal preliminary solution under a limited decoration budget (Phase 3). Finally, the consistency between the overall color scheme and pattern is fine-tuned by VR in order to obtain the final solution (Phase 4).

The questionnaire survey results show that decision makers generally affirm the operation and application of VR, and especially recognize the advantages in the improvement of VR-based interior design feasibility, communication efficiency and design decision-making speed. The optimization of the costs and benefits enables decision makers to effectively evaluate the impact of design decisions on subsequent project implementation during the preliminary design process.

The VR-based decision support system for interior design retains the original immersive experience of VR, and offers a systematic multiple criteria decision- making and operations research optimization method, thus, providing more complete decision-making assistance. Compared with traditional design communication, it can significantly reduce cognitive differences and improve decision-making quality and speed.

This paper describes a method developed to schedule the preventive maintenance tasks in separate and linked cogeneration plants while satisfying the maintenance and production constraints.

The proposed methodology is based on a mixed integer programming model which finds the maximum number of available power and desalting units in separate and linked cogeneration plants. To verify that the model can be implemented for a real system, a case study of scheduling the preventive maintenance tasks of a cogeneration plant in Kuwait is illustrated.

An efficient solution can be achieved for scheduling the preventive maintenance tasks and production in cogeneration plants.

The paper offers a practical model that can be used to schedule preventive maintenance for expensive equipment in cogeneration plans.

The model presented is an effective decision tool that optimises the solution of the maintenance scheduling problem for cogeneration plants under maintenance and production constraints.

Proposes optimization models for spare provisioning. In the first model, considers a series system with m components where each component can have a maximum of (n‐1) spares. The objective function is to maximize the availability of the system satisfying a constraint on space required for the spares. In the second model, considers a series‐parallel system where each component of the system can have a maximum of (n‐1) spares. The optimization models developed in the paper can be solved using general purpose software such as SOLVER of EXCEL. Also presents an efficient branch and bound algorithm which can be used to solve the optimization problem.

The principal concern of organization managers in the global rivalry of commerce environment is how to select the project portfolio among available projects. In this matter, organizations should consider the uncertainty intrinsic in the projects regarding an appropriate valuation technique within an optimization framework. In this research, the purpose of this paper is to formulate using a robust optimization algorithm to deal with the complexities and uncertainty inherent in the construction of the project portfolio.

First, a general mathematical formulation is presented, which in compound real options valuation is highlighted. This formulation gives managerial flexibility by correcting the deficiency of traditional discounted cash flow technique that excludes any form of flexibility. Then, considering a limitation on budget of the organization, an integer programming formulation to maximize the n-fold compound options for project portfolio selection is proposed. Finally, a robust optimization model is developed along with the robust combinatorial optimization algorithm, which is effective for solving problems under uncertainty.

Sensitivity analysis showed that projects in later phases of development, having survived several phases of pre-clinical and clinical tests, are worth more because they are more likely to pertain to business. However, the investment costs related to each project during development phases limit the number of projects that a company can bring to their final portfolio. Additionally, the analysis of conservatism level represented how project managers can quite easily determine their risk attitude and the corresponding portfolio composition. From a managerial point of view, the proposed framework is very useful because it requires only financial estimates. Hence, the proposed decision support tool can assist research and development (R&D) project managers in the pharmaceutical industry for making decisions.

The first is the application of the n-fold compound options on portfolio of R&D projects and the employment of compound options value of a project portfolio as an objective function. The second one is a mathematical formulation of these concepts and solving it by the robust combinatorial optimization algorithm. The literature is lacking in the application of the robust combinatorial optimization algorithm to R&D project portfolio selection based on the generalized n-fold compound option model of Cassimon et al. (2004). Every framework from calculation of the n-fold compound option to solving robust combinatorial algorithm is programmed in Matlab software, since it can be used as a business support tool.