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1 – 10 of over 1000
Article
Publication date: 8 March 2021

Jafar Pourmahmoud and Maedeh Gholam Azad

The purpose of this paper is to propose the data envelopment analysis (DEA) model that can be used as binary-valued data. Often the basic DEA models were developed by assuming…

Abstract

Purpose

The purpose of this paper is to propose the data envelopment analysis (DEA) model that can be used as binary-valued data. Often the basic DEA models were developed by assuming that all of the data are non-negative. However, there are situations where all data are binary. As an example, the information on many diseases in health care is binary data. The existence of binary data in traditional DEA models may change the behavior of the production possibility set (PPS). This study defines a binary summation operator, expresses the modified principles and introduces the extracted PPS of axioms. Furthermore, this study proposes a binary integer programming of DEA (BIP-DEA) for assessing the efficiency scores to use as an alternate tool in prediction.

Design/methodology/approach

In this study, the extracted PPS of modified axioms and the BIP-DEA model for assessing the efficiency score is proposed.

Findings

The binary integer model was proposed to eliminate the challenges of the binary-value data in DEA.

Originality/value

The importance of the proposed model for many fields including the health-care industry is that it can predict the occurrence or non-occurrence of the events, using binary data. This model has been applied to evaluate the most important risk factors for stroke disease and mechanical disorders. The targets set by this model can help to diagnose earlier the disease and increase the patients’ chances of recovery.

Details

Journal of Modelling in Management, vol. 17 no. 1
Type: Research Article
ISSN: 1746-5664

Keywords

Article
Publication date: 25 March 2022

Fatemeh Yazdani, Mehdi Khashei and Seyed Reza Hejazi

This paper aims to detect the most profitable, i.e. optimal turning points (TPs), from the history of time series using a binary integer programming (BIP) model. TPs prediction…

Abstract

Purpose

This paper aims to detect the most profitable, i.e. optimal turning points (TPs), from the history of time series using a binary integer programming (BIP) model. TPs prediction problem is one of the most popular yet challenging topics in financial planning. Predicting profitable TPs results in earning profit by offering the opportunity to buy at low and selling at high. TPs detected from the history of time series will be used as the prediction model’s input. According to the literature, the predicted TPs’ profitability depends on the detected TPs’ profitability. Therefore, research for improving the profitability of detection methods has been never given up. Nevertheless, to the best of our knowledge, none of the existing methods can detect the optimal TPs.

Design/methodology/approach

The objective function of our model maximizes the profit of adopting all the trading strategies. The decision variables represent whether or not to detect the breakpoints as TPs. The assumptions of the model are as follows. Short-selling is possible. The time value for the money is not considered. Detection of consecutive buying (selling) TPs is not possible.

Findings

Empirical results with 20 data sets from Shanghai Stock Exchange indicate that the model detects the optimal TPs.

Originality/value

The proposed model, in contrast to the other methods, can detect the optimal TPs. Additionally, the proposed model, in contrast to the other methods, requires transaction cost as its only input parameter. This advantage reduces the process’ calculations.

Details

Journal of Modelling in Management, vol. 18 no. 5
Type: Research Article
ISSN: 1746-5664

Keywords

Article
Publication date: 1 June 2005

Mustapha Nourelfath and Nabil Nahas

The purpose of this paper is to apply a recent kind of neural networks in a reliability optimization problem for a series system with multiple‐choice constraints incorporated at…

831

Abstract

Purpose

The purpose of this paper is to apply a recent kind of neural networks in a reliability optimization problem for a series system with multiple‐choice constraints incorporated at each subsystem, to maximize the system reliability subject to the system budget and weight. The problem is formulated as a non‐linear binary integer programming problem and characterized as an NP‐hard problem.

Design/methodology/approach

The design of neural network to solve this problem efficiently is based on a quantized Hopfield network (QHN). It has been found that this network allows one to obtain optimal design solutions very frequently and much more quickly than other Hopfield networks.

Research limitations/implications

For systems more complex than series systems considered in this paper, the proposed approach needs to be adapted. The QHN‐based solution approach can be applied in many industrial systems where reliability is considered as an important design measure, e.g. in manufacturing systems, telecommunication systems and power systems.

Originality/value

The paper develops a new efficient method for reliability optimization. The most interesting characteristic of this method is related to its high‐speed computation, since the practical importance lies in the short computation time needed to obtain an optimal or nearly optimal solution for large industrial problems.

Details

Journal of Quality in Maintenance Engineering, vol. 11 no. 2
Type: Research Article
ISSN: 1355-2511

Keywords

Article
Publication date: 12 February 2018

Raed AlHusain and Reza Khorramshahgol

The purpose of this paper is twofold. Initially, a multi-objective binary integer programming model is proposed for designing an appropriate supply chain that takes into…

Abstract

Purpose

The purpose of this paper is twofold. Initially, a multi-objective binary integer programming model is proposed for designing an appropriate supply chain that takes into consideration both responsiveness and efficiency. Then, a responsiveness-cost efficient frontier is generated for the supply chain design that can help organizations find the right balance between responsiveness and efficiency, and hence achieve a strategic fit between organizational strategy and supply chain capabilities.

Design/methodology/approach

The proposed SC design model used both cross-functional and logistical SC drivers to build a binary integer programming model. To this end, various alternative solutions that correspond to different SC design portfolios were generated and a responsiveness-cost efficient frontier was constructed.

Findings

Various alternative solutions that correspond to different SC designs were generated and a responsiveness-cost efficient frontier was constructed to help the decision makers to design SC portfolios to achieve a strategic fit between organizational strategy and SC capabilities.

Practical implications

The proposed methodology enables the decision makers to incorporate both qualitative and quantitative judgements in SC design. The methodology is easy to use and it can be readily implemented by a software.

Originality/value

The proposed methodology allows for subjective value judgements of the decision makers to be considered in SC design and the efficiency-responsiveness frontier generated by the methodology provides a trade-off to be used when choosing between speed and cost efficiency in SC design.

Article
Publication date: 20 February 2009

William Ho and Ali Emrouznejad

A binary integer programming model for the simple assembly line balancing problem (SALBP), which is well known as SALBP‐1, was formulated more than 30 years ago. Since then, a…

1332

Abstract

Purpose

A binary integer programming model for the simple assembly line balancing problem (SALBP), which is well known as SALBP‐1, was formulated more than 30 years ago. Since then, a number of researchers have extended the model for the variants of assembly line balancing problem. The model is still prevalent nowadays mainly because of the lower and upper bounds on task assignment. These properties avoid significant increase of decision variables. The purpose of this paper is to use an example to show that the model may lead to a confusing solution.

Design/methodology/approach

The paper provides a remedial constraint set for the model to rectify the disordered sequence problem.

Findings

The paper presents proof that the assembly line balancing model formulated by Patterson and Albracht may lead to a confusing solution.

Originality/value

No one previously has found that the commonly used model is incorrect.

Details

Assembly Automation, vol. 29 no. 1
Type: Research Article
ISSN: 0144-5154

Keywords

Book part
Publication date: 18 January 2024

Robert T. F. Ah King and Samiah Mohangee

To operate with high efficiency and minimise the risks of power failures, power systems require careful monitoring. The availability of real-time data is crucial for assessing the…

Abstract

To operate with high efficiency and minimise the risks of power failures, power systems require careful monitoring. The availability of real-time data is crucial for assessing the performance of the grid and assisting operators in gauging the present security of the grid. Traditional supervisory control and data acquisition (SCADA)-based systems actually employed provides steady-state measurement values which are the calculation premise of State Estimation. More often, however, the power grid operates under dynamic state and SCADA measurements can lead to erroneous and inaccurate calculation results. The introduction of the phasor measurement unit (PMU) which provides real-time synchronised voltage and current phasors with very high accuracy is universally recognised as an important aspect of delivering a secure and sustainable power system. PMUs are a relatively new technology and because of their high procurement and installation costs, it is imperative to develop appropriate methodologies to determine the minimum number of PMUs as well as their strategic placements to guarantee full observability of a power system. Thus, the problem of the optimal PMU placement (OPP) is formulated as an optimisation problem subject to various constraints to minimise the number of PMUs while ensuring complete observability of the grid. In this chapter, integer linear programming (ILP), genetic algorithm (GA) and non-linear programming (NLP) constrained models of the OPP problem are presented. A new methodology is proposed to incorporate several constraints using the NLP. The optimisation methods have been written in Matlab software and verified on the standard Institute of Electrical and Electronics Engineers (IEEE) 14-bus test system to authenticate their effectiveness. This chapter targets United Nations Sustainable Development Goal 7.

Details

Artificial Intelligence, Engineering Systems and Sustainable Development
Type: Book
ISBN: 978-1-83753-540-8

Keywords

Article
Publication date: 1 December 1997

Bhaba R. Sarker and Kun Li

Presents a mixed‐integer programme to simultaneously select part routeings and form machine cells in the presence of alternate process plans so that the total cost of operating…

699

Abstract

Presents a mixed‐integer programme to simultaneously select part routeings and form machine cells in the presence of alternate process plans so that the total cost of operating and intercell material handling is minimized. Demand for parts, machine capacities, number of cells to be formed, and number of machines in a cell are included in the model. Includes an example to illustrate the solution technique of the problem of practical instance.

Details

Integrated Manufacturing Systems, vol. 8 no. 6
Type: Research Article
ISSN: 0957-6061

Keywords

Article
Publication date: 17 December 2019

Yi-Kai Juan, Hao-Yun Chi and Hsing-Hung Chen

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…

1319

Abstract

Purpose

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.

Design/methodology/approach

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).

Findings

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.

Originality/value

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.

Details

Engineering, Construction and Architectural Management, vol. 28 no. 1
Type: Research Article
ISSN: 0969-9988

Keywords

Article
Publication date: 18 May 2015

Dragana Todovic, Dragana Makajic-Nikolic, Milica Kostic-Stankovic and Milan Martic

The purpose of this paper is to develop a methodology for automatically determining the optimal allocation of police officers in accordance with the division and organization of…

1005

Abstract

Purpose

The purpose of this paper is to develop a methodology for automatically determining the optimal allocation of police officers in accordance with the division and organization of labor.

Design/methodology/approach

The problem is defined as the problem of the goal programming for which the mathematical model of mixed integer programming was developed. In modeling of the scheduling problem the approach police officer/scheme, based on predefined scheduling patterns, was used. The approach is applied to real data of a police station in Bosnia and Herzegovina.

Findings

This study indicates that the determination of monthly scheduling policemen is complex and challenging problem, which is usually performed without the aid of software (self-rostering), and that it can be significantly facilitated by the introduction of scheduling optimization approach.

Research limitations/implications

The developed mathematical model, in its current form, can directly be applied only to the scheduling of police officers at police stations which have the same or a similar organization of work.

Practical implications

Optimization of scheduling significantly reduces the time to obtain a monthly schedule. In addition, it allows the police stations to experiment with different forms of organization work of police officers and to obtain an optimal schedule for each of them in a short time.

Originality/value

The problem of optimal scheduling of employees is often resolved in other fields. To the authors knowledge, this is the first time that the approach of goal programming is applied in the field of policing.

Details

Policing: An International Journal of Police Strategies & Management, vol. 38 no. 2
Type: Research Article
ISSN: 1363-951X

Keywords

Article
Publication date: 1 May 2003

Wei‐Shing Chen and Chiuh‐Cheng Chyu

This paper considers the decision problem for a minimum setup strategy of a production system arising in the assembly of printed circuit boards of different types, using a…

Abstract

This paper considers the decision problem for a minimum setup strategy of a production system arising in the assembly of printed circuit boards of different types, using a placement machine with multi‐slot feeders. We formulate the problem as a binary linear programming model, and propose a heuristic procedure to find the solution that consists of a board‐assembly sequence, an associated component loading and unloading strategy and a feeder‐assignment plan within reasonable computational effort. Computational results from solving the simulated problem instances by using the heuristic method and the mathematical model are provided and compared. The proposed heuristic procedure can be incorporated into the PCB scheduling optimization software to decrease cycle times and increase overall assembly throughput in a high‐mix, low‐volume PCB manufacturing environment.

Details

Integrated Manufacturing Systems, vol. 14 no. 3
Type: Research Article
ISSN: 0957-6061

Keywords

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