A multi-attribute, rank-dependent utility model for selecting dispatching rules

Pergher, Isaac; Almeida, Adiel Teixeira de

doi:10.1016/j.jmsy.2018.01.007

Cited by 14 publications

(9 citation statements)

References 31 publications

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“…These processes can be facilitated by QFD in capturing and contextualising of user and DRs in FED. However as observed by Liu (2011), QFD is limited in accounting for uncertainty in the form of ambiguity and imprecision (Malak Jr et al, 2009); in decision making something that utility theory and multi-attribute decision analysis tools can and have been applied (Pergher and de Almeida, 2018). This extends QFD capabilities.…”

Section: 1front End Designmentioning

confidence: 99%

“…A typical project can have many with different, often different subjective interpretations; sometimes conflicting in a design context. Pergher and de Almeida (2018) argue that this gives rise to the stochastic tendency in multi-attribute systems. Lawson (2005) moreover observes that end-users might not even express explicitly what their use need is.…”

Section: 3utility Theorymentioning

confidence: 99%

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Value Generation in Front-End Design of Social Housing with QFD and Multiattribute Utility Theory

Serugga

Kagioglou

Tzortzopoulos³

2020

J. Constr. Eng. Manage.

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The design of social housing is a multi-faceted endeavour like in many social projects such as megaprojects whose expected benefits reach beyond a handful of stakeholders. Understanding the requirements of such projects can be a painstaking endeavour and without a structured process to guide decision making, such projects often fail to deliver on their intended benefits. This paper examines how the use of Utility Theory and Quality function deployment (QFD) can serve as a useful basis to support the delivery of such benefits. The research examines a social housing project in Brazil to draw focus on contextual influences on understanding, structuring and delivery of benefits. The approach adopts QFD for requirements management while Utility Theory (UT) assesses utility decision making in raking the housing models on the basis established user requirements and derived design requirements (DRs). While a medium low-income model is preferred best, the next preferred models are very-low, medium-low, medium-high in the order. The analysis indicates no transitivity in preference from an end-user perspective. The novelty of the paper is representing, quantitatively, a process of requirements management that supports the delivery of project benefits on a Utilitarian basis in social housing design. The method is able to account for interdependencies between design and user attributes in supporting social housing design decision making in an integrated process. This understanding can be key to the delivery of the right social projects that reflect context-specific end-2 user needs.

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Section: 1front End Designmentioning

confidence: 99%

Section: 3utility Theorymentioning

confidence: 99%

Value Generation in Front-End Design of Social Housing with QFD and Multiattribute Utility Theory

Serugga

Kagioglou

Tzortzopoulos³

2020

J. Constr. Eng. Manage.

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“…Salah satu model MCDM yang dapat membantu dalam pengambilan keputusan adalah metode Multi-Atribut Utility Teori (MAUT), di mana sejumlah kriteria harus diperhitungkan saat membuat keputusan penting (Suner et al, 2017). Memiliki banyak kriteria dan berbagai alternatif merupakan sebuah teknik dalam proses pengambil keputusan menggunakan metode MAUT (Pergher dan Almeida, 2018). Metode MAUT telah banyak digunakan dalam penelitian sebelumnya.…”

Section: Pendahuluanunclassified

The Multi Attribute Utility Theory (Death) Method In The Decision Of The Distributor Distributor Selection (Metode Multi Attribute Utility Theory (Maut) Dalam Keputusan Pemilihan Distributor Barang)

2020

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Distributors are intermediaries who distribute products from factories to retailers. While the distributor of goods is the distributor of goods from factories to shops that need these goods. Incorrect selection of distributors can interfere with the sales process at the store. To improve the quality and quality of a store, it requires the best distributor of goods. This study aims to determine the best distributor of goods. The method used is the Multi Attribute Utility Theory (MAUT) of distributor data at the Padang Luar Sundanese Convenience Store. The data processed in this study consisted of a number of distributor data selected by the Multipurpose Store. From some of the distributor data, the Decision Support System is very necessary in the selection of distributors who aim for the selection of appropriate alternative decisions. The selection of distributors uses 15 samples of distributor data and 5 criteria data that are used as the basis for selecting distributors, namely quality of goods, affordable prices, strategic locations, service responses, and giving bonuses. The results of testing on this method obtained an accuracy rate of 86.67% of the right distributors and in accordance with the realization of the UI data. So this research is very suitable in choosing the best distributor. From the test results, it has got the 5 best distributors by assigning a weight of 11.50 to the best distributor, so the criteria set by the All-Round Shop can be used as a reference in the selection of distributors of goods.

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“…Gahm et al [30] extended prior research on the ''decision theory'' approach to scheduling. Pergher and Almeida [31] proposed a multi-attribute, rankdependent utility model to identify the best dispatching rule for dynamic job shop environments. Amina and El-Bouri [32] proposed a minimax linear programming (LP) model for dispatching rule selection in the presence of multiple criteria.…”

Section: B Heuristicsmentioning

confidence: 99%

Multi-Agent Based Hyper-Heuristics for Multi-Objective Flexible Job Shop Scheduling: A Case Study in an Aero-Engine Blade Manufacturing Plant

2019

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In the paper, a case study focusing on multi-objective flexible job shop scheduling problem (MO-FJSP) in an aero-engine blade manufacturing plant is presented. The problem considered in this paper involves many attributes, including working calendar, due dates, and lot size. Moreover, dynamic events occur frequently in the shop-floor, making the problem more challenging and requiring real-time responses. Therefore, the priority-based methods are more suitable than the computationally intensive search-based methods for the online scheduling. However, developing an effective heuristic for online scheduling problem is a tedious work even for domain experts. Furthermore, the domain knowledge of the practical production scheduling needs to be integrated into the algorithm to guide the search direction, accelerate the convergence of the algorithm, and improve the solution quality. To this end, three multi-agentbased hyper-heuristics (MAHH) integrated with the prior knowledge of the shop floor are proposed to evolve scheduling policies (SPs) for the online scheduling problem. To evaluate the performance of evolved SPs, a 5fold cross-validation method which is frequently used in machine learning is adopted to avoid the overfitting problem. Both the training and test results demonstrate that the bottleneck-agent-based hyper-heuristic method produces the best result among the three MAHH methods. Furthermore, both the effectiveness and the efficiency of the evolved SPs are verified by comparison with the well-known heuristics and two multiobjective particle swarm optimization (MOPSO) algorithms on the practical case. The proposed method has been embedded in the manufacturing execution system that is built on JAVA and successfully applied in several manufacturing plants. INDEX TERMSScheduling, flexible job shop, multi-agent, hyper-heuristics, genetic programming. NOMENCLATURE NSGAII Nondominated sorting genetic algorithm II. SPEA2 Strength Pareto evolutionary algorithm 2. 2/3/MPGP Multi-objective cooperative coevolution genetic programming with two/three/multiple sub-populations. The associate editor coordinating the review of this manuscript and approving it for publication was Kuo-Ching Ying. 2/3/MTGP Multi-objective genetic programming with single population that an individual contains two/three/multiple sub-trees. OMOPSO Optimized multi-objective particle swarm optimization. SMPSO Speed-constrained multi-objective particle swarm optimization. MAHH Multi-agent based hyper-heuristics.

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A multi-attribute, rank-dependent utility model for selecting dispatching rules

Cited by 14 publications

References 31 publications

Value Generation in Front-End Design of Social Housing with QFD and Multiattribute Utility Theory

Value Generation in Front-End Design of Social Housing with QFD and Multiattribute Utility Theory

The Multi Attribute Utility Theory (Death) Method In The Decision Of The Distributor Distributor Selection (Metode Multi Attribute Utility Theory (Maut) Dalam Keputusan Pemilihan Distributor Barang)

Multi-Agent Based Hyper-Heuristics for Multi-Objective Flexible Job Shop Scheduling: A Case Study in an Aero-Engine Blade Manufacturing Plant

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