Algorithmic Aspects of Scenario-Based Multi-stage Decision Process Optimization

2009 ◽  
pp. 365-376 ◽  
Author(s):  
Ronald Hochreiter
Keyword(s):  
Process Optimization ◽  
Decision Process ◽  
Multi Stage

Modelling the adoption of sustainable procurement in construction organisations

2018 ◽  
Vol 8 (5) ◽  
pp. 461-476 ◽  
Author(s):  
Kwaku Agbesi ◽  
Frank D. Fugar ◽  
Theophilus Adjei-Kumi
Keyword(s):  
Decision Process ◽  
Structural Equation ◽  
Adoption Process ◽  
Adoption Studies ◽  
Content Type ◽  
Adoption Decision ◽  
Sustainable Procurement ◽  
Top Managers ◽  
Multi Stage ◽  
Adoption Model

Purpose The adoption of sustainable procurement in construction clients’ organisation remains a difficult concept. Current research of sustainable procurement adoption studies fails to focus on a multi-stage adoption process. The purpose of this paper is to develop an organisational adoption model in a multi-stage process for the adoption of sustainable procurement in construction. Design/methodology/approach The paper developed an organisational adoption model. The model was tested against data obtained from survey administered to 193 respondents of central and local government institutions with a response rate of 63.7 per cent. Structural equation modelling using the partial least squares was employed to determine and confirm the factor structure of the model, and to measure the relationships between the model constructs. Findings An organisational adoption model is developed, tested and is robust to aid the adoption decision process of sustainable procurement within construction organisations. Research limitations/implications The study is limited in scope affecting generalisation of the results. Future study should expand the scope to include consultants, contractors and suppliers. Practical implications The adoption model will assist policy makers and top managers to understand the adoption decision process and prioritise on the technological, organisational and environmental factors that significantly affect sustainable adoption decision process within construction organisations. Originality/value This study appears to be among the first to empirically develop an organisational adoption model to aid the adoption of sustainable procurement in construction.

scholarly journals Selling science: optimizing the research funding evaluation and decision process

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Claartje J. Vinkenburg ◽  
Carolin Ossenkop ◽  
Helene Schiffbaenker
Keyword(s):  
Process Optimization ◽  
Group Dynamics ◽  
Decision Process ◽  
Research Funding ◽  
Evaluation Process ◽  
Negative Effects ◽  
Content Type ◽  
Lack Of Fit ◽  
Biased Language ◽  
Recruitment And Selection

PurposeIn this contribution to EDI's professional insights, the authors develop practical and evidence-based recommendations that are developed for bias mitigation, discretion elimination and process optimization in panel evaluations and decisions in research funding. An analysis is made of how the expectation of “selling science” adds layers of complexity to the evaluation and decision process. The insights are relevant for optimization of similar processes, including publication, recruitment and selection, tenure and promotion.Design/methodology/approachThe recommendations are informed by experiences and evidence from commissioned projects with European research funding organizations. The authors distinguish between three aspects of the evaluation process: written applications, enacted performance and group dynamics. Vignettes are provided to set the stage for the analysis of how bias and (lack of) fit to an ideal image makes it easier for some than for others to be funded.FindingsIn research funding decisions, (over)selling science is expected but creates shifting standards for evaluation, resulting in a narrow band of acceptable behavior for applicants. In the authors' recommendations, research funding organizations, evaluators and panel chairs will find practical ideas and levers for process optimization, standardization and customization, in terms of awareness, accountability, biased language, criteria, structure and time.Originality/valueShowing how “selling science” in research funding adds to the cumulative disadvantage of bias, the authors offer design specifications for interventions to mitigate the negative effects of bias on evaluations and decisions, improve selection habits, eliminate discretion and create a more inclusive process.

scholarly journals The multi-stage dynamic stochastic decision process with unknown distribution of the random utilities

2019 ◽  
Vol 14 (5) ◽  
pp. 1207-1218 ◽  
Author(s):  
Roberto Tadei ◽  
Guido Perboli ◽  
Daniele Manerba
Keyword(s):  
Decision Process ◽  
Unknown Distribution ◽  
Multi Stage ◽  
Dynamic Stochastic

scholarly journals An Analysis of Reliability of Military Vehicles

2019 ◽  
Vol 49 (3) ◽  
pp. 527-546
Author(s):  
Kamil Przybysz ◽  
Sławomir Dygnatowski ◽  
Norbert Grzesik
Keyword(s):  
Process Optimization ◽  
Decision Process ◽  
Reliability Level ◽  
Military Vehicles ◽  
Extensive Analysis ◽  
Peculiar Character ◽  
Military Units ◽  
Exploitation System

Abstract The paper describes issues related to reliability of military vehicles based on recorded operational events. Seeking the quantification of reliability for exploiting vehicles in military units, an extensive analysis of factors shaping the reliability level was made, taking into consideration all phases of military vehicles existence and a peculiar character of the exploitation process of military vehicles. The importance of reliability research in the decision process optimization was emphasized, controlling the efficiency and availability of the exploitation system.

scholarly journals Stochastic single machine scheduling problem as a multi-stage dynamic random decision process

2021 ◽  
Author(s):  
Mina Roohnavazfar ◽  
Daniele Manerba ◽  
Lohic Fotio Tiotsop ◽  
Seyed Hamid Reza Pasandideh ◽  
Roberto Tadei
Keyword(s):  
Single Machine ◽  
Decision Process ◽  
Single Machine Scheduling ◽  
Setup Times ◽  
Scheduling Problem ◽  
Multi Stage ◽  
Machine Configuration ◽  
Sequence Dependent Setup ◽  
Dependent Setup Times ◽  
Sequence Dependent Setup Times

AbstractIn this work, we study a stochastic single machine scheduling problem in which the features of learning effect on processing times, sequence-dependent setup times, and machine configuration selection are considered simultaneously. More precisely, the machine works under a set of configurations and requires stochastic sequence-dependent setup times to switch from one configuration to another. Also, the stochastic processing time of a job is a function of its position and the machine configuration. The objective is to find the sequence of jobs and choose a configuration to process each job to minimize the makespan. We first show that the proposed problem can be formulated through two-stage and multi-stage Stochastic Programming models, which are challenging from the computational point of view. Then, by looking at the problem as a multi-stage dynamic random decision process, a new deterministic approximation-based formulation is developed. The method first derives a mixed-integer non-linear model based on the concept of accessibility to all possible and available alternatives at each stage of the decision-making process. Then, to efficiently solve the problem, a new accessibility measure is defined to convert the model into the search of a shortest path throughout the stages. Extensive computational experiments are carried out on various sets of instances. We discuss and compare the results found by the resolution of plain stochastic models with those obtained by the deterministic approximation approach. Our approximation shows excellent performances both in terms of solution accuracy and computational time.

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