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In the present work, we formulated an integrated method in order to confront two problems regarding a project portfolio that contains energy projects and/or proposals for projects to be implemented in the future. More specifically, the problems are: the selection of the most beneficial projects and then the their scheduling. Our approach is based on extending a tested and well-performed Dynamic Programming solution for project portfolio scheduling in order to serve the selection problem as well. The approach is utilizing the Incremental Funding Method (IFM). Most of the existing bibliography is focused on project scheduling problems, although an adequate part is dedicated to the project portfolio scheduling problem. Moreover, the mainstream line of thought until recently, considered these two fields of study as relevant but the majority of the researchers tried to concentrate on only one of them treating them as two distinct domains. However, contemporary organizations ought to handle simultaneously a bundle of project proposals, to select a subset of them due to budget's constraints, and then to schedule them in order to maximize their potential profit. The proposed method extends the IFM concept in order to cover the energy projects and stands for a tool to select and schedule simultaneously the implementation of project proposals that give the maximum value to the performing organization. The outcome of this enhanced method produces quite promising results when tried on a tailor-made set of benchmark instances. [ABSTRACT FROM AUTHOR]

There are two main methods commonly used for decision-making: multi-criteria analysis (MCA) and cost-benefit analysis (CBA). The significance of this research lies in the fact that currently, the rationale behind the final decision in choosing among multiple options is often based solely on the anticipated financial and economic outcomes of the project. However, the project itself encompasses other strategic implications that are disregarded during the decision-making process. These include, but are not limited to, social, environmental, cultural, and natural consequences. This paper aims to propose a more comprehensive multi-criteria approach, providing a multi-agent solution for selecting from a diverse range of alternatives that align with national interests. The objective of this research is to identify the most promising subproject within the scope of the national transport project, considering not only financial aspects but also other crucial factors such as social, cultural impacts, human capital development, the creation of a new environment, the emergence of fresh opportunities, and the dismantling of institutional obstacles. The paper discusses two main methods used for decision-making in transportation policy: MCA and CBA. A practical experiment was conducted to select and rank promising sub-projects using T. Saaty's hierarchy analysis method, which takes into account the multi-agent approach to the development of transportation systems and their integration with state development in Russia. As part of the study, a practical experiment was conducted to select and rank promising subprojects using the T. Saati method of hierarchical analysis. Criteria for evaluating and prioritizing projects were developed, as well as new tools for future use in public administration of the transport industry. Research methods and methodology included T. Saati's hierarchical analysis method, statistical analysis, specification, mathematical modeling, and statistical tests. The main results of the study were the analysis of four existing promising projects: modernization of railway infrastructure, development of high-speed rail communication, modernization of automotive infrastructure, and development of inland waterway transport. Based on these projects, an applied multi-agent toolkit was proposed for decision-making in public transport administration. The results of the study can be used to inform decision-making processes in transport policy and to evaluate the effectiveness and economic feasibility of projects. The study used methods such as multi-criteria decision analysis (MCDA) and cost-benefit analysis (CBA) to analyze various aspects of transport projects and make informed decisions about their implementation.

A significant challenge in asset management is the selection of investment projects for infrastructures, which often relies on subjective judgement and lacks structured decision support methods. This challenge is particularly complex in water systems due to the diverse and heterogeneous nature of the components requiring investment. While the infrastructure value index (IVI) is widely used to characterise assets and support investment decisions in the water sector, its application in optimisation models for generating efficient project portfolios remains unexplored. To address this research gap, this study introduces optimisation models for generating investment portfolio plans in water systems' asset management. The proposed approach includes two mixed‐integer linear programming (MILP) models that determine optimal solutions and an evolutionary algorithm that offers sub‐optimal alternative investment selection plans to provide decision‐makers with additional choices for balancing optimal outcomes. The primary contribution of this research is the combined utilisation of MILP and evolutionary algorithms, integrating the IVI into the decision‐making process. These tools provide decision‐makers with structured methods for defining investment plans and minimising the subjective elements typically associated with such processes. To illustrate the effectiveness of the models, a case study is presented involving a pumping station of a Portuguese water company. The results demonstrate the practical application and benefits of the proposed approach in optimising investment decisions. This research contributes to advancing asset management practices by integrating quantitative optimisation techniques and leveraging the IVI, thereby enhancing the objectivity and efficiency of investment planning in water systems' asset management. [ABSTRACT FROM AUTHOR]

Prioritizing Lean Six Sigma (LSS) projects that align with company objectives is crucial, yet traditional methods struggle with associated subjective criteria. This study proposed a hybrid Best Worst Method - Fuzzy TOPSIS approach to prioritize LSS projects for a project consulting company. The method integrates expert opinion from 3 decision-makers on 7 main criteria and 24 sub-criteria to select the optimal LSS projects in project management consulting company. Triangular fuzzy numbers were used to describe the responses. The fuzzy positive and negative solutions of the five alternatives were calculated. Results indicate project alternative 3 (ERP Deployment Project) is the optimal choice with the highest closeness coefficient (0.68651), while project alternative 2 (Warehouse Automation Project - 0.54077), project 1 (Data Warehousing Project -- 0.46731), project 4 (Battery life improvement -- 0.54077), and project 5 (Improvement of OEE -- 0.34093) follow closely, thus ensuring efficient project selection. Emphasis should be placed on project 3 when considering the 7 criteria while the other projects are closely monitored in the ranking order. Future research can explore the combination of other multi-criterion decision making approaches that enrich criteria weights and address the subjectivity of decision-makers' opinion. The hybrid methodology used in this work is applicable in other disciplines where selection and ranking problems exists. [ABSTRACT FROM AUTHOR]

When deciding which project to fund, the comparison of project-required resources and monetary or financial benefits is relatively straightforward (as these can be presented in dollar values). In contrast, comparing the nonmonetary benefits of projects can be challenging. The literature often overlooks this problem and only provides high-level approaches (e.g., comparing a project's contributions to the organization's strategic goals). Such approaches are inefficient as they are highly subjective and may be impacted by decision-makers' bias. To this point in time, the literature has not effectively addressed this problem. This literature gap and the practical importance of the problem motivate the development of detailed step-by-step methodological processes to reduce subjectivity and enable a quantitative comparison of the nonmonetary benefits of projects. This paper develops such a novel straightforward methodology and illustrates its application through a case study where nonmonetary benefits of a railway construction project are compared against a hospital expansion project. In addition to this methodological contribution, this paper also contributes to the current literature on construction engineering and management by showing how to perform an effective comparison of the nonmonetary benefits in the project investment decision. The paper also sheds light on how the reasoning structures of different project decision makers may be impacted by their degree of optimism. Practical Applications: Construction projects may deliver a range of dissimilar benefits. Unlike monetary values (such as required resources and financial or monetary benefits), the comparison of nonmonetary benefits (e.g., social values) of different proposals can be challenging. This paper provides a two-phased methodology to facilitate this comparison. In the first phase, two straightforward questions are asked to draw benefit satisfaction graphs, and in the second phase, different future scenarios are integrated to make the comparison process more reliable. Ultimately, a methodology is developed to show how the nonmonetary benefits of different projects can be compared. It is then illustrated on a case from the Australian government where the nonmonetary benefits of a railway construction project are compared against those of a hospital expansion project to assist with the decision as to which project should be given a higher priority to be funded. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to test and develop a method for generation and selection of six sigma projects. This is done by testing the use of the generation and selection method for six sigma projects (GSM_SSP) in a Brazilian manufacturing industry with the participation of managers, aiming to gather the user's perspective and improvement opportunities for the approach itself. Design/methodology/approach: The work adopts the action research (AR) approach once the researchers were busily involved in the training, implementation and use of the GSM_SSP. The intervention was performed in on a series of 15 workshops, with a group of managers, during six months. Findings: The application of the eight steps of the GSM_SSP approach assisted the company's management team to generate nine project candidates and also to select three six sigma projects. This study also finds and discusses barriers and lessons learned used to improve the GSM_SSP. Research limitations/implications: This study presents an example of how six sigma project generation and selection has been applied to a manufacturing industry by adapting AR to the process using the eight steps of GSM_SSP, demonstrating how the management team was involved. This study should be replicated in different companies because AR is limited in its generalization. Originality/value: To the best of the authors' knowledge, this study represents the first use of AR methodology in six sigma project selection. This study contributes a method that can generate and select six sigma projects. In doing so, the research offers a simple approach that can be used by managers. In addition, the steps of the approach before selection were explored. [ABSTRACT FROM AUTHOR]

The body of literature on goal programming (GP) approaches in modeling preferences and the satisfaction philosophy in multi-objective programming (MOP) decision-making processes is extensive. However, there has been little focus on how preferences change in relation to the decision-maker's (DM) behavior within this satisfaction philosophy, particularly in situations involving risk. To address this challenge, we propose introducing a behavior-type utility function into the GP model using the concept of a behavior function. This idea offers an innovative perspective for modeling DM's behavioral preferences in the imprecise GP approach by integrating a risk-aversion parameter specific to each objective. We then formulate a generalized behavioral-based GP approach for decision-making based on this new behavior-type utility function. To validate our proposed approach, we present an illustrative example of project selection in health service institutions, followed by a sensitivity analysis and comparisons with other approaches. The results demonstrate that DM's behavioral preferences significantly impact the decision-making process, and the proposed model provides more reasonable and convenient decisions for DMs with varying degrees of risk aversion.

This article explores the implementation of Neutrosophic TOPSIS, an advanced decision-making framework that extends classical and fuzzy set theories to handle the complexities of project selection amid uncertainty and indeterminacy. Neutrosophic sets are characterized by three parameters: truth, indeterminacy, and falsehood, which allow for a nuanced assessment of alternatives against defined criteria. Utilizing neutrosophic scales and expert evaluations, this method prioritizes projects by efficiently balancing multiple truth levels and addressing specific challenges such as judicial process optimization and labor education enhancement. The case study within the article demonstrates the application of Neutrosophic TOPSIS to select the most suitable project for improving labor relations and judicial efficiency in a post-pandemic world. The methodology proved effective in identifying the Digital Platform for Labor Education project as the optimal solution, given its alignment with strategic objectives and potential to handle identified challenges robustly. Future work could integrate Neutrosophic TOPSIS with other decision-making models and expand its application to more complex scenarios, potentially incorporating automated tools for a broader and more dynamic evaluation process. [ABSTRACT FROM AUTHOR]

Under competitive business scenarios, project selection is a crucial process for ensuring that projects appropriately align with an organization's goals, available resources, and relationships. This study proposes a novel hybrid decision support model that integrates the best–worst method (BWM) with the elimination and choice expressing reality (ELECTRE) method to solve project selection problems. The approach can be used to determine the weights of criteria by comparing two vectors and to provide the best result based on concordance and discordance analysis. An empirical study regarding the selection of strategic marketing information system projects is conducted to demonstrate the proposed methodology's benefits and rationality. The findings show that using the BWM–ELECTRE approach systematically leads to solid decision-making results involving compromise rankings based on quantitative and qualitative data, including decision-makers' preferences. This study contributes to the literature with a new methodology that consumes less time, provides high consistency, and enables subjectivity reduction in human-based judgments while providing helpful information to decision-makers in choosing the best project and/or outranking results among different relationships, resource constraints, or dynamic environments. [ABSTRACT FROM AUTHOR]

This paper uses a novel dataset on investments by the European Bank for Reconstruction and Development to quantify a (sizeable) trade‐off between the impact and financial objectives of a large lender. The unique feature of this dataset is ex ante records of impact. These are made at the early stages of work on each transaction alongside probability‐of‐default scores. Impact scores are further updated at the final approval stage with around 55 percent of transaction concepts translating into signed deals. We show that this approach delivers a simultaneous selection of debt investments on the quality of credit and impact with a sizable trade‐off between pursuing commercial and development objectives. For commercially riskier investments, impact characteristics have a greater bearing on the probability of an investment going ahead. We further use machine‐learning analysis to show that the impact of some investments is strengthened prior to project approval. [ABSTRACT FROM AUTHOR]

The project selection criteria are required for an effective project lifecycle, and the selected projects should correspond with organizational aims and objectives; yet, the organization lacks severely in its ability to act decisively on the problem. Hence, this study aims to highlight the best project selection criteria that are related to organizational success. In this manner, a quantitative survey approach was used, with closed-ended questions distributed to employees in Pakistan's construction industry. The relationship between project selection criteria and organizational performance was evaluated using structural equation modelling in SmartPLS through measurement and structural models. The findings reveal that a significant positive correlation was found for all the project selection criteria, except for the financial criteria. Furthermore, the results demonstrate that project selection criteria are critical for organizational success; hence, the findings will assist organizations in making better project selection decisions.

In this study, a customized version of a less-preferred methodology in decision-making processes, i.e., the interval type-2 fuzzy ORESTE (IT2F-ORESTE), is proposed, and its effectiveness for selecting the most viable projects is demonstrated. The findings are evaluated against those of fuzzy TOPSIS, which is among the most preferred methods, to provide evidence that the proposed method achieves comparable and even superior results. To this end, multicriteria decision-making studies conducted between 2016 and 2021 were examined. Subsequently, 30 automotive manufacturing projects were evaluated over seven criteria using the fuzzy TOPSIS and customized IT2F-ORESTE methods. The results revealed that IT2FORESTE assigned the highest ranks to projects with high earning potential, low cost, low number of operations, and high production capacity, whereas fuzzy TOPSIS failed to select the best project. To the best of the authors' knowledge, this is the first study to utilize this new IT2F-ORESTE method in project evaluation within the automotive industry and demonstrate its superiority over that of conventional methods. [ABSTRACT FROM AUTHOR]

While there are numerous studies on factors affecting the project and business performance in the literature, no research published on the concurrent roles played by formal (FPM) and informal (IPM) project management on project success. The study's three main objectives are: first, give a conceptual framework for evaluating the individual and concurrent contributions of FPM and IPM to project success. Second, the project management methods' mediation effects on the link between senior management support and project success are evaluated. Third, the effects of using multiple environmental control variables are investigated. A sample of 103 small to medium-sized enterprises was used to collect managerial and business operational data. Using structural equation modelling and correlational relationships, ten different models are examined. The findings revealed that IPM has a partial mediation effect, whereas FPM has no effect. Furthermore, the analysis revealed that IPM has the greatest impact on project success. Integration of FPM and IPM with environmental factors is considered. These findings are explained in terms of business, economic, and commercial diversities. [ABSTRACT FROM AUTHOR]

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The main purpose of this article is to identify the main characteristics of Lean Six Sigma projects executed by efficient companies. To do so, this article explores a large empirical database, with 112,421 projects, executed from 2001 to 2018 in 57 strategic business units from a heavy machinery producer, which is recognised as a worldwide reference in the use of Lean Six Sigma methodology. The research was conducted in two stages, first, the data was analysed with a Network Data Envelopment Analysis (DEA) model, which provided an efficiency ranking formed by the strategic business units. In the second stage, a Classification Tree (CHAID) method was used to identify the common characteristics of the projects accomplished by the most efficient ones. The findings show that these strategic business units are characterised by projects executed in departments closely related to senior management, especially Corporate Governance and Business Strategy. In addition, their projects usually focus on people, innovation, or cost reduction and are structured more frequently with the DMAIC cycle. These findings broaden the understanding of the criteria that should be considered in Lean Six Sigma project selection and prioritisation, by providing new insights (not identified in previous studies) to decision-makers and researchers, from projects associated with the efficient performance of Lean Six Sigma, which can be particularly valuable considering the multiple projects conducted in this initiative, that frequently compete for resources. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Business & Projects / Revista de Gestão e Projetos is the property of Revista de Gestao e Projetos and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The motivation for cost-effective management of highway pavements is evidenced not only by the massive expenditures associated with these activities at a national level but also by the consequences of poor pavement condition on road users. This paper presents a state-of-the-art review of multi-objective optimization (MOO) problems that have been formulated and solution techniques that have been used in selecting and scheduling highway pavement rehabilitation and maintenance activities. First, the paper presents a taxonomy and hierarchy for these activities, the role of funding sources, and levels of jurisdiction. The paper then describes how three different decision mechanisms have been used in past research and practice for project selection and scheduling (historical practices, expert opinion, and explicit mathematical optimization) and identifies the pros and cons of each mechanism. The paper then focuses on the optimization mechanism and presents the types of optimization problems, formulations, and objectives that have been used in the literature. Next, the paper examines various solution algorithms and discusses issues related to their implementation. Finally, the paper identifies some barriers to implementing multi-objective optimization in selecting and scheduling highway pavement rehabilitation and maintenance activities, and makes recommendations to overcome some of these barriers.

The purpose of this study is to determine the most important criteria for the selection of regional industrial projects. As part of the study, the authors conducted a review of modern literature on this topic and identified 19 criteria for the selection of regional industrial projects. To determine the most priority of these criteria, an expert survey was conducted using the ranking method. Entrepreneurs who have implemented several projects in the Zhambyl region, as well as employees of state bodies, representatives of the scientific sphere and employees of financial institutions were selected as experts. To summarize the data obtained as a result of the expert survey, descriptive statistics methods were used (indicators characterizing the degree of consistency of expert opinions, and coefficients of variation were calculated). According to the results of the study, the most important criteria for the selection of regional industrial projects were determined. After determining the criteria for the selection of regional industrial projects, an analysis was carried out on those projects that, a few years after the launch of the project, did not reach their design capacity. Analysis of the reasons for their stoppage or failure to reach the design capacity showed that the criteria identified by us as a result of this study are a priority. The results of this study may be of interest to both researchers and heads of regional government agencies when selecting projects for inclusion in the industrialization map of the region.

Due to resource limitations such as time, money, and human forces, projectoriented organizations always decide to choose among their candidate projects. In sustainable decision-making, in addition to responding to the internal needs of the organization, responding to social needs and protecting the environment are also taken into consideration. More specifically, in sustainable project selection, projects are selected with a wider view including economic, social, and environmental pillars. In this article, a combined approach has been presented to select investment projects sustainably, determining the ranking of projects based on the combined output of several multi-attribute decision-making methods. Since the data needed in decision-making is often associated with uncertainty, the problem of project selection has been modeled and analyzed in a non-deterministic way using a robust optimization approach. In the studied case, six projects were selected by the deterministic model, while the robust model reduced them to three projects due to the pessimistic modeling approach. It was also observed that by taking into account the uncertainty, the optimal values of three objective functions have been reduced by 28%, 46%, and 28% respectively; but the validity of the answers is guaranteed in non-deterministic real-world situations, which is very important in the investment problems. The main benefits of the proposed approach are: 1) integrating evaluation and selection phases in order to make wise and optimal decisions, 2) combining the results of different MCDM methods which helps the managers with selecting the projects that are generally acceptable by MCDM methods, 3) because a robust optimization model is implemented, the model solutions remain more feasible in resource fluctuations, and 4) the model prevents ignoring high-priority projects which can be outranked by permutations of lowerpriority projects. [ABSTRACT FROM AUTHOR]

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Project selection decisions are complex because they must balance not only financial returns, project risk, and fit with strategy, but also competitive circumstances. A rival's project development efforts provide two pieces of information: a market rivalry signal, indicating potentially heightened competition in a market, and a technological signal, indicating a possible solution to a problem in that market. We hypothesize that these signals affect a firm's likelihood of project selection in opposite directions, and that the timing of the signals matters for selection. We examine the drug development pipelines of the top 15 pharmaceutical companies from 1999 to 2016 to examine how rival projects drive the decision to progress a drug from preclinical laboratory trials to clinical trials in humans. Early-stage rival projects provide a stronger market rivalry signal, and they are associated with a decreased likelihood of the firm selecting its own project to compete in the same market. Late-stage rival projects signal technological feasibility and are associated with an increase in the likelihood of selection. We then exploit heterogeneity in market potential (i.e., disorder prevalence/incidence) and a molecular compound's technology (i.e., therapeutic modality) to independently manipulate the salience of the two signals. Finally, we provide evidence on how selection based on rival signals informs project success. Information from rival projects prompts the selection of more successful drugs, but only after a threshold when sufficient uncertainty has been resolved. This paper was accepted by Jayashankar Swaminathan, operations management. Supplemental Material: Data and the online appendix are available at https://doi.org/10.1287/mnsc.2022.4642. [ABSTRACT FROM AUTHOR]

Copyright of Revista Espacios is the property of Talleres de Impresos Oma and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, a multi-period, multi-objective mathematical model is presented for selecting oil and gas investment projects based on the Sustainable Development Goals (SDGs) approach, considering resource limitations. Based on the economic dimension of sustainability, the profit from the implementation of petrochemical projects is maximized, and based on the environmental dimension, the amount of greenhouse gas emissions, energy consumption, and produced waste is minimized. According to the social dimension, the number of job opportunities, the number of people covered by insurance, the job satisfaction of employees, the project’s impact on the regional economy, and the number of lost workdays is examined. The uncertainty of the strategic and operational parameters of the model has also been considered, and to deal with the uncertainty, fuzzy possibility programming (FPP) is used. The model is solved in GAMS optimization software with a two-stage approach based on fuzzy programming and the best-worst group decision-making method (BWM). Numerical results confirm the effectiveness of the proposed model and show that SDG will lead to a significant improvement in economic, environmental, and social dimensions without significantly reducing the profits of the selected oil and gas projects. [ABSTRACT FROM AUTHOR]

One of the constant concerns in public and private organizations is choosing a project from among the multitude of potential projects to be implemented. Due to the limited resources in different sectors, projects should be prioritized in order to obtain the maximum benefit. In national and government projects, it is not necessarily important to pay attention to financial components, and more dimensions should be considered. Sustainability is a component that considers various economic, environmental, and social aspects in the evaluation of projects. In this regard, in this study, the main goal is to evaluate and select rail transportation projects according to sustainability criteria. In general, 15 indicators were identified in three economic, environmental, and social sectors, which were weighted using the best–worst fuzzy method (FBWM). The most important indicators in the evaluation of projects are the investment cost, the rate of internal return from a national perspective, and the lesser impact of the plan on environmental destruction. According to the weighted indicators, the stochastic VIKOR approach is developed for the first time in this article, which was evaluated according to two scenarios of demand changes and cost changes of candidate projects. In the stochastic VIKOR approach, to deal with uncertainty, different scenarios are defined, through which it is possible to respond to different conditions and evaluate projects more realistically. Validation of this method is compared to other multi-criteria decision-making methods. The main contribution of this study is presenting the stochastic VIKOR approach for the first time and considering the uncertainty in project evaluation. The findings show that the projects that have the most economic gains from the national and environmental aspects are selected as the best projects. [ABSTRACT FROM AUTHOR]

Natural language processing, text mining and deep learning methods are used to obtain the desired information from textual data in many different sectors. Recently increasing number of R&D projects and differentiated project activity areas, as well as the determination of research areas to which R & D projects belong and the determination of reviewer suitable for these research areas, the support processes of projects may be negatively affected. In this article, in order to classify R&D projects, the data in the database where the study was carried out was primarily cleared and in order to classify the features by using the word representation method and automatic feature learning approach "Word2Vec", which is one of the natural language techniques, Convolutional Neural Network (CNN) models have been tried to be created. The evaluation results obtained from experimental studies on the data set of R&D projects and articles with R&D project submitted in the last ten years content selected from the TUBITAK Dergipark site were compared with other classic algorithms and showed that CNNs with Word2Vec models in particular produce more effective results with many performance parameters. [ABSTRACT FROM AUTHOR]

Overview: Effective portfolio management is vital to maximizing the value of the business' new product development (NPD) portfolio; it is also one of the weakest facets of NPD. Ongoing evaluation tends to be backward looking and process focused, and business cases are not updated with real time and more reliable data. A dynamic portfolio management approach is one solution, including the use of the Productivity Index (PI). Building in multiple iterations to get updated and robust data improves estimates of financial value and the PI. And since the value of a project depends on many factors, we present a multidimensional Value Based Scorecard that we developed using proven, research-based criteria, that companies can use to select and prioritize NPD projects effectively. [ABSTRACT FROM AUTHOR]

Manufacturing organizations have struggled with selecting right projects for sustainable LSS (SLSS) programs for operational excellence. This study is to propose a method for effective assessment of optimal SLSS projects. The importance weight of project selection criteria and prioritization of available projects are calculated using the novel best-worst-method (BWM) approach. The proposed methodology was authenticated through a real case example. The outcome reveals that out of five SLSS projects, P1 is the optimal project. Project P1 is the most significant production line for deploying the SLSS in case organization. The optimality in project ranking is tested through sensitivity analysis, the outcome noticed minimum sensitivity that claimed robust findings. This study is to be unique as there was very little evidence of prioritizing SLSS projects by utilizing a large set of criteria and applying the BWM approach. Further, BWM is the most suitable and reliable approach for prioritizing the alternatives when a large number of criteria are involved and it provides consistent outcomes with fewer inputs. The applied methodology will help top management to select the right project and opportunities in complex situations. Decision-makers and LSS consultants can also adopt the same approach for effective assessment of optimal SLSS projects for sustainable development. [ABSTRACT FROM AUTHOR]

The railway network design problem is one of the critical issues in the transportation sector due to its significance and variety of necessary applications. The major issue of this field relates to the decision of whether to increase the railways' capacity or construct a new route to meet demand. Although the budget is a great concern of the managers for making such a decision, environmental factors should be necessarily included in the decision-making process. Therefore, this research proposes a novel robust bi-objective mixed-integer linear programming (MILP) model to simultaneously minimize the total cost and environmental impact under uncertain conditions and within a given time horizon. The proposed problem addresses strategic and operational decisions through railway project selection and product flow determination. To deal with the bi-objectiveness of the model and tackle the complexity of the problem, a nondominated sorting genetic algorithm (NSGA-II) is employed. The proposed NSGA-II could reach near-optimal Pareto solutions in a reasonable solution time and showed a reliable performance for being employed in large-sized instances. It also indicates that the proposed NSGA-II can be utilized for solving large-sized samples in a very short time. [ABSTRACT FROM AUTHOR]

An agent and a principal are engaged in an ongoing relationship, where the agent receives project opportunities of random value and is biased towards implementation. Each period, the principal elicits a (non-contractible) recommendation from the agent, compares it to a random outside option and decides whether to implement the project or not. The key distortions in the stationary decision rule used by the principal that will sustain honest reporting by the agent are (i) discrimination against best projects, so that sometimes very good projects are not implemented even if they would benefit the principal, (ii) general favoritism, where proposals other than the best are sometimes implemented even when they have negative value to the principal, and (iii) focused leniency towards average projects, where the bias towards implementation is larger for average projects than for bad or good projects. The first limits the agent's incentives to exaggerate by decreasing the effectiveness of exaggeration, the second increases the overall value of the relationship to the agent while the last targets the projects for which the incentive to exaggerate is the largest to sustain truth-telling. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to deal with the selection of the sustainability-oriented Green Lean Six Sigma (GLS) project for the manufacturing industry in the complex decision-making environment. Moreover, the study also proposes a GLS implementation framework for improved organizational performance. Design/methodology/approach: GLS project selection has been done based on the six sustainability-oriented criteria formed from 17 sub-criteria (found from the literature and developed by authors). The weights of the criteria have been determined through the entropy method. The projects have been ranked based on the criteria through the advanced decision-making approach: grey relation analysis (GRA). The results of the study were validated using best worst method and sensitivity analysis. Findings: It has been found that the productivity-related criterion is the most significant among other criteria with entropy weight of 0.2721. GRA has been used in this research work to rank the potential GLS projects in a manufacturing industry based on six sustainability criteria, to select a project that exhibits the maximum potential for sustainable improvement. The machine shop has been found as the most significant GLS project with grey relation grade of 0.4742. Practical implications: The present study facilitates practitioners and industrial managers to implement an inclusive GLS approach for improved sustainability dynamics through effective GLS project selection and implementation framework. Originality/value: With increased globalized competition in recent times, new projects are being considered as the foundation stone for organizational success. The decision-making becomes quite complex to select an effective project due to the intriguing nature of various criteria, sub-criteria and different aspects of sustainability. To the best of the authors' knowledge, the present study is the first of its kind that provides ways for the selection of sustainability-oriented GLS projects. [ABSTRACT FROM AUTHOR]

Resources scarcity, available capabilities and cost-benefit point of view, make it essential to select the best project(s) from available project portfolio. Project selection process has a significant role in the success. Here the main problem is what projects must be selected and how manage simultaneous projects. Used approach to answer these questions must be real, fast, global, flexible, economic and easy to use. It is clear that choosing a good approach for project selection problem with economic and non-economic criteria can be vital for a project manager to success within constraints. The complexity of this problem increases as the number of projects and the number of objectives increase. Therefore, in this research we aim to present a heuristic based on genetic and simulates annealing to select and prioritize available projects based on economic and non-economic criteria. Considered issues are benefit, credit and risk (technical and financial). Presented method starts from multi population of generated solutions and moves toward the final solution. Comparison studies between our method with other recently method in the literature demonstrates the capability of it to find a good basket of projects. Experimental results demonstrate that this method can be used for all kinds of projects basket.

Undefinable nature of overseas markets substantially impacts the enterprises' decision to pursue projects abroad. This study aims at identifying and prioritizing the determinant factors which considerably influence the decision to go/not go for projects in overseas from the Indonesian construction enterprises' (ICEs) perspective. A comprehensive literature review purposed to identify the determinant factors of enterprises' decision in selecting overseas projects was undertaken at the beginning of study. Of 131 successfully identified, 31 factors were specified after a series of following screening methods. Firstly, the factors having similar meaning were incorporated into a term. A tally technique to indicate frequency of the factor appearance then was applied for which four times emergence as cut-off point. A two-round Delphi survey involving 11 industrial experts was carried out to assess the importance and frequency level of risk occurrence of the 31-factors. Significant index (SI) were calculated to prioritize these factors to which 21 items were defined as the determinant factors to go/not go for projects in overseas. The top ten ranking factors are: (1) quality and clarity of contract condition, (2) project scale/size, (3) complexity of project, (4) financial capability and support, (5) types of contract, (6) type of contract, (7) project/contract duration, (8) client's reputation, (9) political stability, and (10) economic health and stability. Besides defining the determinant factors, the findings of this research may assist other typical contracting companies to spotlight the central features of OCM in order to manifest their global vision. [ABSTRACT FROM AUTHOR]

In a Stackelberg max closure game, we are given a digraph whose vertices correspond to projects from which firms can choose and whose arcs represent precedence constraints. Some projects are under the control of a leader who sets prices in the first stage of the game, while in the second stage, the firms choose a feasible subset of projects of maximum value. For a single follower, the leader's problem of finding revenue-maximizing prices can be solved in strongly polynomial time. In this paper, we focus on the setting with multiple followers and distinguish two situations. In the case in which only one copy of each project is available (limited supply), we show that the two-follower problem is solvable in strongly polynomial time, whereas the problem with three or more followers is NP-hard. In the case of unlimited supply, that is, when sufficient copies of each project are available, we show that the two-follower problem is already APX-hard. As a side result, we prove that Stackelberg min vertex cover on bipartite graphs with a single follower is APX-hard. [ABSTRACT FROM AUTHOR]