This paper develops a new goal programming variant, extended revised multi-choice goal programming (ERMCGP), that combines the strengths of revised multi-choice goal programming (RMCGP) and extended goal programming (EGP). To achieve this, the revised multi-choice goal programming (RMCGP) variant is first examined from the perspective of modelling the decision maker(s) underlying preferences. The contribution of the RMCGP to the goal programming paradigm and its range of applications are detailed. Five preferential modelling areas for enhancement are then highlighted: (i) algebraic simplification, (ii) separate weighting of deviational variables, (iii) preferential compatibility with one-sided goals, (iv) normalisation, and (v) addition of a Chebyshev term to allow balanced solutions. By implementing enhancements (i)-(iv), a modified version of the RMCGP is proposed which is more computationally efficient and preferentially straightforward. Its linkage to a weighted goal programming model with a penalty function is shown. Preferential enhancement (v) leads to the new ERMCGP variant. The modified RMCGP and the ERMCGP are demonstrated using both a small literature example and a newly developed renewable energy allocation example and comparisons are drawn with the original RMCGP and EGP. Conclusions are drawn as to the future development of the ERMCGP within the goal programming framework.

This paper studies a household waste collection problem to serve a set of rural islands. The problem is modelled as an Insular Travelling Salesman Problem (InTSP), which simultaneously optimises port selection decisions for a set of islands or isolated regions to be served, along with the optimal maritime visit sequence for the selected ports. The problem considers two types of costs, the Maritime Transportation Cost incurred by a vehicle for visiting all the selected ports, and the Ground Transportation Cost incurred by island inhabitants to move the freight from (to) the selected ports. Based on an existing Extended Stakeholder Network Structure, this research aims to simultaneously balance fairness and efficiency among the islands, in addition to control the centralisation level in decision making process by contrasting the central level (i.e., maritime operations), and the regional or island level (i.e., ground operations). This research enhances the literature by proposing a baseline formulation of an Extended Goal Programming Model along with three distinct variants of normalisation. Results of a real-world application of insular routing in Southern Chile are presented, discussing the advantages and shortcomings of each formulation. The results show the advantages of employing the proposed modelling approach when addressing the studied problem from managerial and political perspectives. Choosing the appropriate normalization significantly affects the number and diversity of obtained solutions, which may finally enhance the decision-making process. The results denote how increasing efficiency may lead to “unfair” solutions in terms of perceived ground transportation cost by inhabitants of different islands.

This paper aims to contribute to the 2nd United Nations sustainable development goal, specifically the targets of tackling hunger, achieving food security and improved nutrition. For this purpose, a multi-objective model is developed to enhance strategic resource allocation in Brazilian food banks. In numerous instances, managers of food banks are confronted with the task of allocating resources to serve the most disadvantaged social institutions, whilst ensuring an equitable, efficient, and effective distribution of food. This paper hence formulates an extended goal programming (EGP) model to devise enhanced strategies for food allocation amongst a set of institutional demand points served by a food bank. A clustering methodology is applied in order to effectively group the institutions. A pairwise comparison methodology is utilised to derive the individual weights of each institution. A comprehensive sensitivity analysis is then undertaken including the cluster level weights and the EGP Alpha parameter than controls the mix of equity and efficiency in the model. The methodology is applied to a case study of a food bank serving 41 institutions in the Brazilian city of Botucatu. The results are analysed in the both in the context of methodology and of the application and conclusions are drawn.

The decarbonisation of energy systems has been a key driver for the advancement of renewable energy sources, including offshore wind, tidal, wave, and ocean thermal energies. As these resources increasingly become integral to national energy matrices, particularly in marine energy, it is crucial to analyse them using Multi-Criteria Decision-Making (MCDM) methods. This study aims to identify the primary topics addressed in MCDM applications related to Marine Renewable Energies (MREs) through a systematic literature review spanning the years 2000–2024. The research process involved five stages: (1) defining the problem, (2) surveying relevant papers, (3) conducting a preliminary analysis of the papers, (4) performing a detailed review, and (5) selecting the final sample of articles for analysis. The findings reveal a strong focus on offshore wind, wave, tidal, and ocean thermal energy studies, often combining multiple technologies. A classification of the research topics indicates that the majority of publications address site selection and spatial planning problems, employing methods such as AHP, PROMETHEE, TOPSIS, and Fuzzy techniques, frequently integrated with GIS tools. Conversely, research on governance, legal, and licencing aspects of these technologies within the broader marine sector remains limited. While the development of MREs is recognised as a significant opportunity, challenges such as technological maturity, design standardisation, reliability, accessibility, and governance must be addressed to enable their widespread adoption.

As a popular approach to last-mile delivery, third-party logistics (3PL) has drawn much attention in managing supply chains. Due to the presence of multiple factors, 3PL service provider evaluation is a multi-criteria decision-making problem. This study introduces methodological enhancements to the EDAS (Evaluation based on Distance from the Average Solution) method to generate more intuitive insights into decision-making. First, instead of the L1-metric, we introduced Euclidean-based EDAS (EDAS-E) to capture the underlying geometry of the space generated by a relatively high number of decision criteria in more comprehensive 3PL provider evaluations. Second, adopting the notion of "thinking in threes," we developed an algorithm to implement a sequential three-way decision (3WD) that addresses ambiguity and provides more explainability in the evaluation process. An actual case study evaluating five 3PL service providers under 17 criteria demonstrates the efficacy of the proposed sequential 3WD-EDAS-E, aiding decision-makers to systematically distinguish between acceptable and unacceptable alternatives and those requiring further evaluation. The comparative analysis yields a high agreement between the results generated by the proposed approach and those of other multi-criteria methods. Finally, implications for the field of decision making and avenues for future research are detailed.

This paper proposes a methodology for situations where strategic, large-scale, difficult to reverse decisions need to be made in the presence of multiple criteria and stakeholders. A novel nine-stage framework is proposed that contributes to the discrete multi-criteria paradigm by allowing a more holistic approach to the analysis of the underlying decision. This is achieved by introducing an underlying distance metric to allow for the consideration of balance between the criteria and a revised form of sensitivity analysis. The specific case study of the location of the offshore wind port in North-Eastern Brazil, a region that has good offshore wind potential but lacks sufficient infrastructure is developed. A hierarchical sustainability network of relevant infrastructural, socioeconomic, environmental, and physical characteristic criteria and sub-criteria is built by preferential elicitation from regional stakeholders. The proposed methodology is applied in order to analyze four potential port site locations in depth. The effect of the enhanced sensitivity analysis and consideration of balance between criteria is demonstrated by the results. The methodology is hence shown to lead to a more informed choice of port location. The paper concludes by drawing conclusions with respect to the use, limitations and potential further research development of the proposed methodology.

The climate crisis, driven by greenhouse gas emissions, necessitates a transition from fossil fuels to renewable energy. Offshore wind, with a global potential of 71,000 GW, eight times surpasses the current global installed electrical grid capacity. This potential provides a pathway toward the decarbonization of global energy systems. Additionally, its capacity is essential for producing green hydrogen, which plays a pivotal role in decarbonizing key sectors such as metallurgy, fertilizers, and maritime and air transportation. Mature offshore wind markets provide valuable learned lessons for emerging markets. This study identifies and systematizes those lessons learned, progress, and trends in offshore wind to enable a more inclusive energy transition. The findings offer recommendations for new markets, addressing regulation, infrastructure, technological innovation, and value chain optimization. Emphasis is placed on inclusive development through socioeconomic and environmental impact management, stakeholder engagement, and policy frameworks that promote local content and sustainable development.

Shipping activities continue to experience growth across a multitude of industrial sectors within the Arctic, hence there are risks in terms of severity and likelihood of accidents. The Arctic region is inherently dangerous to transportation and human existence due to its extreme climate and environmental conditions, and hence the complexities associated with emergency situations within the maritime domain are amplified when operating within the Arctic and North-Atlantic (ANA). The definition and characterisation of potential seaborne disasters and catastrophic incidents in the ANA region are significant enablers in providing a set of critical and sustainable tools for Search and Rescue (SAR), Oil Spill Response (OSR), and emergency management practitioners. Therefore, in this paper we aim to identify and characterise high-priority potential seaborne disasters and catastrophic incidents in the ANA region such as cruise ship accidents, oil leaks, radiological leaks, and fishing boat groundings. These were compiled as an outcome of a set of workshops carried out as part of the ARCSAR, EU Horizon 2020 funded project, and from analysis of the literature. We also provide root cause analysis techniques, tools for strategic decision-making, and means of mitigation. We demonstrate how such tools can be used by applying some of them to a selective case study and drawing lessons learned from the application of root cause analysis, which can help emergency response organisations with preparedness work and hence more efficient response. In doing so, we provide a set of tools that can be used for strategic and operational learning. Such approaches can help standardise the definition and characterisation of potential seaborne disasters and catastrophic incidents in the ANA region in both prospective and retrospective analysis.

This paper studies an innovative bi-objective optimization model for the dry port hub-and-spoke network that considers both the minimization of the total costs and the total amount of carbon emissions. The model is formulated as a two-stage stochastic program due to the uncertain nature of demand. Here, the decision variables include the optimal locations of dry ports, their respective numbers, and their connections, together with the flows of containers. Two types of dry ports are taken into account, where there may be container flows from a relatively small dry port (feeder dry port) to a large dry port (hub dry port). As the problem is NP-hard and practically too complex to solve by an exact method, an efficient hybrid Genetic Algorithm (GA) with interesting ingredients is developed to obtain a promising set of non-dominated solutions. The performance of the proposed methodology is evaluated on a case study of Tianjin Port, China. The computational experiments reveal that the proposed method is promising while providing a useful practical optimization tool that can provide insightful directions for governmental and industrial stakeholders as well as logistic companies on which dry ports can make a suitable addition to their portfolio.

Business fluctuations and pandemics such as COVID 19 have revealed the need for more resilient approaches and processes in the asset management domain. This research aims to design a resilience-based maintenance optimisation (RbMO) framework that absorbs the fluctuations in the operating context and sustains asset performance at an optimum maintenance cost and an acceptable level of risk. The paper proposes a framework that employs the analytical hierarchy process (AHP) to translate the different operating context parameters into risk aspects with relative weights that differ from one operating scenario to another. The Knapsack method then uses these relative weights to define the risk reduction of each maintenance task and pick the optimum ones within the allocated maintenance budget. Additionally, the approach introduces the nested criticality grid (NCG), which graphically demonstrates the inherent, Knapsack and residual risk profiles from the failure mode level up to the unit level enabling an informative decision-making process, where the asset owner can wisely distribute the maintenance budget or achieve efficient cost savings. Finally, the main advantage of this model lies in its comprehensive continuous improvement cycle of the proposed RbMO framework, which ties the different components of the proposed approach together and forms an integrated system that promotes a more resilient strategy for asset maintenance management, enabling asset owners to effectively manage business fluctuations and operating context changes.

In recent years, offshore wind power has become increasingly relevant as a key alternative for contributing to the global economy’s decarbonization. Also, the accelerated technological development of the offshore wind turbine influences the increase in size and weight of its main components. This requires an appropriate port infrastructure to support the installation, operation, and maintenance and future decommissioning of offshore wind farms, and especially to serve as an area for manufacturing these components, addressing logistical challenges associated with land transport. This research aims to identify the factors that characterize a suitable port to support the offshore wind industry, also bringing the new green port industry concept. A systematic literature review was conducted via analyses of 126 documents, and a survey procedure was applied to validate the proposed model. As a result, a characterization model was proposed that includes 71 factors classified into 6 dimensions: physical characteristics, port layout, connectivity, port operation, port–farm performance optimization, and governance for sustainability, which is the main novelty of this study. The results contribute to the advancement of the offshore wind energy sector and can provide significant benefits for regional development and local communities with offshore wind potential.

Offshore wind has developed significantly over the past decade, and promising new markets are emerging, such as Brazil, South Africa, India, Poland, and Turkey. As logistic transport activities increase complexities, developing regional supply chains can help to reduce costs and enhance the sector’s competitiveness. This article proposes a framework for the industrial development of the offshore wind supply chain in new markets. This study is grounded in a systematic literature review and is validated through a multi-case study, identifying key variables and factors influencing industrial growth. Adopting a process-based approach, factors and variables were modeled into a framework, encompassing the following four phases: (1) demand assessment of a new sector, (2) sectorial and industrial planning, (3) industrial development and maturity, and (4) sectorial and industrial renewal or decline. Each phase brings together a group of policies. Our findings show the policies’ interrelations. These results complement the few studies that have examined the industrial development process, providing a clear guide as to the process for the development of the offshore wind industry in specific regions. Thus, the framework provides elements that contribute as a valuable tool to the debate, structuring, and development of public policies for the industrial development of a new sector.

This paper presents a methodology for the incorporation of poverty related concepts into the goal programming paradigm. The concepts of poverty line, absolute poverty and relative poverty are discussed in the context of their potential usage in the field of Operations Research. The synergy between goal programming and poverty lines is examined. Two additional meta-goals are proposed for inclusion in a meta-goal programming framework based around absolute and relative poverty respectively. The resulting poverty incorporating meta-goal programming model is compared against other goal programming variants over a test set of school budget allocation models. The results demonstrate how the incorporation of poverty principles allows for greater modelling flexibility in the goal programming framework. This in turn allows decision makers to avoid solutions that are below threshold levels for a subset of stakeholders in either a relative or absolute sense. Appropriate conclusions and suggestions for future research are given.

Offshore wind energy has achieved significant reductions in its levelized cost of energy (LCoE) in the past decade, but still needs efficiency improvements. Approximately 18% of the LCoE is related to logistical costs, underscoring the need for optimization in this area. Despite its importance, logistical decisions during offshore wind farm installations remain underexplored in the literature. This article aims to identify and structure the relationships of logistic decisions to optimize total installation costs. A conceptual framework is proposed, detailing logistical decisions and their influencing factors. The results are based on a literature review and survey research for validation with specialists in logistics and offshore wind farms. The findings include the key decisions: port installation selection; vessel fleet selection; installation strategy selection; turbine pre-assembly method selection; aggregate planning approach; installation schedule coverage; storage strategy of components; and the degree of sharing information. The framework reveals the importance of coordinating the value chain in the installation process, mainly due to the influence of weather factors; the logistic decisions, when considered in a systemic view, can contribute to a global efficiency gain in the installation process.

This study presents a two-phase approach of Data Envelopment Analysis (DEA) and Goal Programming (GP) for portfolio selection, representing a pioneering attempt at combining these techniques within the context of portfolio selection. The approach expands on the conventional risk and return framework by incorporating additional financial factors and addressing data uncertainty, which allows for a thorough examination of portfolio outcomes while accommodating investor preferences and conservatism levels. The initial phase employs a super-efficiency DEA model to streamline asset selection by identifying suitable investment candidates based on efficiency scores, setting the stage for subsequent portfolio optimization. The second phase leverages the Extended GP (EGP) framework, which facilitates the comprehensive incorporation of investor preferences to determine the optimal weights of the efficient assets previously identified within the portfolio. Each goal is tailored to reflect specific financial factors spanning both technical and fundamental aspects. To tackle data uncertainty, robust optimization is applied. The research contributes to the robust GP (RGP) literature by analyzing new RGP variants, overcoming limitations of traditional and other uncertain GP models by incorporating uncertainty sets. Robust counterparts of the EGP models are accordingly developed using polyhedral and combined interval and polyhedral uncertainty sets, providing a flexible representation of uncertainty in financial markets. Empirical results, based on real data from the Tehran Stock Exchange comprising 779 assets, demonstrate the superiority of the proposed approach over traditional portfolio selection methods across various uncertainty settings. Additionally, a comprehensive sensitivity analysis investigates the impact of uncertainty levels on the robust EGP models. The proposed framework offers guidance to investors and fund managers through a pragmatic approach, enabling informed and robust portfolio decisions by considering efficiency, uncertainty, and extended financial factors.

The presence of multiple criteria for evaluating wastewater reuse applications indicates the potential usage of Multi-Criteria Decision-Making (MCDM) methods for this purpose. However, there is currently a scarcity of studies in the domain literature that utilize MCDM approaches in this application topic. This paper therefore advances the domain literature in two distinctive ways. Firstly, it analyzes and advances the reuse agenda of wastewater from thermal power plants, recognized as large-scale users of water, thus promoting greater water circularity. Secondly, it provides a methodological advance by integrating the notion of Three-Way Decision (3WD) into the computational structure of MCDM methods by introducing a middle reference point. Such an initiative results in a novel 3WD extension of the Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS) method. Additionally, this work provides a proof that the MARCOS method utilizes a compromise solution in identifying priority alternatives, along with the integration of a Weighted Aggregated Sum Product ASsessment (WASPAS) metric. An initial hypothetical example illustrates how the proposed approach augments the canonical MARCOS method, particularly in promoting the “thinking in threes” as a more natural information processing approach and the high degree of distinguishability of priorities between decision alternatives. An actual case study in a thermal power plant then demonstrates the contributions of this work. With the best-worst method used to determine the priorities of the decision attributes, the findings reveal that wastewater reuse applications achieving reduced costs for needed infrastructures, operational simplicity, technological compatibility, consumer safety and household savings are preferred by stakeholders. The 3WD-MARCOS approach identifies industrial and commercial use, municipal use, environmental restoration, and household use as the high-priority alternatives, with cooking and drinking as least preferred. These insights guide stakeholders in their design of initiatives that allocate resources for greater wastewater reuse. A comparative analysis yields high consistency of these findings with similar MCDM methods. In addition, the efficacy of the novel 3WD-MARCOS method highlights its potential in handling MCDM problems, including those promoting water circularity.

The formulation of the duals of the original goal programming variants took place in the decades following its introduction, principally for computational purposes. However the development of more recent Goal Programming variants has not been matched by the formulation and analysis of their associated duals. This paper furthers the topic of goal programming duality by formulating the duals of a progressive sequence of up to the most recent Goal Programming models: Weighted, Chebyshev, Extended and Extended Network Goal Programming models. It interprets the results and shows the insights that the duals can provide, principally by providing an understanding of the interactions between the multiple objectives in each case. The paper concludes with a comparison of the results and shows how the sequence of duals mirrors the sequence of the associated primals.

This paper aims to address the problem of locating/upgrading health care facilities with appropriate service levels while considering the preferences of all stakeholders mapped across a hierarchical decision network. To this end, an extended network goal programming model is adapted to ensure that a mix of balance and optimisation is attained in order to offer the decision that best satisfies the objectives of the national health care system, which are: network coverage, service level, network cost, and social impact of health centres. In addition, due to the highly uncertain environment of the health care systems, the robust counterpart of this model using the budget-of-uncertainty approach is developed in order to analyse the health care network’s performance to deal with uncertain parameters such as the national allocated budget and social impact. Key parameters which indicate the level of non-compensation between objectives, level of non-compensation between stakeholders, and level of centralisation in the health network along with the uncertainty budget are utilised to analyse the dynamics of decision network. The effectiveness of the model is demonstrated through use of a case study. The best-worst method is used to select a number of appropriate potential projects that serve as an input for the proposed model. To highlight the practical implications, different parametric analyses under both deterministic and uncertain environments are conducted. In addition, these experiments explore the compensatory behaviour between objectives and stakeholders in the network. Finally, some managerial insights are provided arising from the analytical results.

It is fair to assume that selection of a post-warranty maintenance strategy is one of the main challenges the capital-good owners face; however, there is relatively little research done in this area. This paper aims to assist asset owners in selecting the equipment maintenance package at the warranty termination time; it proposes the shape package process (SPP), which represents a comprehensive process that facilitates the choice between multiple packages, considering the cost and risk associated with each package.The main novelty of this paper is introducing two new parameters to compare maintenance packages: the risk reduction factor (RRF) to assess the maintenance task effectiveness in reducing non-financial risk and the value-added indicator (VAI) to evaluate the task efficiency in reducing the financial risk. Additionally, the suggested process includes mapping the risk profile associated with each package, enabling a cost-cutting process where the tasks with less value and risk mitigation can be easily identified and eliminated.A case study then demonstrates the process flexibility in comparing different maintenance packages using the analytical hierarchy process (AHP); while presenting RRF and VAI at the task and package levels when imple-mented in a cooling water system to select the post-warranty maintenance package from an asset owner's perspective.

Platelet supply chain (PLT SC) management is always a challenging task for healthcare systems due to the nature of platelets (PLTs). PLTs have an extremely short shelf life and their demand is highly uncertain, which may lead to a high percentage of wastage and shortage in the corresponding PLT SC. This paper proposes a two-stage stochastic programming (2SSP) model to investigate the opportunity of incorporating frozen PLTs (FPLTs) into the PLT SC to see how it can improve the performance of the PLT SC in which the PLTs are used only in liquid form with respect to the platelet shortage, wastage and substitution penalties in transfusions. To investigate a more realistic situation when clear targets of blood shortage, wastage and substitution penalties are available, an extended goal programming model is built based on the proposed 2SSP model. Furthermore, we generate scenarios based on the real data provided by healthcare practitioners using the combination of a topdown forecasting approach and a Monte Carlo based scenario generation method. From the experimental results we note that, when comparing the model that incorporates FPLTs with the one that doesn't, the average reduction rates for platelet shortages, wastage, and substitution penalties in transfusions are 0.55, 0.55, and 0.23, respectively, when 40% of the overall demand is from patients who can receive both liquid PLTs and thawed PLTs sourced from FPLTs (Patient Type I). These rates can be further improved to 0.64, 0.55 and 0.23 or 0.65, 0.55 and 0.23 when 50% or 60% of the total demand is from Patient Type I. Furthermore, in contrast to realworld performance, the output of the 2SSP model, when not incorporating FPLTs, results in notable reductions in platelet shortage, wastage, and substitution penalties by 95%, 56% and 18%, respectively.