Best-Worst Method (BWM) is a structured approach for eliciting criteria weights in multi-criteria decision-making. This review paper revisits the original BWM formulation and procedural logic, and discusses behavioral motivations for dual anchoring and structured elicitation. It then synthesizes major methodological developments, covering linear and multiplicative formulations, interaction modeling extensions (nonadditive models), Bayesian formulations, group-aggregation models, tradeoff-based elicitation, parsimonious and disaggregation-based variants, sorting extensions, and fuzzy and belief-based treatments of imprecision and epistemic uncertainty. Because the reliability of inferred weights depends on judgment quality, the paper consolidates and compares consistency checking approaches and discusses their implications for practice. Representative application domains are reviewed to illustrate how BWM is deployed in practice. Finally, future research directions are outlined, emphasizing behavioral validation, integration into complete decision pipelines, scalable elicitation support, and cautious human–AI co-production that preserves problem-specific preference meaning. These directions also include transferring BWM's anchored elicitation principle to other preference elicitation approaches, including MACBETH, UTA, and conjoint analysis.

Eliciting the weights of attributes is a key step in multi-attribute decision-making methods. The weights usually represent the relative importance of the attributes or the tradeoffs among them in forming a decision. Various weight elicitation methods exist, each based on different assumptions and procedures. Still, many of these methods do not explicitly account for the potential influence of cognitive biases in their design. This study examines the anchoring bias, a well-known cognitive bias, in the weight elicitation step (the Tradeoff procedure) of multi-attribute value theory (MAVT). We developed the following three hypotheses: (i) Using the most important (best) attribute to construct the indifference pairs in the Tradeoff procedure leads to higher weights for the best and worst attributes and lower weights for the other attributes, (ii) using the least important (worst) attribute to construct the indifference pairs in the Tradeoff procedure leads to lower weights for the best and worst attributes and higher weights for the other attributes, and (iii) using both best and worst attributes to construct the indifference pairs (i.e., the best–worst tradeoff: BWT) mitigates the anchoring bias. To test the hypotheses, we conducted an experiment by designing a questionnaire based on MAVT and collected data from 336 participants for a decision problem. The findings indicate that the anchoring bias has a significant impact on the Tradeoff procedure and that the BWT is effective in mitigating this bias.

Cognitive biases significantly influence decision-making by distorting how individuals perceive and evaluate outcomes over time. This systematic review synthesizes research from various domains, including behavioral economics, psychology, and health, to explore six time-related biases affecting intertemporal judgments and trade-offs. We analyze the underlying mechanisms of each bias, map their interrelationships, and uncover their impacts on both individual choices and societal decisions. Drawing upon empirical evidence, we propose tailored strategies to mitigate the adverse effects of these biases. Our findings contribute to the literature not only by enhancing the understanding of time-related cognitive biases but also by providing practical insights for improving decision-making and policy design aimed at promoting long-term well-being. The review concludes by highlighting critical gaps in the literature and outlining a future research agenda to further investigate and address biases in intertemporal decision-making.

Alignment of core supply chain functions with corporate supply chain strategy is a key success factor for firms. Misalignments can lead to inefficiencies, higher costs, risks, and weaker performance. In particular, misalignment between transportation strategy and supply chain strategy can reduce responsiveness and flexibility, increase risks, and amplify environmental impacts. Although classic supply chain frameworks established the theoretical foundations of supply chain strategy, they treated transportation as a secondary issue and overlooked contextual decision factors such as product density, perishability, product life cycle, resilience, and sustainability. Consequently, a conceptual model that systematically integrates the complexity of the transportation and supply chain strategy alignment is not yet present in the literature. To address this gap, we develop a conceptual framework that introduces four transportation strategies, i.e. Cost-Oriented, Flexibility-Reliant, Modal-Control-Intensive, and Value-Enhanced, mapped against supply chain strategies across thirteen contextual criteria. Using the Best-Worst Method, we supplement the framework with an illustrative survey study of Global Fortune 500 companies. The results indicate that no single transportation strategy is right across all supply chains, but tailor-made service bundles can lead to alignment. Our study extends alignment theory to transportation and offers practical insights for managers of shipper firms and logistics service providers.

This study examines the power dynamics and their impact on involvement levels and compromise complexities in B2B joint decision-making within Dutch high-tech firms. Using a five-phase framework, it investigates the asymmetrical participation of co-owning decision-makers in dyadic buyer-supplier relationships and presents a conceptual model to explain how different power sources influence business interactions. A qualitative multiple case study involving Dutch high-tech firms was conducted. The research reveals that power bases significantly determine both involvement levels and the complexities of compromises, showing a positive correlation between involvement and compromise complexity. This study provides guidance for B2B stakeholders, offering diagnostic tools for understanding power structures and strategies to enhance collaborative decision-making. Additionally, the paper recommends governance frameworks and role delineations to improve participation levels, especially for companies with less power. This scholarly work enriches the literature by clarifying the relationship between power bases, involvement levels, and compromise complexities, and extends the application of social power theory to high-tech B2B contexts.

As ports are experiencing heavier traffic, the pressure to improve port call processes is increasing. Port call optimization (PCO) is one of these improvement initiatives, enabling the arrival of vessels to the port just-in-time when the vessel services, like pilotage, towage, and mooring, are all readily available. Otherwise, vessels that sailed at full speed to arrive at the port may have to wait, idling at anchorage, occupying space, burning fuel, and leading to increased congestion. One of the main challenges in the implementation of PCO is determining the time at which availability of these services can be guaranteed. The paper addresses this challenge by presenting a model that jointly schedules vessels and service providers. It extends the current approaches to allow application to larger and busier ports, where repositioning times for pilots and tugboats is highly variable and vessels experience waiting times between services. The problem is formulated as a mixed-integer linear programming one and is modelled in continuous time. We test alternative scheduling strategies using three different objective functions, based on the current ‘first-come-first-serve’ approach, a minimal level of service, and the best capacity utilization. The model is applied on data made available by the Port of Rotterdam, and it provides a full-service schedule for vessels and service providers.

Anchoring bias refers to the human tendency to rely heavily on an initial piece of information when making judgments. This bias has significant implications for decision analysis methods that rely on human judgments. This study examines the influence of anchoring bias in the value function elicitation step of the multiattribute value theory, specifically within the midvalue splitting procedure. We hypothesize that the starting point provided by the analyst during elicitation creates a bias in decision makers’ judgments, leading to distorted value functions and ultimately affecting decision outcomes. We also hypothesize that counter-anchoring and avoiding the use of anchors mitigate the effect of anchoring bias. To test the hypotheses, we designed an experiment and collected data from 320 subjects. The findings show that the starting point in the midvalue splitting procedure could change the attribute-specific value functions and, consequently, the overall value of the alternatives. Additionally, two debiasing strategies, counter-anchoring and avoiding the use of anchors, were found to be effective in reducing the effect of anchoring bias. The implications of this study can extend to other structured value function elicitation methods.

We analyze the stability of a game-theoretic model of a polymorphic eco-evolutionary system in the presence of human intervention. The goal is to understand how the intensity of this human intervention and competition within the system impact its stability, with cancer treatment as a case study. In this case study, the physician applies anti-cancer treatment, while cancer, consisting of treatment-sensitive and treatment-resistant cancer cells, responds by evolving more or less treatment-induced resistance, according to Darwinian evolution. We analyze how the existence and stability of the cancer eco-evolutionary equilibria depend on the treatment dose and rate of competition between cancer cells of the two different types. We also identify initial conditions for which the resistance grows unbounded. In addition, we adopt the level-set method to find viscosity solutions of the corresponding Hamilton–Jacobi equation to estimate the basins of attraction of the found eco-evolutionary equilibria and simulate typical eco-evolutionary dynamics of cancer within and outside these estimated basins. While we illustrate our results on the cancer treatment case study, they can be generalized to any situation where a human aims at containing, eradicating, or saving Darwinian systems, such as in managing antimicrobial resistance, fisheries management, and pest management. The obtained results help our understanding of the impact of human interventions and intraspecific competition on the possibility of containing, eradicating, or saving evolving species. This will help us with our ability to control such systems.

Game-theoretic models are frequently used to analyse the effects of information availability and quality on supply chain decision-making. Although information asymmetry plays a vital role in shaping sustainable supply chains, a comprehensive review of these models within this context remains lacking. This study conducts a systematic literature review and performs an in-depth content-based analysis of 73 peer-reviewed journal articles, categorising them based on their assumptions regarding supply chain structure, information structure, and interaction among supply chain members. We find that researchers are extending traditional supply chain models to address emerging challenges and opportunities driven by sustainable practices under asymmetric information. However, the research remains in a preliminary phase, with most models relying on simplified settings – typically dyadic, single-product, and single-period frameworks – and focusing primarily on demand and cost information asymmetries. Multilateral information structures and non-contractual coordination mechanisms remain largely unexplored. Theoretical advancements have considerably outpaced empirical validation, revealing a critical gap in the integration of real-world practices. Our findings highlight the importance of information sharing and coordination mechanisms in achieving sustainability outcomes and improving supply chain performance. These insights enrich the theoretical discourse on information asymmetry in sustainable supply chains.

Evolutionary cancer therapy (ECT) delays or forestalls the progression of metastatic cancer by adjusting treatment based on individual patient and disease characteristics. Clinical implementation of ECT can improve patient outcomes but faces technical and cultural challenges. To address those, we propose a systems approach incorporating systems modeling, problem structuring, and stakeholder engagement. This approach identifies and addresses barriers to implementation, ensuring the feasibility of ECT in clinical practice and enabling better metastatic cancer care.

Despite its recent introduction in literature, the Best–Worst Method (BWM) is among the most well-known and applied methods in Multicriteria Decision-Making. The method can be used to elicit the relative importance (weight) of the criteria as well as to get the priorities of the alternatives on the criteria at hand. In this paper, we will present an extension of the method, namely, the parsimonious Best–Worst-Method (P-BWM) permitting to apply the BWM to get the priorities of the alternatives in case they are in a large number. At first, the Decision-Maker (DM) is asked to give a rating to the alternatives under consideration; after, the classical BWM is applied to a set of reference alternatives to get their priorities used to compute, then, the priorities of all the alternatives under consideration. We propose also a procedure to select reference alternatives, possibly in cooperation with the DM, providing a well-distributed coverage of the rating range. The new proposal requires the DM a fewer number of pairwise comparisons than the original BWM. Another contribution of the paper is related to the comparison between BWM, P-BWM, the Analytic Hierarchy Process (AHP), and the parsimonious AHP in terms of the amount of preference information provided by the DM in each method to apply it. In addition to the standard approach, we propose one alternative way of inferring the priority vectors in BWM and P-BWM based on the barycenter of the space of alternatives priorities compatible with the preferences given by the DM. Finally, an experiment with university students has been conducted to test the new proposal. Results of the experiments show that P-BWM performs better than BWM in terms of capability to represent the DM's preferences and the difference between the results of the two methods is significant from the statistical point of view. The new proposal will permit to use the potentialities of the BWM to get the alternatives’ priorities in real-world decision-making problems where a large number of alternatives must be taken into account.

Since the development of the best–worst method (BWM) in 2015, it has become a popular research focus in multi-criteria decision-making. The original optimization problem of the BWM is a nonlinear min–max model that can lead to multiple optimal solutions, while the linear model of the BWM produces a unique solution. The two models need to be solved by optimization software packages. In addition, although the linear model of the BWM can obtain a unique solution, it produces different feasible regions than the nonlinear model of the BWM, and it changes the objective function. This study aims to solve the nonlinear model of the BWM mathematically to obtain the analytical forms of the optimal solutions. First, we transform the original nonlinear model of BWM into an equivalent optimization model driven by the optimally modified comparison vectors. The equivalent BWM provides a solid basis for computing the analytical solutions. Second, for not-fully consistent pairwise comparison systems, we strictly prove that there is only one unique optimal solution with three criteria, and there might be multiple optimal solutions with more than three criteria. We further develop the analytical forms of these unique and multiple optimal solutions and the optimal interval weights. Third, we develop a secondary objective function to select a unique solution for the BWM. The secondary objective function retains all the characteristics of the original nonlinear model of the BWM, and we find the unique solution analytically. Finally, some numerical examples are examined, and a comparative analysis is performed to demonstrate the effectiveness of our analytical solution approach.

This paper presents a new multi-criteria decision-making (MCDM) method, namely the ratio product model (RPM). We first overview two popular aggregating models: the weighted sum model (WSM) and the weighted product model (WPM). Then, we argue that the two models suffer from some fundamental issues mainly due to ignoring the ratio nature of the alternatives' scores with respect to the criteria and the importance weights of the criteria. Building on the notion of compositional data analysis, the developed RPM regards performance scores and criteria weights as compositions, which solves the issues around the WSM and WPM. Using several examples, we show that the WSM and WPM could lead to erroneous conclusions, whereas the RPM could lead to fully reliable conclusions. Since many MCDM methods rely on some aggregation approaches, the proposed method is a significant contribution to the field and puts forward the correct way to analyze decision problems while respecting the nature and constraints of the input data.

To design a bioethanol supply chain, along with the transportation and operational costs, it is vital to consider more factors categorized into three sustainability pillars (i.e. economy, social and environment). In this paper, to develop a mathematical model for bioethanol supply chain (BSC), we propose a two-phase methodology; in the first phase, using a sustainable framework of attributes contributing to the facility location selection in the BSC network, we calculate the sustainability score of alternatives through employing the best-worst method (BWM). Then, considering the results of the multi-attribute step as the parameters of an objective function called the sustainability value function, we develop a bi-objective multi-level bioethanol supply chain model. To solve the proposed model, a Nested bi-objective Optimization Genetic Algorithm (NbOGA) is introduced in this research. Finally, we evaluate the performance of the presented BSC model and the algorithm for a real-world problem. The results show that using the proposed structure, both sustainability attributes and transportation costs are appropriately satisfied in the BSC network.

Joint decision-making is one of the coordination mechanisms to address the inherent complexity of business-to-business (B2B) processes within a supply chain. Joint decision-making can be helpful to define shared goals and objectives, identify supply chain failures and opportunities, and consolidate supply chain success. Parties may benefit directly from a partnership's potential and synergies by collaboratively making decisions. However, specific business conditions need to be in place to enable joint decision-making. This paper investigates how companies in a dyadic relationship arrive at joint and individual supply chain decision-making structure. We examine the drivers, facilitators, and barriers of making joint as well as individual decisions within the supplier-buyer dyad and frame our arguments borrowing perspectives from resource dependency theory, transaction cost economics, collaboration theory, and social exchange theory. The paper presents a case study of Dutch high-tech companies, analysing experiences of supply chain managers via semi-structured interviews. High-tech firms often collaborate and share supply chain decisions due to the high-value capital equipment as well as a shared dependency on highly specific scarce resources. Our study provides new empirical insight into how firms cope with conflicting drivers, facilitators, and barriers in collaborations, controlling their decision-making structure. From the case study, we identify the combinations of facilitators and drivers that tend to promote the existence of joint decisions. We conclude with providing a list of suggestions for decision-makers and future research.

Priorities in multi-criteria decision-making (MCDM) convey the relevance preference of one criterion over another, which is usually reflected by imposing the non-negativity and unit-sum constraints. The processing of such priorities is different than other unconstrained data, but this point is often neglected by researchers, which results in fallacious statistical analysis. This article studies three prevalent fallacies in group MCDM along with solutions based on compositional data analysis to avoid misusing statistical operations. First, we use a compositional approach to aggregate the priorities of a group of DMs and show that the outcome of the compositional analysis is identical to the normalized geometric mean, meaning that the arithmetic mean should be avoided. Furthermore, a new aggregation method is developed, which is a robust surrogate for the geometric mean. We also discuss the errors in computing measures of dispersion, including standard deviation and distance functions. Discussing the fallacies in computing the standard deviation, we provide a probabilistic criteria ranking by developing proper Bayesian tests, where we calculate the extent to which a criterion is more important than another. Finally, we explain the errors in computing the distance between priorities, and a clustering algorithm is specially tailored based on proper distance metrics.

Cooperation between vessel service providers can improve the performance of ports. However, the potential impact of such cooperation has not yet been quantitatively addressed in the literature. We present an assessment using a port simulation model where the exchange of information has been made explicit. Cooperation is modelled as information exchange between the pilotage and towage service providers for the deployment of pilots and tugboats. A first application of the model is shown for the case of Port of Rotterdam. We find that time savings of up to 30% in waiting times can be achieved, while both service providers improve their performance. These findings provide empirical confirmation of the expected benefits of cooperation in ports as voiced in the literature. Furthermore, the results underscore the importance of moving beyond an ad-hoc synchronizations of these services towards systematic cooperation, to the benefit of ports as well as the service providers.

This study introduces a general framework, called Bi-sided facility location, for a wide range of problems in the area of combined facility location and routing problems such as locating test centres and designing the network of supermarkets. It is based on a multi-objective optimisation model to enhance the service quality which the clients received, called client-side, and enhance the interconnection quality and eligibility among the centres, called center-side. Well-known problems such as k-median and k-centre for the client-side and the travelling salesman problem for the centre-side are taken into account in this paper. After discussing the complexity of this kind of combination, we propose a heuristic approximation algorithm to find approximation Pareto-optimal solutions for the problem. The algorithm is an efficient local search utilising geometric objects such as the Voronoi diagram and Delaunay triangulation as well as algorithms for computing approximation travelling salesman tour. In addition to the comprehensive theoretical analysis of the proposed models and algorithm, we apply the algorithm to different instances and benchmarks, and compare it with NSGA-II based on set coverage and spacing metrics. The results confirm the efficiency of the algorithm in terms of running time and providing a diverse set of efficient trade-off solutions. Highlights: Introducing a general bi-side location model considering centres and clients’ utilities Discussing and proving the NP-hardness of the model in the general framework Considering two instances; k-centre and k-median for client-side and TSP for centre-side Proposing an efficient geometric-based algorithm for solving the problems Implementing, testing, and comparing the proposed algorithm on several benchmarks.

The Physical Internet (PI) is a paradigm-changing and technology-driven vision, which is expected to significantly impact the development of the freight transport and logistics (FTL) system of today. However, the development of the FTL system towards the PI creates much uncertainty for its current stakeholders. Ports are one of those stakeholders that are expected to be profoundly affected by these developments. However, research that focuses on port policy, under the uncertain developments towards the PI, is still lacking. By providing port authorities with insights and recommendations on robust policy areas, we address this void in literature. We conduct a scenario analysis in combination with multi-criteria decision analysis (MCDA) to determine the importance of port performance indicators and policy areas in different scenarios. The most significant, uncertain, and orthogonal factors for the development of the PI are technological development and institutional development. We find that for a proper alignment with the PI vision, in three out of four scenarios, ports should prioritize the implementation of digital solutions and standards, as opposed to an infrastructure focused policy.

Consumer goods supply chains are intensifying their efforts to develop and offer green products, in order to seize new business opportunities and improve profitability. A specific type of green products concerns marginal and development cost-intensive green products (MDIGPs), like electric vehicles. As greening these products affects both marginal and development costs, their design presents special challenges, especially within the context of uncertain demand. This paper formulates the joint product pricing-ordering-greening decision problem in the supply chains of MDIGPs and examines the impact of demand uncertainty. A sequential game-theoretic framework is developed, providing analytical expressions of the optimal solutions for the stochastic model. A bargaining game on the wholesale price between supply chain members is proposed to coordinate decisions. We compare the optimal decisions numerically in the stochastic and deterministic cases and find that, although demand uncertainty creates inefficiency in the green supply chain, it might positively impact product greenness and prices. Given the impact of the unit-variable greening costs of MDIGPs, we are able to identify cases where–contrary to common belief–demand uncertainty does not always lead firms to reduce greenness or increase prices.