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.

Supply chain networks face the critical challenge of enhancing resilience to disruptions while controlling the costs associated with resilience improvements. In this paper, we introduce an adaptive resilience improvement framework designed to sustain material flow by responding dynamically to emerging network vulnerabilities. Our framework centers on the production chain as a core element in resilience planning, integrating vulnerability assessment and reinforcement strategies through a tri-level optimization model. This model adapts to the network's changing conditions by (i) incorporating disruption scenario generation as an integral part of the decision-making process, allowing for the dynamic identification of vulnerabilities, and (ii) optimizing reinforcement strategies in response to them. We demonstrate the framework's effectiveness through two distinct case studies: a steel supply chain, where production flexibility improves resilience by 30%, and a pharmaceutical supply chain affected by climate-related disruptions. Our computational results confirm the scalability and effectiveness of this approach in strengthening network-wide resilience as vulnerabilities evolve.

The evolving field of electric moped sharing systems is shaped by various determinants influencing user preferences, including range anxiety, pricing strategies, and regulatory changes. Utilizing a stated preference approach with a hybrid choice model, this research explores how these factors, along with attitudinal constructs, impact user decisions. The findings reveal that remaining driving range plays a critical role, with significant individual variability in its sensitivity, while perceived range anxiety did not significantly influence choices. Recent changes in helmet regulations have shifted preferences towards faster vehicles. Furthermore, dynamic pricing strategies, such as adjusting ride or unlock fees, can incentivize the use of less desirable vehicles with lower battery range or aid in user-based relocation. Nevertheless, low-range vehicles are less likely to be chosen, even with incentives. These insights provide valuable guidance for operators of electric moped sharing system to improve fleet management and optimize user satisfaction through strategic pricing and battery management.

This study introduces an optimization framework for deploying Mobile Fleet Inventories (MFIs) to address operational inefficiencies in on-demand delivery systems. Traditionally, these systems rely on stationary facilities to organize operations and manage resources. While stationary facilities provide stability and structured coverage, they are inherently rigid and struggle to adapt to the spatial and temporal fluctuations characteristic of urban service demand. By leveraging urban waterways, MFIs act as dynamic, mobile facilities, enabling real-time resource redistribution and offering greater flexibility to meet evolving demand patterns efficiently. We formulate the problem as a mixed-integer linear programming model to optimize MFI deployment, minimizing total system costs. The model incorporates both capital investments (e.g., MFI leasing and docking infrastructure) and operational expenses (e.g., rider idle time). Key decisions include determining the optimal number, placement of MFIs, and fleet size. To validate the approach, we apply it to a meal delivery platform in Amsterdam, demonstrating its practicality and scalability. Results show that implementing MFIs reduces overall system costs by 17% and decreases rider idle time by 35% compared to stationary facility operations. These findings underscore the transformative potential of MFIs to enhance the efficiency, sustainability, and adaptability of on-demand delivery systems in urban settings.

The rapid growth of the online food delivery industry has led to a significant increase in the number of delivery riders navigating urban streets, predominantly using bikes and e-bikes. This growth has been accompanied by a concerning rise in crashes involving these riders, posing a critical challenge for city authorities and policymakers. Promoting safer riding behavior, such as choosing safer routes while delivering food, can potentially reduce crash risks. With this motivation, this paper aims to evaluate the effectiveness of strategies that encourage riders to choose safer routes and estimate the value riders place on reducing the risk of road crashes. The paper presents a stated preference experiment conducted with food delivery riders in Amsterdam and Copenhagen to assess two targeted strategies: ’safety information’ and ’monetary incentives’, designed to encourage riders toward selecting safer routes. The results from the route choice model show that presenting information about safety against crashes on different routes and offering monetary incentives can effectively motivate riders to choose safer routes, even if these are longer. The trade-offs riders make between safer and shorter routes were quantified by calculating the Value of Risk Reduction (VRR) and Willingness to Accept (WTA) indicators, which offer valuable insights into riders’ safety preferences. These indicators highlight how much riders value risk reduction and the compensation required to choose safer routes. Furthermore, the findings reveal that factors related to riders’ working arrangements and socio-demographic profiles significantly influence their route choice decisions. The paper concludes with a discussion about the practical challenges associated with implementing the strategies to enhance rider safety and proposing potential solutions that can be useful for food delivery platforms and policymakers.

This paper addresses the challenges of charging infrastructure design (CID) for electrified public transport networks using Battery Electric Buses (BEBs) under conditions of sparse energy consumption data. Accurate energy consumption estimation is critical for cost-effective and reliable electrification but often requires costly field experiments, resulting in limited data. To address this issue, we propose two mathematical models designed to handle uncertainty and data sparsity in energy consumption. The first is a robust optimization model with box uncertainty, addressing variability in energy consumption. The second is a data-driven distributionally robust optimization model that leverages observed data to provide more flexible and informed solutions. To evaluate these models, we apply them to the Rotterdam bus network. Our analysis reveals three key insights: (1) Ignoring variations in energy consumption can result in operational unreliability, with up to 55% of scenarios leading to infeasible trips. (2) Designing infrastructure based on worst-case energy consumption increases costs by 67% compared to using average estimates. (3) The data-driven distributionally robust optimization model reduces costs by 28% compared to the box uncertainty model while maintaining reliability, especially in scenarios where extreme energy consumption values are rare and data exhibit skewness. In addition to cost savings, this approach provides robust protection against uncertainty, ensuring reliable operation under diverse conditions.

Mobility is vital for societal wellbeing, economic growth, social inclusion, and access to essential amenities. However, the current system faces significant challenges, including environmental impact, unequal access, and safety concerns. […]

The emergence of Intelligent Transportation Systems (ITS) for freight transport in recent times has created interest among practitioners and researchers to extend freight ITS to support broader logistics processes, including dynamic tour scheduling, loading and unloading, warehousing, and even production. However, connecting transport data, ITS and logistics information systems require collaboration between different organizations and new business models to create business value for logistics actors. It is critical for these stakeholders to consider how their business models connect to create meaningful new data-to-information value chains. This study develops a conceptual framework to identify opportunities for logistics value creation with freight transport data. Building on the literature we construct a framework that reconciles multi-firm and firm-level business modelling. The main component is a generalized framework for Data-to-Value (DtV) chains for applications in information and communications technology. In order to support its business validity, we extend this framework with Business Model Canvases (BMCs) of the actors in the value chain. Three real-life use cases from a freight ITS community in the Netherlands are used to evaluate and illustrate the framework.

Urban mobility services face the challenge of planning their operations efficiently while complying with user preferences. In this paper, we introduce a new mathematical model called a choice-driven dial-a-ride problem (CD-DARP) which is a generalization of the dynamic DARP where passenger behavior is integrated in the operational planning using choice models and assortment optimization. We look at two types of mobility services, private and shared. Our problem extends the dynamic DARP by (i) changing its objective function to profit maximization, where both cost and revenue are variables, and (ii) incorporating assortment optimization with routing decisions in a dynamic setting. We propose a pricing scheme based on a choice model designed to offer service alternatives at the time a customer makes a request. We introduce a tailored algorithm to efficiently solve the dynamic CD-DARP. Computational results indicate that our proposed approach outperforms dynamic DARP in terms of reducing routing costs and improving the number of customers served.

Due to the uncertain and dynamic environment around scheduling systems, timely revisions or reschedules of the master plans are essential for achieving optimal utilization. With the recent development of Industry 4.0 technologies, many researchers perceive the creation of cyber-physical systems as a solution for managing systems under uncertainty. This article focuses on a loading facility under uncertain truck arrivals due to road congestion and proposes utilizing real-time truck location information to improve performance. We do this by developing an integrated system consisting of a predictive model using machine learning (MC) classifiers and a mathematical model for real-time slot rescheduling. The ML classifier is used to predict the presence probabilities of all the incoming trucks at a particular slot based on the historical traffic data and the real-time truck location. Subsequently, a Mixed-Integer Quadratic Programming (MIQP) model is developed to solve a Probabilistic Slot Rescheduling Problem (P-SRP), which uses the estimated truck presence probabilities and minimizes the total expected cost of rescheduling. We implemented this by first testing multiple ML classifiers and selected the ANN classifier for prediction as it outperformed others. Our limited experiments showed that the proposed method reduced the total rescheduling cost by 42%. Furthermore, our sensitivity analysis with different congestion levels, complexity, and rescheduling strategy also showed the practicality of the proposed approach.

Traffic congestion is uncertain and undesirable in logistics and leads to arrival uncertainty at downstream locations engendering disruptions. This paper considers a loading facility that uses Truck Appointment System (TAS) for slot management and faces incoming truck arrival uncertainty due to traffic congestion. Due to the recent advancements in cyber-physical systems, we propose an adaptive system that uses the real-time truck Estimated Time of Arrival (ETA) data to make informed decisions. We develop an integer mathematical model to represent the adaptive behavior that determines the optimal reschedules by minimizing the average truck waiting time. We developed a simulation model of the adaptive system and reported the estimated benefits from our initial experiments.

In this paper, we evaluate the cost of the electrification of an existing bus network. We propose a family of bi-objective mathematical models to demonstrate the trade-off between strategic (i.e., battery sizing and the locations of charging stations) and operational decisions (i.e., battery degradation). The proposed mathematical models investigate different charging policies and measure their impacts on overall cost. Battery degradation is estimated by a tailored and linearized semi-empirical approach and is explicitly incorporated in the proposed mixed-integer linear models. The impact of different charging policies on reducing the overall costs is evaluated for a bus network in Rotterdam. The results show that allowing for flexibility in the loss of energy levels at each bus cycle results in savings up to 17% in battery aging.

Interconnected logistics system can play an important role towards having a more sustainable green freight transport. Recently, after introducing the concept of Physical Internet (PI), researchers have started to explore the opportunities and challenges that a collaborative and interconnected network could create in different aspects of the supply chain. In this research, we study the last mile delivery as well as vehicle dispatching problems under the assumptions of collaborative supply chain networks while assuming that modularized boxes are applied inside the network from the provider to the final customer. Our research aims at proposing a more efficient resource planning with the minimal number of empty vehicle movements running on roads that ultimately leads to decrease carbon dioxide emission. The assumptions have been tested and verified using real data coming from a major retail company in Europe.

In this paper, we address a multi-objective residential waste collection problem with an integrated territory planning and vehicle routing approach. Dividing the problem into territories enables drivers to carry out the same route every week so they get familiar with it and residents put out their bins at the appropriate time. Another benefit is to reduce the computation time for large problems, since the complex characteristics of the involved vehicle routing problem make it otherwise difficult to solve. There are three characteristics that are important for good territory planning: minimum overlap, minimum travel time, and balanced workload. The purpose of this paper is to investigate the influence these three objectives have on each other, since they might be contradictory. Moreover, an Adaptive Large Neighborhood Search (ALNS) algorithm is developed for this specific problem which uses a K-means algorithm to generate the initial solution for territories. The results with the three objectives are shown to be useful for planners seeking to make informed decisions through the trade-off across different solutions with the Pareto frontiers provided. Moreover, the ALNS algorithm is shown to find good quality solutions in a reasonable computational time.

Unexpected disruptions commonly occur in the railway network, causing delays, and extra cost for operators and inconvenience for passengers by missing their connection and facing overcrowded trains. This paper presents a passenger-centric approach for timetable rescheduling in case of disruption. We study a railway system in which passengers are free to choose their itinerary and compete over limited train capacity. We explicitly model the passengers’ decisions using a choice model. We propose a multi-objective algorithmic approach to solve the problem. Service punctuality, operating cost, and passengers’ inconvenience are selected as objectives. Computational experiments are performed on the Swiss and Dutch railway networks. The results demonstrate the performance of the algorithm in finding high-quality solutions in a computationally efficient manner.

We solve a rich routing problem inspired from practice, in which a heterogeneous fixed fleet is used for collecting recyclable waste from large containers over a finite planning horizon. Each container is equipped with a sensor that communicates its level at the start of the day. Given a history of observations, a forecasting model is used to estimate the expected demands and a forecasting error representing the level of uncertainty. The problem falls under the framework of the stochastic inventory routing problem and our main contribution is the modeling of the dynamic probability-based cost of container overflows and route failures over the planning horizon. We cast the problem as a mixed integer non-linear program and, to solve it, we develop an adaptive large neighborhood search algorithm that integrates a purpose-designed forecasting model, tested and validated on real data. We demonstrate the strength of our modeling approach on a set of rich inventory routing instances derived from real data coming from the canton of Geneva, Switzerland. Our approach significantly outperforms alternative deterministic policies in its ability to limit the occurrence of container overflows for the same routing cost. Finally, we show the benefit of a rolling horizon solution and derive lower and upper bounds on its cost.

Hinterland freight transportation is managed according to a pre-designed schedule. In daily operations, unexpected uncertainties cause deviation from the original plan. Thus replanning is needed to deal with the perturbations and complete the transportation tasks. This paper proposes a mixed-integer programming model to re-plan hinterland freight transportation, based on the framework of synchromodality. It is a holistic resolution of shipment flow rerouting, consequence transshipment organization in the intermediate terminals, and corresponding service rescheduling. The replanning benefits from a high operational flexibility and coordination via a split of shipment and aligning the departure time of service flows with the shipment flows.

In the context of on-demand logistics systems, the facility location is becoming even more critical as demand characteristics and customer preferences are changing with respect to the location, time, customer segments etc. Classic facility location models do not take into account customer preferences when the set of facility locations are optimized and therefore the expected profit of the classic facility location models is not an accurate representation of reality. This paper develops a preference-based facility location model which incorporates customer preferences while maximizing the system-wide expected profit in the context of an on-demand logistics provider. The customers are first segmented based on historical data and segment specific preferences are estimated by logit mixture where we take into account heterogeneity within the segment. The performance of the preference-based facility location model is measured by total expected profit and consumer surplus. It is found that, the preference-based facility location model is not only a more accurate representation of reality but also has the potential to increase the expected profit compared to typical facility location models.

This paper presents a route optimization algorithm for light aircraft operating under visual flight rules. The problem aims at finding a minimum-duration, collision-free route in three spatial dimensions with possible aircraft maneuvers. The optimal route takes into account the aircraft kinodynamic characteristics and its interaction with external wind. A data processing approach is presented to recast the flying environment as a series of polyhedrons based on which a mixed-integer linear model is formulated. A two-stage route optimization model is then introduced to solve real-life instances. Computational experiments depict the efficiency of this approach.