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

We consider a decision maker who performs a stochastic decision process over a multiple number of stages, where the choice alternatives are characterized by random utilities with unknown probability distribution. The decisions are nested each other, i.e. the decision taken at each stage is affected by the subsequent stage decisions. The problem consists in maximizing the total expected utility of the overall multi-stage stochastic dynamic decision process. By means of some results of the extreme values theory, the probability distribution of the total maximum utility is derived and its expected value is found. This value is proportional to the logarithm of the accessibility of the decision maker to the overall set of alternatives in the different stages at the start of the decision process. It is also shown that the choice probability to select alternatives becomes a Nested Multinomial Logit model

How to locate services optimizing redundancy: A comparative analysis of K-Covering Facility Location models

Redundancy aspects related to covering facility location problems are of extreme importance for many applications, in particular those regarding critical services. For example, in the healthcare sector, facilities such as ambulances or first-aid centers must be located robustly against unpredictable events causing disruption or congestion. In this paper, we propose different modeling tools that explicitly address coverage redundancy for the underlying service. We also evaluate, both theoretically and experimentally, the properties and behavior of the models, and compare them from a computational and managerial point of view. More precisely, by starting from three classical double-covering models from the literature (BACOP1, BACOP2, and DSM), we define three parametric families of models (namely, K-BACOP1, K-BACOP2, and K-DSM) which generalize the former to any possible Kth coverage level of interest. The study of such generalizations allows us to derive interesting managerial insights on location decisions at the strategic level. The CPU performance and the quality of the solutions returned are assessed through ad-hoc KPIs collected over many representative instances with different sizes and topological characteristics, and also by dynamically simulating scenarios involving possible disruption for the located facilities. Finally, a real case study concerning ambulance service in Morocco is analyzed. The results show that, in general, K-BACOP1 performs very well, even if intrinsic feasibility issues limit its broad applicability. Instead, K-DSM achieves the best coverage and equity performances for lower levels of redundancy, while K-BACOP2 seems the most robust choice when high redundancy is required, showing smoother and more predictable trends

How to locate services optimizing redundancy: A comparative analysis of K-Covering Facility Location models

Redundancy aspects related to covering facility location problems are of extreme importance for many applications, in particular those regarding critical services. For example, in the healthcare sector, facilities such as ambulances or first -aid centers must be located robustly against unpredictable events causing disruption or congestion. In this paper, we propose different modeling tools that explicitly address coverage redundancy for the underlying service. We also evaluate, both theoretically and experimentally, the properties and behavior of the models, and compare them from a computational and managerial point of view. More precisely, by starting from three classical double -covering models from the literature (BACOP1, BACOP2, and DSM), we define three parametric families of models (namely, K-BACOP1, K-BACOP2, and K-DSM) which generalize the former to any possible Kth coverage level of interest. The study of such generalizations allows us to derive interesting managerial insights on location decisions at the strategic level. The CPU performance and the quality of the solutions returned are assessed through ad -hoc KPIs collected over many representative instances with different sizes and topological characteristics, and also by dynamically simulating scenarios involving possible disruption for the located facilities. Finally, a real case study concerning ambulance service in Morocco is analyzed. The results show that, in general, K-BACOP1 performs very well, even if intrinsic feasibility issues limit its broad applicability. Instead, K-DSM achieves the best coverage and equity performances for lower levels of redundancy, while K-BACOP2 seems the most robust choice when high redundancy is required, showing smoother and more predictable trends

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

We consider a decision maker who performs a stochastic decision process over a multiple number of stages, where the choice alternatives are characterized by random utilities with unknown probability distribution. The decisions are nested each other, i.e. the decision taken at each stage is affected by the subsequent stage decisions. The problem consists in maximizing the total expected utility of the overall multi-stage stochastic dynamic decision process. By means of some results of the extreme values theory, the probability distribution of the total maximum utility is derived and its expected value is found. This value is proportional to the logarithm of the accessibility of the decision maker to the overall set of alternatives in the different stages at the start of the decision process. It is also shown that the choice probability to select alternatives becomes a Nested Multinomial Logit model

Smart Steaming: A New Flexible Paradigm for Synchromodal Logistics

Slow steaming, i.e., the possibility to ship vessels at a significantly slower speed than their nominal one, has been widely studied and implemented to improve the sustainability of long-haul supply chains. However, to create an efficient symbiosis with the paradigm of synchromodality, an evolution of slow steaming called smart steaming is introduced. Smart steaming is about defining a medium speed execution of shipping movements and the real-time adjustment (acceleration and deceleration) of traveling speeds to pursue the entire logistic system’s overall efficiency and sustainability. For instance, congestion in handling facilities (intermodal hubs, ports, and rail stations) is often caused by the common wish to arrive as soon as possible. Therefore, smart steaming would help avoid bottlenecks, allowing better synchronization and decreasing waiting time at ports or handling facilities. This work aims to discuss the strict relationships between smart steaming and synchromodality and show the potential impact of moving from slow steaming to smart steaming in terms of sustainability and efficiency. Moreover, we will propose an analysis considering the pros, cons, opportunities, and risks of managing operations under this new policy

Synchromodal logistics: An overview of critical success factors, enabling technologies, and open research issues

Abstract As supply chain management is becoming demand driven, logistics service providers need to use real-time information efficiently and integrate new technologies into their business. Synchromodal logistics has emerged recently to improve flexibility in supply chains, cooperation among stakeholders, and utilization of resources. We survey the existing scientific literature and real-life developments on synchromodality. We focus on the critical success factors of synchromodality and six categories of enabling technologies. We identify open research issues and propose the introduction of a new stakeholder, which takes on the role of orchestrator to coordinate and provide services through a technology-based platform

Real-Time Rough Extraction of Foreground Objects in MPEG1,2 Compressed Video

This paper describes a new approach to extract foreground objects in MPEG1,2 video streams, in the framework of “rough indexing paradigm”, that is starting from rough data obtained by only partially decoding the compressed stream. In this approach we use both P-frame motion information and I-frame colour information to identify and extract foreground objects. The particularity of our approach with regards to the state of the art methods consists in a robust estimation of camera motion and its use for localisation of real objects and filtering of parasite zones. Secondly, a spatio-temporal filtering of roughly segmented objects at DC resolution is fulfilled using motion trajectory and gaussian-like shape characteristic function. This paradigm results in content description in real time, maintaining a good level of details

The synchronized multi-commodity multi-service Transshipment-Hub Location Problem with cyclic schedules

The synchronized multi-commodity multi-service Transshipment-Hub Location Problem is a hub location problem variant faced by a logistics service provider operating in the context of synchromodal logistics. The provider must decide where and when to locate transshipment facilities in order to manage many customers’ origin–destination shipments with release and due dates while minimizing a total cost given by location costs, transportation costs, and penalties related to unmet time constraints. The considered synchromodal network involves different transportation modes (e.g., truck, rail, river and sea navigation) to perform long-haul shipments and the freight synchronization at facilities for transshipment operations. To the best of our knowledge, this variant has never been studied before. Considering a time horizon in which both transportation services and demand follow a cyclic pattern, we propose a time–space network representation of the problem and an ad-hoc embedding of the time-dependent parameters into the network topology and the arcs’ weight. This allows to model the flow synchronization required by the problem through a Mixed-Integer Linear Programming formulation with a simplified structure, similar to well-known hub location problems and avoiding complicating constraints for managing the time dimension. Through an extensive experimental campaign conducted over a large set of realistic instances, we present a computational and an economic analysis. In particular, we want to assess the potential benefits of implementing synchromodal logistics operations into long-haul supply-chains managed by large service providers. Since flexibility is one of the main features of synchromodality, we evaluate the impact on decisions and costs of different levels of flexibility regarding terminals’ operations and customers’ requirements

Social Bias Probing: Fairness Benchmarking for Language Models

Large language models have been shown to encode a variety of social biases, which carries the risk of downstream harms. While the impact of these biases has been recognized, prior methods for bias evaluation have been limited to binary association tests on small datasets, offering a constrained view of the nature of societal biases within language models. In this paper, we propose an original framework for probing language models for societal biases. We collect a probing dataset to analyze language models' general associations, as well as along the axes of societal categories, identities, and stereotypes. To this end, we leverage a novel perplexity-based fairness score. We curate a large-scale benchmarking dataset addressing drawbacks and limitations of existing fairness collections, expanding to a variety of different identities and stereotypes. When comparing our methodology with prior work, we demonstrate that biases within language models are more nuanced than previously acknowledged. In agreement with recent findings, we find that larger model variants exhibit a higher degree of bias. Moreover, we expose how identities expressing different religions lead to the most pronounced disparate treatments across all models

A Generalized Bin Packing Problem for parcel delivery in last-mile logistics

Abstract In this paper, we present a new problem arising at a tactical level of setting a last-mile parcel delivery service in a city by considering different Transportation Companies (TC), which differ in cost and service quality. The courier must decide which TCs to select for the service in order to minimize the total cost and maximize the total service quality. We show that the problem can be modeled as a new packing problem, the Generalized Bin Packing Problem with bin-dependent item profits (GBPPI), where the items are the parcels to deliver and the bins are the TCs. The aim of the GBPPI is to select the appropriate fleet from TCs and determine the optimal assignment of parcels to vehicles such that the overall net cost is minimized. This cost takes into account both transportation costs and service quality. We provide a Mixed Integer Programming formulation of the problem, which is the starting point for the development of efficient heuristics that can address the GBPPI for instances involving up to 1000 items. Extensive computational tests show the accuracy of the proposed methods. Finally, we present a last-mile logistics case study of an international courier which addresses this problem