The soft-clustered vehicle routing problem is a natural generalization of the classic capacitated vehicle routing problem, where the routing decision must respect the already taken clustering decisions. It is a relevant routing problem with numerous practical applications, such as packages or parcels delivery. Population-based evolutionary algorithms have already been adapted to solve this problem

Emergency response refers to the systematic response to an unexpected, disruptive occurrence such as a natural disaster. The response aims to mitigate the consequences of the occurrence by providing the affected region with the necessary supplies. A critical factor for a successful response is its timely execution, but the unpredictable nature of disasters often prevents quick reactionary measures

Food fraud is driven by unethical enterprises’ economic incentives and endures due to consumers’ delayed quality perceptions, while present solutions make it impossible for ethical firms to verify food quality in a timely and convincing manner. To that end, this paper focuses on a duopoly competition between an ethical firm (H) and an unethical firm (L) incorporating consumers’ delayed quality perceptions

Co-creation, a new business model that requires platform enterprises, manufacturers, and even consumers to participate in product research and development, has become increasingly popular in recent years. Simultaneously, technological advances in platforms have provided a convenient channel for consumers to contribute creative ideas in co-creation activities, providing an opportunity to rebuild business

The hybrid seru system (HSS), which is an innovative production pattern that emerges from real-world production situations, is practical because it includes both serus and a flow line, allowing temporary workers who are unable to complete all tasks to be assigned to the flow line. We focus on the HSS by minimising both makespan and total labour time. The HSS includes two complicated coupled NP-hard

Given a network with capacities and blocker costs associated with its arcs, we study the maximum flow blocker problem (FB). This problem seeks to identify a minimum-cost subset of arcs to be removed from the network, ensuring that the maximum flow value from the source to the destination in the remaining network does not exceed a specified threshold. The FB finds applications in telecommunication networks

Operators of vehicle-sharing systems such as carsharing or ride-hailing can benefit from integrating driverless vehicles into their fleet. In this context, we study the impact of optimal fleet size and composition on an operator’s profitability, which entails a non-trivial tradeoff between operational benefits and higher upfront investment for driverless vehicles.

This paper addresses the problem of maximizing the expected size of a matching in the case of unreliable vertices and/or edges. The assumption is that the solution is built in several steps. In a given step, edges with successfully matched vertices are made permanent; but upon edge or vertex failures, the remaining vertices become eligible for reassignment. This process may be repeated a given number

Traditional queuing models mostly leave human judgment and decision making outside the scope of the system, ignoring their role as determinants of system performance. However, empirical evidence has shown that human behavior can substantially alter the system’s output. In this paper, we develop a hybrid approach that improves our understanding of the interplay between individual heterogeneous human

Decision problems are often characterized by complex criteria dependencies, which can hamper the development of an efficient and theoretically accurate multicriteria decision aid model. These criteria interactions have the form of either a redundancy or synergistic effect and require arduous and demanding preference statements for their quantification. This paper investigates interactions between pairs

The challenge of optimizing multiple objectives while considering job groups and partial due dates is prevalent in the flowshop group scheduling problem (FGSP). Despite its significance, the multi-objective FGSP with partial due dates (MFGSP) remains largely unaddressed in existing FGSP literature. In this paper, we bridge this gap by introducing a mixed integer linear programming model and an iterated

In this paper, we introduce a novel, profit-driven classification approach for churn prevention by framing the task of targeting customers for a retention campaign as a regret minimization problem within a predict-and-optimize framework. This is the first churn prevention model to utilize this approach. Our main objective is to leverage individual customer lifetime values (CLVs) to ensure that only

This paper investigates a variant of an inventory-routing problem (IRP) that enforces two conditions on the structure of the solution: time-invariant routes, and a fixed, injective (i.e., one-to-one) assignment of routes to vehicles. The practical benefits of concurrent route invariance and driver assignments are numerous. Fixed routes reduce the solution space of the problem and improve its tractability;

This work addresses a variant of the maximum flow problem where specific pairs of arcs are not allowed to carry positive flow simultaneously. Such restrictions are known in the literature as negative disjunctive constraints or conflict constraints. The problem is known to be strongly NP-hard and several exact approaches have been proposed in the literature. In this paper, we present a heuristic algorithm

High-dimensional matrix regression has been studied in various aspects, such as statistical properties, computational efficiency and application to specific instances including multivariate regression, system identification and matrix compressed sensing. Current studies mainly consider the idealized case that the covariate matrix is obtained without noise, while the more realistic scenario that the

Natural disasters are recurring emergencies that can result in numerous deaths and injuries. When a natural disaster occurs, rescue teams can be sent to help affected survivors, but deploying them efficiently is a challenge. Rescuers not knowing where affected survivors are located poses a significant challenge in delivering aid. With the development of new technologies, there are new possibilities

E-commerce retailers are challenged to maintain cost-efficiency and customer satisfaction while pursuing sustainability, especially in the last mile. In response, retailers are offering a range of delivery speeds, including same-day and instant options. Faster deliveries, while trending, often increase costs and emissions due to limited planning time and reduced consolidation opportunities in the last

This paper studies residential users’ electricity consumption in response to dynamic pricing in smart grid, from the behavioral economics angle. Particularly, this research employs a novel approach, i.e., the boundedly rational user decision (BRUD) modeling framework, which assumes users will accept an electricity consumption schedule whose total utility is within an “acceptable bound” of his/her maximum

To improve routing efficiency, transport service providers can enter a horizontal transport collaboration that uses a combinatorial auction to reallocate delivery orders. To find the optimal allocation, the carriers have to report bids for all possible combinations of available delivery orders. As this number grows exponentially with the number of orders to be reallocated, they are faced with an enormous

In this paper we provide an analysis of an inventory system with compound Poisson demand and a sequential supply system, specifically focusing on non-crossing lead times. The purpose is to facilitate a comparison with order crossing caused by independent lead times. We apply a new approach for modeling exogenous, non-crossing Erlang distributed lead times. It is assumed that the inventory is controlled

In this work we study an extension of the ordinary one-to-many shortest path problem that also considers additional disjunctive conflict relations between the arcs: an optimal shortest path tree is not allowed to include any conflicting arc pair. As is the case with many polynomially solvable combinatorial optimization problems, the addition of conflict relations makes the problem NP-hard. We propose

The retail market is being increasingly invaded by suppliers who are establishing their own direct selling channels, thanks to the rise of e-commerce and internet technology. The cost of direct sales has been identified as a crucial factor in the strategic interaction between suppliers and retailers. However, retailers often struggle to accurately assess this cost due to their decision biases. To address

We explore the value of using operational sensor data to improve the risk assessment of service contracts that cover all maintenance-related costs during a fixed period. An initial estimate of the contract risk is determined by predicting the maintenance costs via a gradient-boosting machine based on the machine’s and contract’s characteristics observable at the onset of the contract period. We then

Scoring models support decision-making in financial institutions. Their estimation and evaluation rely on labeled data from previously accepted clients. Ignoring rejected applicants with unknown repayment behavior introduces sampling bias, as the available labeled data only partially represents the population of potential borrowers. This paper examines the impact of sampling bias and introduces new

We study stochastic dominance between portfolios of independent and identically distributed (iid) extremely heavy-tailed (i.e., infinite-mean) Pareto random variables. With the notion of majorization order, we show that a more diversified portfolio of iid extremely heavy-tailed Pareto random variables is larger in the sense of first-order stochastic dominance. This result is further generalized for

The Physical Internet (PI or π) is a design metaphor that applies the digital internet as an archetype to rethink freight logistics and transportation in a more sustainable, interoperable, and cooperative way. Analogously to the protocols of the digital internet, freight should be encapsulated into standardized π-containers and transported through an open network of cooperating π-hubs. Despite the

Although there are techniques to address large scale Markov decision processes (MDP), a computationally adequate solution of the so-called curse of dimensionality still eludes, in many aspects, a satisfactory treatment. In this paper, we advance in this issue by introducing a novel multi-subset partitioning scheme to allow for a distributed evaluation of the MDP, aiming to accelerate convergence and

This paper discusses an iterative simulation–optimization approach to estimate high-quality solutions for the multi-level capacitated lot-sizing problem with linked lot sizes and backorders (MLCLSP-L-B) based on probabilistic demand. It presents the application of the Generalized Uncertainty Framework (GUF) to the MLCLSP-L-B. The research provides an exact mathematical problem formulation and a variable

This paper proposes a new mechanism for coordinating master planning in a buyer–supplier supply chain. Parties take different roles in the mechanism: there is an informed party (IP) and a reporting party (RP). The mechanism consists of three steps. First, the RP defines a lump sum payment, which he will receive if a supply proposal that deviates from the default is implemented. Second, parties generate

Assortment optimization presents a complex challenge for retailers, as it depends on numerous decision factors. Changes in assortment can result in demand redistribution with multi-layered consequences. This complexity is even more pronounced for omnichannel retailers, which have to manage assortments across multiple sales channels. Choice modeling has emerged as an effective method in assortment optimization

We study single-machine scheduling problems where processing each job requires both processing time and rechargeable energy. Subject to a predefined energy capacity, energy can be recharged after each job during a fixed recharging period. Our focus is on two due date-related scheduling criteria: minimizing the number of late jobs and maximizing the weighted number of jobs completed exactly at their

We propose a general approach for modeling production technologies, allowing for modeling both inefficiency and noise that are specific for each input and each output. The approach is based on amalgamating ideas from nonparametric activity analysis models for production and consumption theory with stochastic frontier models. We do this by effectively re-interpreting the activity analysis models as

The application of Deep Reinforcement Learning (DRL) to inventory management is an emerging field. However, traditional DRL algorithms, originally developed for diverse domains such as game-playing and robotics, may not be well-suited for the specific challenges posed by inventory management. Consequently, these algorithms often fail to outperform established heuristics; for instance, no existing DRL

The distribution of relief supplies to shelters is a critical aspect of post-disaster humanitarian logistics. In major disasters, prepositioned supplies often fall short of meeting all demands. We address the problem of planning vehicle routes from a distribution center to shelters while allocating limited relief supplies. To balance efficiency and equity, we formulate a bi-objective problem: minimizing

As technology evolves, a seller may offer sequential releases of its product over time, with new versions offering superior performance. Beyond offering new product releases over time, sellers now increasingly have the option of offering post-sale software updates, thereby potentially extending product longevity. The potential to change product life cycles via software updates is of strategic importance

This paper provides a transparent and dynamic decision support tool that ranks clusters of listed banks worldwide by riskiness. It is designed to be flexible in updating and editing the values and quantities of banks, indicators, and clusters. For constructing this tool, a large set of stand-alone and systemic risk indicators are computed and reduced to fewer representative factors. These factors are

We propose a new solution for the Emergency Department (ED) staffing and scheduling problem, considering uncertainty in patient arrival patterns, multiple treatment stages, and resource capacity. A two-stage stochastic mathematical programming model was developed. We employed a Sample Average Approximation (SAA) method to generate scenarios and a discrete event simulation to evaluate the results. The

The Prisoner Transportation Problem is an NP-hard combinatorial problem and a complex variant of the Dial-a-Ride Problem. Given a set of requests for pick-up and delivery and a homogeneous fleet, it consists of assigning requests to vehicles to serve all requests, respecting the problem constraints such as route duration, capacity, ride time, time windows, multi-compartment assignment of conflicting

We consider the problem arising when two agents, each owning a set of jobs, compete to schedule their jobs on a common processing resource. Each schedule implies a certain utility for each agent and an overall system utility. We are interested in solutions that incorporate some criterion of fairness for the agents and, at the same time, are satisfactory from the viewpoint of system utility. More precisely

Recommender systems (RS) are highly relevant for multiple domains, allowing to construct personalized suggestions for consumers. Previous studies have strongly focused on collaborative filtering approaches, but the inclusion of longitudinal data (LD) has received limited attention. To address this gap, we investigate the impact of incorporating LD for recommendations, comparing traditional collaborative

This paper examines a single-echelon inventory system that fulfills stochastic orders from a production facility using a time-based shipment consolidation strategy. In this system, the production facility provides advance demand information to the warehouse, ensuring that all orders are placed with a positive demand lead time. Using value iteration, we identify the optimal outbound shipment quantities

Urban logistics has been recognized as one of the most complex and expensive part of e-commerce supply chains. An increasing share of this complexity comes from the first mile, where shipments are initially picked up to be fed into the transportation network. First-mile pickup volumes have become fragmented due to the enormous growth of e-commerce marketplaces, which allow even small-size vendors access

Following the rapid evolution of manufacturing industries, customer demands may change dramatically, which challenges the conventional production systems. Seru production system (SPS) is a key to deal with uncertain varieties and fluctuating volumes. In dynamic scenarios, orders with uncertain demands arrive over time. For each arriving order, appropriate workers should be allocated to assemble it

We tackle the problem of coordinating a three-echelon last-mile delivery system. In the first echelon, trucks transport parcels from distribution centres outside the city to public transport stops. In the second echelon, the parcels move on public transport and reach the city centre. In the third echelon, zero-emission vehicles pick up the parcels at public transport stops and deliver them to customers

Motivated by the emergence of offline and online donations, this paper explores the interplay between charitable donations and strategic choice of sales mode in a philanthropic supply chain consisting of a manufacturer and a platform. We consider two donation strategies, offline donations and both offline and online donations that are traceable by blockchain technology, and two business models, i.e

This study introduces a new dynamic routing problem, namely the Dynamic Team Orienteering Problem (DTOP), which is a dynamic variant of the Team Orienteering Problem (TOP). In the DTOP, some customer locations are known a priori, while others are dynamic, with each location associated with a profit value. The goal is to maximize the sum of collected profits by visiting a set of customer locations within

In this work, we address two variants of the Period Travelling Salesman Problem: one where some nodes cannot be visited consecutively over the time horizon, and another one where this restriction is not imposed. A new flow-based formulation that uses specific information about the visit patterns of nodes is studied and empirical tests show that it is able to solve test instances where a flow-based

Recent supply chain disruptions and crisis response policies (e.g., the COVID-19 pandemic and the Red Sea crisis) have highlighted the role of container terminals as crucial and scarce resources in the global economy. To tackle these challenges, the industry increasingly aims for advanced operational collaboration among multiple stakeholders, as demonstrated by the ambitions of the recently founded

Unexpected failures of operating systems can result in severe consequences and huge economic losses. To prevent them, preventive maintenance based on condition data can be performed. Existing studies either rely on the assumption of a known deterioration process or an abundance of data. However, in practice, it is unlikely that the deterioration process is known, and data is often limited (to a few

Corporate social responsibility (CSR) has a strong impact on the external image of the enterprise. The violation of CSR not only harms the enterprise but also negatively affects other firms in the supply chain. This paper establishes a game-theoretical model to study the management of social responsibility efforts with considerations of violation probability. The upstream manufacturer and downstream

The aim of this paper is to compare different strategies to combine sustainability and optimality in stock portfolios to assess whether there is an association between their average ESG (Environmental, Social, Governance) score and their financial performance and, if so, whether it depends on the specific strategy used. To this end, we confront the risk-adjusted performance of three ESG-compliant optimal

The pharmaceutical industry faces growing pressure to develop innovative, affordable products faster. Completing clinical trials on time is crucial, as revenue strongly depends on the finite patent protection. In this paper, we consider dynamic resource allocation for pharmaceutical product portfolio management and clinical trial scheduling, proposing a modelling framework, where resource profiles

Gradient descent methods are widely used as optimization algorithms for updating neural network weights. With advancements in fractional-order calculus, fractional-order gradient descent algorithms have demonstrated superior optimization performance. Nevertheless, existing fractional-order gradient descent algorithms have shortcomings in terms of structural design and theoretical derivation. Specifically

In today's network era, people's decisions are susceptibly influenced by others, especially the ones they trust. This study confines to studying social network group decision making (SNGDM). Due to the mutual influence of consensus level and consensus adjustment among decision makers (DMs), this study utilizes biform game theory to propose an innovative consensus mechanism for facilitating group decision

This paper explores the incentive of a competitive contract manufacturer (CCM) to adopt a decentralized structure by segregating contract manufacturing from its self-branded business. We consider an original equipment manufacturer (OEM) with the option to outsource production either to a CCM producing its self-branded product, or to a non-competitive contract manufacturer (NCM) also serving another

Tree ensembles are one of the most powerful methodologies in Machine Learning. In this paper, we investigate how to make tree ensembles more flexible to incorporate explainability and fairness in the training process, possibly at the expense of a decrease in accuracy. While explainability helps the user understand the key features that play a role in the classification task, with fairness we ensure

Partially linear partially concave models are semiparametric regression models that can capture linear and concavity-constrained nonlinear effects within one framework. A fundamental problem of this kind of model is deciding which covariates have linear effects and which covariates have strictly concave effects. Assuming that the true regression function is partially linear partially concave and sparse

This study examines the impact of flexibility-based price discrimination (FBPD) on the pricing and quality strategy of the adopting firm and its competitor, as well as the impact on the welfare of consumers. We assume that the inflexible consumers being targeted for price discrimination can be either high-income consumers or low-income consumers, and the high-income consumers are more sensitive to

This paper evaluates the role of volatility-free stock market illiquidity proxies in forecasting monthly stock market returns. We adopt a probabilistic approach to multivariate time-series modelling using Bayesian nonparametric vector autoregressions. These models flexibly capture complex joint dynamics among financial variables through data-driven regime switching. Out-of-sample forecasts maintain