In a survey of Belgian logistics service providers, the efficiency of first-mile pickup operations was identified as a key area for improvement, given the increasing number of returns in e-commerce, which has a significant impact on traffic congestion, carbon emissions, energy consumption and operational costs. However, the complexity of first-mile pickup operations, resulting from the small number

Classical capacitated lot-sizing models include capacity constraints relying on a rough estimation of capacity consumption. The plans resulting from these models are often not executable on the shop floor. This paper investigates the use of constraint learning approaches to replace the capacity constraints in lot-sizing models with machine learning models. Integrating machine learning models into optimization

The outranking approach in Multiple Criteria Decision Analysis (MCDA) uses ranking procedures to exploit a fuzzy outranking relation, which captures the decision maker's notion of a ranking. However, as decision problems become more complex and computer performance improves, new ranking procedures are needed to rank complex data sets that decision-makers may not interpret. This paper discusses recent

Port wine stocks ameliorate during storage, facilitating product differentiation according to age. This induces a trade-off between immediate revenues and further maturation. Varying climate conditions in the limited supply region lead to stochastic purchase prices for wine grapes. Decision makers must integrate recurring purchasing, production, and issuance decisions. Because stocks from different

E-commerce sales rise exponentially and represent an increasing proportion of global retail. To benefit from this, traditional brick-and-mortar stores enter the e-commerce market and become omnichannel retailers. However, the profitability of omnichannel retailers remains questionable due to high shipment and fulfillment costs. This paper addresses this challenge, focusing on using standard shipping

Aircraft recovery, a critical step in airline operations recovery, aims to minimize the cost of disrupted aircraft schedules. The exact methods for aircraft recovery are computationally expensive and operationally infeasible in practice. Heuristics and hybrid approaches offer faster solutions but have inconsistent solution quality, often leading to large losses. We propose a supervised machine learning

In this paper, we discuss portfolio selection strategies for Enhanced Indexation (EI), which are based on stochastic dominance relations. The goal is to select portfolios that stochastically dominate a given benchmark but that, at the same time, must generate some excess return with respect to a benchmark index. To achieve this goal, we propose a new methodology that selects portfolios using the ordered

We consider Ranking and Selection (R&S) in the presence of spatial correlation among designs. The performance of each design can only be evaluated through stochastic simulation with heterogeneous noise. Our primary objective is to maximize the probability of correct selection (PCS) by optimally allocating the simulation budget considering the spatial correlation among designs. We propose using Gaussian

Competing online retail platforms frequently function as both agency and reselling channels. This paper explores a manufacturer’s channel selection strategy in the context of downstream platform competition and information sharing, taking into account the platforms’ varying levels of information capability. Our research indicates that the manufacturer opts for a hybrid channel approach. Competing platforms

Information theory has emerged as a prominent approach for analyzing feature relevance and redundancy in multi-label feature selection. However, traditional information theory-based methods encounter two primary issues. Firstly, when evaluating feature relevance, they fail to consider the differing importance of each label within the entire label set. Secondly, when assessing feature redundancy, they

This paper investigates stochastic scheduling and routing problems in the online meal delivery (OMD) service. The huge increase in meal delivery demand requires the service providers to construct a highly efficient logistics network to deal with a large-volume of time-sensitive and fluctuating fulfillment, often using inhouse and crowdsourced drivers to secure the ambitious service quality. We aim

We formulate a fairly general deterministic problem of scheduling electric vehicle (EV) regular charging tasks on parallel chargers over time. Charging task is called regular if it is performed at the same time and in the same place. The charging time and place can be fixed or selectable The scheduling decision time range can be an interval or a circle. Charging tasks may or may not be preemptive.

Last-mile delivery with traditional delivery trucks is ecologically unfriendly and leads to high road utilization. Thus, cities seek for different delivery options to solve these problems. One promising option is the use of cargo bikes in last-mile delivery. These bikes are typically released at micro hubs, which are small containers or facilities located at advantageous places in the city center.

In this paper we present a multi-stage stochastic optimization model to solve an inventory routing problem for the collection of recyclable municipal waste. The objective is the maximization of the total expected profit of the waste collection company. The decisions are related to the selection of the bins to be visited and the corresponding routing plan in a predefined time horizon. Stochasticity

Recent work in healthcare operations provide empirical evidence for the deterioration of service quality due to congestion. Motivated by these findings, we formulate a novel scheduling problem to study how a service provider should prioritize jobs in order to mitigate the impact of temporary congestion-related issues. We analyze the model and show that the optimal policy can be interpreted as a dynamic

Truck–drone combination involves launch/retrieval of rotary-wing drones on trucks, which can address the issues of limited endurance and capacity of rotary-wing drones in delivery systems. Truck–drone combination technologies provide a compelling alternative to traditional emergency logistics systems that rely on on-ground transportation networks. Thus far, little research has been conducted on the

During urban rail transit (URT) emergencies, stranded passengers may choose to seek alternative modes of transportation instead of waiting in the URT system for the bus bridging service to commence. To tackle this challenge, we present an optimization-based approach focused on identifying promising bus bridging lines and devising efficient services. Specifically, we introduce a candidate line generation

This paper presents a literature review of flow-shop scheduling problems with transportation or delivery of jobs. Flow-shop scheduling problems are one of the most widely studied optimisation problems in the literature on Operations Research. Although these have traditionally been studied assuming negligible or constant transport times, this does not correspond to real manufacturing scenarios in the

The speed-up procedure proposed for the permutation flowshop scheduling problem with makespan minimisation (commonly denoted as Taillard’s acceleration) remains, after 30 years, one of the most important and relevant studies in the scheduling literature. Since its proposal, this procedure has been included in countless approximate optimisation algorithms, and its use is mandatory for several scheduling

We study the staffing and scheduling problem of a multiskilled workforce with uncertain demand. We formulate the problem as a two-stage stochastic integer program. The first stage considers strategic decisions, including recruiting permanent staff from an available pool and training them with additional skills, and the second stage focuses on operational decisions, involving the allocation of the multiskilled

This paper investigates the computation of daily schedules of pumps and boundary valves for the minimization of energy costs in water distribution networks (WDN) with dynamically adaptive configurations. The considered problem combines integer (“on”/“off”) pump control variables, non-convex energy conservation constraints and time-coupling mass conservation constraints. For operational WDNs, the resulting

In the prevalent online marketplaces, vendors manage daily operations while e-commerce platforms (EPs) that provide auxiliary services and charge commission fees. Two commission policies are examined in this article: Fixed Commission Policy (FCP), involving a fixed usage fee, and Ordinary Commission Policy (OCP), incorporating an additional fee proportional to sales revenue alongside the fixed usage

Overfitting is a classical statistical issue that occurs when a model fits a particular observed data sample too closely, potentially limiting its generalizability. While Data Envelopment Analysis (DEA) is a powerful non-parametric method for assessing the relative efficiency of decision-making units (DMUs), its reliance on the minimal extrapolation principle can lead to concerns about overfitting

It is well-known that Excess-of-Loss reinsurance has more marketability than Stop-Loss reinsurance, though Stop-Loss reinsurance is the most prominent setting discussed in the optimal (re)insurance design literature. We point out that optimal reinsurance policy under Stop-Loss leads to a zero insolvency probability, which motivates our paper. We remedy this peculiar property of the optimal Stop-Loss

Data envelopment analysis (DEA) relies on two main postulates of convexity and inefficiency (free disposability). No free disposability postulate is suggested to address undesirable measures. In this study, we demonstrate how no-disposability assumption can be correctly integrated into the DEA framework. We propose the appropriate constraints that should be used in the absence of the free disposability

The network effect, measured by users’ adoption, is considered an important driver of cryptocurrency market dynamics. This study examines the role of adoption timing in cryptocurrency markets by decomposing total adoption into two components: innovators (early adopters) and imitators (late adopters). We find that the innovators’ component is the primary driver of the association between user adoption

Cost-efficient and reliable transports are needed to supply products competitively. Thus, particularly in increasingly complex and global supply chains, identifying the optimal transportation mode is a critical decision. Transportation modes, however, are prone to disruptions, such as hurricanes, low water levels, or port shutdowns, resulting in transportation stops and cost increases. To counteract

Most sports associations regularly face the problem of determining and scheduling games for dozens if not hundreds of non-professional (youth) teams. For practical reasons and player convenience, it is key that the schedule respects venue capacities and minimizes travel distance. A classic approach is to split up teams over leagues, and then have each league play a round robin tournament. In a round

In this paper the classical one-dimensional bin packing problem is integrated with scheduling elements: a due date is assigned to each item and the time required to process each bin depends on the pattern being used. The objective is to minimize a convex combination of the material waste and the delay costs, both significant in many real-world contexts. We present a novel pattern-based mixed integer

In this article, we review the literature on statistical theories of neural networks from three perspectives: approximation, training dynamics, and generative models. In the first part, results on excess risks for neural networks are reviewed in the nonparametric framework of regression. These results rely on explicit constructions of neural networks, leading to fast convergence rates of excess risks

One of the most important tasks in sports analytics is the development of binary response models for head-to-head game outcomes to estimate team and player strength. We discuss commonly used probability models for game outcomes, including the Bradley–Terry and Thurstone–Mosteller models, as well as extensions to ties as a third outcome and to the inclusion of a home-field advantage. We consider dynamic

The discrete time/cost trade-off problem (DTCTP) optimizes the project duration and/or cost while considering the trade-off between activity durations and their direct costs. The complete and non-dominated time-cost profile over the set of feasible project durations is achieved within the framework of Pareto front problem. Despite the importance of Pareto front optimization in project and portfolio

This work introduces a novel methodology for portfolio optimization that is the first to integrate support vector machines (SVMs) with cardinality-constrained mean–variance optimization. We propose augmenting cardinality-constrained mean–variance optimization with a preference for portfolios with the property that a low-dimensional hyperplane can separate assets eligible for investment from those ineligible

Allocating capacity to private cloud computing services is challenging because demand is time-varying, there are often no buffers, and customers can re-submit jobs a finite number of times. We model this setting using a multi-station queueing network where servers represent CPU cores and jobs not immediately processed retry several times. Assuming retrial rates are stationary and that there is a maximum

After-sales service is support provided to a customer after purchase, which potentially leads to higher customer satisfaction and is demand-enhancing. Using a game-theoretic model in which a manufacturer determines its after-sales service and distribution channel strategies in the presence of two asymmetric retailers, we identify channel position as an important criterion in determining the outsourcing

Mixed-integer linear programming (MILP) has become a cornerstone of operations research. This is driven by the enhanced efficiency of modern solvers, which can today find globally optimal solutions within seconds for problems that were out of reach a decade ago. The versatility of these solvers allowed successful applications in many areas, such as transportation, logistics, supply chain management

Asset and Liability Management (ALM) is a critical framework for pension funds, ensuring they have sufficient assets to meet future liabilities (pension payments) while managing investment risks effectively. This paper utilizes Brazilian data to develop an ALM model specifically for pension funds in the country. The model employs an optimization strategy that minimizes expected contributions made by

In this paper, we consider a bi-attribute decision making problem where the decision maker’s (DM’s) objective is to maximize the expected utility of outcomes with two attributes but where the true utility function which captures the DM’s risk preference is ambiguous. To tackle this ambiguity, we propose a maximin bi-attribute utility preference robust optimization (BUPRO) model where the optimal decision

This paper introduces a novel by-production approach to modeling desirable and undesirable output production processes in the US banking sector. We utilize the structural proxy variable framework in which desirable outputs (different types of loans and other income-generating activities) are exogenous, which is a common practice in the banking literature. The undesirable output is non-performing loans

In recent years, there has been a growing trend of applying reinforcement learning (RL) in financial applications. This approach has shown great potential for decision-making tasks in finance. In this review, we present a comprehensive study of the applications of RL in finance and conduct a series of meta-analyses to investigate the common themes in the literature, such as the factors that most significantly

We consider bi-objective ranking and selection problems, where the goal is to correctly identify the Pareto-optimal solutions among a finite set of candidates for which the objective function values have to be estimated from noisy evaluations. When identifying these solutions, the noise perturbing the observed performance may lead to two types of errors: solutions that are truly Pareto-optimal may

In medical studies, time-to-event outcomes such as time to death or relapse of a disease are routinely recorded along with longitudinal data that are observed intermittently during the follow-up period. For various reasons, marginal approaches to model the event time, corresponding to separate approaches for survival data/longitudinal data, tend to induce bias and lose efficiency. Instead, a joint

Insufficient data quality prevents data usage by decision support systems (DSS) in many areas of business. This is the case for data on precedence relations between tasks, which is relevant, for instance, in project scheduling and assembly line balancing. Inaccurate data on unnecessary precedence relations cannot be used, otherwise the recommendations of DSS may turn infeasible. So, unnecessary relations

Ranking & selection (R&S) is concerned with the selection of the best decision from a finite set of alternative decisions when the outcome of the decision has to be estimated using stochastic simulation. In this paper, we extend the R&S problem to a two-stage setting where after a first-stage decision has been made, some information may be observed and a second-stage decision then needs to be made

In this paper we study single-machine preemptive scheduling to minimize the total weighted late work with assignable due dates or assignable weights. For the problem with assignable due dates, we show that it is binary NP-hard, solvable in pseudo-polynomial time, and solvable in polynomial time when all the jobs have agreeable processing times and weights. For the problem with assignable weights, we

Infectious diseases pose a persistent challenge to public health worldwide. Recent global health crises, such as the COVID-19 pandemic and Ebola outbreaks, have underscored the vital role of infectious disease modeling in guiding public health policy and response. Infectious disease modeling is a critical tool for society, informing risk mitigation measures, prompting timely interventions, and aiding

Large amounts of multidimensional data represented by multiway arrays or tensors are prevalent in modern applications across various fields such as chemometrics, genomics, physics, psychology, and signal processing. The structural complexity of such data provides vast new opportunities for modeling and analysis, but efficiently extracting information content from them, both statistically and computationally

Functional data analysis (FDA) studies data that include infinite-dimensional functions or objects, generalizing traditional univariate or multivariate observations from each study unit. Among inferential approaches without parametric assumptions, empirical likelihood (EL) offers a principled method in that it extends the framework of parametric likelihood ratio–based inference via the nonparametric

Estimating the mortality associated with a specific mortality crisis event (for example, a pandemic, natural disaster, or conflict) is clearly an important public health undertaking. In many situations, deaths may be directly or indirectly attributable to the mortality crisis event, and both contributions may be of interest. The totality of the mortality impact on the population (direct and indirect

Simulation-based methods for statistical inference have evolved dramatically over the past 50 years, keeping pace with technological advancements. The field is undergoing a new revolution as it embraces the representational capacity of neural networks, optimization libraries, and graphics processing units for learning complex mappings between data and inferential targets. The resulting tools are amortized

Fuzzy preference relation with self-confidence (FPR-SC) uses semantic self-confidence to illustrate the hesitation of experts in affirming given preference values. However, extant ranking derivation methods of FPRs-SC suffer from self-confidence failure problem. Specifically, when the logical operations of self-confidence levels are replaced by algebraic operations on semantic subscripts, the derived

The most complex challenge facing the energy market is identifying effective solutions to reduce CO2 emissions (CEs) and enhance environmental performance (EP). Coal production within the power sector is the primary source of these emissions. In this study, we developed a novel linear programming model that accounts for undesirable outputs to assess the EP of 15 power enterprises in eastern China from

Rough volatility models are a new class of stochastic volatility models that have been shown to provide a consistently good fit to implied volatility smiles of SPX options. They are continuous-time stochastic volatility models, whose volatility process is driven by a fractional Brownian motion with the corresponding Hurst parameter less than a half. Albeit the empirical success, the valuation of derivative

In this paper, we study the patient planning and surgeon scheduling in the operating room theatre. The problem considers the simultaneous planning of patients and the assignment of time blocks to surgeons so that they can perform the surgery of their patients. The timing and length of the allotted time blocks depend on the patient characteristics on the surgeons’ waiting lists. Solving this problem

Unmanned Aerial Vehicles (UAVs) are widely used for military and civilian purposes. Effective route planning is an important component of their successful missions. In this study, we address the route planning problem of a UAV tasked with collecting information from various target locations in a protected terrain. We consider multiple targets, three objectives, and time-dependent information availability

Cap-and-trade, a widely used carbon regulation policy, encourages firms to adopt carbon abatement technologies to reduce emissions. Traditional supply-chain literature on this policy assumes symmetrical information, overlooking the fact that carbon abatement efforts and costs are often private and vary significantly across geographies, industries, and pollutants. In this paper we explore a dual-channel

We study how managers allocate resources in response to algorithmic recommendations that are programmed with specific levels of risk aversion. Using the anchoring and adjustment heuristic, we derive our predictions and test them in a series of multi-item newsvendor experiments. We find that highly risk-averse algorithmic recommendations have a strong and persistent influence on order decisions, even

In response to the growing recognition of the vital role played by streets as public spaces in enhancing the vibrancy of urban life, various concepts aiming at creating greener and more inclusive streets have gained popularity in recent years, especially in North America. Shared streets are one example of such concepts that have attracted the attention of citizens and of urban and transportation planning

Model misspecification is typical in applied structural equation modeling (SEM). Traditional specification search methods, such as modification indices, search for misspecifications within the mode...

Social distancing reduces infectious disease transmission by limiting contact frequency and proximity within a community. However, compliance varies due to its impact on daily life. This paper explores the effects of compliance on social distancing effectiveness through a “social distancing game”, where community members make decisions based on personal utility. We conducted numerical experiments to