We propose a robust optimization (RO) framework that immunizes some of the central linear algebra problems in the presence of data uncertainty. Namely, we formulate linear systems, matrix inversion, eigenvalues-eigenvectors and matrix factorization under uncertainty, as robust optimization problems using appropriate descriptions of uncertainty. The resulting optimization problems are computationally

In marine container terminals, truck dispatching optimization is often considered as the primary focus as it provides crucial synergy between the sea-side operations and yard-side activities and hence can greatly affect the terminal throughput and quay crane utilization. However, many existing studies rely on strong assumptions that often overlook the uncertainties and dynamics innate to real-life

We investigate a condition-based group maintenance problem for multi-component systems, where the degradation process of a specific component is affected only by its neighbouring ones, leading to a special type of stochastic dependence among components. We formulate the maintenance problem into a factored Markov decision process taking advantage of this dependence property, and develop a factored value

In this note, the authors propose correcting one erroneous formula from [Vidal, T., Crainic, T. G., Gendreau, M., & Prins, C. (2014). A unified solution framework for multi-attribute vehicle routing problems. European Journal of Operational Research, 234(3), 658-673] in charge of lunch breaks. In the original paper, the authors propose to compute several attribute values from the solution of a vehicle

A finite order multivariate Hermite polynomial expansion, as an approximation of a joint density function, can handle complex correlation structures. However, it does not construct copulas, because the density function can take negative values. In this study, we propose a formulation of multivariate Hermite polynomial expansion suitable for the application of correction that recovers non-negativity

Given the considerable number of actors in the humanitarian space, coordination is essential for successful disaster response. Furthermore, the sheer size of challenges and limited resources increasingly highlight the need for improved cooperation and collaboration in humanitarian supply chains. A significant number of studies in the literature explore the 3Cs (coordination, cooperation and collaboration)

Agents may form coalitions. Each coalition shares its endowment among its agents by applying a sharing rule. The sharing rule induces a coalition formation problem by assuming that agents rank coalitions according to the allocation they obtain in the corresponding sharing problem. We characterize the sharing rules that induce a class of stable coalition formation problems as those that satisfy a natural

With the global effort to reduce carbon emissions, clean technologies such as electric vehicles and heat pumps are increasingly introduced into electricity distribution networks. These technologies considerably increase electricity flows and can lead to more coincident electricity demand. In this paper, we analyze how such increases in demand coincidence impact future distribution network investments

Time-sync comment (TSC) has emerged as a new type of textual comment for real-time user interactions on online video platforms. The sentiment classification of TSCs provides considerable potential for platforms to optimize operation strategies but inevitably faces great challenges due to the TSCs’ often uninformative and informal text. Considering the contextual dependency among TSCs posted within

In this paper, we present a survey on vehicle routing problems with multiple commodities. In most routing problems, only one commodity is explicitly considered. This may be due to the fact that, indeed, a single commodity is involved, or multiple commodities are transported, but they are aggregated and modeled as a single commodity, as no specific requirement imposes their explicit consideration. However

This paper studies purchasing a commodity or a perishable item under stochastically evolving and correlated prices for a distribution system environment. We consider the central purchasing of the commodity under the demand process correlated with the random price and decide on the timing and quantity of allocation to demand locations. As an implementation of the physical pooling concept, we investigate

We develop an additive Cox proportional hazard model with time-varying covariates, including spatio-temporal characteristics of weather events, to study the impact of weather extremes (heavy rains and tropical cyclones) on the probability of mortgage default and prepayment. We compare the survival model with a flexible logistic model and an extreme gradient boosting algorithm. We estimate the models

In this work, the authors address the Optimal Transport (OT) problem on graphs using a proximal stabilized Interior Point Method (IPM). In particular, strongly leveraging on the induced primal–dual regularization, the authors propose to solve large scale OT problems on sparse graphs using a bespoke IPM algorithm able to suitably exploit primal–dual regularization in order to enforce scalability. Indeed

This paper proposes efficient exact and heuristic approaches for minimizing the makespan on a single machine scheduling problem with sequence-dependent setup times and release dates. The exact procedure consists of a branch-and-price (B&P) algorithm implemented over an arc-time-indexed formulation with a pseudo-polynomial number of variables and constraints. Our B&P algorithm includes several modern

Retailers are embracing cryptocurrency payments to gain a competitive edge. However, the fierce volatility of traditional cryptocurrencies like Bitcoin deters risk-averse consumers from using them regularly. This issue is particularly pronounced in retail markets with high product return rates, as consumers may bear the volatility risk by directly holding cryptocurrencies after claiming a refund. In

This paper characterises dynamic linkages arising from shocks with heterogeneous degrees of persistence. Using frequency domain techniques, we introduce measures that identify smoothly varying links of a transitory and persistent nature. Our approach allows us to test for statistical differences in such dynamic links. We document substantial differences in transitory and persistent linkages among US

Third-party sellers on online platforms primarily rely on banks to meet their financing requirements and are often constrained by the lack of sufficient working capital. Online platforms such as Amazon and Alibaba have ushered new dynamics in the e-commerce financing landscape by offering working capital loans to these sellers, thereby directly competing with banks and influencing market competition

In several areas of economics and engineering, it is often necessary to fit discrete data points or approximate non-linear functions with continuous functions. Piecewise linear (PWL) functions are a convenient way to achieve this. PWL functions can be modeled in mathematical problems using only linear and integer variables. Moreover, there is a computational benefit in using PWL functions that have

A common approach that governments use to combat the potential environmental harm caused by industry is to set an environmental standard for firms to either comply with by a specified deadline or face a penalty. Two penalty policies that governments often rely on to pressure firms to comply with a new standard are a per-period penalty policy and a per-unit penalty policy. We examine how a government

Online portfolio selection is a practical problem in financial engineering and quantitative trading. Many empirical studies show that stock performance in the market is likely to follow mean reversion, and strategies based on mean reversion show better return performance than the market average. However, the existing mean reversion strategies are not universal and short selling is not allowed, which

We consider single machine scheduling problems in the context of adversarial bilevel optimization where two agents, the leader and the follower, take decisions on the same jobset and the leader acts first with the aim of inducing the worst possible solution for the follower. Thus, the follower schedules the jobs in order to optimize a given criterion. The considered criteria are the total completion

The traditional classification algorithms focus on the maximization of classification accuracy which might lead to poor performance in practice by forcing classifiers to overfit to the majority class. In order to overcome this issue, various approaches focus on the optimization of alternative loss functions such as the Area Under the Curve (AUC). AUC is a Receiver Operating Characteristics (ROC) metric

Decisions under uncertainty or with multiple objectives usually require the decision maker to formulate a preference regarding risks or trade-offs. If this preference is known, the ordered weighted averaging (OWA) criterion can be applied to aggregate scenarios or objectives into a single function. Formulating this preference, however, can be challenging, as we need to make explicit what is usually

Counterfactual Explanations are becoming a de-facto standard in post-hoc interpretable machine learning. For a given classifier and an instance classified in an undesired class, its counterfactual explanation corresponds to small perturbations of that instance that allows changing the classification outcome. This work aims to leverage Counterfactual Explanations to detect the important decision boundaries

This paper studies the Zero Emission passenger Vessel Service Network Design Problem (ZEVSNDP) in order to investigate how technical and economic challenges related to diffusion of battery electric vessels can be alleviated by appropriate planning of services. The ZEVSNDP considers decisions that are strategic (i.e., vessel fleet and charging locations), tactical (i.e., routes, whether to omit servicing

Most analytics models are built on complex internal learning processes and calculations, which might be unintuitive, opaque, and incomprehensible to humans. Analytics-based decisions must be transparent and intuitive to foster greater human acceptability and confidence in analytics. Explainable analytics models are transparent models in which the primary factors and weights that lead to a prediction

We consider the dynamic and stochastic resource-constrained multi-project scheduling problem which allows for the random arrival of projects and stochastic task durations. Completing projects generates rewards, which are reduced by a tardiness cost in the case of late completion. Multiple types of resource are available, and projects consume different amounts of these resources when under processing

Abstract not available

We consider dynamic portfolio selection under ambiguity in the classical multi-period binomial market model. Ambiguity is incorporated in the real-world probability measure through an epsilon-contamination, that gives rise to a completely monotone capacity conveying a pessimistic investor’s ambiguous beliefs. The dynamic portfolio selection problem is formulated as a Choquet expected utility maximization

Motivated by current green manufacturing standards, in this paper we study a parallel-machine scheduling model in which the energy cost incurred on each machine is machine-dependent and proportional to the load of the machine. The objective is to determine a production schedule with the minimum makespan subject to the energy constraint that the total energy cost does not exceed a given bound. We provide

Convenience stores are small shops typically located in dense urban areas. They are an important element in modern retailing considering growing urbanization. The layout of convenience stores usually differs significantly for individual stores depending on the local conditions and the types of store (e.g., grocery stores, kiosks, gas stations). Convenience stores have specific delivery requirements

A sequential quadratic programming numerical method is proposed for American option pricing based on the variational inequality formulation. The variational inequality is discretized using the θ-method in time and the finite element method in space. The resulting system of algebraic inequalities at each time step is solved through a sequence of box-constrained quadratic programming problems, with the

In this study, we compose a new metaheuristic algorithm for solving the resource-constrained multi-project scheduling problem. Our approach is based on a general metaheuristic strategy which incorporates two resource-buffered scheduling tactics. We build on the most effective evolutionary operators and other well-known scheduling methods to create a novel genetic algorithm with resource buffers. We

Empirical studies have shown that stockouts may adversely affect future demand. However, much of the literature on inventory optimization has ignored this effect. In this paper, we investigate the effect of stockouts on a firm’s operational policy and total discounted profit. We show that compared with classic periodic review inventory models with no stockout effect, the optimal inventory control policies

Remanufacturing is well-established as a means for original equipment manufacturers (OEMs) to reduce waste, save on raw materials costs, and tap into new markets. At the same time, OEMs are often skeptical of remanufacturing due to the potential for remanufactured items to steal market share from new items. Third-party remanufacturers (TPRs) experience no such skepticism, since these market players

The number of people affected by disasters, including man-made ones, is on the rise globally, with rising food insecurity being one of the most critical impacts. Disasters, both sudden-onset and slow-onset ones, can cause disruptions to the production and transportation of agricultural commodities. Having the tools that can quantitatively assess the changes in agricultural commodity shipment volumes

Rail freight distribution operations require assembling trains consisting of multiple freight cars with a common destination, and assigning departure times to the assembled trains. Limits on railyard train assembly capacity restrict the number of trains that may be assembled and, therefore, depart the railyard, per unit time. Each individual car, or unit, in a railyard typically has an associated subsequent

Interest in citizen participation is increasing generally. Almost all operational research (OR) is engaged with clients, but it is mainly in the areas of Soft and Community OR that wider stakeholder and citizen participation has been a significant focus. It is the involvement of citizens that is the subject of this paper. We surveyed OR literature and compiled a corpus of 62 studies, the earliest from

With the burgeoning of the Internet of everything, the amount of data generated by edge devices increases dramatically. In order to relieve the huge pressure of the could computing center, a popular computing scheme, called edge computing, is to select and process part of the computation tasks on edge servers of the network. In this paper we model the task offloading problem motivated by the popular

Nowadays, green loans have been widely promoted to support the capital-constrained supply chain under carbon regulation. However, the borrowers in the supply chain may use the preferential green loans for other purposes which are not related to green development. That phenomenon is mainly attributed to the lack of valid information investigation under traditional information technology. Blockchain

In practice, agency e-commerce, also known as e-marketplace, faces the challenges of copycat products with nearly identical “consumption quality” to genuine products. The e-marketplace may lack incentives to combat copycat sellers, leading many brands to adopt a novel “Channel Separation” strategy to protect their brand image. Under this strategy, although the brand seems to lose profit from stopping

In the paper Torrealba et al. (2021) an augmented Lagrangian algorithm was proposed for resource allocation problems with the intriguing characteristic that instead of solving the box-constrained augmented Lagrangian subproblem, they propose projecting the solution of the unconstrained subproblem onto such box. A global convergence result for the quadratic case was provided, however, this is somewhat

In strategic conflicts, decision-makers (DMs) do not constantly make their best decisions, which makes room for DMs to feel regretful. In this context, this work proposes the Minimax Regret Stability within the graph model for conflict resolution (GMCR) for modelling and analysing conflicts, considering that DMs would feel regret for an unsatisfactory decision. This concept does not require knowledge

Exact simulation schemes under the Heston stochastic volatility model (e.g., Broadie–Kaya and Glasserman–Kim) suffer from computationally expensive Bessel function evaluations. We propose a new exact simulation scheme without the Bessel function, based on the observation that the conditional integrated variance can be simplified when conditioned by the Poisson variate used for simulating the terminal

In this paper, I report some investigation discussing team selection, whence hierarchy, through ranking indicators, for example when “measuring” professional cyclist team’s “sportive value”, in particular in multistage races. A logical, it seems, constraint is introduced on the riders: they must finish the race. Several new indicators are defined, justified, and compared. These indicators are mainly

Hundreds of scholarly papers on optimisation of preventive maintenance policies for items under warranty have been published in the reliability related literature. They typically have two limitations: they make a simplified assumption on the relationship between age and usage for items under two-dimensional warranty and they assume that it is cost-effective to conduct preventive maintenance (PM) on

Estimating missing judgements is a key component in many multi-criteria decision making techniques, especially in the Analytic Hierarchy Process. Inspired by the Koczkodaj inconsistency index and a widely used solution concept of cooperative game theory called the nucleolus, the current study proposes a new algorithm for this purpose. In particular, the missing values are substituted by variables,

In this article, we address the family capacitated vehicle routing problem (F-CVRP), an NP-hard problem that generalizes both the FTSP and the capacitated vehicle routing problem. The F-CVRP has practical application in warehouse management in warehouses with scattered storage. We present several mixed integer linear programming formulations for this problem and establish a theoretical and empirical

We present an integrated prediction-optimization (PredOpt) framework to efficiently solve sequential decision-making problems by predicting the values of binary decision variables in an optimal solution. We address the key issues of sequential dependence, infeasibility, and generalization in machine learning (ML) to make predictions for optimal solutions to combinatorial problems. The sequential nature

Slow-moving goods are common in many retail settings and occupy a vast part of retail shelves. Since stores sell these products irregularly and in small quantities, the replenishing distribution center may only place batched orders with manufacturers every few weeks. While order quantities are often fixed, the challenge for manufacturers facing such intermittent demand is to forecast the order timing

The planning fallacy describes the tendency of people to underestimate the costs and to overestimate the benefits of investments. It is typically associated with cost overruns and decreased performance. In this paper, we demonstrate that in a simple distribution channel with an upstream manufacturer and a downstream retailer that both make demand-enhancing investments, there is a bright side of the

The objective of facility location models is to maximize the profit or minimize the cost of a company that wishes to expand or enter the market. The market share captured by facilities is a function of the distances to the facilities and their attractiveness. The analysis involves how to estimate the market share captured by facilities, where to locate new facilities, how to determine their attractiveness

Despite being consistently outperformed by machine learning (ML) in forecasting competitions, simple statistical forecasting techniques remain standard in retail. This is partly because, for all their advantages, these top-performing ML methods are often too complex to implement. We have experimented with various tree-based ML methods and find that a ‘simple’ implementation of these can (substantially)

Small and medium-sized enterprises (SMEs) often rely on credit guarantee companies (guarantors) to obtain financing from banks. To encourage guarantors’ engagement and reduce SMEs’ guarantee costs, governments commonly implement two subsidy policies: risk compensation, which shares a fraction of guarantors’ losses, and fee reduction, which reimburses SMEs for a fraction of their guarantee fees. This

This study presents a comprehensive literature survey on facility location problems for drone (uncrewed vehicle) delivery, where either (i) drones are the only vehicles, or (ii) drones and other vehicles (e.g., trucks) work together for delivery, but drones do not ride in or on the other vehicles. The main goals of this review are to identify and categorize fundamental facility location problems associated

This study establishes a dual-channel supply chain (DCSC) wherein the e-retailer is endowed with superior demand information, and investigates power structure preferences of the supplier and e-retailer. Game models are investigated based on two information structures (information symmetry and asymmetry) and three power structures, i.e., vertical Nash, e-retailer-led Stackelberg, and supplier-led Stackelberg

Cross-efficiency evaluation and meta-frontier analysis (MFA) have been widely used to measure performance in many areas. However, cross-efficiency MFA (CMFA) has rarely been studied due to its potential violation of the basic MFA property, that is, the efficiency relative to the group frontier is not less than that relative to the meta-frontier. In this paper, we deduce the conditions under which the

Innovative online interfaces informing and consulting citizens about their preferences for multicriteria decision analysis (MCDA) could make public decision-making more participatory. We propose a three-faceted learning for decision-making framework and used it to test newly-designed online weight elicitation interfaces. We investigated two features meant to enhance learning: fully-fledged gamification

Cluster ensembles have emerged as a powerful tool to obtain clusters of data points by combining a library of clustering solutions into a consensus solution. In this paper, we address the cluster ensemble selection problem and design a multi-objective optimization-based solution framework to produce consensus solutions. Given a library of clustering solutions, we first design a preprocessing procedure

We address the problem of determining the start times of activities in order to maximize the expected net present value of a project given precedence constraints. We assume that each activity has a random duration and profit with a known probability distribution. Most approaches generate either: a baseline schedule that is robust to uncertainty (using proactive approaches), or a policy that reacts