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 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

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

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

An accurate and interpretable credit scoring model plays a crucial role in helping financial institutions reduce losses by promptly detecting, containing, and preventing defaulters. However, existing models often face a trade-off between interpretability and predictive accuracy. Traditional models like Logistic Regression (LR) offer high interpretability but may have limited predictive performance

Every sport needs rules. Tournament design refers to the rules that determine how a tournament, a series of games between a number of competitors, is organized. This study aims to provide an overview of the tournament design literature from the perspective of operational research. Three important design criteria are discussed: efficacy, fairness, and attractiveness. Our survey classifies the papers

This paper studies a mean-risk portfolio choice problem for log-returns in a continuous-time, complete market. It is a growth-optimal portfolio choice problem under risk control. The risk of log-returns is measured by weighted Value-at-Risk (WVaR), which is a generalization of Value-at-Risk (VaR) and Expected Shortfall (ES). We characterize the optimal terminal wealth and obtain analytical expressions

In recent years, there has been considerable interest in the transformative potential of additive manufacturing (AM) since it allows for producing highly customizable and complex components while reducing lead times and costs. The rise of AM for traditional and new business models enforces the need for efficient planning procedures for AM facilities. In this area, the assignment and sequencing of components

This study focuses on two-echelon synchronized logistics problems in the context of integrated water- and land-based transportation (IWLT) systems. The aim is to meet the increasing demand in city logistics as a result of the growth in transport activities, including parcel delivery, food delivery, and waste collection. We propose two models, a novel mixed integer linear joint model, and a logic-based

Practical planning and decision-making problems are often better and more accurately formulated with multiple conflicting objectives rather than a single objective. This study investigates a situation relevant for Multiple Criteria Decision Making (MCDM) as well as Evolutionary Multi-objective Optimization (EMO), where the decision-maker needs to make a series of choices between nondominated options

The Resource Constrained Project Scheduling Problem with a flexible Project Structure (RCPSP-PS) is a generalization of the Resource Constrained Project Scheduling Problem (RCPSP). In the RCPSP, the goal is to determine a minimal makespan schedule subject to precedence and resource constraints. The generalization introduced in the RCPSP-PS is that, instead of executing all activities, only a subset

In this paper, a latency location-routing problem with stochastic travel times is investigated. The problem is cast as a two-stage stochastic program. The ex-ante decision comprises the location of the depots. The ex-post decision regards the routing, which adapts to the observed travel times. A risk-averse decision-maker is assumed, which is conveyed by adopting the latency CVaRα as the objective

A Sigma-Mu approach is proposed for mutual funds portfolio selection. The mean and variance of the overall performance of each asset are considered, according to an additive aggregation model, subject to weights’ preferences provided by the decision maker. These preferences concern two independent sets of weights, i.e., those pertaining to the investment indicators and those pertaining to the time

During project execution, the actual project progress shows deviations from the baseline schedule due to uncertainty. To complete the project timely, project monitoring is performed at discrete control points to identify project opportunities/problems and take possible corrective actions. These control points affect the quality of project monitoring and corrective actions, but little guidance is available

This article considers a sequential investment project which starts with a product innovation phase, and subsequently, once R&D is completed, a production phase. The investment decisions are the timing and size of the R&D investment, and the size of the production capacity. We show that from a social welfare perspective the firm starts the R&D project too late and installs a too low production capacity

In decision-making under uncertainty, several criteria have been studied to aggregate the performance of a solution over multiple possible scenarios. This paper introduces a novel variant of ordered weighted averaging (OWA) for optimization problems. It generalizes the classic OWA approach, which includes the robust min–max optimization as a special case, as well as the min–max regret optimization

This paper focuses on the price of fairness in several scheduling problems with two agents, each with a set of nonpreemptive jobs, competing to execute their respective jobs on a single machine. Each agent expects to minimize its objective function, which depends on the completion times of its own jobs. Several objective functions are considered, including makespan, total (weighted) completion time

The Multilinear Target-based Preference Functions (MTPFs) support multi-attribute decision problems characterized by attribute interactions and targets. However, existing research falls short in flexibly modeling high-dimensional interactions and lacks robustness in decision-making recommendations when faced with uncertain parameters and targets. The paper proposes a robust multilinear target-based

Theorems are established on the interrelationships among non-cooperative, classical and Pareto coalitional stability definitions within the framework of the graph model for conflict resolution. The classical coalition stability concepts are first redefined and then, based on the concept of Pareto coalition improvement, new definitions are proposed for Pareto coalition Nash, Pareto coalition general

Large manufacturing companies often manage a network of multiple factories, creating a distributed flow shop scheduling problem for flowline manufacturing processes. This problem involves assigning jobs to one of several distributed factories, each equipped with identical flow shops, and completing the jobs within their designated factory. We expand upon the traditional distributed flow shop scheduling

This paper presents an optimization model to distribute logistical items from a warehouse to shelters in the case of humanitarian flood disaster relief. The model utilizes three transportation modes, namely, a truck, a drone, and an inflatable boat. We refer to this problem as the Traveling Salesman Problem with Drone and Boat (TSP-DB). The truck acts as a mothership vehicle, carrying a drone and a

Shared input occurs often when a production system performs two or more functions and some of the functions share the same input. To determine the proportion of the shared input devoted to each function, the conventional data envelopment analysis (DEA) models that allow each decision making unit (DMU) to select the most favorable value to attain the highest efficiency score is usually used. The result

A new practice among online retail platforms, e.g., Amazon and Wayfair, is to offer their own private label product and a substitutable exclusive manufacturer product. We employ a game theoretic approach to examine conditions under which a retailer and a manufacturer find it optimal to enter into such a partnership. Our analysis reveals that a retailer finds it profitable to partner with a manufacturer

Strategic behavior is common in multi-criteria sorting, where manipulated alternatives are allocated to the target category to achieve the intended goal. Engaging in such strategic behavior in sorting consensus often comes at a cost, which is closely tied to the degree of preference adjustments and the level of trust relationships among decision makers. This study investigates strategic behavior using

We introduce a novel two-phase matheuristic for assignment and truck loading. The scope of our approach involves scenarios with an extensive amount of items requiring assignment to a heterogeneous fleet of trucks and subsequent transportation. A distinguishing feature of our matheuristic lies in the fact that the complex underlying problem is divided into subproblems which later are merged into a global

With the significant advancements in communication technology, group decision-making (GDM) can now be implemented online, allowing a large number of decision-makers (DMs) to participate concurrently. However, current methods for large-scale group decision-making (LSGDM) are primarily suitable for 20 to 50 DMs, and their effectiveness in scenarios involving thousands or even tens of thousands of participants

Despite numerous use cases, enterprise-wide implementations of blockchains have seen limited success. This raises the question of when do firms adopt blockchains and do blockchains benefit supply chains. To answer this, we examine a dyadic supply chain consisting of a buyer and a supplier and analyze their traceability effort and pricing decisions. Our results show that the demand-side, supply-side

We show that internationally diversified portfolios carry sizeable political risk premia and expose investors to tail risk. We obtain political efficient frontiers with and without hedging political risk using a portfolio selection model for skewed distributions and develop a new asymptotic inference test to compare portfolio performance. Politically hedged portfolios outperform a broad market index

The advent of e-commerce and omnichannel retailing has sparked renewed interest in picker routing in warehouses. This paper presents two significant methodological advances in this well-established field. First, it is a well-known fact that the parallel-aisle layout of warehouses, as opposed to general graphs, allows for polynomial-time solutions of the Traveling Salesman Problem. We show that the

Motivated by the real-world inventory management problem of a large network of pharmacies, this paper proposes and studies a practically relevant Prescriptive Analytics approach for data-driven dynamic inventory control of large portfolios of interrelated products. We extend existing research on weighted Sample Average Approximation by integrating a ‘global learning’ model that effectively exploits

The efficiency decomposition and frontier projection of traditional two-stage network data envelopment analysis (DEA) model under variable returns to scale (VRS) are often not equivalent; which not only contradicts DEA theory, but also reduces the scientificity of the model. The main reason for this inequivalence is that there is a synergistic effect of variable scale return in two different stages

Planning in the power sector has to take into account the physical laws of alternating current (AC) power flows as well as uncertainty in the data of the problems, both of which greatly complicate optimal decision making. We propose a computationally tractable framework to solve multi-stage stochastic optimal power flow (OPF) problems in AC power systems. Our approach uses recent results on dual convex

We study a pickup-and-delivery problem that arises when customers randomly submit requests over the course of a day from a choice of vendors on a collaborative e-commerce portal. Based on the attributes of a customer request, a dispatcher dynamically schedules the delivery service on either a dedicated vehicle or a crowdshipper, both of whom experience time-dependent travel times. While dedicated vehicles

We introduce a tractable multivariate pure jump process in which the trading time is described by an additive subordinator. The multivariate process retains the additivity property, and therefore is time inhomogeneous, i.e., its increments are independent but non stationary. We provide the theoretical framework of our process, perform a sensitivity analysis with respect to the time inhomogeneity parameters

We consider a long-term engine maintenance planning problem for an aircraft fleet. The objective is to guarantee sufficient on-wing engines to reach service levels while effectively organizing shop visits for engines. However, complexity arises from intricate maintenance policies and uncertainty in engine deterioration. To address this problem, we propose a graph-based approach representing high-dimensional

The existing inverse graph model for conflict resolution (GMCR) research primarily concentrates on identifying the required preferences of decisions makers (DMs) such that a desired state is an equilibrium. However, the process of transitioning from the current state to the desired equilibrium is not explored. In this paper, we propose a minimum adjustment cost model taking account of preference adjustment

The analytic hierarchy process (AHP) is a structured technique used to analyze complex decision-making situations such as resource allocation, benchmarking, and quality management. In the weight valuation step of using AHP to select the best design, pairwise comparison matrices are used to calculate the local priorities for designs that have contentious and unresolved criticisms. In this study, we

In this work, we address a forest management problem for timber production with fire concerns, employing a novel simulation-based optimization approach wherein forest management is iteratively guided by the feedback from fire spread simulations.

This research examines the problem of routing multiple assets of different types over a network to service demands in a collaborative manner. The servicing is collaborative in that, when servicing a demand, the different types of assets must do so nearly simultaneously. Moreover, whereas some asset types must service demands by visiting them, other asset types may provide service proximally. This study

Supply chain fairness issues have become crucial and prevalent recently, whereas the operational decisions in the fair chain are more and more challenging when involving information asymmetry. Considering the fact that the upstream supplier of a chain generally has private production cost information, this paper investigates how the supplier strategically makes pricing decisions under the own private

We consider a logistics planning problem of prepositioning relief commodities in preparation for an impending hurricane landfall. We model the problem as a multi-period network flow problem where the objective is to minimize the total expected logistics cost of operating the network to meet the demand for relief commodities. We assume that the hurricane’s attributes evolve over time according to a

This paper explores the calculation of risk-minimization hedging strategies, specifically local and global risk minimization strategies for contingent claims under affine and non-affine GARCH models with the known closed forms of its first four moments across times under the physical measure. A unified and efficient willow tree method is introduced for various GARCH models. Unlike methods that provide

This paper first studies an expected utility problem with chance constraints and incomplete information on a decision maker’s utility function. The model maximizes the worst-case expected utility of random outcome over a set of concave functions within a novel ambiguity set, while the underlying probability distribution is known with the assumption of a discretization of possible realizations for the

In this paper we introduce a generalized extension of the Eisenberg–Noe model of financial contagion to allow for time dynamics of the interbank liabilities, including a dynamic examination of default risk. This framework separates the cash account and long-term capital account to more accurately model the health of a financial institution. In doing so, such a system allows us to distinguish between

We propose a new unbiased stochastic gradient estimator for a family of stochastic models driven by uniform random numbers as inputs. Dropping the requirement that the tails of the density of the input random variables decay smoothly, the estimator extends the applicability of the generalized likelihood ratio (GLR) method. We demonstrate the new estimator for several general classes of input random

We investigate a multi-skill technician and reassignable task scheduling problem in a cloud computing company. In the problem, multi-skill technicians are assigned to process a large number of tasks from customer requests in a certain scheduling horizon. The tasks are allowed to be reassigned to another technician multiple times, and one technician can process multiple tasks in parallel. The company

We study single-machine scheduling problems with step-learning, where an improvement in processing time is experienced if a job is started at, or after, a job-dependent learning-date. We consider minimizing two functions: the number of late jobs and the total late work, and we show that when at least a common due-date or common learning-date is assumed, the problem is NP-hard in the ordinary sense;

Eco-driving and eco-routing problems are both concerned with minimizing the fuel consumption of a single vehicle; the former does so by optimizing the vehicle’s speed profile on a given road segment, and the latter selects the best path for a vehicle from a given origin to a given destination. This paper studies a problem that combines the two, namely an eco-routing-and-driving problem, in which the

Focusing on gains & losses relative to a risk-free benchmark instead of terminal wealth, we consider an asset allocation problem to maximize time-consistently a mean-risk reward function with a general risk measure which is (i) law-invariant, (ii) cash- or shift-invariant, and (iii) positively homogeneous, and possibly plugged into a general function. Examples include (relative) Value at Risk, coherent

In this paper, we discuss the worst-case distortion riskmetrics for general distributions when only partial information (mean and variance) is known. This result is applicable to a general class of distortion risk measures and variability measures. Furthermore, we also consider the worst-case weighted entropy for general distributions when only partial information is available. Specifically, we provide

This study explores the question of adapting a likelihood-ratio test in the two-stage data envelopment analysis (DEA) framework, where DEA estimates are regressed against external factors. We focus on the hypotheses of testing the technological difference across time periods (or groups) and propose two bootstrapping procedures. Our Monte Carlo (MC) simulation shows that the proposed test has a substantially

The main objective of this study is to introduce machine learning-type extensions for the measurement of environmental inefficiency based on regression trees under shape constraints. The new methods developed are implemented using a by-production approach that distinguishes two technologies, one related to the generation of pollution and the other to the production of good outputs. In particular, we

We challenge the dichotomy of network effects and highlight that they are not an exogenous characteristic of networks, but endogenous to the decisions of network users. When users choose which activities to perform in a network, multi-activity users transform indirect into direct network effects and a network effectively becomes one-sided if merely multi-activity users frequent it. Our work contributes

This paper considers a parallel queueing system with multiple stations, each of which contains many statistically identical servers and has a dedicated queue. Upon each customer arrival, the system manager must decide to which station the customer should be routed, with the objective of minimizing the system’s long-run average delay cost. One feature of this paper is that a customer’s delay cost depends

In this paper we investigate a two-echelon vehicle routing problem for liquefied natural gas (LNG) delivery to determine how to transport LNG from an overseas production terminal to a set of import terminals by vessels, and transport the LNG from the import terminals to a set of filling stations either by tanker trucks or bunker barges. Some important features of this problem are that part of LNG will

Rationalizing the relatively low levels of hedging observed in the oil market, compared to those predicted by pure risk minimization, has proven difficult. This article examines whether the objectives of oil producers can explain this discrepancy. From a theoretical perspective, it appears that the observed level of hedging is well explained by risk averse producers who also exhibit regret aversion