This paper investigates an unrelated parallel machine scheduling problem with practical production constraints, where the upper limit of additional resources varies across distinct time intervals, and the precedence relationships between jobs can be violated by paying specified penalty costs. We formulate a novel mixed integer linear programming model to minimize the makespan and total penalty cost

This paper presents a new statistical process control method for monitoring the number of waiting entities for queues. It is based on dynamic characterization of the number-in-system (NIS) data trajectory via Fourier coefficient magnitudes. Since monitoring periods are necessarily short, we investigate windowing methods for dampening the impact of the Gibbs phenomenon, which can contaminate the Fourier

The classical multi-armed bandit problem involves a learner and a collection of arms with unknown reward distributions. At each round, the learner selects an arm and receives new information. The learner faces a tradeoff between exploiting the current information and exploring all arms. The objective is to maximize the expected cumulative reward over all rounds. Such an objective does not involve a

Choice behavior reflects consumer preferences. Consumers often purchase products online nowadays, which can be viewed as a choice process. If a consumer makes multiple transactions over a period of time, then we can say the consumer make multiple intertemporal choices. This study focuses on the problem of learning consumer preferences from intertemporal choice data. The main challenges in this research

This article presents a comprehensive literature review of the Cutting Stock Problem with Usable Leftovers (CSPUL). The most recent review on this topic dates to 2014, covering articles published before 2013. Since then, the number of publications on CSPUL has increased significantly, driven by new applications and more efficient solution approaches. We analyze fifty two relevant articles from twenty

This study presents a mathematical framework and solution approach aimed at enhancing wireless sensor network coverage, specifically focusing on agricultural applications. Sensor networks in precision agriculture can efficiently monitor environmental parameters and control factors affecting crop yield and quality. However, challenges such as sensor failures and communication disruptions due to vegetation

Service composition and optimal selection in cloud manufacturing involves the allocation of available manufacturing cloud services (MCSs) derived from a diverse array of manufacturing resources to satisfy personalized demand of customers. Existing studies generally neglect the uncertainty of manufacturing capabilities for providing MCSs. To this end, we use an event-dependent hybrid ambiguity set consisting

We examine whether a factor-based framework to construct the covariance matrix can enhance the performance of minimum-variance portfolios. We conduct a comprehensive comparative analysis of a wide range of factor models, which can differ based on the machine learning dimensionality reduction approach used to construct the latent factors and whether the covariance matrix is static or dynamic. The results

Due to difference in strength, power, or status among decision makers (DMs), traditional strategic conflict may be evolved into a super conflict problem where a powerful DM can force others to approach his strategy although other DMs suffer loss. For instance, the government can mobilize relevant enterprises to engage in ecological and environmental governance. Also, DMs may conduct irrational behaviors

When distributors, as small and medium-sized enterprises (SMEs), encounter financial constraints and market uncertainty, manufacturers can offer guarantees and product buyback (credit-buyback-financing strategy, CBF) to mitigate risk and enhance output. Recently, the rise in popularity of third-party guarantee institutions introduces additional options for supply chain members, including coguarantee

In this paper, we propose a Bayesian Nash equilibrium (BNE) model for analyzing price competition under multinomial logit demand where firm’s marginal cost is private information: each firm may predict the range of the marginal cost of its rival but does not know the true marginal cost. Differing from the existing Nash equilibrium models (Aksoy-Pierson et al., 2013; Pang et al., 2015) where the market

The aim of this paper is to develop an approach for evaluating various maritime transportation routes in light of ongoing disruptions and evolving global factors, including changes in demand, fluctuations in fuel prices, geopolitical shifts, and environmental considerations. For each alternative route, a profit maximization liner shipping problem with speed optimization is solved. Both edge-based speed

Credit scoring is pivotal in financial risk management and has attracted significant research interest. While existing studies primarily concentrate on enhancing model predictive power and economic value, they often overlook the crucial aspect of predictive uncertainty, especially in the context of deep neural networks applied to credit scoring. This study addresses uncertainty estimation in credit

With the increasing incidence of fatal crowd stampede disasters at public events due to rapid urbanization and escalating population density, there is an urgent need for real-time monitoring of crowd flow to prevent such tragic incidents. This paper proposes a novel nonparametric online control chart for crowd density monitoring, called the RDR-CUSUM chart. This chart utilizes a new statistic based

Drug availability in clinics is essential for patient services, whose demand for medication is uncertain. Thus, clinics must have a variety of drugs available, leading to high inventory holding costs. In Germany, it is common for a larger central clinic to take over the procurement of drugs and distribute them to smaller surrounding clinics, which results in a two-echelon network structure. The clinics

We consider a single-period fuel delivery problem in which a distribution company has to transport multiple types of fuel from a depot to a set of service stations using a heterogeneous set of multi-compartment vehicles. Among other characteristics, the problem includes time windows, a limit on the duration of each route, unsplit compartments, and split deliveries. We propose two mixed integer programming

Fixed cost allocation (FCA) poses a significant challenge for decision-making units (DMUs) contributing to a shared cost. Each DMU aims to allocate the minimum possible cost to itself. As a result, different DMUs hold varying and often conflicting allocation proposals and preferences, which prevents them from reaching a consensus on the FCA outcome. This paper develops a dynamic bargaining game-based

Randomized controlled trials (RCTs) are conducted in many business applications including online marketing or customer churn prevention to investigate the effect of specific treatments (coupons, retention offers, mailings, etc.). RCTs allow for the estimation of average treatment effects and the training of (uplift) models for the heterogeneity of treatment effects across individuals. The problem with

High-speed railway stations are crucial junctions in high-speed railway networks. Compared to operations on the tracks between stations, trains have more routing possibilities within stations. As a result, track allocation at a station is relatively complicated. In this study, we aim to solve the train platforming problem for a busy high-speed railway station by considering comprehensive track resources

In recent times, extreme weather conditions have been playing havoc on supply chains. Companies are investing in climate-specific assets to mitigate weather-related risks and ensure continuous supply chain activities. In this paper, we analytically investigate a supplier’s decision regarding climate-specific investment to understand how the probability of weather disruption, its effort during extreme

To streamline order fulfillment, many e-commerce retailers apply scattered storage in their picker-to-parts warehouses. Instead of putting homogeneous unit loads into racks, they break up the pallets and store individual products within mixed shelves. This increases the probability that (somewhere in the vast warehouses) products, ending up jointly on hardly predictable pick lists, can be picked from

This paper studies a novel crane scheduling problem with unconstrained double-load operations (CSP-UDL) in slab yards. It aims to optimize the sequence of crane operations to minimize makespan. Unlike conventional crane operations, which handle one or two slabs per trip, the unconstrained double-load operation enables the transport of more than two slabs in a single trip, thereby improving logistic

To find one of the M target entries in an unstructured database that has N entries, Grover’s algorithm performs π4NM iterations. In each iteration, a function is called that evaluates whether an entry is a target entry. Compared to a classical procedure, that requires NM evaluations, Grover’s algorithm achieves a quadratic speedup. This quadratic speedup, combined with its general applicability as

The three-way decision (3WD) model has lately gained a lot of attention in data analysis and decision-making, and has even become a methodological framework that brings new insights into old problems. Our research is inspired by Bisht and Pal (2024) who introduced an innovative 3WD model which frames multi-attribute decision-making (MADM) problems. Considering both the loss and utility associated with

Bike Sharing Systems (BSSs) offer a sustainable and efficient urban transportation solution, bringing flexible and eco-friendly alternatives to city logistics. During their operation, BSSs may suffer from unbalanced bike distribution among stations, requiring rebalancing operations throughout the system. The inherent uncertain demand at the stations further complicates these rebalancing operations

Traditional classification and regression trees (CARTs) utilize a top-down, greedy approach to split the feature space into sharply defined, axis-aligned sub-regions (leaves). Each leaf treats all of the samples therein uniformly during the prediction process, leading to a constant predictor. Although this approach is well known for its interpretability and efficiency, it overlooks the complex local

Kidney Exchange Programs (KEPs) typically maximize overall patient benefit through donor exchanges. This aggregation of benefits (utilitarian objective) calls into question potential individual patient disparities in terms of access to transplantation in KEPs. Moreover, current KEP policies are all-or-nothing, meaning that only one exchange plan is determined — each patient is either selected or not

Patient-to-room assignment (PRA) is a scheduling problem in decision support for hospitals. It consists of assigning patients to rooms during their stay at a hospital according to certain conditions and objectives, e.g., ensuring gender separated rooms, avoiding transfers and respecting single-room requests. This work presents combinatorial insights about the feasibility of PRA and about how (many)

This paper addresses seru formation problem in divisional seru production system (SPS), which focuses on job-seru assignment, worker-seru assignment and operation-worker assignment in each seru. The problem is formulated as a mixed-integer nonlinear programming (MINLP) model with the objective of minimizing training and processing costs of workers. Once the job-seru assignment is determined, we employ

This is a comprehensive review of the Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic and its hybridization with Path Relinking (PR). GRASP with PR has become a widely adopted approach for solving hard optimization problems since its proposal in 1999. The paper covers the historical development of GRASP with PR and its theoretical foundations, as well as recent advances in its implementation

We study the problem of mechanism design for allocating a set of indivisible items among agents with private preferences on items. We aim to design a mechanism that is strategyproof (in which agents find it optimal to report their true preferences) and ensures a certain level of fairness and efficiency. We first establish that no deterministic mechanism can simultaneously be strategyproof, fair, and

The Traveling Purchaser Problem (TPP) is a generalization of the Traveling Salesman Problem (TSP) in which a list of items must be acquired by visiting a subset of markets. The objective is to minimize the total cost sustained along the route, including purchasing and traveling costs. Due to the NP-hard nature of the problem, solving the TPP in an exact manner is computationally challenging, implying

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

Motivated by the observation that some online retail platforms begin to enter offline channels by acquiring traditional retailers, this paper studies the impact of platform acquisition in a context where a manufacturer sells a product via an online agency selling and two offline reselling channels. Considering three changes brought by platform acquisition in practice, namely, showrooming, “pay online

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

The finite nature of certain fossil fuels, which are essential inputs in production, imposes a substantial constraint on the economy's long-term growth potential. This paper develops an endogenous directed technical change model under uncertainty regarding the timing of a potential backstop technology discovery. The model directs R&D towards increasing the probability of a technological breakthrough

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 study examines a supply chain comprising a live-streaming platform and a manufacturer that sells its products via the platform in the agency or resale mode. Under the carbon trading system, the manufacturer adopts green technology and exhibits the greenwashing behaviour, and the platform chooses whether to use blockchain to eliminate this behaviour. We find that the existence of greenwashing generates

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

This paper presents an overview of scheduling research done over the last half century. The main focus is on what is typically referred to as machine scheduling. The first section describes the general framework for machine scheduling models and introduces the notation. The second section discusses the basic deterministic machine scheduling models, including single machine, parallel machines, flow

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