Showing papers in "European Journal of Operational Research in 2021"

TL;DR: A survey of machine learning and combinatorial optimization problems can be found in this paper, where the main point is to see generic optimization problems as data points and inquire what is the relevant distribution of problems to use for learning on a given task.

TL;DR: Predictive analytics tools for forecasting and planning during a pandemic using statistical, epidemiological, machine- and deep-learning models, and a new hybrid forecasting method based on nearest neighbors and clustering are provided.

TL;DR: A review of a total of 63 performance indicators partitioned into four groups according to their properties: cardinality, convergence, distribution and spread is proposed.

TL;DR: This study identifies and classifies research related to the planning and control of AMRs in intralogistics, and provides an extended literature review that highlights how AMR technological advances affect planning and Control decisions.

TL;DR: The results showed that the proposed consensus model can integrate opinions of participants using diverse preference formats and reach an agreement efficiently, and was proven by theoretical and simulation analyses.

TL;DR: This study examines the disruption propagation through simulating simple interaction rules of firms inside the SCN, and demonstrates that it is practically important to differentiate between forward and backward disruption propagation, as they are distinctive in the associated mitigation strategies and in the effects on network and individual firm performance.

TL;DR: This study investigates a supply chain system consisting of a capital-constrained manufacturer and a well-funded supplier facing uncertain demand, in which the manufacturer may seek GCF from banks, and designs a GCF model by imposing a hard constraint on carbon emissions.

TL;DR: An analytical model is proposed to examine how the selling mode choice interacts with the logistics service strategy and finds that the supplier's preference aligns with the improvement of the Logistics service level.

TL;DR: This study builds a game model which consists of a government and two competing firms to investigate the equilibrium solutions that the two symmetric firms can reach on their technology improvement strategies, and analyzes the role of government subsidy.

TL;DR: The machine learning literature is surveyed and in an optimization framework several commonly used machine learning approaches are presented for regression, classification, clustering, deep learning, and adversarial learning as well as new emerging applications in machine teaching, empirical modelLearning, and Bayesian network structure learning.

TL;DR: Deep learning algorithms do not seem to be appropriate models for credit scoring based on this comparison and XGBoost should be preferred over the other credit scoring methods considered here when classification performance is the main objective of credit scoring activities.

TL;DR: In this paper, a supply chain game theory network framework was constructed to capture labor constraints under three different scenarios, and the appropriate equilibrium constructs were defined, along with their variational inequality formulations.

TL;DR: This paper investigates the joint optimization of inspection and condition based mission abort policies for systems subject to continuous degradation and numerically evaluates several heuristic policies where mission reliability and system survivability are derived.

TL;DR: It is found that whether information sharing with compensation improves the expected social welfare depends on the freshness elasticity and that information sharing cooperation is more likely to occur when the supplier is more economical in terms of freshness-keeping investment, or when the e-tailer is more efficient in Terms of service investment.

TL;DR: A taxonomy is presented to provide a framework for the broad array of hub location models, and key gaps in the literature are identified that provide opportunities for better models.

TL;DR: Numerical study shows that the altruistic preference can help increase the SMM's profit and system efficiency but decrease the retailer's profit in the coordination contract.

TL;DR: The computational results illustrate that the proposed approaches reduce the level of crowdedness of metro stations by around 8% in comparison with the current practical timetable of the investigated Beijing urban rail network.

TL;DR: A hybrid meta-heuristic algorithm is designed to solve a routing problem in urban transportation which considers time-dependent travel time, multiple trips per vehicle, and loading time at the depot simultaneously, and is shown to be robust and efficient under different speed profiles and maximum trip duration limits.

TL;DR: This paper investigates the condition based mission abort policies for systems subject to two-stage degradation process with normal and defective stages and compares the optimal policy against several heuristic policies where mission reliability and system survivability are evaluated.

TL;DR: This work proposes a decision-dependent distributionally robust optimization model, and develops its exact mixed-integer linear programming reformulation after extensively testing problem characteristics and derives valid inequalities to strengthen the formulation.

TL;DR: A solution approach for a time-dependent vehicle routing problem with time windows in which travel speeds are associated with road segments in the road network is proposed, which involves a tabu search heuristic that considers different shortest paths between any two customers at different times of the day.

TL;DR: Analysis of real-world practices and analytical models show that the “R-collect scheme with GT” leads to the highest levels of supply chain profit and social welfare but more emissions may be generated, while implementing GT does not always benefit the environment in both R-collect and M-collect schemes.

TL;DR: A typology and classifies the literature on inventory models with multiple sourcing options and identifies research gaps on multi-echelon supply chain structures is presented.

TL;DR: This work studies three such omnichannel configurations and shows that depending on whether a premium product is highly personalized or standardized, opening an additional showroom or a B&M store becomes optimal for the retailer.

TL;DR: A meta-learning framework based on newly developed deep convolutional neural networks, which can first learn a feature representation from raw sales time series data automatically, and then link the learnt features with a set of weights which are used to combine a pool of base-forecasting methods.

TL;DR: An Adaptive Large Neighborhood Search heuristic algorithm is developed for solving the vehicle routing problem with time windows and delivery robots (VRPTWDR), and insights are provided on the use of self-driving parcel delivery robots as an alternative last mile service.

TL;DR: Firms’ endogenous CSR compliance strategies are studied, finding that CSR compliant operations will be implemented either when the exposure risk is sufficiently high or when the cost premium is sufficiently low, and that the practice of sustainability requires firms to consider both the external risk of exposure and cost premium.

TL;DR: A model to analyze a manufacturer's channel selection decision among three channel strategies finds that symmetric manufacturers can adopt asymmetric strategies as Nash equilibria and also that there are situations where no Nash equilibrium exists.

TL;DR: This study forms the system as a Markov decision process and provides optimal joint condition-based maintenance and production policies for multi-unit systems with economic dependency and shows that substantial cost savings can be realized.

TL;DR: A variable neighbourhood search (VNS) algorithm to solve JIT-JSS, a variant of the job-shop scheduling problem, in which each operation has a distinct due-date and any deviation of the operation completion time from its due- date incurs an earliness or tardiness penalty.