Showing papers in "European Journal of Operational Research in 2019"

TL;DR: This paper discusses suited systems, surveys the relevant literature, and defines future research needs for these warehousing systems especially suited for e-commerce retailers.

TL;DR: A generalized soft cost consensus model under a certain degree of consensus is developed, which is built by defining a consensus level function and a generalized aggregation operator and is applied to a loan consensus scenario using data from an online peer-to-peer lending platform.

TL;DR: A comprehensive Multiple Criteria Group Decision Making (MCGDM) method with probabilistic linguistic information based on a new consensus measure and a novel outranking method, Gained and Lost Dominance Score (GLDS), validated by a case study of selecting optimal green enterprises.

TL;DR: This work analyzed both centralized and decentralized systems for unknown distribution function of the random variables through a distribution-free approach and also for known distribution function and examines the effect of delivery lead time and customers’ channel preference on the optimal operation.

TL;DR: This paper surveys the state-of-the-art literature on applications and theoretical/methodological aspects of ARO and provides a tutorial and a road map to guide researchers and practitioners on how to apply ARO methods, as well as, the advantages and limitations of the associated methods.

TL;DR: A comprehensive review regarding the different approaches to CRP is reported, and a series of CRPs as the comparison objects are presented, and the following comparison criteria for measuring the efficiency of CPRs are proposed.

TL;DR: A review of the hybrid simulation literature is presented, using a novel framework based on the simulation lifecycle that will be useful for future modellers and authors alike, and promising areas for future research are identified.

TL;DR: A Trust Relationship-based Conflict Detection and Elimination decision making model applicable for Large-scale Group Decision Making (LSGDM) problems in social network contexts is proposed and a new selection method for LSGDM is proposed that determines decision makers’ weights based on their conflict degree.

TL;DR: This work presents a meta-analyses of the determinants of infectious disease in eight operation rooms of the immune system and three of them are connected to each other through the immune response to infectious disease.

TL;DR: The results provide the first large-sample evidence for the predictive power of textual disclosures and suggest that simpler models such as averaging embedding are more effective than convolutional neural networks.

TL;DR: It is argued that the principles of bandit problems should become a core dimension of mainstream stochastic optimization, and a universal modeling framework is proposed that encompasses all of these competing approaches.

TL;DR: Modern modeling approaches to address problems related to ambulance fleet management, particularly those related to vehicle location and relocation, as well as dispatching decisions are discussed.

TL;DR: It is found that although the traditional manufacturing process accepts incremental improvement, remanufacturing in general requires stepwise innovation; thus, the optimal strategy of managing process innovation in a forward supply chain does not directly apply to manage process innovation for re manufacturing in a closed-loop supply chain.

TL;DR: The model is applied to a case from a steel converter plant, and the results show that the proposed maintenance policy outperforms some existing maintenance policies in terms of the net revenue.

TL;DR: The recovery efficiency of a two-echelon decentralized model consisting of a manufacturer and a retailer and its performance with a centralized model is analysed and two coordination mechanisms are proposed to ameliorate the decentralized model.

TL;DR: In this paper, the authors introduce an electric vehicle routing problem combining conventional, plug-in hybrid, and electric vehicles, and design a sophisticated metaheuristic which combines a genetic algorithm with local and large neighborhood search.

TL;DR: The model is extended to several directions, finding that longer product line, manufacturer competition, lower marginal production cost, and higher platform cost all tend to induce the platform to put forward a fixed fee contract; while if quality decision is less flexible than contract decision, the platform is more ready to embrace revenue sharing.

TL;DR: This paper proposes an integer programming formulation and a hybrid algorithm that combines a column generation and an adaptive large neighborhood search (CG-ALNS) to solve the two-echelon capacitated electric vehicle routing problem with battery swapping stations.

TL;DR: In this article, a computational intelligence technique that uses a hybrid Neuro-Fuzzy controller, namely PATSOS, was proposed to forecast the direction in the change of the daily price of Bitcoin.

TL;DR: Investigating manufacturer encroachment with both endogenous quality decision and asymmetric demand information to examine the effects of encroachment and information structure on quality and profits for chain members shows that encroachment leads to a lower quality when the manufacturer’s direct selling cost is intermediate.

TL;DR: A new simheuristic approach that is an integration of Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) and Monte Carlo simulation is developed to overcome the stochastic combinatorial optimization problem of this study.

TL;DR: The basic principles behind and an overview of the existing body of recent research on ‘greening’ freight transportation using OR-based planning techniques for road and maritime transportation are presented.

TL;DR: This paper is the first to consider a broad range of cyber risk events and actual cost data from an operational risk database and uses the peaks-over-threshold method from extreme value theory to identify “cyber risks of daily life” and “extreme cyber risks”.

TL;DR: In this article, an integrated operational model for electricity and natural gas systems under uncertain power supply by applying two-stage stochastic programming is proposed to optimize day-ahead and real-time dispatch of both energy systems and aims at minimizing the total expected cost.

TL;DR: The main purpose of this paper is to answer this problem by considering the combined effects of manufacturers’ leader-follower relationships, the e-tailer’s referral fees, the difference in products’ substitutable degrees and the different in products' market bases.

TL;DR: It is presented an interesting insight that the marketplace channel should be introduced under not only a low degree but also a high degree of upstream sales inefficiency, which also means that a weak direct channel would not necessarily become a burden for the two.

TL;DR: This paper proposes a novel approach for energy-efficient timetabling by adjusting the running time allocation of given timetables using train trajectory optimization, and suggests that the proposed approach is able to improve the energy efficiency of a timetable.

TL;DR: This paper provides an empirical example to evaluate cross-efficiency with several selected universities directly managed by the Ministry of Education of China to illustrate the effectiveness of the PCE model in ranking DMUs.

TL;DR: This study provides a novel text analytics–driven approach to fake news detection for reducing the risks posed by fake news consumption through the implementation and validation of a novel FakE News Detection (FEND) system.

TL;DR: It is shown that only a little intervention of the operator can significantly enhance the quality of service, and that the rule of thumb for bike repositioning is to prioritize the closer, the more active, the closer to be full or empty, and the more imbalanced stations if no reversing in the imbalance is anticipated.