This paper proposes a new integer L-shaped method for solving two-stage stochastic integer programs whose first-stage solutions can decompose into disjoint components, each one having a monotonic ...recourse function. In a minimization problem, the monotonicity property stipulates that the recourse cost of a component must always be higher or equal to that of any of its subcomponents. The method exploits new types of optimality cuts and lower bounding functionals that are valid under this property. The stochastic vehicle routing problem is particularly well suited to be solved by this approach, as its solutions can be decomposed into a set of routes. We consider the variant with stochastic demands in which the recourse policy consists of performing a return trip to the depot whenever a vehicle does not have sufficient capacity to accommodate a newly realized customer demand. This work shows that this policy can lead to a non-monotonic recourse function, but that the monotonicity holds when the customer demands are modeled by several commonly used families of probability distributions. We also present new problem-specific lower bounds on the recourse that strengthen the lower bounding functionals and significantly speed up the solving process. Computational experiments on instances from the literature show that the new approach achieves state-of-the-art results.
•We propose a new integer L-shaped method for two-stage stochastic programs.•The new method is for problems where the recourse function is monotonic.•The method is applied to the vehicle routing problem with stochastic demands.•We show that the recourse is monotonic for many distributions under some conditions.•Our new approach achieves state-of-the-art results on instances from the literature.
Abstract
Corrective justice and civil recourse theorists aim to provide coherent and unified theories of tort law—and private law more generally. In doing so, they have identified relationality as a ...key unifying concept. For corrective justice theorists, relational rights and wrongs are based on the internal moral structure of private law—namely a notion of rights that protect a person’s capacity to exercise purposive agency. For civil recourse theorists, on the other hand, relational rights and wrongs are grounded in the positive law. This essay assesses whether relationality does, in fact, provide a strong foundation for grounding a theory of tort law. It argues that, while relationality certainly describes aspects of the remedial relationship between right and wrong, it does not generally provide sufficient guidance for understanding what kinds of “relational wrongs” should be redressable by tort in the first instance.
Energy storage systems (ESS) are indispensable building blocks of power systems with a high share of variable renewable energy. As energy-limited resources, ESS should be carefully modeled in ...uncertainty-aware multistage dispatch. On the modeling side, we develop a two-stage model for ESS that respects the nonanticipativity of multistage dispatch, and implement it into a distributionally robust model predictive control scheme. The ESS model features multiple selective operational modes, which enable its power interval to be scheduled in chronological dispatch. On the algorithm side, we propose to evaluate the future worst-case cost expectation over a convex relaxation of the non-convex and discrete value function of the recourse. This gives rise to a novel algorithm that efficiently solves a typical class of two-stage distributionally robust optimization problems, which are equipped with discrete recourse and infinite support. The advantage of the algorithm over the state-of-the-art nested column-and-constraint generation method is theoretically interpreted. Simulation is performed on the modified IEEE 118-bus system and 300-bus system. Results show that ESS function well on the basis of the proposed model and control scheme, and also demonstrate the superiority of the novel algorithm.
This study explores the critical success factors of ERP implementation in the manufacturing sector of Karachi. Data were collected from 252 such employees of the manufacturing sector of Karachi who ...have been using ERP solution for the time duration of at least one year. Exploratory Factor Analysis and Multiple Regression Analysis were used. Results of the factor analysis indicate the presence of four factors. Three of these factors, namely Data Quality (DQ), System Quality (SQ) and the Consultant Support (CS) were the critical success factors, whereas, the fourth was the dependent variable i.e. successful ERP Implementation or ERP Success. Once the factors were extracted, the reliability was tested and then the CFA was used to test convergent and discriminant validity of instrument. Multiple Regression Analysis was then applied to determine the impact of each factor on successful ERP Implementation or ERP Success. It was found that the Data Quality (DQ), System Quality (SQ) and the Consultant Support (CS) have significant impact on Successful ERP Implementation (hence forth known as ERP Success). Therefore, it is recommended that while implementing the ERP systems, firms must ensure that the data accuracy while they are entering the data or providing the data to the vendor / consultant. It is also recommended that the firms must be mindful about the system quality and the consultancy services (software support services) provided by the ERP vendor.
Stochastic vehicle routing, which deals with routing problems in which some of the key problem parameters are not known with certainty, has been an active, but fairly small research area for almost ...50 years. However, over the past 15 years we have witnessed a steady increase in the number of papers targeting stochastic versions of the vehicle routing problem (VRP). This increase may be explained by the larger amount of data available to better analyze and understand various stochastic phenomena at hand, coupled with methodological advances that have yielded solution tools capable of handling some of the computational challenges involved in such problems.
In this paper, we first briefly sketch the state-of-the-art in stochastic vehicle routing by examining the main classes of stochastic VRPs (problems with stochastic demands, with stochastic customers, and with stochastic travel or service times), the modeling paradigms that have been used to formulate them, and existing exact and approximate solution methods that have been proposed to tackle them. We then identify and discuss two groups of critical issues and challenges that need to be addressed to advance research in this area. These revolve around the expression of stochastic phenomena and the development of new recourse strategies. Based on this discussion, we conclude the paper by proposing a number of promising research directions.
The phase II J003 (N = 169) and phase III RECOURSE (N = 800) trials demonstrated a significant improvement in survival with trifluridine (FTD)/tipiracil (TPI) versus placebo in patients with ...refractory metastatic colorectal cancer. This post hoc analysis investigated pharmacokinetic data of FTD/TPI exposure and pharmacodynamic markers, such as chemotherapy-induced neutropenia (CIN) and clinical outcomes.
A total of 210 patients from RECOURSE were enrolled in this substudy. A limited sampling approach was used, with three pharmacokinetic samples drawn on day 12 of cycle 1. Patients were categorized as being above or below the median area under the plasma concentration–time curve (AUC) for FTD and TPI. We conducted a post hoc analysis using the entire RECOURSE population to determine the correlations between CIN and clinical outcome. We then carried out a similar analysis on the J003 trial to validate the results.
In the RECOURSE subset, patients in the high FTD AUC group had a significantly increased CIN risk. Analyses of the entire population demonstrated that FTD/TPI-treated patients with CIN of any grade in cycles 1 and 2 had significantly longer median overall survival (OS) and progression-free survival (PFS) than patients who did not develop CIN and patients in the placebo group. Patients who required an FTD/TPI treatment delay had increased OS and PFS versus those in the placebo group and those who did not develop CIN. Similar results were obtained in the J003 cohort.
In RECOURSE, patients with higher FTD drug exposure had an increased CIN risk. FTD/TPI-treated patients who developed CIN had improved OS and PFS versus those in the placebo group and those who did not develop CIN. Similar findings were reported in the J003 cohort, thus validating the RECOURSE results. The occurrence of CIN may be a useful predictor of treatment outcomes for FTD/TPI-treated patients.
NCT01607957 (RECOURSE).
JapicCTI-090880 (J003).
•Patients with higher FTD exposure had a significantly increased risk of CIN.•In RECOURSE, FTD/TPI-treated patients who developed CIN had improved OS and PFS compared with placebo and those with no CIN.•Similar results from J003 validated the RECOURSE results.
Proactive preparedness to cope with emergencies, especially those of nature origins, significantly improves the resilience and minimizes the restoration cost of electric power systems. In this paper, ...a proactive resource allocation model for repair and restoration of potential damages to the power system infrastructure located on the path of an upcoming hurricane is proposed. The objective is to develop an efficient framework for system operators to minimize potential damages to power system components in a cost-effective manner. The problem is modeled as a stochastic integer program with complete recourse. The large-scale mixed-integer equivalence of the original model is solved by the Benders' decomposition method to handle computation burden. The standard IEEE 118-bus system is employed to demonstrate the effectiveness of the proposed model and further discuss its merits.
Networked microgrids that integrate the hydrogen fueling stations (HFSs) with the on-site renewable energy sources (RES), power-to-hydrogen (P2H) facilities, and hydrogen storage could help ...decarbonize the energy and transportation sectors. In this paper, to support the hydrogen-based networked microgrids planning subject to multiple uncertainties (e.g., RES generation, electric loads, and the refueling demands of hydrogen vehicles), we propose a two-stage stochastic formulation with mixed integer conic program (MICP) recourse decisions. Our formulation involves the holistic investment and operation modeling to optimally site and configure the microgrids with HFSs. The MICP problems appearing in the second-stage capture the nonlinear power flow of networked microgrids system with binary decisions on storage charging/discharging status and energy transactions (including the trading of electricity, hydrogen, and carbon credits to recover the capital expenditures). To handle the computational challenges associated with the stochastic program with MICP recourse, an augmented Benders decomposition algorithm (ABD) is developed. Numerical studies on 33- and 47-bus exemplary networks demonstrate the economics viability of electricity-hydrogen coordination on microgrids level, as well as the benefits of stochastic modeling. Also, our augmented algorithm significantly outperforms existing methods, e.g., the progressive hedging algorithm (PHA) and the direct use of a professional MIP solver, which has largely improved the solution quality and reduced the computation time by orders of magnitude. Note to Practitioners-This paper proposes an optimal planning model for electricity-hydrogen microgrids with the renewable hydrogen production, storage, and refueling infrastructures. Our planning model is extended under a two-stage stochastic framework to address the multi-energy-sector uncertainties, e.g., RES generation, electric loads, and the refueling demands of hydrogen vehicles. The first-stage problem is to optimize the siting and sizing plan of microgrids. Then, in the second-stage problem, the coordinated scheduling of electricity and hydrogen supply systems is modeled as second-order conic programs (SOCPs) to accurately capture the power flow representation under stochastic scenarios. Also, the logical constraints with binary variables are introduced to describe the energy transactions and storage operations, which results in an MICP recourse structure. Note that the stochastic MICP formulation could be very challenging to compute even with a moderate number of scenarios. One challenge certainly comes from integer variables that cause the problem nonconvex. Another challenge follows from the fact that the strong duality of SOCPs might not hold in general. To mitigate those two challenges, we prove that the continuous relaxation of our recourse problem has strong duality, and make use of that continuous relaxation and other enhancements to design an augmented decomposition algorithm. As revealed by our numerical tests, the proposed decomposition method outperforms PHA in both the solution quality and computational efficiency. Comparing to the PHA, our ABD method often achieves tighter bounds with trivial optimality gaps. Also, it could reduce the computation time by orders of magnitude. With the help of advanced analytical tool, the proposed planning framework can be readily implemented in real-world applications.
Background
Precision medicine, space exploration, drug discovery to characterization of dark chemical space of habitats and organisms, metabolomics takes a centre stage in providing answers to ...diverse biological, biomedical, and environmental questions. With technological advances in mass-spectrometry and spectroscopy platforms that aid in generation of information rich datasets that are complex big-data, data analytics tend to co-evolve to match the pace of analytical instrumentation. Software tools, resources, databases, and solutions help in harnessing the concealed information in the generated data for eventual translational success.
Aim of the review
In this review, ~ 85 metabolomics software resources, packages, tools, databases, and other utilities that appeared in 2020 are introduced to the research community.
Key scientific concepts of review
In Table
1
the computational dependencies and downloadable links of the tools are provided, and the resources are categorized based on their utility. The review aims to keep the community of metabolomics researchers updated with all the resources developed in 2020 at a collated avenue, in line with efforts form 2015 onwards to help them find these at one place for further referencing and use.
In steel industries, oxygen demands are generally uncertain due to unpredictable disturbances in steel production, which seriously impacts system safety. Conventional static oxygen scheduling ...exhibits excessive conservativeness and limited robustness. Thus, a Multi-Stage Adaptive Robust Scheduling method for oxygen systems is proposed to adaptively address uncertain demands over time. To achieve this, an oxygen system scheduling model that integrates multiple load-adjustment modes of the air separation unit is first established to improve responsiveness to oxygen demands. Then, the scheduling time horizon is divided into multiple stages. For each stage, a data-driven uncertainty set is constructed to capture the most critical uncertainty distribution interval with low conservativeness. In addition, computationally efficient recourse decisions are designed with a few additional variables. These designs upgrade the deterministic oxygen scheduling model to a computationally tractable Multi-Stage Adaptive Robust Scheduling model that can dynamically adjust evaporators and emissions to address observed uncertainty promptly. The superiority of the proposed model is demonstrated using real data of an oxygen system. Compared to conventional oxygen scheduling, the adjustable robust scheduling improves the ability to handle uncertainty at least three-fold and saves cost, thereby enhancing system robustness and while reducing conservativeness. The designed recourse decisions greatly improve solution efficiency by two orders of magnitude, and the constructed data-driven uncertainty set also benefits robustness and cost optimality.
•Adjustable multi-stage robust oxygen scheduling, adapting to demand uncertainty.•An oxygen scheduling model with multiple air separation unit adjustment modes.•Computationally efficient recourse decisions, depending on a recent time period.•Data-driven uncertainty sets, accurately capturing the densest distribution region.
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