General Programming Model Research Articles

Enhancing DC distribution network efficiency through optimal power coordination in lithium-ion batteries: A sparse nonlinear optimization approach

This work deals with the problem regarding the optimal operation of lithium-ion batteries to improve the economic, technical, and environmental indices of standalone and grid-connected direct current (DC) distribution networks.To this effect, a general nonlinear programming model has been developed, which considers three objective functions: (i) the daily energy purchasing costs at the terminals of the substation, considering the maintenance costs of renewable generation (photovoltaic) and energy storage technologies (batteries); (ii) the daily energy losses associated with energy transport; and (iii) the CO2 emissions per day of operation, associated with conventional generators. This is done by integrating all the operations and technical constraints of the DC network and the devices it comprises. The sparse nonlinear optimization (SNOPT) solution method is employed, comparing its results against those of three recently reported metaheuristic optimization methodologies: the continuous genetic algorithm and the parallel versions of the particle swarm optimizer and the vortex search algorithm. The performance of the proposed methodology is validated on standalone and grid-connected networks, considering the technical and economic conditions of two regions of Colombia (rural and urban territories) and evaluating its effectiveness in terms of solution quality, standard deviation, and processing times. The results show that the SNOPT tool of the General Algebraic Modeling System (GAMS) achieves the global optimum in all test scenarios, exhibiting a standard deviation of zero and requiring less than three seconds on average to generate a solution for an entire day of operation.

Dynamic rebalancing optimization for bike-sharing systems: A modeling framework and empirical comparison

Station-based Bike-sharing systems have been implemented in multiple major cities, offering a low-cost and environmentally friendly transportation alternative. As a remedy to unbalanced stations, operators typically rebalance bikes by trucks. The resulting dynamic planning has received significant attention from the Operations Research community. Due to its modeling flexibility, mixed-integer programming remains a popular choice. However, the complex planning problem requires significant simplifications to obtain a computationally tractable model. As a result, existing models have used a large variety of modeling assumptions and techniques regarding decision variables and constraints. Unfortunately, the impact of such assumptions on the solutions’ performance in practice remains generally unexplored.In this paper, we first systematically survey the literature on rebalancing problems and their modeling assumptions. We then propose a general mixed-integer programming model for multi-period rebalancing problems that can be easily adapted to different assumptions, including trip modeling, time discretization, trip distribution, and event sequences. We develop an instance generator to synthesize realistic station networks and customer trips, as well as a realistic fine-grained simulator to evaluate the operational performance of rebalancing strategies. Finally, extensive numerical experiments are carried out, both on the synthetic and real-world data, to analyze the effectiveness of various modeling assumptions and techniques. Based on our results, we identify the assumptions that empirically provide the most effective rebalancing strategies in practice. Specifically, a set of specific trip distribution constraints and event sequences ignored in the previous literature seem to provide particularly good results.

Optimizing sustainable multimodal distribution networks in the context of carbon pricing, with a case study in the Thai sugar industry

Transportation is a major cause of energy consumption and emissions which can be reduced by optimizing routings and using alternative modes of transport. This paper relates to the strategic design of multimodal transportation networks. It presents a general model of green vehicle routing problems that supports strategic decision-making by identifying optimal solutions and provides data on costs and emissions. Three general linear programming models were developed that optimize multimodal distribution networks that could be applied in many industries. Model I evaluates carbon emissions; model II assesses carbon emissions and capacity constraints; and model III establishes total costs including transportation, handling, storage, fuel and carbon costs.Thailand is the third largest world sugar exporter in the world and is piloting carbon pricing, which will affect energy intensive industries, including the sugar industry. The models are applied using data obtained from a collaborating company. The research contributed to practice by informing managerial decisions relating to the export of sugar from the factory. This included evaluating the possible use of a dry port with rail connections, which could reduce transportation and carbon costs by 54.3 % and facilitate the building of another factory to increase exports.

Master Production Scheduling with Consideration of Utilization-Dependent Exhaustion and Capacity Load

A large number of researchers have addressed social aspects in hierarchical production planning. This article responds to research gaps identified in our previous literature review. Accordingly, consideration of social aspects and the economic implications of social improvements are required in a longer term planning approach. For this, we integrate work intensity as employee utilization in a general mixed-integer programming model for master production scheduling. Following existing fatigue functions, we represent the relationship between work intensity and exhaustion through an employee-utilization-dependent exhaustion function. We account for the economic implications through exhaustion-dependent capacity load factors. We solve our model with a CPLEX standard solver and analyze a case study based on a realistic production system and numerical data. We demonstrate that the consideration of economic implications is necessary to evaluate social improvements. Otherwise, monetary disadvantages are overestimated, and social improvements are, thus, negatively affected. Moreover, from a certain level of work-intensity reduction, demand peaks are smoothed more by pre-production, which requires more core employees, while temporary employment is reduced. Further potential may arise from considering and quantifying other interdependencies, such as employee exhaustion and employee days off. In addition, the relationship between social working conditions and employee turnover can be integrated.

The Floating-Cuts model: a general and flexible mixed-integer programming model for non-guillotine and guillotine rectangular cutting problems

Cutting and packing problems are challenging combinatorial optimization problems that have many relevant industrial applications and arise whenever a raw material has to be cut into smaller parts while minimizing waste, or products have to be packed, minimizing the empty space. Thus, the optimal solution to these problems has a positive economic and environmental impact.In many practical applications, both the raw material and the cut parts have a rectangular shape, and cutting plans are generated for one raw material rectangle (also known as plate) at a time. This is known in the literature as the (two-dimensional) rectangular cutting problem. Many variants of this problem may arise, led by cutting technology constraints, raw-material characteristics, and different planning goals, the most relevant of which are the guillotine cuts. The absence of the guillotine cuts imposition makes the problem harder to solve to optimality.Based on the Floating-Cuts paradigm, a general and flexible mixed-integer programming model for the general rectangular cutting problem is proposed. To the best of our knowledge, it is the first mixed integer linear programming model in the literature for both non-guillotine and guillotine problems. The basic idea of this model is a tree search where branching occurs by successive first-order non-guillotine-type cuts. The exact position of the cuts is not fixed, but instead remains floating until a concrete small rectangle (also known as item) is assigned to a child node. This model does not include decision variables either for the position coordinates of the items or for the coordinates of the cuts. Under this framework, it was possible to address various different variants of the problem.Extensive computational experiments were run to evaluate the model’s performance considering 16 different problem variants, and to compare it with the state-of-the-art formulations of each variant. The results confirm the power of this flexible model, as, for some variants, it outperforms the state-of-the-art approaches and, for the other variants, it presents results fairly close to the best approaches. But, even more importantly, this is a new way of looking at these problems which may trigger even better approaches, with the consequent economic and environmental benefits.

Sharing the electric bus charging stations by scheduling the charging strategy

Electric cars (ECs) play a vital role in reducing carbon emissions and improving sustainable development. Due to the lack of public charging facilities, the application of private ECs is limited. Motivated by the sharing economy, this is the first study to investigate the idea of sharing electric bus (EB) charging stations to alleviate the plight of private EC owners. However, existing EB charging strategies do not cater to the sharing and hinder the implementation of the policy. Therefore, this paper proposed a general mathematic mixed-integer non-linear programming model to coordinate the optimal charging strategy and sharing operation to meet the external and internal goals of bus systems, namely, maximizing charging station availability and satisfying the EB charging demand. Harnessing real-world bus operating data, the proposed model effectively generates multiple charging strategies. The results show that some hybrid strategies can achieve a similar sharing availability period as the public-oriented strategy with lower costs. In addition, sensitivity analysis indicates that increasing battery capacity can extend the sharing availability period while increasing charging power has only a minor impact. Overall, the proposed model obtains desired charging scheduling and provides concrete suggestions to promote the sustainable development of ECs and EBs.

A general robust dynamic Bayesian network method for supply chain disruption risk estimation under ripple effect

Robust dynamic Bayesian network (DBN) is a valid tool for disruption propagation estimation in the supply chain under data scarcity. However, one of assumptions in robust DBN is that the Markov transition matrix is fixed and fully known, which is unpractical. To make up this deficiency, a novel and general robust DBN is, for the first time, proposed in this work to assess the worst-case oriented supply chain disruption risk under ripple effect. The study focuses on a supply chain with multiple suppliers and one manufacturer over a time horizon, in which only probability intervals of related probabilities are known. The objective is to obtain the worst-case supply chain disruption risk, measured by the probability of the manufacturer in the fully disrupted state in the final time period. For the problem, a new and general nonconvex programming model is established. Then, a case study is conducted to compare our approach with the classic DBN and robust DBN in the literature.

Patient Mix Optimization in Admission Planning under Multitype Patients and Priority Constraints.

Hospital beds are one of the most critical medical resources. Large hospitals in China have caused bed utilization rates to exceed 100% due to long-term extra beds. To alleviate the contradiction between the supply of high-quality medical resources and the demand for hospitalization, in this paper, we address the decision of choosing a case mix for a respiratory medicine department. We aim to generate an optimal admission plan of elective patients with the stochastic length of stay and different resource consumption. We assume that we can classify elective patients according to their registration information before admission. We formulated a general integer programming model considering heterogeneous patients and introducing patient priority constraints. The mathematical model is used to generate a scientific and reasonable admission planning, determining the best admission mix for multitype patients in a period. Compared with model II that does not consider priority constraints, model I proposed in this paper is better in terms of admissions and revenue. The proposed model I can adjust the priority parameters to meet the optimal output under different goals and scenarios. The daily admission planning for each type of patient obtained by model I can be used to assist the patient admission management in large general hospitals.

Hubs for VirtuosoNext: Online verification of real-time coordinators

• Modelling of the coordination mechanism between tasks in a RTOS (VirtuosoNext); • Build new coordinators by plugging existing ones; • Specify timed scenarios to interact with the coordinator; • Verify timed behavioural properties of coordinators using UPPAAL; • Implementation: open source and running as a service online. VirtuosoNext TM is a distributed real-time operating system (RTOS) featuring a generic programming model dubbed Interacting Entities . This paper focuses on these interactions, implemented as so-called Hubs . Hubs act as synchronisation and communication mechanisms between the application tasks and implement the services provided by the kernel. While the kernel provides the most basic services, each carefully designed, tested and optimised, tasks are limited to this handful of basic hubs, leaving the development of more complex mechanisms up to application specific implementations. This work presents a toolset that supports the building of new services compositionally, using notions borrowed from the Reo coordination language, on which the developer can delegate coordination-related duties. This toolset uses a formal compositional semantics for hubs that captures dataflow and time, formalising the behaviour of existing hubs, and allowing the definition of new ones. Furthermore, it enables the analysis and verification of hubs under our automata interpretation, including time-sensitive behaviour via the Uppaal model checker, usable on http://arcatools.org/hubs . We illustrate the proposed tools and methods by verifying key properties on different interaction scenarios between tasks and a composed hub.

Developing Optimal Policies to Fight Pandemics and COVID-19 Combat in the United States

The world has faced many outbreaks and pandemics with hundreds of millions of deaths throughout its history. Those epidemics are global health concerns and as well as serious economic issues. There is certain need to allocate scarce sources efficiently to fight such epidemics in both personal and global dimensions. Here we develop and propose two optimization models to maximize the total protection from any epidemic, pandemic or bioterror attacks; the first one is personal protection model or protocol and the other one is mass protection model that is inspired by the well-known weapon-target assignment problem of operations research. These efforts are optimal allocation of scarce medical and economic resources to save millions of lives- gift of life. We implement our general mathematical programming models with real-world data to fight the coronavirus pandemic for a person and the United States. Our personal protection protocol provides 99.99% protection from COVID-19 for an American through personal strategies when the mass protection model supplies 96.961% protection on average from coronavirus pandemic for the United States through across country policies. The mass protection model which recommends general policy frame for health care policy makers could be applied for any epidemic at any level from county to city, to state and country as well as in global scale. The mathematical relation between the personal protection protocol and the mass protection model also highlights the fact of unus pro omnibus, omnes pro uno (one for all, all for one) for fighting epidemics- particularly the moving enemy, novel coronavirus which is double invisible due to its viral nature and the availability of the high level of asymptomatic cases. Recognize the enemy, as protecting yourself means protecting people, love life, follow the rules and stay at home. That is the greatest ever social impact.Published on 28 April 2020.

A General Model for Periodic Chemical Production Scheduling

We present a general mixed-integer programming model for periodic production scheduling. The formulation, which is based on the state-task network (STN) representation, allows us to model (1) accur...

Failure Recovery in Resilient X10

Cloud computing has made the resources needed to execute large-scale in-memory distributed computations widely available. Specialized programming models, e.g., MapReduce, have emerged to offer transparent fault tolerance and fault recovery for specific computational patterns, but they sacrifice generality. In contrast, the Resilient X10 programming language adds failure containment and failure awareness to a general purpose, distributed programming language. A Resilient X10 application spans over a number of places. Its formal semantics precisely specify how it continues executing after a place failure. Thanks to failure awareness, the X10 programmer can in principle build redundancy into an application to recover from failures. In practice, however, correctness is elusive, as redundancy and recovery are often complex programming tasks. This article further develops Resilient X10 to shift the focus from failure awareness to failure recovery, from both a theoretical and a practical standpoint. We rigorously define the distinction between recoverable and catastrophic failures. We revisit the happens-before invariance principle and its implementation. We shift most of the burden of redundancy and recovery from the programmer to the runtime system and standard library. We make it easy to protect critical data from failure using resilient stores and harness elasticity—dynamic place creation—to persist not just the data but also its spatial distribution. We demonstrate the flexibility and practical usefulness of Resilient X10 by building several representative high-performance in-memory parallel application kernels and frameworks. These codes are 10× to 25× larger than previous Resilient X10 benchmarks. For each application kernel, the average runtime overhead of resiliency is less than 7%. By comparing application kernels written in the Resilient X10 and Spark programming models, we demonstrate that Resilient X10’s more general programming model can enable significantly better application performance for resilient in-memory distributed computations.

A Variability-Aware Robust Design Methodology for Integrated Circuits by Geometric Programming

Process variations have continuously posed significant challenges to the performance and yield of integrated circuits (ICs). The performance modeling and robust optimization method considering process variations has become an important research task in today’s IC design. Aiming at solving the problems of strong nonlinearity and high-dimensional problems in circuit design, this paper proposes a general robust optimization method for ICs by geometric programming. This method first employs regularization sparse models to model a specific performance metric as a posynomial function in terms of design parameters, in order to reduce parameter space dimensionality and to accurately capture the nonlinear relationship between performance perturbations and process variations. Based on the posynomial performance models, this method further uses an uncertainty set to represent the uncertainties of process variations, and formulates the problem of robust optimization under process variations as a general geometric programming model that can be efficiently solved. Experimental results demonstrate that, the proposed method not only enhances the accuracy and efficiency of circuit performance modeling, but also improves the performance yield significantly compared with traditional circuit design methods.

Observability of power systems with optimal PMU placement

Phasor Measurement Units (PMUs) are measuring devices that, when placed in electrical networks, observe their state by providing information on the currents in their branches (transmission lines) and voltages in their buses. Compared to other devices, PMUs have the capability of observing other nodes besides the ones they are placed on. Due to a set of observability rules, depending on the placement decisions, the same number of PMUs can monitor a higher or smaller percentage of a network. This leads to the optimization problem hereby addressed, the PMU Placement Problem (PPP) which aims at determining the minimum number and location of PMUs that guarantee full observability of a network at minimum cost.In this paper we propose two general mathematical programming models for the PPP: a single-level and a bilevel integer programming model. To strengthen both formulations, we derive new valid inequalities and promote variable fixing. Furthermore, to tackle the bilevel model, we devise a cutting plane algorithm amended with particular features that improve its efficiency. The efficiency of the algorithm is validated through computational experiments. Results show that this new approach is more efficient than state-of-the-art proposals.

MapReduce particle filtering with exact resampling and deterministic runtime

Particle filtering is a numerical Bayesian technique that has great potential for solving sequential estimation problems involving non-linear and non-Gaussian models. Since the estimation accuracy achieved by particle filters improves as the number of particles increases, it is natural to consider as many particles as possible. MapReduce is a generic programming model that makes it possible to scale a wide variety of algorithms to Big data. However, despite the application of particle filters across many domains, little attention has been devoted to implementing particle filters using MapReduce.In this paper, we describe an implementation of a particle filter using MapReduce. We focus on a component that what would otherwise be a bottleneck to parallel execution, the resampling component. We devise a new implementation of this component, which requires no approximations, has O(N) spatial complexity and deterministic O((logN)2) time complexity. Results demonstrate the utility of this new component and culminate in consideration of a particle filter with 224 particles being distributed across 512 processor cores.

Optimal Synthesis and Operation of Wastewater Treatment Process with Dynamic Influent

This work addresses the problem of optimal synthesis and operation of wastewater treatment processes considering multiscenario influent streams under different discharge standards and penalty rates of noncompliant emissions. We develop a general disjunctive programming model to address this problem, which is then reformulated as a multiperiod mixed-integer nonlinear programming model using hull reformulation for the disjunctions. To solve the resulting model to global optimality, we propose a Lagrangean-based decomposition algorithm. Numerical studies verify the effectiveness of the algorithm, and comparison studies provide useful management insights for policy makers.

A general model for the home health care routing and scheduling problem with route balancing

Home health care organizations are inclined to optimize their activities since the rise of the demand for home care. Defining the routing of the caregivers are complex activities to perform because of the different objectives to optimize and constraints to handle. The objectives considered are often in conflict leading one objective to deteriorate the other ones. Indeed, balancing the traveling time may increase the total traveling time of caregivers as well as increase the soft patient time window and shared visits non-satisfaction. Therefore, a good balance must be found between these objectives. Thus, we propose a general mixed-integer programming model for the home health care routing and scheduling problem with route balancing. The proposed model handles most of the known characteristics in order to be application-based independent. A memetic algorithm is proposed to evaluate the multi-objective approach on several instances and support decision-making. The impact of the focus on route balancing is analyzed on the behavior of the objectives. The results highlight that the focus on route length balancing may become harmful by deteriorating the other objectives while not improving anymore the maximal route length difference. Moreover, the route length balancing shows some different sensitivities on the other objective functions depending on the instance structure.

Solving the maximum vertex weight clique problem via binary quadratic programming

In recent years, the general binary quadratic programming (BQP) model has been widely applied to solve a number of combinatorial optimization problems. In this paper, we recast the maximum vertex weight clique problem (MVWCP) into this model which is then solved by a probabilistic tabu search algorithm designed for the BQP. Experimental results on 80 challenging DIMACS-W and 40 BHOSLIB-W benchmark instances demonstrate that this general approach is viable for solving the MVWCP problem.

Discovery of semantic associations in an RDF graph using bi-directional BFS on massively parallel hardware

Resource description framework (RDF) data model provides a framework to capture the meaning of an entity by specifying how it relates to other entities. Large RDF graph involving millions of entities are common in many semantic graph applications and are challenging to process. For example, finding complex relationships called semantic associations between two entities in an RDF graph is a tedious process. Graphics processing units (GPUs) provides high computation power at low price. Today, the GPUs expose a general data-parallel programming model in the form of CUDA. In this paper, we present the implementation of bi-directional breadth-first-search algorithm to discover the semantic association in RDF graph using CUDA programming model. In the experimental evaluation, we prove that our proposed algorithm is faster than the existing algorithms.

数值模拟领域并行编程模型的要素与实例研究

This paper analyzes general parallel programming models and identifies the general parallel programming models stack used for high-performance numerical simulation. Then, on the basis of this analysis, domain-specific parallel programming models are presented and their main constitution, including data structures, computational patterns, component models, and programming frameworks, discussed. The inherent relationship among these elements is also analyzed. The J Adaptive Structured Mesh application INfrastructure (JASMIN) framework is used to verify and validate the possibility and effectiveness of these models. Domain-specific parallel programming models will significantly improve the development efficiency of parallel application software.