This open access book constitutes the proceedings of the 31st European Symposium on Programming, ESOP 2022, which was held during April 5-7, 2022, in Munich, Germany, as part of the European Joint ...Conferences on Theory and Practice of Software, ETAPS 2022. The 21 regular papers presented in this volume were carefully reviewed and selected from 64 submissions. They deal with fundamental issues in the specification, design, analysis, and implementation of programming languages and systems.
•An MILP model is formulated to strategically design the allocation plans of EVs and staff members to parking stations•This model considers the EV rebalancing and staff relocation operations with ...spatial-time-dependent user reservations.•A Lagrangian relaxation framework is developed to obtain the lower bound of the model.•A three-phase implementing algorithm based on DP is proposed for generating good-quality solutions.
In one-way electric vehicle (EV) carsharing systems, a practical issue that needs to be addressed is the imbalance of EVs with respect to the spatial time-dependent user reservations at different carsharing stations. In practice, appropriate EV rebalancing operations can satisfy user reservations with limited resources and effectively save system investments. This paper proposes an integrated framework that can determine the optimal allocation plan of EVs and staff on the strategic level while considering the operational EV relocation and staff relocation decisions, in order to minimize the total cost, including the EV and staff investment, EV rebalancing and staff relocation costs. In this framework, the dispatching routes of EVs and staff are represented by two sets of space-time paths in the planning time horizon by using a space-time network representation, and the considered problem is then formulated into a mixed-integer linear programming model (MILP). This model explicitly considers (1) the satisfaction of time-dependent user reservations through dynamically rebalancing EVs and relocating staff to keep the service quality of carsharing system, and (2) the EV battery capacity with limited traveling distance and the charging process of EVs at parking stations. A Lagrangian relaxation-based solution approach is developed to decompose the primal problem into several sets of computationally efficient subproblems. In order to generate good-quality solutions, we also propose a three-phase implementing algorithm based on dynamic programming according to the values of Lagrangian multipliers. An illustrative numerical example and a real-world case study (based on the operation data of Seattle, WA) are conducted to verify the applicability of the formulated model and effectiveness of the proposed approach.
•Minimum Spanning Trees with second order cone-representable neighborhoods and lengths are studied.•Two Mixed Integer Nonlinear models are presented.•A decomposition approach is proposed which is ...embedded within an branch-and-cut scheme.•An alternate convex search heuristic is developed, by using a biconvex representation.•The results of our computational experiments are reported.
This paper studies Minimum Spanning Trees under incomplete information assuming that it is only known that vertices belong to some neighborhoods that are second order cone representable and distances are measured with a ℓq-norm. Two Mixed Integer Non Linear mathematical programming formulations are presented, based on alternative representations of subtour elimination constraints. A solution scheme is also proposed, resulting from a reformulation suitable for a Benders-like decomposition, which is embedded within an exact branch-and-cut framework. Furthermore, a mathheuristic is developed, which alternates in solving convex subproblems in different solution spaces, and is able to solve larger instances. The results of extensive computational experiments are reported and analyzed.
Using OpenCL Kowalik, J; Puźniakowski, T
2012, Letnik:
21
eBook
Recenzirano
In 2011 many computer users were exploring the opportunities and the benefits of the massive parallelism offered by heterogeneous computing. In 2000 the Khronos Group, a not-for-profit industry ...consortium, was founded to create standard open APIs for parallel computing, graphics and dynamic media. Among them has been OpenCL, an open system for programming heterogeneous computers with components made by multiple manufacturers. This publication explains how heterogeneous computers work and how to program them using OpenCL. It also describes how to combine OpenCL with OpenGL for displaying graphical effects in real time. Chapter 1 describes briefly two older de facto standard and highly successful parallel programming systems: MPI and OpenMP. Collectively, the MPI, OpenMP, and OpenCL systems cover programming of all major parallel architectures: clusters, shared-memory computers, and the newest heterogeneous computers. Chapter 2, the technical core of the book, deals with OpenCL fundamentals: programming, hardware, and the interaction between them. Chapter 3 adds important information about such advanced issues as double-versus-single arithmetic precision, efficiency, memory use, and debugging. Chapters 2 and 3 contain several examples of code and one case study on genetic algorithms. These examples are related to linear algebra operations, which are very common in scientific, industrial, and business applications. Most of the book's examples can be found on the enclosed CD, which also contains basic projects for Visual Studio, MinGW, and GCC. This supplementary material will assist the reader in getting a quick start on OpenCL projects.
JuMP is an algebraic modeling language embedded in the Julia programming language. JuMP allows users to model optimization problems of a variety of kinds, including linear programming, integer ...programming, conic optimization, semidefinite programming, and nonlinear programming, and handles the low-level details of communicating with solvers. After nearly 10 years in development, JuMP 1.0 was released in March, 2022. In this short communication, we highlight the improvements to JuMP from recent releases up to and including 1.0.
We develop a modular and tractable framework for solving an adaptive distributionally robust linear optimization problem, where we minimize the worst-case expected cost over an
ambiguity set
of ...probability distributions. The adaptive distributionally robust optimization framework caters for dynamic decision making, where decisions adapt to the uncertain outcomes as they unfold in stages. For tractability considerations, we focus on a class of second-order conic (SOC) representable ambiguity set, though our results can easily be extended to more general conic representations. We show that the adaptive distributionally robust linear optimization problem can be formulated as a classical robust optimization problem. To obtain a tractable formulation, we approximate the adaptive distributionally robust optimization problem using linear decision rule (LDR) techniques. More interestingly, by incorporating the primary and auxiliary random variables of the lifted ambiguity set in the LDR approximation, we can significantly improve the solutions, and for a class of adaptive distributionally robust optimization problems, exact solutions can also be obtained. Using the new LDR approximation, we can transform the distributionally adaptive robust optimization problem to a classical robust optimization problem with an SOC representable uncertainty set. Finally, to demonstrate the potential for solving management decision problems, we develop an algebraic modeling package and illustrate how it can be used to facilitate modeling and obtain high-quality solutions for medical appointment scheduling and inventory management problems.
The electronic companion is available at
https://doi.org/10.1287/mnsc.2017.2952
.
This paper was accepted by Noah Gans, optimization.
In this paper, we study unit commitment (UC) problems considering the uncertainty of load and wind power generation. UC problem is formulated as a chance-constrained two-stage stochastic programming ...problem where the chance constraint is used to restrict the probability of load imbalance. In addition to the conventional mixed integer linear programming formulation using Big-M, we present the bilinear mixed integer formulation of chance constraint, and then derive its linear counterpart using the McCormick linearization method. Then, we develop a bilinear variant of the Benders decomposition method, which is an easy-to-implement algorithm, to solve the resulting large-scale linear counterpart. Our results on typical IEEE systems demonstrate that (i) the bilinear mixed integer programming formulation is stronger than the conventional one and (ii) the proposed Benders decomposition algorithm is generally an order of magnitude faster than using a professional solver to directly compute both linear and bilinear chance-constrained UC models.
This study presents the authors’ recent research and application of a new visual programming language and its development environment: VIPLE (Visual IoT/Robotics Programming Language Environment) at ...Arizona State University (ASU). ASU VIPLE supports a variety of IoT devices and robots based on an open architecture. Based on computational thinking, VIPLE supports the integration of engineering design process, workflow, fundamental programming concepts, control flow, parallel computing, event-driven programming, and service-oriented computing seamlessly into a wide range of curricula, such as introduction to computing, introduction to engineering, service-oriented computing, and software integration. It is actively used at ASU in several sections of FSE 100: Introduction to Engineering and in CSE 446: Software Integration and Engineering, as well as in several other universities worldwide.
Unreal Engine 4 is a hugely popular game development framework. This book is a practical guide, and starts off by quickly introducing you to the Unreal Engine ecosystem. You will become familiar with ...its core systems and by the end of the book will have a solid knowledge base from which to get started building games.
A history of the Oz multiparadigm language Van Roy, Peter; Haridi, Seif; Schulte, Christian ...
Proceedings of ACM on programming languages,
2020, Letnik:
4, Številka:
HOPL
Journal Article
Recenzirano
Odprti dostop
Oz is a programming language designed to support multiple programming paradigms in a clean factored way that is easy to program despite its broad coverage. It started in 1991 as a collaborative ...effort by the DFKI (Germany) and SICS (Sweden) and led to an influential system, Mozart, that was released in 1999 and widely used in the 2000s for practical applications and education. We give the history of Oz as it developed from its origins in logic programming, starting with Prolog, followed by concurrent logic programming and constraint logic programming, and leading to its two direct precursors, the concurrent constraint model and the Andorra Kernel Language (AKL). We give the lessons learned from the Oz effort including successes and failures and we explain the principles underlying the Oz design. Oz is defined through a kernel language, which is a formal model similar to a foundational calculus, but that is designed to be directly useful to the programmer. The kernel language is organized in a layered structure, which makes it straightforward to write programs that use different paradigms in different parts. Oz is a key enabler for the book Concepts, Techniques, and Models of Computer Programming (MIT Press, 2004). Based on the book and the implementation, Oz has been used successfully in university-level programming courses starting from 2001 to the present day.
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