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"Programmierung."
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Coders at work : reflections on the craft of programming
Presents an overview of computer programming and interviews with some of the well-known programmers currently working in the field as they discuss their experiences and techniques.
Book
Data-driven distributionally robust chance-constrained optimization with Wasserstein metric
We study distributionally robust chance-constrained programming (DRCCP) optimization problems with data-driven Wasserstein ambiguity sets. The proposed algorithmic and reformulation framework applies to all types of distributionally robust chance-constrained optimization problems subjected to individual as well as joint chance constraints, with random right-hand side and technology vector, and under two types of uncertainties, called uncertain probabilities and continuum of realizations. For the uncertain probabilities (UP) case, we provide new mixed-integer linear programming reformulations for DRCCP problems. For the continuum of realizations case with random right-hand side, we propose an exact mixed-integer second-order cone programming (MISOCP) reformulation and a linear programming (LP) outer approximation. For the continuum of realizations (CR) case with random technology vector, we propose two MISOCP and LP outer approximations. We show that all proposed relaxations become exact reformulations when the decision variables are binary or bounded general integers. For DRCCP with individual chance constraint and random right-hand side under both the UP and CR cases, we also propose linear programming reformulations which need the ex-ante derivation of the worst-case value-at-risk via the solution of a finite series of linear programs determined via a bisection-type procedure. We evaluate the scalability and tightness of the proposed MISOCP and (MI)LP formulations on a distributionally robust chance-constrained knapsack problem.
Journal Article
Building digital experience platforms : a guide to developing next-generation enterprise applications
Use digital experience platforms (DXP) to improve your development productivity and release timelines. Leverage the pre-integrated feature sets of DXPs in your organization's digital transformation journey to quickly develop a personalized, secure, and robust enterprise platform. In this book the authors examine various features of DXPs and provide rich insights into building each layer in a digital platform. Proven best practices are presented with examples for designing and building layers. A special focus is provided on security and quality attributes needed for business-critical enterprise applications. The authors cover modern and emerging digital trends such as Blockchain, IoT, containers, chatbots, artificial intelligence, and more. The book is divided into five parts related to requirements/design, development, security, infrastructure, and case study. The authors employ proven real-world methods, best practices, and security and integration techniques derived from their rich experience. An elaborate digital transformation case study for a banking application is included.
Book
Gamification design to improve student motivation on learning object-oriented programming
Object-Oriented Programming (OOP) is a skill that must be mastered by students to survive in information technology industry competition. The problem that occurs during OOP learning is that some students are not motivated during the class because of a passive learning style and the lack of understanding from previous programming classes. Based on these problems, this study aims to design gamification to increase student's involvement and motivation in OOP learning. Gamification provides an element of pleasure obtained in the game so that it stimulates the activeness and creativity of students. This research applied the Marczewski Gamification Framework. To find out student's motivation in learning OOP, the Hexad Gamification Questionnaire test was used. The test results showed that 43% of students have the type of achiever. The game mechanics used in this gamification design are for achiever types of users such as levels, challenges and achievements with game elements such as points, badges and trophies. The results showed that the Marczewski Gamification Framework has been implemented in gamification design according to the functional needs of users. This research contributes to the use of gamification in increasing student motivation in learning OOP programming.
Journal Article
A heuristic for a special case of the generalized assignment problem with additional conditions
We consider the following variant of the generalized assignment problem (GAP). There are a set of agents and a set of jobs for which a single resource use. Each job is to assign to one and only one agent subject to the constraints on the capacity and loading of agents. The resource expense for executing any job is independent of the agents choice unlike the profit from the job. Each job has a certain type (or color). For every agent, the maximum possible number of the job types given. It is necessary to find a feasible assignment of agents to jobs so that all jobs were completed and total profit was maximized. Finding a feasible solution to this problem is NP-hard. We present a heuristic algorithm based on the ideas of random search and local improvement of solutions. Used the mixed-integer programming (MIP) relaxation and variables fixing, we construct a set of integer linear programming (ILP) subproblems similar to the original problem and solve them by the general MIP solver. The results of a computational experiment for tasks with random initial data are presented.
Journal Article
Drone Delivery Scheduling Optimization Considering Payload-induced Battery Consumption Rates
This paper addresses the design of a parcel delivery system using drones, which includes the strategic planning of the system and operational planning for a given region. The amount of payload affects the battery consumption rate (BCR), which can cause a disruption in delivery of goods if the BCR was under-estimated in the planning stage or cause unnecessarily higher expenses if it was over-estimated. Hence, a reliable parcel delivery schedule using drones is proposed to consider the BCR as a function of payload in the operational planning optimization. A minimum set covering approach is used to model the strategic planning and a mixed integer linear programming problem (MILP) is used for operational planning. A variable preprocessing algorithm and primal and dual bound generation methods are developed to improve the computational time for solving the operational planning model. The optimal solution provides the least number of drones and their flight paths to deliver parcels while ensuring the safe return of the drones with respect to the battery charge level. Experimental data show that the BCR is a linear function of the payload amount. The results indicate the impact of including the BCR in drone scheduling, 3 out of 5 (60%) flight paths are not feasible if the BCR is not considered. The numerical results show that the sequence of visiting customers impacts the remaining charge.
Journal Article
Reverse logistics optimization of an industrial air conditioner manufacturing company for designing sustainable supply chain: a fuzzy hybrid multi-criteria decision-making approach
Magnified resource consumption and depletion of natural resources calls for non-flexible or strict regulations and penalties on industrial operations, increased rate of processing and reuse of waste material as a substitute for raw material and political and legal interventions at global scale. Product recovery involves reuse, repair, refurbishing, remanufacturing and materials recycling, requires an efficient network design known as reverse logistic network and offers economical benefits in terms of fewer procurement of raw material, inventory management and less disposal. In current study, a mixed integer linear programming model designed on a multi-stage reverse logistics network for product recovery is proposed which considers different recovery options-product remanufacturing, component reprocessing and material recycling for sustainable outcomes. The model is designed to find optimal solutions for fulfilling demand and revenue needs by focusing on strategic locations for collection centers, reprocessing centers, remanufacturing plants and transportation options and simultaneously achieving sustainability goals. The model is applied on an Indian based manufacturing unit of a Saudi Arabian Industrial Air conditioner manufacturing organization and the case is presented here. The model is converted into a multi-objective programming model in accordance with the importance of each objective suiting the business needs. All relevant objective functions are evaluated using BWM, AHP and FAHP methods to obtain weights for integration into a fuzzy linear programming model which eventually provides three separate results. The model applied has originality and uniqueness for applications to solve multi-objective problems under uncertain environment and tends to strike a balance between economic and environmental objectives. The study provides for a base for further scope covering uncertainty about the amount and quality of returned products and even can be implemented by practitioners and academics for making a significant contribution in improving the efficiency of supply chains.
Journal Article
Dynamic programming for an optimization of production plan
This study discusses the optimization of production plans in a convection company using a dynamic programming. Fluctuating demand requires the convection company to implement a strategy to control the production process so that there are no shortages or excess products. The purpose of this study is to find out the total minimum costs in preparation of school uniform production plans for the next one year using dynamic programming. The initial steps used in the production planning is predicting the demand of product from average demand in the previous 3-year period. The implementation of dynamic programming divides the problem into 12 stages due to monthly periods for one year. The results of this study obtained the total minimum cost of production plan for the next 1 year. The production plan through dynamic programming is able to maintain the existence of a convection company for the next few years.
Journal Article
Multi-task scheduling of distributed 3D printing services in cloud manufacturing
The problem of service matching and scheduling in cloud manufacturing (CMfg) is complex for different types of manufacturing services. 3D printing, as a rapidly developing manufacturing technology, has become an important service form in the CMfg platform due to its characteristics of personalized manufacturing. How to solve the task scheduling problem for distributed 3D printing services in CMfg needs further research. In this paper, a service transaction model of 3D printing services in CMfg is built. Based on the service transaction model, we propose 3D printing service matching strategies and matching rules of different service attributes, including model size, printing material, printing preciseness, task cost, task time, and logistics. To reduce the delivery time of tasks from service suppliers to service demanders, a 3D printing service scheduling (3DPSS) method is proposed to generate optimal service scheduling solutions. In 3DPSS, optimization objective, constraints, and optimization algorithm are presented in detail. Experimental results show that the average task delivery time of 3DPSS is shorter than that of typical scheduling methods, such as particle swarm optimization, pattern search, and sequential quadratic programming, when the amounts of tasks change.
Journal Article