Klaus Neumann

Bio: Klaus Neumann is an academic researcher from Karlsruhe Institute of Technology. The author has contributed to research in topics: Scheduling (computing) & Dynamic priority scheduling. The author has an hindex of 19, co-authored 46 publications receiving 3116 citations.

Project Scheduling with Time Windows and Scarce Resources: Temporal and Resource-Constrained Project Scheduling with Regular and Nonregular Objective Functions

Abstract: 1 Temporal Project Scheduling.- 1.1 Minimum and maximum time lags.- 1.2 Activity-on-node project networks.- 1.3 Temporal project scheduling computations.- 1.4 Orders in the set of activities.- 2 Resource-Constrained Project Scheduling - Minimization of Project Duration.- 2.1 Formulation of the problem.- 2.2 Cycle structures in activity-on-node project networks.- 2.3 Properties of the feasible region.- 2.3.1 Strict orders and order polyhedra.- 2.3.2 Forbidden sets and resolution of resource conflicts.- 2.4 Different types of shifts and sets of schedules.- 2.5 Branch-and-bound and truncated branch-and-bound methods.- 2.5.1 Enumeration scheme.- 2.5.2 Preprocessing.- 2.5.3 Lower bounds.- 2.5.4 Branch-and-bound algorithm.- 2.5.5 Truncated branch-and-bound methods.- 2.5.6 Alternative enumeration schemes.- 2.5.7 Alternative preprocessing and constraint propagation.- 2.5.8 Alternative lower bounds.- 2.6 Priority-rule methods.- 2.6.1 Direct method.- 2.6.2 Decomposition methods.- 2.6.3 Priority rules.- 2.6.4 Serial generation scheme.- 2.6.5 Parallel generation scheme.- 2.7 Schedule-improvement procedures.- 2.7.1 Genetic algorithm.- 2.7.2 Tabu search.- 2.8 Experimental performance analysis.- 2.8.1 Random generation of projects.- 2.8.2 Computational experience.- 2.9 Application to make-to-order production in manufacturing industry.- 2.10 Regular objective functions different from project duration.- 2.11 Calendarization.- 2.12 Project scheduling with cumulative resources.- 2.12.1 Discrete cumulative resources.- 2.12.2 Continuous cumulative resources.- 2.13 Project scheduling with synchronizing resources.- 2.14 Project scheduling with sequence-dependent changeover times.- 2.15 Multi-mode project scheduling problems.- 2.15.1 Problem formulation and basic properties.- 2.15.2 Solution methods.- 2.16 Application to batch production in process industries.- 2.16.1 Case study.- 2.16.2 Batching problem.- 2.16.3 Project scheduling model for batch scheduling.- 2.16.4 Solution procedure for batch scheduling.- 3 Resource-Constrained Project Scheduling - Minimization of General Objective Functions.- 3.1 Different objective functions.- 3.2 Additional types of shifts and sets of schedules.- 3.3 Classification of objective functions.- 3.3.1 Separable and resource-utilization dependent objective functions.- 3.3.2 Class 1 of regular objective functions.- 3.3.3 Class 2 of antiregular objective functions.- 3.3.4 Class 3 of convex objective functions.- 3.3.5 Class 4 of binary-monotone objective functions.- 3.3.6 Class 5 of quasiconcave objective functions.- 3.3.7 Class 6 of locally regular objective functions.- 3.3.8 Class 7 of locally quasiconcave objective functions.- 3.4 Time complexity of time-constrained project scheduling.- 3.5 Relaxation-based approach for function classes 1 to 5.- 3.5.1 General enumeration scheme.- 3.5.2 Branch-and-bound algorithm for the net present value problem.- 3.5.3 Branch-and-bound algorithm for the earliness-tardiness problem.- 3.6 Tree-based approach for function classes 6 and 7.- 3.6.1 General enumeration scheme.- 3.6.2 Branch-and-bound algorithms for resource investment, resource levelling, and resource renting problems.- 3.6.3 Experimental performance analysis.- 3.6.4 Alternative lower bounds.- 3.7 Priority-rule methods.- 3.7.1 Time-constrained project scheduling.- 3.7.2 Resource-constrained project scheduling.- 3.7.3 Experimental performance analysis.- 3.8 Schedule-improvement procedures.- 3.8.1 Neighborhoods for project scheduling problems.- 3.8.2 A tabu search procedure.- 3.9 Application to investment projects.- 3.9.1 Computation of the net present value function.- 3.9.2 Decision support.- 3.10 Hierarchical project planning.- References.- List of Symbols.- Three-Field Classification for Resource-Constrained Project Scheduling.

Project Scheduling with Time Windows and Scarce Resources

Abstract: 1 Temporal Project Scheduling.- 1.1 Minimum and maximum time lags.- 1.2 Activity-on-node project networks.- 1.3 Temporal project scheduling computations.- 1.4 Orders in the set of activities.- 2 Resource-Constrained Project Scheduling - Minimization of Project Duration.- 2.1 Formulation of the problem.- 2.2 Cycle structures in activity-on-node project networks.- 2.3 Properties of the feasible region.- 2.3.1 Strict orders and order polyhedra.- 2.3.2 Forbidden sets and resolution of resource conflicts.- 2.4 Different types of shifts and sets of schedules.- 2.5 Branch-and-bound and truncated branch-and-bound methods.- 2.5.1 Enumeration scheme.- 2.5.2 Preprocessing.- 2.5.3 Lower bounds.- 2.5.4 Branch-and-bound algorithm.- 2.5.5 Truncated branch-and-bound methods.- 2.5.6 Alternative enumeration schemes.- 2.5.7 Alternative preprocessing and constraint propagation.- 2.5.8 Alternative lower bounds.- 2.6 Priority-rule methods.- 2.6.1 Direct method.- 2.6.2 Decomposition methods.- 2.6.3 Priority rules.- 2.6.4 Serial generation scheme.- 2.6.5 Parallel generation scheme.- 2.7 Schedule-improvement procedures.- 2.7.1 Genetic algorithm.- 2.7.2 Tabu search.- 2.8 Experimental performance analysis.- 2.8.1 Random generation of projects.- 2.8.2 Computational experience.- 2.9 Application to make-to-order production in manufacturing industry.- 2.10 Regular objective functions different from project duration.- 2.11 Calendarization.- 2.12 Project scheduling with cumulative resources.- 2.12.1 Discrete cumulative resources.- 2.12.2 Continuous cumulative resources.- 2.13 Project scheduling with synchronizing resources.- 2.14 Project scheduling with sequence-dependent changeover times.- 2.15 Multi-mode project scheduling problems.- 2.15.1 Problem formulation and basic properties.- 2.15.2 Solution methods.- 2.16 Application to batch production in process industries.- 2.16.1 Case study.- 2.16.2 Batching problem.- 2.16.3 Project scheduling model for batch scheduling.- 2.16.4 Solution procedure for batch scheduling.- 3 Resource-Constrained Project Scheduling - Minimization of General Objective Functions.- 3.1 Different objective functions.- 3.2 Additional types of shifts and sets of schedules.- 3.3 Classification of objective functions.- 3.3.1 Separable and resource-utilization dependent objective functions.- 3.3.2 Class 1 of regular objective functions.- 3.3.3 Class 2 of antiregular objective functions.- 3.3.4 Class 3 of convex objective functions.- 3.3.5 Class 4 of binary-monotone objective functions.- 3.3.6 Class 5 of quasiconcave objective functions.- 3.3.7 Class 6 of locally regular objective functions.- 3.3.8 Class 7 of locally quasiconcave objective functions.- 3.4 Time complexity of time-constrained project scheduling.- 3.5 Relaxation-based approach for function classes 1 to 5.- 3.5.1 General enumeration scheme.- 3.5.2 Branch-and-bound algorithm for the net present value problem.- 3.5.3 Branch-and-bound algorithm for the earliness-tardiness problem.- 3.6 Tree-based approach for function classes 6 and 7.- 3.6.1 General enumeration scheme.- 3.6.2 Branch-and-bound algorithms for resource investment, resource levelling, and resource renting problems.- 3.6.3 Experimental performance analysis.- 3.6.4 Alternative lower bounds.- 3.7 Priority-rule methods.- 3.7.1 Time-constrained project scheduling.- 3.7.2 Resource-constrained project scheduling.- 3.7.3 Experimental performance analysis.- 3.8 Schedule-improvement procedures.- 3.8.1 Neighborhoods for project scheduling problems.- 3.8.2 A tabu search procedure.- 3.9 Application to investment projects.- 3.9.1 Computation of the net present value function.- 3.9.2 Decision support.- 3.10 Hierarchical project planning.- References.- List of Symbols.- Three-Field Classification for Resource-Constrained Project Scheduling.

Advanced production scheduling for batch plants in process industries

Abstract: An Advanced Planning System (APS) offers support at all planning levels along the supply chain while observing limited resources. We consider an APS for process industries (e.g. chemical and pharmaceutical industries) consisting of the modules network design (for long–term decisions), supply network planning (for medium–term decisions), and detailed production scheduling (for short–term decisions). For each module, we outline the decision problem, discuss the specifi cs of process industries, and review state–of–the–art solution approaches. For the module detailed production scheduling, a new solution approach is proposed in the case of batch production, which can solve much larger practical problems than the methods known thus far. The new approach decomposes detailed production scheduling for batch production into batching and batch scheduling. The batching problem converts the primary requirements for products into individual batches, where the work load is to be minimized. We formulate the batching problem as a nonlinear mixed–integer program and transform it into a linear mixed–binary program of moderate size, which can be solved by standard software. The batch scheduling problem allocates the batches to scarce resources such as processing units, workers, and intermediate storage facilities, where some regular objective function like the makespan is to be minimized. The batch scheduling problem is modelled as a resource–constrained project scheduling problem, which can be solved by an efficient truncated branch–and–bound algorithm developed recently. The performance of the new solution procedures for batching and batch scheduling is demonstrated by solving several instances of a case study from process industries.

Network Function Virtualization: State-of-the-Art and Research Challenges

Abstract: Network function virtualization (NFV) has drawn significant attention from both industry and academia as an important shift in telecommunication service provisioning. By decoupling network functions (NFs) from the physical devices on which they run, NFV has the potential to lead to significant reductions in operating expenses (OPEX) and capital expenses (CAPEX) and facilitate the deployment of new services with increased agility and faster time-to-value. The NFV paradigm is still in its infancy and there is a large spectrum of opportunities for the research community to develop new architectures, systems and applications, and to evaluate alternatives and trade-offs in developing technologies for its successful deployment. In this paper, after discussing NFV and its relationship with complementary fields of software defined networking (SDN) and cloud computing, we survey the state-of-the-art in NFV, and identify promising research directions in this area. We also overview key NFV projects, standardization efforts, early implementations, use cases, and commercial products.