The elastic generalized assignment problem (eGAP) is a natural extension of the generalized assignment problem (GAP) where the capacities are not fixed but can be adjusted which is expressed by continuous variables. These variables might be un-bounded or restricted by a lower or upper bound,...

The 0-1 knapsack problem with a single continuous variable (KPC) is a natural extension of the binary knapsack problem (KP), where the capacity is not any longer fixed but can be extended which is expressed by a continuous variable. This variable might be unbounded or restricted by a lower or...

This paper introduces a new general framework for genetic algorithms to solve a broad range of optimization problems. When designing a genetic algorithm, there may be several alternatives for a component such as crossover, mutation or decoding procedure, and it may be difficult to determine the...

For most computationally intractable problems there exists no heuristic that is equally effective on all instances. Rather, any given heuristic may do well on some instances but will do worse on others. Indeed, even the 'best' heuristics will be dominated by others on at least some subclasses of...

Manpower still is one of the most expansive resources, in spite of increasing automation. While employee scheduling and rostering has been the topic of extensive research over the past decades, usually it is assumed that the demand for staff is either given or can be obtained without difficulty....

Based upon an empirical survey among the 200 biggest CPA firms in Germany an hierarchical modeling framework for audit-staff scheduling with three levels has been developed. For the second level, the so-called medium-to-short-term planning, a binary optimization model is introduced which is...

This paper addresses a general class of nonpreemptive resource-constrained project scheduling problems in which activity durations are discrete functions of committed renewable and nonrenewabe resources. We provide a 0-1 problem formulation and stress the importance of the outlined model by...

Several approaches for subset recovery and improved forecasting accuracy have been proposed and studied. One way is to apply a regularization strategy and solve the model selection task as a continuous optimization problem. One of the most popular approaches in this research field is given by...

This paper discusses the theoretical and practical aspects of new methods for solving DEA problems under real-life geometrical uncertainty and probability uncertainty of sample data. The proposed minimax approach to solve problems with geometrical uncertainty of sample data involves an...

This paper introduces a method for solving numerical dynamic stochastic optimization problems that avoids rootfinding operations. The idea is applicable to many microeconomic and macroeconomic problems, including life cycle, buffer-stock, and stochastic growth problems. Software is provided.