Abstract In this article, we present a flipped classroom based teaching concept and related open book exam for Master’s courses in the field of Operational Research while paying particular attention to dual curricula management students. The characteristics include that students study part-time complemented by practical stages cooperating firms. As consequence, there is limited number lectures ...

Suppose we are given k randomized heuristics to use in solving a combinatorial problem. Each heuristic, when run, produces a solution with an associated quality or value. Given a budget of n runs, our goal is to allocate runs to the heuristics so as to maximize the number of sampled solutions whose value exceeds a specified threshold. For this special case of the classical k-armed bandit proble...

Since Resource-Constrained Project Scheduling Problem (RCPSP) is a well-known NP-hard problem, it is difficult to solve large-scale practical cases by using traditional exact algorithms. Genetic algorithm (GA) is a kind of intelligent algorithm for approximate optimization, which can ascertain global optimization or suboptimal solution within a reasonable time. This article presented a new simu...

Shunting scheduling problems at railway depot can be regarded as a sort of resource constrained project scheduling problem (RCPSP). Unlike the normal RCPSPs, however, shunting scheduling problems require that the number of activities which form a project has to be dynamically changed in the problem solving process and some of the activities have to be performed at a prescribed timing, for examp...

In the paper, we give illustrated reviews, new approaches and results of how rcps –problems are being solved with Hybrid algorithm’s. Firstly, considering Hybrid Genetic Algorithm (HGA), Vicente a et al 2007, introducing many changes in Genetic algorithm (GA) paradigm, resulting in HGA giving fast and high quality algorithms out performing state-of-the-art-algorithm (SOTAA). for rcps-problem. S...

Resource constrained project scheduling problems (RCPSPs) are complex optimization that aim to minimize completion time after considering limited resources and precedence-related activities with known durations. However, due the dynamic nature of real-world applications, activity durations vulnerable change. In addition, resource demands along availability at any stage a can also vary disruptio...

The intrinsic and pervasive uncertainty of the real world causes unforeseen disturbances during the execution of plans. The continuous adaptation of existing schedules is necessary in response to the corresponding disruptions. Affected Operations Rescheduling (AOR) and Matchup Scheduling (MUP) have proven to be efficient techniques for this purpose, as they take only a subset of future activiti...

Project scheduling with limited resources is a challenging management problem that is of immense importance to both practitioners and researchers. This problem is known to be NP-hard even under the simplifying assumptions of single renewable resource constraint, its constant availability over time and minimization of makespan as objective. This paper presents an improved Genetic Algorithm (GA) ...

In this paper, we propose an extended local search framework to solve combinatorial optimization problems with data uncertainty. Our approach represents a major departure from scenario-based or stochastic programming approaches often used to tackle uncertainty. Given a value 0 < ≤ 1, we are interested to know what the robust objective value is, i.e. the optimal value if we allow an chance of no...