This paper presents a search technique for scheduling problems, called Heuristic-Biased Stochastic Sampling (HBSS). The underlying assumption behind the HBSS approach is that strictly adhering to a search heuristic often does not yield the best solution and, therefore, exploration off the heuristic path can prove fruitful. Within the HBSS approach, the balance between heuristic adherence and exploration can be controlled according to the confidence one has in the heuristic. By varying this balance, encoded as a bias function, the HBSS approach encompasses a family of search algorithms of which greedy search and completely random search are extreme members. We present empirical results from an application of HBSS to the realworld problem of observation scheduling. These results show that with the proper bias function, it can be easy to outperform greedy search. Introducing HBSS This paper presents a search technique, called Heuristic-Biased Stochastic Sampling (HBSS), that was design...

Abstract. The success of stochastic algorithms is often due to their ability to effectively amplify the performance of search heuristics. This is certainly the case with stochastic sampling algorithms such as heuristic-biased stochastic sampling (HBSS) and value-biased stochastic sampling (VBSS), wherein a heuristic is used to bias a stochastic policy for choosing among alternative branches in the search tree. One complication in getting the most out of algorithms like HBSS and VBSS in a given problem domain is the need to identify the most effective search heuristic. In many domains, the relative performance of various heuristics tends to vary across different problem instances and no single heuristic dominates. In such cases, the choice of any given heuristic will be limiting and it would be advantageous to gain the collective power of several heuristics. Toward this goal, this paper describes a framework for integrating multiple heuristics within a stochastic sampling search algorithm. In its essence, the framework uses online-generated statistical models of the search performance of different base heuristics to select which to employ on each subsequent iteration of the search. To estimate the solution quality distribution resulting from repeated application of a strong heuristic within a stochastic search, we propose the use of models from extreme value theory (EVT). Our EVT-motivated approach is validated on the NP-Hard problem of resource-constrained project scheduling with time windows (RCPSP/max). Using VBSS as a base stochastic sampling algorithm, the integrated use of a set of project scheduling heuristics is shown to be competitive with the current best known heuristic algorithm for RCPSP/max and in some cases even improves upon best known solutions to difficult benchmark instances. 1

The Coors warehouse scheduling problem involves finding a permutation of customer orders that minimizes the average time that customers' orders spend at the loading docks while at the same time minimizing the running average inventory. Search based solutions require fast objective functions. Thus, a fast low-resolution simulation is used as an objective function. A slower high-resolution simulation is used to validate solutions. We compare the performance of a constructive scheduling algorithm to a genetic algorithm and local search approach. The constructive algorithm is based on a heuristic built specifically for this application. We also tested a hybrid of the genetic algorithm and local search approaches by initializing the search using the domain-specific heuristic. This hybrid genetic algorithm was able to find the best solutions when evaluated by the high-resolution simulation. Finally, we consider the effect of using the high-resolution simulation to filter a set ...

The choice of search algorithm can play a vital role in the success of a scheduling application. In this paper, we investigate the contribution of search algorithms in solving a real-world warehouse scheduling problem. We compare performance of three types of scheduling algorithms: heuristic, genetic algorithms and local search.

In many combinatorial domains, simple stochastic algorithms often exhibit superior performance when compared to highly customized approaches. Many of these simple algorithms outperform more sophisticated approaches on difficult benchmark problems; and often lead to better solutions as the algorithms are taken out of the world of benchmarks and into the real-world. Simple stochastic algorithms are often robust, scalable problem solvers.

This paper describes our experiences in solving a real staff scheduling problem. We experimented with a variety of approaches that included: studying the structure of the problem as list-coloring in interval graphs, using constraint-based methods, using greedy algorithms, and using a variety of iterative improvement approaches. In the end, a very simple randomized greedy algorithm proved able to generate good schedules using very little computational resources. Track: Emerging Application, Technology, and Issues Application Domain: Scheduling (staff,labour,employee) AI Techniques: Constraint-based scheduling, Taboo search Application Status: Research prototype 1 Introduction Over the last year, we have been developing and experimenting with a system to schedule security officers at a contract security company. The InTime Visual Scheduler r fl has been developed as an easy to use system that helps human schedulers create and maintain schedules. This paper describes some experie...

This paper presents a technique, called GENH, that automatically generates search heuristics for scheduling problems. The impetus for developing this technique is the growing consensus that heuristics encode advice that is, at best, useful in solving most, or typical, problem instances, and, at worst, useful in solving only a narrowly defined set of instances. In either case, heuristic problem solvers, to be broadly applicable, should have a means of automatically adjusting to the idiosyncrasies of each problem instance. GENH generates a search heuristic for a given problem instance by hillclimbing in the space of possible multi-attribute heuristics, where the evaluation of a candidate heuristic is based on the quality of the solution found under its guidance. We present empirical results obtained by applying GENH to the real world problem of telescope observation scheduling. These results demonstrate that GENH is a simple and effective way of improving the performance of an heuristic scheduler.