Squeaky Wheel Optimization

Abstract

We describe a general approach to optimization which we term `Squeaky Wheel' Optimization (SWO). In SWO, a greedy algorithm is used to construct a solution which is then analyzed to find the trouble spots, i.e., those elements, that, if improved, are likely to improve the objective function score. The results of the analysis are used to generate new priorities that determine the order in which the greedy algorithm constructs the next solution. This Construct/Analyze/Prioritize cycle continues until some limit is reached, or an acceptable solution is found. SWO can be viewed as operating on two search spaces: solutions and prioritizations. Successive solutions are only indirectly related, via the re-prioritization that results from analyzing the prior solution. Similarly, successive prioritizations are generated by constructing and analyzing solutions. This `coupled search' has some interesting properties, which we discuss. We report encouraging experimental results on two domains, scheduling problems that arise in fiber-optic cable manufacturing, and graph coloring problems. The fact that these domains are very different supports our claim that SWO is a general technique for optimization

Improved Squeaky Wheel Optimisation for Driver Scheduling

“…This constructanalyze-prioritize cycle continues until a stopping condition is reached. Joslin and Clements (1999) applied this technique on production line scheduling problems and graph colouring problems with some satisfactory results. developed an adaptive heuristic framework for examination timetabling problems which was based on SWO.…”

“…a shift pattern assigned to a particular employee, may be a strong candidate in its own right, but it also has to fit well with other components. To deal with these components, Joslin and Clements (1999) proposed a technique called Squeaky Wheel Optimisation (SWO), and claimed it could be a general approach for various combinatorial optimisation problems. In this paper, we analyse the limitations of the original SWO and revise it by incorporating some evolutionary features into the searching process.…”

Bounds-Consistent Local Search

“…A local search algorithm chooses an initial configuration at random and then modifies it step by step. Usually the configuration is a total variable assignment, but in some cases [6,1,2] (including our new algorithm) partial assignments are used.…”

“…The basic idea of the Squeaky Wheel Optimization algorithm [2] is to restart the search whenever a dead-end is reached. But now an informed search runs every time.…”

The Vertex Coloring Problem and its generalizations

“…In a successive work, the same author proposed a hybrid local search for VCP and BMCP [96]. In [82], Lim et al proposed a method for solving VCP, BCP, MCP and BMCP combining hill-climbing techniques and Squeaky Wheel Optimization, a general heuristic approach for optimization, originally proposed by Joslin and Clements [74]. In a recent work Lim et al [81] studied the performance of different heuristic methods, including Squeaky Wheel and Tabu Search and their hybridization, for the solution of BCP, MCP and BMCP.…”