Promise Neighborhoods Research Articles

Exploring relaxation induced neighborhoods to improve MIP solutions

Given a feasible solution to a Mixed Integer Programming (MIP) model, a natural question is whether that solution can be improved using local search techniques. Local search has been applied very successfully in a variety of other combinatorial optimization domains. Unfortunately, local search relies extensively on the notion of a solution neighborhood, and this neighborhood is almost always tailored to the structure of the particular problem being solved. A MIP model typically conveys little information about the underlying problem structure. This paper considers two new approaches to exploring interesting, domain-independent neighborhoods in MIP. The more effective of the two, which we call Relaxation Induced Neighborhood Search (RINS), constructs a promising neighborhood using information contained in the continuous relaxation of the MIP model. Neighborhood exploration is then formulated as a MIP model itself and solved recursively. The second, which we call guided dives, is a simple modification of the MIP tree traversal order. Loosely speaking, it guides the search towards nodes that are close neighbors of the best known feasible solution. Extensive computational experiments on very difficult MIP models show that both approaches outperform default CPLEX MIP and a previously described approach for exploring MIP neighborhoods (local branching) with respect to several different metrics. The metrics we consider are quality of the best integer solution produced within a time limit, ability to improve a given integer solution (of both good and poor quality), and time required to diversify the search in order to find a new solution.

Determining buffer location and size in production lines using tabu search

In this paper, we address the problem of buffer location and storage size in a manufacturing line. A simulation-search heuristic procedure based on tabu search, combined with simulation, was developed and tested. Simulation is used to model the manufacturing process and tabu search is used to guide the search to overcome the problem of being trapped at local optimal solutions. The procedure employs a Swap Search routine and a Global Search routine. With the Swap Search routine, the procedure identifies good performing buffer profiles and determines the maximum output level for any given storage level. With the Global Search routine, the procedure can locate promising neighborhood of buffer profiles quickly. The results obtained are then used to plot a line specific output curve. The procedure is capable of modeling a variety of manufacturing processes with a variety of scheduling policies and dispatching rules.

A Combined Tchebycheff/Aspiration Criterion Vector Interactive Multiobjective Programming Procedure

In this paper we combine the Tchebycheff method of Steuer and Choo with Wierzbicki's Aspiration Criterion Vector method in order to form an improved procedure for interactive multiple objective programming. The Combined procedure is sensible because the Tchebycheff and Aspiration Criterion Vector methods possess complementary distinguishing characteristics, solve similar optimization problems to probe the nondominated set, and share a similar computer/user interface. In the early iterations, Tchebycheff probes of the nondominated set might be conducted to locate promising neighborhoods of search. In later iterations, Aspiration Criterion Vector probes might be used to pinpoint a final solution. Computational experience is reported showing the improved effectiveness of the Combined Procedure when employed in this fashion when compared against the Tchebycheff and Aspiration Criterion Vector methods run separately.