Iterative Deepening Branch and Bound

TL;DR

This paper introduces the Iterative Deepening Branch and Bound algorithm, an efficient search method that combines space and time advantages, and demonstrates its effectiveness on flow shop scheduling problems.

Contribution

It presents a novel algorithm that improves upon IDA* for real-valued problems, addressing its limitations in optimality and efficiency.

Findings

Effective in flow shop scheduling

Reduces space requirements compared to traditional methods

Achieves optimal solutions efficiently

Abstract

In tree search problem the best-first search algorithm needs too much of space . To remove such drawbacks of these algorithms the IDA* was developed which is both space and time cost efficient. But again IDA* can give an optimal solution for real valued problems like Flow shop scheduling, Travelling Salesman and 0/1 Knapsack due to their real valued cost estimates. Thus further modifications are done on it and the Iterative Deepening Branch and Bound Search Algorithms is developed which meets the requirements. We have tried using this algorithm for the Flow Shop Scheduling Problem and have found that it is quite effective.