This paper presents three techniques for using an iterated local search algorithm to improve the performance of a state-of-the-art branch and bound algorithm for job shop scheduling. We use iterated local search to obtain (i) sharpened upper bounds, (ii) an improved branchordering heuristic, and (iii) and improved variableselection heuristic. On randomly-generated instances, our hybrid of itera...

We present new local search heuristics for the problem of scheduling jobs on identical parallel machines with the goal of minimizing total weighted completion time. Our proposed algorithm is a local search method based on combining a variable number of insertion moves. We develop an efficient heuristic for finding a profitable sequence of insertion moves. In a computational study, we compare th...

We consider the problem of scheduling a single machine to minimize total tardiness with sequence dependent setup times. We present two algorithms, a problem space-based local search heuristic and a Greedy Randomized Adaptive Search Procedure (GRASP) for this problem. With respect to GRASP, our main contributions are—a new cost function in the construction phase, a new variation of Variable Neig...

We recently developed a new randomized optimization framework, the Nested Partitions (NP) method. This approach uses partitioning, random sampling, promising index, and backtracking to create a Markov chain that has global optima as its absorbing states. This new method combines global search and local search (heuristic) procedures in a nature way and it is highly matched to emerging massively ...

In this paper we explore the influence of adaptive memory in the performance of heuristic methods when solving a hard combinatorial optimization problem. Specifically, we tackle the adaptation of tabu search and scatter search to the bandwidth minimization problem. It consists of finding a permutation of the rows and columns of a given matrix which keeps the non-zero elements in a band that is ...

Neighborhood search heuristics like local search and its variants are some of the most popular approaches to solve discrete optimization problems of moderate to large size. Apart from tabu search, most of these heuristics are memoryless. In this paper we introduce a new neighborhood search heuristic that makes effective use of memory structures in a way that is different from tabu search. We re...

This paper presents a computational study of a digital line network design problem arising in the telecommunication industry. The objective can be formulated as that of nding an optimal degree constrained Steiner tree in a graph whose nodes and edges are weighted by costs. We develop a Tabu Search heuristic for this problem that incorporates long term memory and probabilistic move selections. W...

Recent work aimed at developing a deeper understanding of suboptimal heuristic search has demonstrated that the use of a cost-based heuristic function in the presence of large operator cost ratio and the decision to allow re-opening of visited nodes can have a significant effect on search effort. In parallel research, phase transitions in problem solubility have proved useful in the study of pr...

Greedy Best-First Search (GBFS) is a powerful algorithm at the heart of many state of the art satisficing planners. One major weakness of GBFS is its behavior in so-called uninformative heuristic regions (UHRs) parts of the search space in which no heuristic provides guidance towards states with improved heuristic values. This work analyzes the problem of UHRs in planning in detail, and propose...

We address the important step of determining an effective subset of heuristics in selection hyper-heuristics. Little attention has been devoted to this in the literature, and the decision is left at the discretion of the investigator. The performance of a hyper-heuristic depends on the quality and size of the heuristic pool. Using more than one heuristic is generally advantageous, however, an u...