Showing papers on "Heuristic published in 2001"

The vehicle routing problem

Abstract: The Vehicle Routing Problem covers both exact and heuristic methods developed for the VRP and some of its main variants, emphasizing the practical issues common to VRP. The book is composed of three parts containing contributions from well-known experts. The first part covers basic VRP, known more commonly as capacitated VRP. The second part covers three main variants of VRP with time windows, backhauls, and pickup and delivery. The third part covers issues arising in real-world VRP applications and includes both case studies and references to software packages. The book will be of interest to both researchers and graduate-level students in the communities of operations research and matematical sciences. It focuses on a specific family of problems while offering a complete overview of the effective use of the most important techniques proposed for the solution of hard combinatorial problems. Practitioners will find this book particularly usef

The FF planning system: fast plan generation through heuristic search

Abstract: We describe and evaluate the algorithmic techniques that are used in the FF planning system. Like the HSP system, FF relies on forward state space search, using a heuristic that estimates goal distances by ignoring delete lists. Unlike HSP's heuristic, our method does not assume facts to be independent. We introduce a novel search strategy that combines hill-climbing with systematic search, and we show how other powerful heuristic information can be extracted and used to prune the search space. FF was the most successful automatic planner at the recent AIPS-2000 planning competition. We review the results of the competition, give data for other benchmark domains, and investigate the reasons for the runtime performance of FF compared to HSP.

A unified tabu search heuristic for vehicle routing problems with time windows

Abstract: This paper presents a unified tabu search heuristic for the vehicle routing problem with time windows and for two important generalizations: the periodic and the multi-depot vehicle routing problems with time windows. The major benefits of the approach are its speed, simplicity and flexibility. The performance of the heuristic is assessed by comparing it to alternative methods on benchmark instances of the vehicle routing problem with time windows. Computational experiments are also reported on new randomly generated instances for each of the two generalizations.

An integrated survey of deterministic project scheduling

Abstract: There have been many survey papers in the area of project scheduling in recent years. These papers have primarily emphasized modeling and algorithmic contributions for specific classes of project scheduling problems, such as net present value (NPV) maximization and makespan minimization, with and without resource constraints. Paralleling these developments has been the research in the area of project scheduling decision support, with its emphasis on data sets, data generation methods, and so on, that are essential to benchmark, evaluate, and compare the new models, algorithms and heuristic techniques. These investigations have extended the frontiers of research and application in all areas of project scheduling and management. In this paper, we survey the vast literature in this area with a perspective that integrates models, data, and optimal and heuristic algorithms, for the major classes of project scheduling problems. We also include recent surveys that have compared commercial project scheduling systems. Finally, we present an overview of web-based decision support systems and discuss the potential of this technology in enabling and facilitating researchers and practitioners in identifying new areas of inquiry and application.

An integrated survey of deterministic project scheduling

Abstract: There have been many survey papers in the area of project scheduling in recent years. These papers have primarily emphasized modeling and algorithmic contributions for specific classes of project scheduling problems, such as net present value (NPV) maximization and makespan minimization, with and without resource constraints. Paralleling these developments has been the research in the area of project scheduling decision support, with its emphasis on data sets, data generation methods, and so on, that are essential to benchmark, evaluate, and compare the new models, algorithms and heuristic techniques. These investigations have extended the frontiers of research and application in all areas of project scheduling and management. In this paper, we survey the vast literature in this area with a perspective that integrates models, data, and optimal and heuristic algorithms, for the major classes of project scheduling problems. We also include recent surveys that have compared commercial project scheduling systems. Finally, we present an overview of web-based decision support systems and discuss the potential of this technology in enabling and facilitating researchers and practitioners in identifying new areas of inquiry and application.

Observations on using genetic algorithms for dynamic load-balancing

Abstract: Load-balancing problems arise in many applications, but, most importantly, they play a special role in the operation of parallel and distributed computing systems. Load-balancing deals with partitioning a program into smaller tasks that can be executed concurrently and mapping each of these tasks to a computational resource such as a processor (e.g., in a multiprocessor system) or a computer (e.g., in a computer network). By developing strategies that can map these tasks to processors in a way that balances out the load, the total processing time will be reduced with improved processor utilization. Most of the research on load-balancing focused on static scenarios that, in most of the cases, employ heuristic methods. However, genetic algorithms have gained immense popularity over the last few years as a robust and easily adaptable search technique. The work proposed here investigates how a genetic algorithm can be employed to solve the dynamic load-balancing problem. A dynamic load-balancing algorithm is developed whereby optimal or near-optimal task allocations can "evolve" during the operation of the parallel computing system. The algorithm considers other load-balancing issues such as threshold policies, information exchange criteria, and interprocessor communication. The effects of these and other issues on the success of the genetic-based load-balancing algorithm as compared with the first-fit heuristic are outlined.

Project Scheduling with Multiple Modes: A Genetic Algorithm

Abstract: In this paper we consider the resource-constrained project scheduling problem with multiple execution modes for each activity and makespan minimization as objective. We present a new genetic algorithm approach to solve this problem. The genetic encoding is based on a precedence feasible list of activities and a mode assignment. After defining the related crossover, mutation, and selection operators, we describe a local search extension which is employed to improve the schedules found by the basic genetic algorithm. Finally, we present the results of our thorough computational study. We determine the best among several different variants of our genetic algorithm and compare it to four other heuristics that have recently been proposed in the literature. The results that have been obtained using a standard set of instances show that the new genetic algorithm outperforms the other heuristic procedures with regard to a lower average deviation from the optimal makespan.

A Mixed Integer Disjunctive Model for Transmission Network Expansion

Abstract: The classical nonlinear mixed integer formulation of the transmission network expansion problem cannot guarantee finding the optimal solution due to its nonconvex nature. We propose an alternative mixed integer linear disjunctive formulation, which has better conditioning properties than the standard disjunctive model. The mixed integer program is solved by a commercial branch and bound code, where an upper bound provided by a heuristic solution is used to reduce the tree search. The heuristic solution is obtained using a GRASP metaheuristic, capable of finding sub-optimal solutions with an affordable computing effort. Combining the upper bound given by the heuristic and the mixed integer disjunctive model, optimality can be proven for several hard problem instances.

A Memetic Approach to the Nurse Rostering Problem

Abstract: Constructing timetables of work for personnel in healthcare institutions is known to be a highly constrained and difficult problem to solve. In this paper, we discuss a commercial system, together with the model it uses, for this rostering problem. We show that tabu search heuristics can be made effective, particularly for obtaining reasonably good solutions quickly for smaller rostering problems. We discuss the robustness issues, which arise in practice, for tabu search heuristics. This paper introduces a range of new memetic approaches for the problem, which use a steepest descent improvement heuristic within a genetic algorithm framework. We provide empirical evidence to demonstrate the best features of a memetic algorithm for the rostering problem, particularly the nature of an effective recombination operator, and show that these memetic approaches can handle initialisation parameters and a range of instances more robustly than tabu search algorithms, at the expense of longer solution times. Having presented tabu search and memetic approaches (both with benefits and drawbacks) we finally present an algorithm that is a hybrid of both approaches. This technique produces better solutions than either of the earlier approaches and it is relatively unaffected by initialisation and parameter changes, combining some of the best features of each approach to create a hybrid which is greater than the sum of its component algorithms.

Using Experimental Design to Find Effective Parameter Settings for Heuristics

Abstract: In this paper, we propose a procedure, based on statistical design of experiments and gradient descent, that finds effective settings for parameters found in heuristics. We develop our procedure using four experiments. We use our procedure and a small subset of problems to find parameter settings for two new vehicle routing heuristics. We then set the parameters of each heuristic and solve 19 capacity-constrained and 15 capacity-constrained and route-length-constrained vehicle routing problems ranging in size from 50 to 483 customers. We conclude that our procedure is an effective method that deserves serious consideration by both researchers and operations research practitioners.

Knowledge discovery and measures of interest

Abstract: List of Figures. List of Tables. Preface. Acknowledgments. 1. Introduction. 2. Background and Related Work. 3. A Data Mining Technique. 4. Heuristic Measures of Interestingness. 5. An Interestingness Framework. 6. Experimental Analyses. 7. Conclusion. Appendices. Index.

Generating Experimental Data for Computational Testing with Machine Scheduling Applications

Abstract: The operations research literature provides little guidance about how data should be generated for the computational testing of algorithms or heuristic procedures. We discuss several widely used data generation schemes, and demonstrate that they may introduce biases into computational results. Moreover, such schemes are often not representative of the way data arises in practical situations. We address these deficiencies by describing several principles for data generation and several properties that are desirable in a generation scheme. This enables us to provide specific proposals for the generation of a variety of machine scheduling problems. We present a generation scheme for precedence constraints that achieves a target density which is uniform in the precedence constraint graph. We also present a generation scheme that explicitly considers the correlation of routings in a job shop. We identify several related issues that may influence the design of a data generation scheme. Finally, two case studies illustrate, for specific scheduling problems, how our proposals can be implemented to design a data generation scheme.

A new pruning heuristic based on variance analysis of sensitivity information

Abstract: Architecture selection is a very important aspect in the design of neural networks (NNs) to optimally tune performance and computational complexity. Sensitivity analysis has been used successfully to prune irrelevant parameters from feedforward NNs. This paper presents a new pruning algorithm that uses the sensitivity analysis to quantify the relevance of input and hidden units. A new statistical pruning heuristic is proposed, based on the variance analysis, to decide which units to prune. The basic idea is that a parameter with a variance in sensitivity not significantly different from zero, is irrelevant and can be removed. Experimental results show that the new pruning algorithm correctly prunes irrelevant input and hidden units. The new pruning algorithm is also compared with standard pruning algorithms.

Event-driven power management

Abstract: Energy consumption of electronic devices has become a serious concern in recent years. Power management (PM) algorithms aim at reducing energy consumption at the system-level by selectively placing components into low-power states. Formerly, two classes of heuristic algorithms have been proposed for PM: timeout and predictive. Later, a category of algorithms based on stochastic control was proposed for PM. These algorithms guarantee optimal results as long as the system that is power managed can be modeled well with exponential distributions. We show that there is a large mismatch between measurements and simulation results if the exponential distribution is used to model all user request arrivals. We develop two new approaches that better model system behavior for general user request distributions. Our approaches are event-driven and give optimal results verified by measurements. The first approach we present is based on renewal theory. This model assumes that the decision to transition to low-power state can be made in only one state. Another method we developed is based on the time-indexed semi-Markov decision process (TISMDP) model. This model has wider applicability because it assumes that a decision to transition into a lower-power state can be made upon each event occurrence from any number of states. This model allows for transitions into low-power states from any state, but it is also more complex than our other approach. It is important to note that the results obtained by renewal model are guaranteed to match results obtained by TISMDP model, as both approaches give globally optimal solutions. We implemented our PM algorithms on two different classes of devices: two different hard disks and client-server wireless local area network systems such as the SmartBadge or a laptop. The measurement results show power savings ranging from a factor of 1.7 up to 5.0 with insignificant variation in performance.

A Competitive Heuristic Solution Technique for Resource-Constrained Project Scheduling

Abstract: In this work a new heuristic solution technique for the Resource-Constrained Project Scheduling Problem (RCPSP) is proposed This technique is a hybrid multi-pass method that combines random sampling procedures with a backward–forward method The impact of each component of the algorithm is evaluated through a step-wise computational analysis which in addition permits the value of their parameters to be specified Furthermore, the performance of the new technique is evaluated against the best currently available heuristics using a well known set of instances The results obtained point out that the new technique greatly outperforms both the heuristics and metaheuristics currently available for the RCPSP being thus competitive with the best heuristic solution techniques for this problem

Constraint-based clustering in large databases

Abstract: Constrained clustering--finding clusters that satisfy user-specified constraints--is highly desirable in many applications. In this paper, we introduce the constrained clustering problem and show that traditional clustering algorithms (e.g., k-means) cannot handle it. A scalable constraint-clustering algorithm is developed in this study which starts by finding an initial solution that satisfies user-specified constraints and then refines the solution by performing confined object movements under constraints. Our algorithm consists of two phases: pivot movement and deadlock resolution. For both phases, we show that finding the optimal solution is NP-hard. We then propose several heuristics and show how our algorithm can scale up for large data sets using the heuristic of micro-cluster sharing. By experiments, we show the effectiveness and efficiency of the heuristics.

Reviving partial order planning

Abstract: This paper challenges the prevailing pessimism about the scalability of partial order planning (POP) algorithms by presenting several novel heuristic control techniques that make them competitive with the state of the art plan synthesis algorithms. Our key insight is that the techniques responsible for the efficiency of the currently successful planners–viz., distance based heuristics, reachability analysis and disjunctive constraint handling–can also be adapted to dramatically improve the efficiency of the POP algorithm. We implement our ideas in a variant of UCPOP called REPOP. Our empirical results show that in addition to dominating UCPOP, REPOP also convincingly outperforms Graphplan in several “parallel” domains. The plans generated by REPOP also tend to be better than those generated by Graphplan and state search planners in terms of execution flexibility.

Matrix multiplication on heterogeneous platforms

Abstract: We address the issue of implementing matrix multiplication on heterogeneous platforms. We target two different classes of heterogeneous computing resources: heterogeneous networks of workstations and collections of heterogeneous clusters. Intuitively, the problem is to load balance the work with different speed resources while minimizing the communication volume. We formally state this problem in a geometric framework and prove its NP-completeness. Next, we introduce a (polynomial) column-based heuristic, which turns out to be very satisfactory: We derive a theoretical performance guarantee for the heuristic and we assess its practical usefulness through MPI experiments.

A backbone-search heuristic for efficient solving of hard 3-SAT formulae

Abstract: Of late, new insight into the study of random k -SAT formulae has been gained from the introduction of a concept inspired by models of physics, the ‘backbone’ of a SAT formula which corresponds to the variables having a fixed truth value in all assignments satisfying the maximum number of clauses. In the present paper, we show that this concept, already invaluable from a theoretical viewpoint in the study of the satisfiability transition, can also play an important role in the design of efficient DPL-type algorithms for solving hard random k -SAT formulae and more specifically 3 -SAT formulae. We define a heuristic search for variables belonging to the backbone of a 3 -SAT formula which are chosen as branch nodes for the tree developed by a DPL-type procedure. We give in addition a simple technique to magnify the effect of the heuristic. Implementation yields DPL-type algorithms with a significant performance improvement over the best current algorithms, making it possible to handle unsatisfiable hard 3-SAT formulae up to 700 variables.

The Batch Loading and Scheduling Problem

Abstract: This paper discusses the problem of batching and scheduling of certain kinds of batch processors. Examples of these processors include heat treatment facilities, particularly in the steel and ceramics industries, as well as a variety of operations in the manufacture of integrated circuits. In general, for our problem there is a set of jobs waiting to be processed. Each job is associated with a given family and has a weight or delay cost and a volume. The scheduler must organize jobs into batches in which each batch consists of jobs from a single family and in which the total volume of jobs in a batch does not exceed the capacity of the processor. The scheduler must then sequence all the batches. The processing time for a batch depends only on the family and not on the number or the volume of jobs in the batch. The objective is to minimize the mean weighted flow time.The paper presents an integer programming formulation for this problem, generates a lower bound from a partial LP relaxation, provides a polynomial algorithm to solve a special case, and tests a set of heuristics on the general problem. The ability to pack jobs into batches is the key to efficient solutions and is the basis of the different solution procedures in this paper. The heuristics include a greedy heuristic, a successive knapsack heuristic, and a generalized assignment heuristic. Optimal solutions are obtained by complete enumeration for small problems.The conclusions of the computational study show that the successive knapsack and generalized assignment heuristics perform better than the greedy. The generalized assignment heuristic does slightly better than the successive knapsack heuristic in some cases, but the latter is substantially faster and more robust. For problems with few jobs, the generalized assignment heuristic and the knapsack heuristic almost always provide optimal solutions. For problems with more jobs, we compare the heruistic solutions' values to lower bounds; the computational work suggests that the heuristics continue to provide solutions that are optimal or close to the optimal. The study also shows that the volume of the job relative to the capacity of the facility and the number of jobs in a family affect the performance of the heuristics, whereas the number of families does not. Finally, we give a worst-case analysis of the greedy heuristic.

Efficient methods for qualitative spatial reasoning

Abstract: The theoretical properties of qualitative spatial reasoning in the RCC-8 framework have been analyzed extensively. However, no empirical investigation has been made yet. Our experiments show that the adaption of the algorithms used for qualitative temporal reasoning can solve large RCC-8 instances, even if they are in the phase transition region - provided that one uses the maximal tractable subsets of RCC-8 that have been identified by us. In particular, we demonstrate that the orthogonal combination of heuristic methods is successful in solving almost all apparently hard instances in the phase transition region up to a certain size in reasonable time.

Optimizing Inventory Replenishment of Retail Fashion Products

Abstract: We consider the problem of determining (for a short lifecycle) retail product initial and replenishment order quantities that minimize the cost of lost sales, back orders, and obsolete inventory. We model this problem as a two-stage stochastic dynamic program, propose a heuristic, establish conditions under which the heuristic finds an optimal solution, and report results of the application of our procedure at a catalog retailer. Our procedure improves on the existing method by enough to double profits. In addition, our method can be used to choose the optimal reorder time, to quantify the benefit of leadtime reduction, and to choose the best replenishment contract.