Frederik Stork

Solving Project Scheduling Problems by Minimum Cut Computations

In project scheduling, a set of precedence-constrained jobs has to be scheduled so as to minimize a given objective. In resource-constrained project scheduling, the jobs additionally compete for scarce resources. Due to its universality, the latter problem has a variety of applications in manufacturing, production planning, project management, and elsewhere. It is one of the most intractable problems in operations research, and has therefore become a popular playground for the latest optimization techniques, including virtually all local search paradigms. We show that a somewhat more classical mathematical programming approach leads to both competitive feasible solutions and strong lower bounds, within reasonable computation times. The basic ingredients of our approach are the Lagrangian relaxation of a time-indexed integer programming formulation and relaxation-based list scheduling, enriched with a useful idea from recent approximation algorithms for machine scheduling problems. The efficiency of the algorithm results from the insight that the relaxed problem can be solved by computing a minimum cut in an appropriately defined directed graph. Our computational study covers different types of resource-constrained project scheduling problems, based on several notoriously hard test sets, including practical problem instances from chemical production planning.

Scheduling with AND/OR Precedence Constraints

A procedure for controlling a data processing system by a computer program that compares two versions of a source program and identifies the difference between the two. The program compares the two versions until a noncomparison is determined. The program then continues to compare each line in the base version to each line in the modified version until a comparison is found. The program then verifies that it is in the same area of both files by checking for an identical symbolic address and proceeds to check the statements preceding the identical symbolic addresses by working backwards until a noncompare is again detected. The test that defines the smallest area of noncomparison delineates the changes. The program then examines the statements in the noncomparing area to signify whether the noncomparison is due to an addition, deletion or modification.

A Computational Study on Bounding the Makespan Distribution in Stochastic Project Networks

Due to the practical importance of stochastic project networks (PERT-networks), many methods have been developed over the past decades in order to obtain information about the random project completion time. Of particular interest are methods that provide (lower and upper) bounds for its distribution, since these aim at balancing efficiency of calculation with accuracy of the obtained information.We provide a thorough computational evaluation of the most promising of these bounding algorithms with the aim to test their suitability for practical applications both in terms of efficiency and quality. To this end, we have implemented these algorithms and compare their behavior on a basis of nearly 2000 instances with up to 1200 activities of different test-sets. These implementations are based on a suitable numerical representation of distributions which is the basis for excellent computational results. Particularly a distribution-free heuristic based on the Central Limit Theorem provides an excellent tool to evaluate stochastic project networks.

Linear preselective policies for stochastic project scheduling

Abstract. In the context of stochastic resource-constrained project scheduling we introduce a novel class of scheduling policies, the linear preselective policies. They combine the benefits of preselective policies and priority policies; two classes that are well known from both deterministic and stochastic scheduling. We study several properties of this new class of policies which indicate its usefulness for computational purposes. Based on a new representation of preselective policies as and/orprecedence constraints we derive efficient algorithms for computing earliest job start times and state a necessary and sufficient dominance criterion for preselective policies. A computational experiment based on 480 instances empirically validates the theoretical findings.

On project scheduling with irregular starting time costs

Maniezzo and Mingozzi (Oper. Res. Lett. 25 (1999) 175-182) study a project scheduling problem with irregular starting time costs. Starting from the assumption that its computational complexity status is open, they develop a branch-and-bound procedure and they identify special cases that are solvable in polynomial time. In this note, we present a collection of previously established results which show that the general problem is solvable in polynomial time. This collection may serve as a useful guide to the literature, since this polynomial-time solvability has been rediscovered in different contexts or using different methods. In addition, we briefly review some related results for specializations and generalizations of the problem.

On the generation of circuits and minimal forbidden sets

Abstract.We present several complexity results related to generation and counting of all circuits of an independence system. Our motivation to study these problems is their relevance in the solution of resource constrained scheduling problems, where an independence system arises as the subsets of jobs that may be scheduled simultaneously. We are interested in the circuits of this system, the so-called minimal forbidden sets, which are minimal subsets of jobs that must not be scheduled simultaneously. As a consequence of the complexity results for general independence systems, we obtain several complexity results in the context of resource constrained scheduling. On that account, we propose and analyze a simple backtracking algorithm that generates all minimal forbidden sets for such problems. The performance of this algorithm, in comparison to a previously suggested divide-and-conquer approach, is evaluated empirically using instances from the project scheduling library PSPLIB.

Resource-Constrained Project Scheduling: Computing Lower Bounds by Solving Minimum Cut Problems

We present a novel approach to compute Lagrangian lower bounds on the objective function value of a wide class of resource-constrained project scheduling problems. The basis is a polynomial-time algorithm to solve the following scheduling problem: Given a set of activities with start-time dependent costs and temporal constraints in the form of time windows, find a feasible schedule of minimum total cost. In fact, we show that any instance of this problem can be solved by a minimum cut computation in a certain directed graph.We then discuss the performance of the proposed Lagrangian approach when applied to various types of resource-constrained project scheduling problems. An extensive computational study based on different established test beds in project scheduling shows that it can significantly improve upon the quality of other comparably fast computable lower bounds.

Enumeration of circuits and minimal forbidden sets

In resource-constrained scheduling, it is sometimes important to know all inclusion-minimal subsets of jobs that must not be scheduled simultaneously. These so-called minimal forbidden sets are given implicitly by a linear inequality system, and can be interpreted more generally as the circuits of a particular independence system. We present several complexity results related to computation, enumeration, and counting of the circuits of an independence system. On this account, we also propose a backtracking algorithm that enumerates all minimal forbidden sets for resource constrained scheduling problems.