Robert J. Wittrock

Scheduling algorithms for flexible flow lines

This paper discusses scheduling algorithms for a certain kind of manufacturing environment, called the “flexible flow line.” Two scheduling problems are considered. “Loading” decides when each part should be loaded into the system. “Mix allocation” selects the daily part mix. The goals are to maximize throughput and reduce WIP. New heuristic algorithms specially suited to solve these problems in the context of a flexible flow line are described. The paper also discusses experience with the use of an experimental implementation of these algorithms to solve such problems arising in a real production line.

An adaptable scheduling algorithm for flexible flow lines

Consider a manufacturing line that produces parts of several types. Each part must be processed by at most one machine in each of several banks of machines. This paper presents an algorithm that schedules the loading of parts into such a line. The objective is primarily to minimize the makespan and secondarily to minimize queueing. The problem is decomposed into three subproblems and each of these is solved using a fast heuristic. The most challenging subproblem is that of finding a good loading sequence, and this is addressed using workload concepts and an approximation to dynamic programming. We make several extensions to the algorithm in order to handle limited storage capacity, expediting, and reactions to system dynamics. The algorithm was tested by computing schedules for a real production line, and the results are discussed.

Scheduling Parallel Machines with Major and Minor Setup Times

This article discusses the problem of scheduling a large set of parts on an FMS so as to minimize the total completion time. Here, the FMS consists of a set of parallel identical machines. Setup time is incurred whenever a machine switches from one type of part to another. The setup time may be large or small depending on whether or not the two part types belong to the same family. This article describes a fast heuristic for this scheduling problem and derives a lower bound on the optimal solution. In computational tests using random data and data from an IBM card test line, the heuristic archieves nearly optimal schedules.

Applying supply chain optimization techniques to workforce planning problems

The IBM Research Division has developed the Resource Capacity Planning (RCP) Optimizer to support the Workforce Management Initiative (WMI) of IBM. RCP applies supply chain management techniques to the problem of planning the needs of IBM for skilled labor in order to satisfy service engagements, such as consulting, application development, or customer support. This paper describes two RCP models and presents two approaches to solving each of them. We also describe the motivation for using one approach over another. The models are built using the Watson Implosion Technology toolkit, which consists of a supply chain model, solvers for analysis and optimization, and an Application Programming Interface (API) for developing a solution. The models that we built solve two core resource planning problems, gap/glut analysis and resource action planning. The gap/glut analysis is similar to material requirements planning (MRP), in which shortages (gaps) and excesses (gluts) of resources are determined on the basis of expected demand. The goal of the resource action planning problem is to determine what resource actions to take in order to fill the gaps and reduce the gluts. The gap/glut analysis engine is currently deployed within the IBM service organization to report gaps and gluts in personnel.

The "Orchard" scheduler for manufacturing systems

Abstract This paper describes Orchard, which is a heuristic algorithm for scheduling the loading of jobs into a manufacturing system. While Orchard was specifically designed for use at a particular printed circuit board line, it is sufficiently generic to be used at other manufacturing systems as well. In general, such a manufacturing system performs several operations on each job with multiple machines performing each operation. The routing of each job may involve stochastic branching including rework loops, and there may be a need to assign human operators to operations. The Orchard heuristic can be adapted to two different objective functions: weighted mean flow time, and an objective specially formulated for the circuit board line. The present paper describes all aspects of Orchard except its operator assignment module (which is described in another paper) and concludes with results of tests of the algorithm as applied to the circuit board line.