S.L. van de Velde

Minimizing Makespan in a Class of Reentrant Shops

We study the problem of scheduling a chain-reentrant shop, in which each job goes for its processing first to a machine called the primary machine, then to a number of other machines in a fixed sequence, and finally back to the primary machine for its last operation. The problem is to schedule the jobs so as to minimize the makespan. This problem is unary NP-hard for a general number of machines. We focus in particular on the two-machine case that is also at least binary NP-hard. We prove some properties that identify a specific class of optimal schedules, and then use these properties in designing an approximation algorithm and a branch-and-bound type optimization algorithm. The approximation algorithm, of which we present three versions, has a worst-case performance guarantee of 3/2 along with an excellent empirical performance. The optimization algorithm solves large instances quickly. Finally, we identify a few well solvable special cases and present a pseudo-polynomial algorithm for the case in which the first and the last operations of any job (on the primary machine) are identical.

Duality-Based Algorithms for Scheduling Unrelated Parallel Machines

We consider the following parallel machine scheduling problem. Each of n independent jobs has to be scheduled on one of m unrelated parallel machines. The processing of job J[sub l] on machine Mi requires an uninterrupted period of positive length p[sub lj]. The objective is to find an assignment of jobs to machines so as to minimize the maximum Job completion time. The objective of this paper is to design practical algorithms for this NP-hard problem. We present an optimization algorithm and an approximation algorithm that are both based on surrogate relaxation and duality. The optimization algorithm solves quite large problems within reasonable time limits. The approximation algorithm is based upon a novel concept for iterative local search, in which the search direction is guided by surrogate multipliers.

A branch-and-bound algorithm for single-machine earliness-tardiness scheduling with idle time

We address the NP-hard single-machine problem of scheduling n independent jobs so as to minimize the sum of α times total completion time and β times total earliness with β > α, which can be rewritten as an earliness–tardiness problem. Postponing jobs by leaving the machine idle may then be advantageous. The allowance of machine idle time between the execution of jobs singles out our problem from most concurrent research on problems with earliness penalties. Solving the problem to optimality poses a computational challenge, since the possibility of leaving the machine idle has a major effect on designing a branch-and-bound algorithm in general, and on computing lower bounds in particular. We present a branch-and-bound algorithm which is based upon many dominance rules and various lower bound approaches, including relaxation of the machine capacity, data manipulation, and Lagrangian relaxation. The algorithm is shown to solve small instances with up to 20 jobs.

An agile planning and control framework for customer‐order driven discrete parts manufacturing environments

In this paper, we present a planning and control framework for manufacture-to-order environments that enables and supports agile-based discrete parts manufacturing. The characteristic elements of our framework are that it is decentralized, logistics and business oriented, and that it recognizes that more detailed and more reliable data become available as orders advance through the different manufacturing stages and departments. Furthermore, it is a generic framework in that it applies to any discrete parts manufacturer, ranging from an engineer-to-order to an assemble-to-order company. We also point out the necessity of an organizational structure that supports and reinforces the framework. Particularly, we discuss the adoption and implementation of the new framework by creating multi-disciplinary teams and structural and operational supporting groups to strengthen the organization for agile manufacturing.

Dual decomposition of a single-machine scheduling problem

We design a fast ascent direction algorithm for the Lagrangian dual problem of the single-machine scheduling problem of minimizing total weighted completion time subject to precedence constraints. We show that designing such an algorithm is relatively simple if a scheduling problem is formulated in terms of the job completion times rather than as an 0–1 linear program. Also, we show that upon termination of such an ascent direction algorithm we get a dual decomposition of the original problem, which can be exploited to develop approximative and enumerative approaches for it. Computational results exhibit that in our application the ascent direction leads to good Lagrangian lower and upper bounds.

A new lower bound approach for single-machine multicriteria scheduling

The concept of maximum potential improvement has played an important role in computing lower bounds for single-machine scheduling problems with composite objective functions that are linear in the job completion times. We introduce a new method for lower bound computation; objective splitting. We show that it dominates the maximum potential improvement method in terms of speed and quality.

Shop Floor Scheduling in Discrete Parts Manufacturing

This paper discusses the architecture and the algorithmic framework of an automatic shop floor planning and scheduling system that is currently used in practice. After reviewing some general trends in manufacturing and the role of planning and scheduling systems in particular, it outlines a decomposition framework which forms the basis of the system proposed here. Next, a number of algorithmic enhancements, needed to deal with more complex but realistic machining systems, are discussed, such as multi-resource scheduling techniques, the inclusion of set-up times in scheduling with due dates, techniques for jobs with assembly structures as well as several minor additional features. A brief exposition of the architecture of the system and a discussion of some experiences in practice conclude the paper.

Agent-based Human-computer-interaction for Real-time Monitoring Systems in the Trucking Industry

Auto ID systems can replace time-consuming, costly and error-prone processes of human data entry and produce detailed real time information. However, they would add value only to the extent that data is presented in a user-friendly manner. As model-based decision support is not always adequate, an agent-based approach is often chosen. Real life entities such as orders and trucks are represented by agents, which negotiate in order to solve planning problems. For the respective data representation at least two forms can be distinguished, focusing either on (1) resources (account-based) or (2) orders (order-centric). Applying cognitive fit theory we describe how the different interfaces affect decision making. The hypotheses would be tested in a laboratory experiment. The intended contribution should support that order-centric interfaces have higher user-friendliness and are especially beneficial to low-analytics and planners working under time pressure