Michael Pinedo

Sequencing in an assembly line with blocking to minimize cycle time

We consider an assembly line with m stations in series having finite capacity buffers. Blocking occurs when buffers are full. There are M different types of products to be assembled, each with its own processing requirements. There is a production target set for each type. The problem is to operate the line to maximize throughput. We propose heuristic approaches to this problem based on an equivalent maximum flow problem and on critical path techniques.

A heuristic to minimize the total weighted tardiness with sequence-dependent setups

We propose a three-phase heuristic for the problem of minimizing the total weighted tardiness on a single machine in the presence of sequence-dependent setup times. In the first phase a number of parameters characterizing the problem instance at hand are calculated. In the second phase we develop a schedule by using a new priority rule whose parameters are calculated based on the results of the first phase. Computational experiments show that this rule significantly outperforms the only other rule so far developed in the literature. The third phase consists of a local improvement procedure to improve the schedule obtained in the second phase. The procedure we suggest has been successfully implemented in an industrial scheduling system.

Scheduling jobs on parallel machines with sequence-dependent setup times

Abstract Consider a number of jobs to be processed on a number of identical machines in parallel. A job has a processing time, a weight and a due date. If a job is followed by another job, a setup time independent of the machine is incurred. A three phase heuristic is presented for minimizing the sum of the weighted tardinesses. In the first phase, as a pre-processing procedure, factors or statistics which characterize an instance are computed. The second phase consists of constructing a sequence by a dispatching rule which is controlled through parameters determined by the factors. In the third phase, as a post-processing procedure, a simulated annealing method is applied starting from a seed solution which is the result of the second phase. In the dispatching rule of the second phase there are two parameters of which the values are dependent on the particular problem instance at hand. Through extensive experiments rules are developed for determining the values of the two parameters which make the priority rule work effectively. The performance of the simulated annealing procedure in the third phase is evaluated for various values of the factors.

Stochastic Scheduling with Release Dates and Due Dates

We consider stochastic scheduling problems in which the processing times of jobs are independent exponentially distributed random variables, the release dates are random variables with an arbitrary joint distribution, and the due dates are random variables with a joint distribution that satisfies certain conditions. Our development establishes simple policies that minimize such criteria as the expected weighted sum of completion times and the expected weighted number of late jobs. These results contrast markedly with the deterministic counterparts of these models for which no polynomial time algorithms are known.

Competitive Two-Agent Scheduling and Its Applications

We consider a scheduling environment with m (m ≥ 1) identical machines in parallel and two agents. Agent A is responsible for n1 jobs and has a given objective function with regard to these jobs; agent B is responsible for n2 jobs and has an objective function that may be either the same or different from the one of agent A. The problem is to find a schedule for the n1 + n2 jobs that minimizes the objective of agent A (with regard to his n1 jobs) while keeping the objective of agent B (with regard to his n2 jobs) below or at a fixed level Q. The special case with a single machine has recently been considered in the literature, and a variety of results have been obtained for two-agent models with objectives such as fmax, Σ wjCj, and Σ Uj. In this paper, we generalize these results and solve one of the problems that had remained open. Furthermore, we enlarge the framework for the two-agent scheduling problem by including the total tardiness objective, allowing for preemptions, and considering jobs with different release dates; we consider also identical machines in parallel. We furthermore establish the relationships between two-agent scheduling problems and other areas within the scheduling field, namely rescheduling and scheduling subject to availability constraints.

Scheduling tasks with exponential service times on non-identical processors to minimize various cost functions

Abstract : We consider preemptive scheduling of N tasks on m processors; processors have different speeds, tasks require amounts of work which are exponentially distributed, with different parameters. The policies of assigning at every moment the task with shortest (longest) expected processing time among those not yet completed to the fastest processor available, 2nd shortest (longest) to the 2nd fastest etc., are examined, and shown to minimize expected values of various cost functions. As special cases we obtain policies which minimize expected flowtime, expected makespan and expected lifetime of a series system with m component locations and N spares. (Author)

Minimizing the Expected Makespan in Stochastic Flow Shops

The optimization problem of minimizing the completion time in permutation flow shop scheduling is considered under the assumption that the processing times of a job on different machines are independent and identically distributed random variables. Models with and without intermediate storage are considered. Solutions for special cases are found and based on these results a more general rule of thumb is obtained.

A computational study of branch and bound techniques for minimizing the total weighted tardiness in job shops

We present and compare a number of branch and bound algorithms for minimizing the total weighted tardiness in job shops. There are basically two types of branching schemes. The first one inserts operations in a partial schedule, while the second one fixes arcs in the disjunctive graph formulation of the problem. The bounding schemes are based on the analysis of precedence constraints, and on the solution of nonpreemptive single machine subproblems that are subject to so-called delayed precedence constraints. We obtain optimal solutions for all the instances with ten jobs and ten machines that we consider, including three tardiness versions of a well-known 10 × 10 instance introduced by Muth and Thompson [1] in 1963.

Approximation algorithms for multi-agent scheduling to minimize total weighted completion time

We consider a multi-agent scheduling problem on a single machine in which each agent is responsible for his own set of jobs and wishes to minimize the total weighted completion time of his own set of jobs. It is known that the unweighted problem with two agents is NP-hard in the ordinary sense. For this case, we can reduce our problem to a Multi-Objective Shortest-Path (MOSP) problem and this reduction leads to several results including Fully Polynomial Time Approximation Schemes (FPTAS). We also provide an efficient approximation algorithm with a reasonably good worst-case ratio.

Scheduling two agents with controllable processing times

We consider several two-agent scheduling problems with controllable job processing times, where agents A and B have to share either a single machine or two identical machines in parallel while processing their jobs. The processing times of the jobs of agent A are compressible at additional cost. The objective function for agent B is always the same, namely a regular function fmax. Several different objective functions are considered for agent A, including the total completion time plus compression cost, the maximum tardiness plus compression cost, the maximum lateness plus compression cost and the total compression cost subject to deadline constraints (the imprecise computation model). All problems are to minimize the objective function of agent A subject to a given upper bound on the objective function of agent B. These problems have various applications in computer systems as well as in operations management. We provide NP-hardness proofs for the more general problems and polynomial-time algorithms for several special cases of the problems.