Ammar Oulamara

Permutation flowshop scheduling problems with maximal and minimal time lags

In this paper, we study permutation flowshop problems with minimal and/or maximal time lags, where the time lags are defined between couples of successive operations of jobs. Such constraints may be used to model various industrial situations, for instance the production of perishable products. We present theoretical results concerning two-machine cases, we prove that the two-machine permutation flowshop with constant maximal time lags is strongly NP-hard. We develop an optimal branch and bound procedure to solve the m-machine permutation flowshop problem with minimal and maximal time lags. We test several lower bounds and heuristics providing upper bounds on different classes of benchmarks, and we carry out a performance analysis.

Makespan minimization in a no-wait flow shop problem with two batching machines

This paper deals with the problem of task scheduling in a no-wait flowshop with two batching machines. Each task has to be processed by both machines. All tasks visit the machines in the same order. Batching machines can process several tasks per batch so that all tasks of the same batch start and complete together. The batch processing time for the first machine is equal to the maximal processing time of the tasks in this batch, and for the second machine is equal to the sum of the processing times of the tasks in this batch. We assume that the capacity of any batch on the first machine is bounded, and that when a batch is completed on the first machine it is immediately transferred to the second machine. The aim is to make batching and sequencing decisions that allow the makespan to be minimized.

Flowshop scheduling problem with a batching machine and task compatibilities

This paper deals with the problem of task scheduling in a flowshop with two (discrete and batching) machines. Each task has to be processed by both machines. All tasks visit the machines in the same order. The first machine is a discrete machine that can process no more than one task at a time, and the second machine is a batching machine that can process several tasks per batch with the additional feature that the tasks of the same batch have to be compatible. A compatibility relation is defined between each pair of tasks, so that an undirected compatibility graph is obtained which turns out to be an interval graph. The batch processing time is equal to the maximal processing time of the tasks in this batch and all tasks of the same batch start and finish together. The aim is to make batching and sequencing decisions and minimize the makespan.

Scheduling hybrid flowshop with parallel batching machines and compatibilities

This paper considers a two-stage hybrid flowshop problem in which the first stage contains several identical discrete machines, and the second stage contains several identical batching machines. Each discrete machine can process no more than one task at time, and each batching machine can process several tasks simultaneously in a batch with the additional feature that the tasks of the same batch have to be compatible. A compatibility relation is defined between each pair of tasks, so that an undirected compatibility graph is obtained which turns out to be an interval graph. The batch processing time is equal to the maximal processing time of the tasks in this batch, and all tasks of the same batch start and finish together. The goal is to make batching and sequencing decisions in order to minimize the makespan. Since the problem is NP-hard, we develop several heuristics along with their worst cases analysis. We also consider the case in which tasks have the same processing time on the first stage, for which a polynomial time approximation scheme (PTAS) algorithm is presented.

Two-Agent scheduling on an unbounded serial batching machine

We study a scheduling problem, in which two agents compete to perform their jobs on the same serial batching machine. On this machine, jobs of the same batch start and complete simultaneously and the batch processing time is equal to the total processing time of its jobs. Each agent aims at minimizing a function which depends only on the completion times of its jobs. The problem is to find a schedule that minimizes the objective function of one agent, subject to the objective function of the other agent does not exceed a given threshold Q. Polynomial and pseudo-polynomial time algorithms are derived for settings with various combinations of the objective functions.

Optimal testing and repairing a failed series system

We consider a series repairable system that includes n components and assume that it has just failed because exactly one of its components has failed. The failed component is unknown. Probability of each component to be responsible for the failure is given. Each component can be tested and repaired at given costs. Both testing and repairing operations are assumed to be perfect, that is, the result of testing a component is a true information that this component is failed or active (not failed), and the result of repairing is that the component becomes active. The problem is to find a sequence of testing and repairing operations over the components such that the system is always repaired and the total expected cost of testing and repairing the components is minimized. We show that this problem is equivalent to minimizing a quadratic pseudo-boolean function. Polynomially solvable special cases of the latter problem are identified and a fully polynomial time approximation scheme (FPTAS) is derived for the general case. Computer experiments are provided to demonstrate high efficiency of the proposed FPTAS. In particular, it is able to find a solution with relative error ɛ = 0.1 for problems with more than 4000 components within 5 minutes on a standard PC with 1.2 Mhz processor.

Permutation flow shops with exact time lags to minimise maximum lateness

In this paper we investigate the m-machine permutation flow shop scheduling problem where exact time lags are defined between consecutive operations of every job. This generic model can be used for the study and analysis of various real situations that may arise, for instance, in the food-producing, pharmaceutical and steel industries. The objective is to minimise the maximum lateness. We study polynomial special cases and provide a dominance relation. We derive lower and upper bounds that are integrated in a branch-and-bound procedure to solve the problem. Three branching schemes are proposed and compared. We perform a computational analysis to evaluate the efficiency of the developed method.

Scheduling a no-wait flow shop containing unbounded batching machines

We study the problem of scheduling n jobs in a no-wait flow shop consisting of m batching machines. Each job has to be processed by all the machines. All jobs visit the machines in the same order. A job completed on an upstream machine should be immediately transferred to the downstream machine. Batching machines can process several jobs simultaneously in a batch so that all jobs of the same batch start and complete together. The processing time of a batch is equal to the maximum processing time of the jobs in this batch. We assume that the capacity of any batch is unbounded. The problem is to find an optimal batch schedule such that the maximum job completion time, that is the makespan, is minimized. For m = 2, we prove that there exists an optimal schedule with at most two batches and construct such a schedule in O(n log n) time. For m = 3, we prove that the number of batches can be limited to nine and give an example where all optimal schedules have seven batches. Furthermore, we prove that the best schedules with at most one, two and three batches are 3-, 2- and 3/2-approximate solutions, respectively. The first two bounds are tight for corresponding schedules. Finally, we suggest an assignment method that solves the problem with m machines and at most r batches in O(n m(r−2)+1+[m/r]) time, if m and r are fixed. The method can be generalized to minimize an arbitrary maximum cost or total cost objective function. Contributed by the Scheduling/Production Planning/Capacity Planning Department

Minimizing total completion time on a batching machine with job processing time compatibilities

Abstract The problem of scheduling n jobs on an unbounded batching machine to minimize the total completion time is studied. The machine can process any number of jobs simultaneously in a batch, subject to an additional constraint that, in the same batch, the job processing times are compatible. There are given normal job processing times. An actual job processing time can exceed its normal value up to a certain percent. This percent is the same for all jobs. Thus, there are processing time intervals for the jobs. The job processing times are compatible if the corresponding processing time intervals intersect. The processing time of a batch is given by the longest processing time of the tasks in the batch and it correspnds to the left endpoint of the intersection of the job processing time intervals in this batch. For the total completion time a dynamic programming algorithm is provided.

Scheduling an unbounded batching machine with job processing time compatibilities

The problem of scheduling n jobs on an unbounded batching machine to minimize a regular objective function is studied. In this problem intervals for job processing times are given. The machine can process any number of jobs in a batch, provided that the processing time intervals of these jobs have a non-empty intersection. The jobs in the same batch start and complete together, and the batch processing time is equal to the left endpoint of the intersection of the processing time intervals in this batch. Properties of an optimal schedule are established and an enumerative algorithm based on these properties is developed. For the total completion time minimization, a dynamic programming algorithm is developed. Minimizing the makespan is shown to be solvable in O(nlogn) time and minimizing the maximum lateness is proved to be NP-hard.