Aymen Sioud

A hybrid genetic algorithm for the single machine scheduling problem with sequence-dependent setup times

This paper presents a hybrid approach based on the integration between a genetic algorithm (GA) and concepts from constraint programming, multi-objective evolutionary algorithms and ant colony optimization for solving a scheduling problem. The main contributions are the integration of these concepts in a GA crossover operator. The proposed methodology is applied to a single machine scheduling problem with sequence-dependent setup times for the objective of minimizing the total tardiness. A sensitivity analysis of the hybrid approach is carried out to compare the performance of the GA and the hybrid genetic algorithm (HGA) approaches on different benchmarks from the literature. The numerical experiments demonstrate the HGA efficiency and effectiveness which generates solutions that approach those of the known reference sets and improves several lower bounds.

Enhanced migrating birds optimization algorithm for the permutation flow shop problem with sequence dependent setup times

Abstract This paper presents an enhanced migrating bird optimization (MBO) algorithm and a new heuristic for solving a scheduling problem. The proposed approaches are applied to a permutation flowshop with sequence dependent setup times and the objective of minimizing the makespan. In order to augment the MBOs intensification capacity, an original problem specific heuristic is introduced. An adapted neighborhood, a tabu list, a restart mechanism and an original process for selecting a new leader also improved the MBO’s behavior. Using benchmarks from the literature, the resulting enhanced MBO (EMBO) gives state-of-the-art results when compared with other algorithms reference. A statistical analysis of the numerical experiments confirms the relative efficiency and effectiveness of both EMBO and the new heuristic.

A genetic algorithm for solving a hybrid flexible flowshop with sequence dependent setup times

In this paper, we propose a genetic algorithm (GA) to solve a realistic variant of flowshop problem. The variant considered here is a hybrid flexible flowshop problem with sequence-dependent setup times, and with the objective of minimizing the makespan. This type of flowshop is frequently used in the batch production, helping toreduce the gap between research and operational use. The proposed approach introduces three new crossover operators. Numerical experiments compare the performance of the GA on different benchmarks from the literature. The results show that the proposed approach is more effective than all other adaptations.

Metaheuristics for Solving a Hybrid Flexible Flowshop Problem with Sequence-Dependent Setup Times

In this paper, we propose three new metaheuristic implementations to address the problem of minimizing the makespan in a hybrid flexible flowshop with sequence-dependent setup times. The first metaheuristic is a genetic algorithm (GA) embedding two new crossover operators, and the second is an ant colony optimization (ACO) algorithm which incorporates a transition rule featuring lookahead information and past information based on archive concepts such as the multiobjective evolutionary computation. The third metaheuristic is a hybridization (HGA) of the GA and the ACO algorithms. Numerical experiments were performed to compare the performance of the proposed algorithms on different benchmarks from the literature. The algorithms are compared with the best algorithms from the literature. The results indicate that our algorithms generate better solutions than those of the known reference sets.

An ant colony optimization for solving a hybrid flexible flowshop

In this paper, we propose an ant colony optimization (ACO) to solve a realistic variant of flowshop problem. The considered scheduling problem is a hybrid flexible flowshop problem with sequence-dependent setup times under the objective of minimizing the makespan. The proposed approach uses concept from multi-objective evolutionary algorithms and look-ahead information to enhance solutions quality. We also introduce new constructive heuristic used in the ACO local improvement. Numerical experiments were performed to compare the performance of the ACO on different benchmarks from the literature. The results indicate that the ACO is very competitive and enhances solutions of the known reference sets.

Hybrid Genetic Algorithms for the Single Machine Scheduling Problem with Sequence-Dependent Setup Times

Several researches on scheduling problems have been done under the assumption that setup times are independent of job sequence. However, in certain contexts, such as the pharmaceutical industry, metallurgical production, electronics and automotive manufacturing, there are frequently setup times on equipment between two different activities. In a survey of industrial schedulers, Dudek et al. (1974) reported that 70% of industrial activities include sequence-dependent setup times. More recently, Conner (2009) has pointed out, in 250 industrial projects, that 50% of these projects contain sequence-dependent setup times, and when these setup times are well applied, 92% of the order deadline could be met. Production of good schedules often relies on management of these setup times (Allahverdi et al., 2008). This present chapter considers the single machine scheduling problem with sequence dependent setup times with the objective to minimize total tardiness of the jobs (SMSDST). This problem, noted as 1|sij|ΣTj in accordance with the notation of Graham et al. (1979), is an NP-hard problem (Du & Leung, 1990).

Solving the Home Health Care Problem with Temporal Precedence and Synchronization

Home health care (HHC) services aim to improve patients’ living condition by providing different services at patients home. This chapter presents a medium-term home health care problem (HHCP) model. This model considers qualification requirements, possible patients/nurses exclusions, temporal precedences, synchronized services, routing with time windows, and working time constraints. As building a proper HHC planning is a challenging task, we proposed a two-stages approach including a new construction heuristic and an enhance basic VNS. Various alternatives in the construction heuristic and tested neighborhoods are exposed and discussed. The obtained results of the solving approach are compared to the literature, on challenging publicly available short-term datasets with temporal precedences and synchronized services. Amongst the 40 instances used, we obtained better results than the literature for 37 of them.

An MBO algorithm for a flow shop problem with sequence-dependent setup times

In this paper, we propose a migrating birds algorithm (MBO) to solve a permutation flowshop with sequence-dependent setup times and the objective of minimizing the makespan. In this approach, we adapt several MBO operators and we embed an intensification process. Indeed, we introduce an original leader selection process, a restart mechanism and an adapted neighborhood. Sensitivity analysis results are further presented to show the effectiveness and performance of the presented MBO on benchmarks from the literature.

An Object Memory Management Prototype Based on Mark and Sweep Algorithm Using Separation of Concerns

C++ applications suffer from the lack of a garbage collector which has been acknowledged as one of their major defects. Therefore, these applications need an automatic memory management lifecycle because their memory management techniques are developed explicitly and manually. For instance, research effort has been done to improve object memory management technique, such as reference counter, incremental garbage collector, conservative garbage collector, smart pointer, and so on. These techniques have some limitations, such as amalgamated functional and technical aspects in the same object-oriented programs and its implementation has to be realized manually. Generally speaking, we propose a mechanism and develop a prototype to i) separate object lifecycle management from functional aspects and ii) implement and integrate this task automatically and implicitly. To eliminate implicitly storage management defects, our prototype is based on mark and sweep algorithm and separation of concerns approaches such as aspect-oriented programming.