Camino R. Vela

A knowledge-based evolutionary strategy for scheduling problems with bottlenecks

Abstract In this paper we confront a family of scheduling problems by means of genetic algorithms: the job shop scheduling problem with bottlenecks. Our main contribution is a strategy to introduce specific knowledge into the initial population. This strategy exploits a probabilistic-based heuristic method that was designed to guide a conventional backtracking search. We report experimental results on two benchmarks, the first one includes a set of small problems and is taken from the literature. The second includes medium and large size problems and is proposed by our own. The experimental results show that the performance of the genetic algorithm clearly augments when the initial population is seeded with heuristic chromosomes, the improvement being more and more appreciable as long as the size of the problem instance increases. Moreover premature convergence which sometimes appears when randomness is limited in any way in a genetic algorithm is not observed.

Local search and genetic algorithm for the job shop scheduling problem with sequence dependent setup times

The Job Shop Scheduling Problem (JSP) is an example of a combinatorial optimization problem that has interested researchers for several decades. In this paper we confront an extension of this problem called JSP with Sequence Dependent Setup Times (SDST-JSP). The approach extends a genetic algorithm and a local search method that demonstrated to be efficient in solving the JSP. For local search, we have formalized neighborhood structures that generalize three well-know structures defined for the JSP. We have conducted an experimental study across conventional benchmark instances showing that the genetic algorithm exploited in combination with the local search, considering all three neighborhoods at the same time, provides the best results. Moreover, this approach outperforms the current state-of-the-art methods.

Semantics of Schedules for the Fuzzy Job-Shop Problem

In the sequel, we consider the fuzzy job-shop problem, which is a variation of the job-shop problem where duration of tasks may be uncertain and where due-date constraints are allowed to be flexible. Uncertain durations are modeled using triangular fuzzy numbers, and due-date constraints are fuzzy sets with decreasing membership functions expressing a flexible threshold ldquoless than.rdquo Also, the objective function is built using fuzzy decision-making theory. We propose the use of a genetic algorithm (GA) to find solutions to this problem. Our aim is to provide a semantics for this type of problems and use this semantics in a methodology to analyze, evaluate, and, therefore, compare solutions. Finally, we present the results obtained using the GA and evaluate them using the proposed methodology.

Genetic tabu search for the fuzzy flexible job shop problem

This paper tackles the flexible job-shop scheduling problem with uncertain processing times. The uncertainty in processing times is represented by means of fuzzy numbers, hence the name fuzzy flexible job-shop scheduling. We propose an effective genetic algorithm hybridised with tabu search and heuristic seeding to minimise the total time needed to complete all jobs, known as makespan. To build a high-quality and diverse set of initial solutions we introduce a heuristic method which benefits from the flexible nature of the problem. This initial population will be the starting point for the genetic algorithm, which then applies tabu search to every generated chromosome. The tabu search algorithm relies on a neighbourhood structure that is proposed and analysed in this paper; in particular, some interesting properties are proved, such as feasibility and connectivity. Additionally, we incorporate a filtering mechanism to reduce the neighbourhood size and a method that allows to speed-up the evaluation of new chromosomes. To assess the performance of the resulting method and compare it with the state-of-the-art, we present an extensive computational study on a benchmark with 205 instances, considering both deterministic and fuzzy instances to enhance the significance of the study. The results of these experiments clearly show that not only does the hybrid algorithm benefit from the synergy among its components but it is also quite competitive with the state-of-the-art when solving both crisp and fuzzy instances, providing new best-known solutions for a number of these test instances.

A Memetic Approach to Fuzzy Job Shop Based on Expectation Model

In the sequel we consider a job shop problem with uncertain processing times modelled using triangular fuzzy numbers. A scheduling model based on the expected value of the makespan is introduced. Later, a genetic algorithm based on codification of permutations with repetitions, a decoding algorithm to generate possibly active schedules and a local search schema are defined in order to solve the job shop problem. Experimental results illustrate the potential of the proposed methods.

Scatter search with path relinking for the flexible job shop scheduling problem

The flexible job shop scheduling is a challenging problem due to its high complexity and the huge number of applications it has in real production environments. In this paper, we propose effective neighborhood structures for this problem, including feasibility and non improving conditions, as well as procedures for fast estimation of the neighbors quality. These neighborhoods are embedded into a scatter search algorithm which uses tabu search and path relinking in its core. To develop these metaheuristics we define a novel dissimilarity measure, which deals with flexibility. We conducted an experimental study to analyze the proposed algorithm and to compare it with the state of the art on standard benchmarks. In this study, our algorithm compared favorably to other methods and established new upper bounds for a number of instances.

Genetic Algorithm Combined with Tabu Search for the Job Shop Scheduling Problem with Setup Times

We face the Job Shop Scheduling Problem with Sequence Dependent Setup Times and makespan minimization. To solve this problem we propose a new approach that combines a Genetic Algorithm with a Tabu Search method. We report results from an experimental study across conventional benchmark instances showing that this hybrid approach outperforms the current state-of-the-art methods.

Coevolutionary makespan optimisation through different ranking methods for the fuzzy flexible job shop

In this paper we tackle a variant of the flexible job shop scheduling problem with uncertain task durations modelled as fuzzy numbers, the fuzzy flexible job shop scheduling problem or FfJSP in short. To minimise the schedules fuzzy makespan, we consider different ranking methods for fuzzy numbers. We then propose a cooperative coevolutionary algorithm with two different populations evolving the two components of a solution: machine assignment and task relative order. Additionally, we incorporate a specific local search method for each population. The resulting hybrid algorithm is then evaluated on existing benchmark instances, comparing favourably with the state-of-the-art methods. The experimental results also serve to analyse the influence in the robustness of the resulting schedules of the chosen ranking method.

An efficient hybrid evolutionary algorithm for scheduling with setup times and weighted tardiness minimization

We confront the job shop scheduling problem with sequence-dependent setup times and weighted tardiness minimization. To solve this problem, we propose a hybrid metaheuristic that combines the intensification capability of tabu search with the diversification capability of a genetic algorithm which plays the role of long term memory for tabu search in the combined approach. We define and analyze a new neighborhood structure for this problem which is embedded in the tabu search algorithm. The efficiency of the proposed algorithm relies on some elements such as neighbors filtering and a proper balance between intensification and diversification of the search. We report results from an experimental study across conventional benchmarks, where we analyze our approach and demonstrate that it compares favorably to the state-of-the-art methods.

An efficient memetic algorithm for total weighted tardiness minimization in a single machine with setups

Graphical abstractDisplay Omitted HighlightsA replacement strategy that improves the diversity of the population is proposed.A decomposition of a computationally expensive neighborhood is defined.Some methods to speed-up the evaluation of the neighbors are proposed and extended.The resulting hybrid algorithm is significantly better than the state-of the-art. The single machine scheduling problem with sequence-dependent setup times with the objective of minimizing the total weighted tardiness is a challenging problem due to its complexity, and has a huge number of applications in real production environments. In this paper, we propose a memetic algorithm that combines and extends several ideas from the literature, including a crossover operator that respects both the absolute and relative position of the tasks, a replacement strategy that improves the diversity of the population, and an effective but computationally expensive neighborhood structure. We propose a new decomposition of this neighborhood that can be used by a variable neighborhood descent framework, and also some speed-up methods for evaluating the neighbors. In this way we can obtain competitive running times. We conduct an experimental study to analyze the proposed algorithm and prove that it is significantly better than the state-of-the-art in standard benchmarks.