Inserting knowledge in multirecombined evolutionary algorithms for the flow shop scheduling problem

Determining an optimal schedule to minimize the completion time of the last job abandoning the system (makespan) becomes a very difficult problem when there are more than two machines in the flow shop. Due both to its economical impact and complexity, different techniques to solve the Flow Shop Scheduling problem (FSSP) has been developed. Current trends addressed to multire-combination, involve distinct evolutionary computation approaches providing not a single but a set of acceptable alternative solutions, which are created by intensive exploitation of multiple solutions previously found. Evolutionary algorithms perform their search based only in the relative fitness of each potential solution to the problem. On the other hand specialised heuristics are based on some specific features of the problem. This work shows alternative ways to insert knowledge in the search by means of the inherent infor-mation carried by solutions coming from that specialised heuristic or gathered by the evolutionary process itself. The present paper compares the performance of multirecombined evolutionary algo-rithms with and without knowledge insertion and their influence in the crossover rate, the popula-tion size and the quality of results when applied to selected instances of the FSSP.Eje: Sistemas distribuidos y paralelismoRed de Universidades con Carreras en Informática (RedUNCI

Evolutionary optimization of due date based objectives in unrestricted identical parallel machine scheduling problems

Parallel machine scheduling, involves the allocation of jobs to the system resources (a bank of machines in parallel). A basic model consisting of m machines and n jobs is the foundation of more complex models. Here, jobs are allocated according to resource availability following some allocation rule. In the specialised literature, minimisation of the makespan has been extensively approached and benchmarks can be easily found. This is not the case for other important objectives such as the maximum tardiness and the number of tardy jobs. These problems are NP-hard for 2 ≤ m ≤ n, and conventional heuristics and evolutionary algorithms (EAs) have been developed to provide acceptable schedules as solutions. To solve the unrestricted identical parallel machine scheduling problems, this paper proposes MCMP-SRI and MCMP-SRSI, which are two multirecombination schemes that combine studs, random and seed immigrants. Evidence of the improved behaviour of the EAs when inserting problem-specific knowledge is provided. Experiments and results are discussed.Eje: V - Workshop de agentes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

Multirecombination and different representation in evolutionary algorithms for the flow shop scheduling problem

The flow shop scheduling problem (FSSP) has held the attention of many researchers. In a simplest usual situation, a set of jobs must follow the same route to be executed on a set of machines (resources) and the main objective is to optimize some performance variable (makespan, tardiness, lateness, etc.). In the case of the makespan, it have been proved that when the number of machines is greater than or equal to three, the problem is NP-hard. EC is an emergent research field, which provides new heuristics to problem optimization where traditional approaches make the problem computationally intractable, is continuously showing its own evolution and enhanced approaches included latest multi-recombinative methods involving multiple crossovers per couple (MCPC) and multiple crossovers on multiple parents (MCMP). The present paper discusses the new multi-recombinative methods and shows the improvement of performance of enhanced evolutionary approaches under permutation and decode representation. Results of the methods proposed for each chromosome representation are here contrasted and results are shown.I Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Parameter control in multirecombinated evolutionary algorithms for the flow shop scheduling problem

Improvements in evolutionary algorithms (EAs) consider multirecombination, allowing multiple crossover operations on a pair of parents (MCPC, multiple crossovers per couple) or on a set of multiple parents (MCMP, multiple crossovers on multiple parents). Evolutionary algorithms have been successfully applied to solve scheduling problems. MCMP-STUD and MCMP-SRI are novel MCMP variants, which considers the inclusion of a stud-breeding individual in a pool of random immigrant parents In this paper the proposal is to generate the stud-breeding individual by means of a robust conventional heuristic, the CDS. In a multirecombined EA, setting of parameters n1 (number of crossovers) and n2 (number of parents) remained as an open question. In previous works; they were empirically determined, or a deterministic rule was applied. In this paper self adaptation of parameters n1 and n2 is implemented, the idea is to code the parameters within the chromosome and undergo genetic operations. Hence it is expected that better parameter values be more intensively propagated. The present paper discusses different multi-recombined methods and contrasts their performance when different parameter control methods are applied, to find the minimum makespan for selected instances of the FSSP.Eje: Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

Evolutionary optimization of due date based objectives in unrestricted identical parallel machine scheduling problems

Parallel machine scheduling, involves the allocation of jobs to the system resources (a bank of machines in parallel). A basic model consisting of m machines and n jobs is the foundation of more complex models. Here, jobs are allocated according to resource availability following some allocation rule. In the specialised literature, minimisation of the makespan has been extensively approached and benchmarks can be easily found. This is not the case for other important objectives such as the maximum tardiness and the number of tardy jobs. These problems are NP-hard for 2 ≤ m ≤ n, and conventional heuristics and evolutionary algorithms (EAs) have been developed to provide acceptable schedules as solutions. To solve the unrestricted identical parallel machine scheduling problems, this paper proposes MCMP-SRI and MCMP-SRSI, which are two multirecombination schemes that combine studs, random and seed immigrants. Evidence of the improved behaviour of the EAs when inserting problem-specific knowledge is provided. Experiments and results are discussed.Eje: V - Workshop de agentes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

Parameter control in multirecombinated evolutionary algorithms for the flow shop scheduling problem

Improvements in evolutionary algorithms (EAs) consider multirecombination, allowing multiple crossover operations on a pair of parents (MCPC, multiple crossovers per couple) or on a set of multiple parents (MCMP, multiple crossovers on multiple parents). Evolutionary algorithms have been successfully applied to solve scheduling problems. MCMP-STUD and MCMP-SRI are novel MCMP variants, which considers the inclusion of a stud-breeding individual in a pool of random immigrant parents In this paper the proposal is to generate the stud-breeding individual by means of a robust conventional heuristic, the CDS. In a multirecombined EA, setting of parameters n1 (number of crossovers) and n2 (number of parents) remained as an open question. In previous works; they were empirically determined, or a deterministic rule was applied. In this paper self adaptation of parameters n1 and n2 is implemented, the idea is to code the parameters within the chromosome and undergo genetic operations. Hence it is expected that better parameter values be more intensively propagated. The present paper discusses different multi-recombined methods and contrasts their performance when different parameter control methods are applied, to find the minimum makespan for selected instances of the FSSP.Eje: Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

Studs and immigrants in multirecombined evolutionary algorithm to face weighted tardiness scheduling problems

Jobs to be delivered in a production system are usually weighted according to clients requirements and relevance. Attempting to achieve higher customer satisfaction trends in manufacturing are focussed today on production policies, which emphasizes minimum weighted tardiness. Evolutionary algorithms have been successfully applied to solve scheduling problems. New trends to enhance evolutionary algorithms introduced multiple-crossovers-on-multiple-parents (MCMP) a multirecombinative approach allowing multiple crossovers on the selected pool of (more than two) parents. MCMP-SRI is a novel MCMP variant, which considers the inclusion of a stud-breeding individual in a pool of random immigrant parents. Members of this mating pool subsequently undergo multiple crossover operations. This paper briefly describes the weighted tardiness problem in a single machine environment, and summarizes implementation details and MCMP-SRI performance for a set of problem instances extracted from the OR-Library.Eje: Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

Influence of crossover operators in evolutionary scheduling under multirecombined schemes

In evolutionary algorithms based on genetics, the crossover operation creates individuals by interchanging genes. On the other side selection mechanisms aim to favour reproduction of better individuals imposing a direction on the search process: copies of better ones replace worst individuals. Consequently, part of the genetic material contained in these worst individuals disappears forever. This loss of diversity can lead to a premature convergence. To prevent a premature convergence to a local optimum under the same selection mechanism and multirecombined scheme then, either a larger population size or adequate crossover and mutation operators are needed. In this work we are showing the effect on genetic diversity, quality of results and required computational effort, when applying different crossover methods to a set of very hard instances of the weighted tardiness scheduling problem in single machine environments. For these experiments we are using multirecombined approaches which allow multiple crossover operations on multiple parent each time a new individual is generated. A description of each method, experiments and preliminary results are reported.Eje: Agentes y Sistemas Inteligentes (ASI)Red de Universidades con Carreras en Informática (RedUNCI

Evolutionary approaches for the parallel task scheduling problem : the representation issue

The problem of how to find a schedule on m > 2 processors of equal capacity that minimises the whole processing time of independent tasks has been shown as belonging to the NP-complete class (Horowitz and Sahni [12]). Evolutionary Algorithms (EAs) have been used in the past to implement the allocation of the components (tasks) of a parallel program to processors [12], [13], [14], [16], [17]. Those approaches showed their advantages when contrasted against conventional approaches and different chromosome representations were proposed. This paper shows four algorithms to solve the problem of allocating a number of non-identical related tasks in a multiprocessor or multicomputer system. The model assumes that the system consists of a number of identical processors and only one task may execute on a processor at a time. All schedules and tasks are non-preemptive. Three evolutionary algorithms, using an indirect-decode representation, are contrasted with the well-known Graham’s [11] list scheduling algorithm (LSA). All of them use the conventional Single Crossover Per Couple (SCPC) approach and indirectdecode representation but they differ in what is represented by the decoders. In the first representation scheme, decoders represent processor dispatching priorities, in the second decoders represent tasks priority lists, and in the third decoders represent both processor dispatching priorities and tasks priority lists in a bipartite chromosome. Chromosome structure, genetic operators, experiments and results are discussed.Eje: Programación concurrenteRed de Universidades con Carreras en Informática (RedUNCI

Multirecombination and different representation in evolutionary algorithms for the flow shop scheduling problem

The flow shop scheduling problem (FSSP) has held the attention of many researchers. In a simplest usual situation, a set of jobs must follow the same route to be executed on a set of machines (resources) and the main objective is to optimize some performance variable (makespan, tardiness, lateness, etc.). In the case of the makespan, it have been proved that when the number of machines is greater than or equal to three, the problem is NP-hard. EC is an emergent research field, which provides new heuristics to problem optimization where traditional approaches make the problem computationally intractable, is continuously showing its own evolution and enhanced approaches included latest multi-recombinative methods involving multiple crossovers per couple (MCPC) and multiple crossovers on multiple parents (MCMP). The present paper discusses the new multi-recombinative methods and shows the improvement of performance of enhanced evolutionary approaches under permutation and decode representation. Results of the methods proposed for each chromosome representation are here contrasted and results are shown.I Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI