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

Knowledge insertion: an efficient approach to reduce search effort in evolutionary scheduling

Evolutionary algorithms (EAs) are merely blind search algorithms, which only make use of the relative fitness of solutions, but completely ignore the nature of the problem. Their performance can be improved by using new multirecombinative approaches, which provide a good balance between exploration and exploitation. Even though in difficult problems with large search spaces a considerable number of evaluations are required to arrive to near-optimal solutions. On the other hand specialized heuristics are based on some specific features of the problem, and the solution obtained can include some features of optimal solutions. If we insert in the evolutionary algorithm the problem specific knowledge embedded in good solutions (seeds), coming from some other heuristic or from the evolutionary process itself, we can expect that the algorithm will be guided to promising sub-spaces avoiding a large search. This work shows alternative ways to insert knowledge in the search process by means of the inherent information carried by solutions coming from that specialised heuristic or gathered by the evolutionary process itself. To show the efficiency of this approach, the present paper compares the performance of multirecombined evolutionary algorithms with and without knowledge insertion when applied to selected instances of the Average Tardiness Problem in a single machine environment.Eje: Agentes y Sistemas Inteligentes (ASI)Red de Universidades con Carreras en Informática (RedUNCI

Optimization of tardiness related objectives in single machine environments via multirecombined evolutionary algorithms

Tardiness related objectives are of utmost importance in production systems when client satisfaction is a main goal of a company. These objectives measure the system response to the client requirements and rate manager´s performance In scheduling problems with diverse single or multiple objectives and environments Evolutionary algorithms (EAs) were successfully applied. Latest improvements in EAs have been developed by means of multirecombination, a method, which allows multiple exchange of genetic material between individuals of the mating pool. These individuals can be provided by the current population or by an external source. The performance of the algorithm depends o the number of individuals in the mating pool and their mating frequency. MCMP-SRI and MCMP-SRSI are multirecombined evolutionary approaches using the concept of the stud (a breeding individual), random immigrants and/or seeds, to avoid premature convergence and adding problem-specific- knowledge. Here, both methods applied to tardiness related problems in single machine environmen are discussed and contrasted against conventional heuristics.Eje: Inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI

Alternative representations and multirecombined approaches for solving the single-machine common due date problem

Balance between exploitation and exploration is a main factor influencing convergence in an evolutionary algorithm. In order to improve this balance new trends in evolutionary algorithms make use of multi-recombinative approaches, known as multiple-crossovers-on-multiple-parents (MCMP). The use of a breeding individual (stud) which repeatedly mates individuals that randomly immigrates to a mating pool can further help the balance between exploration and exploitation. For the single-machine common due date problem an optimal schedule is V-shaped around the due date. To produce V-shaped schedules an appropriate binary representation, associated with a schedule builder, can be used. In this representation each bit indicates if a corresponding job belongs either to the tardy or the non-tardy set. When contrasted with commonly used permutation representations this approach reduces the searching space from n! to 2n. This paper compares three different implementations and shows their performance on a set of instances for the single machine scheduling problem with a common due date. Two of these approaches are based on a binary representation to form V-shaped schedules while the other is based on permutations. All these approaches apply different multirecombined methods. Details on implementation and results are discussed.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

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

Solutions to the dynamic average tardiness problem in single machine environments

In dynamic scheduling arrival times as well, as some or all job attributes are unknown in advance. Dynamism can be classified as partial or total. In simplest partially dynamic problems the only unknown attribute of a job is its arrival time rj. A job arrival can be given at any instant in the time interval between zero and a limit established by its processing time, in order to ensure finishing it before the due date deadline. In the cases where the arrivals are near to zero the problem becomes closer to the static problem, otherwise the problem becomes more restrictive. In totally dynamics problems, other job attributes such as processing time pj, due date dj, and tardiness penalty wj, are also unknown. This paper proposes different approaches for resolution of (partial and total) Dynamic Average Tardiness problems in a single machine environment. The first approach uses, as a list of dispatching priorities a final (total) schedule, found as the best by another method for a similar static problem: same job features, processing time, and due dates. The second approach uses as a dispatching priority the order imposed by a partial schedule created by another heuristic, at each decision point. The details of implementation of the proposed algorithms and results for a group of selected instances are discussed in this work.Eje: Agentes y Sistemas Inteligentes (ASI)Red de Universidades con Carreras en Informática (RedUNCI

Knowledge Insertion: an Efficient Approach to Reduce Search Effort in Evolutionary Scheduling

Evolutionary algorithms (EAs) are merely blind search algorithms, which only make use of the relative fitness of solutions, but completely ignore the nature of the problem. Their performance can be improved by using new multirecombinative approaches, which provide a good balance between exploration and exploitation. Even though in difficult problems with large search spaces a considerable number of evaluations are required to arrive to near-optimal solutions. On the other hand specialized heuristics are based on some specific features of the problem, and the solution obtained can include some features of optimal solutions. If we insert in the evolutionary algorithm the problem specific knowledge embedded in good solutions (seeds), coming from some other heuristic or from the evolutionary process itself, we can expect that the algorithm will be guided to promising subspaces avoiding a large search. This work shows alternative ways to insert knowledge in the search process by means of the inherent information carried by solutions coming from that specialised heuristic or gathered by the evolutionary process itself. To show the efficiency of this approach, the present paper compares the performance of multirecombined evolutionary algorithms with and without knowledge insertion when applied to selected instances of the Average Tardiness Problem in a single machine environment.Facultad de Informátic

Knowledge insertion: an efficient approach to reduce search effort in evolutionary scheduling

Evolutionary algorithms (EAs) are merely blind search algorithms, which only make use of the relative fitness of solutions, but completely ignore the nature of the problem. Their performance can be improved by using new multirecombinative approaches, which provide a good balance between exploration and exploitation. Even though in difficult problems with large search spaces a considerable number of evaluations are required to arrive to near-optimal solutions. On the other hand specialized heuristics are based on some specific features of the problem, and the solution obtained can include some features of optimal solutions. If we insert in the evolutionary algorithm the problem specific knowledge embedded in good solutions (seeds), coming from some other heuristic or from the evolutionary process itself, we can expect that the algorithm will be guided to promising sub-spaces avoiding a large search. This work shows alternative ways to insert knowledge in the search process by means of the inherent information carried by solutions coming from that specialised heuristic or gathered by the evolutionary process itself. To show the efficiency of this approach, the present paper compares the performance of multirecombined evolutionary algorithms with and without knowledge insertion when applied to selected instances of the Average Tardiness Problem in a single machine environment.Eje: Agentes y Sistemas Inteligentes (ASI)Red de Universidades con Carreras en Informática (RedUNCI

Optimization of tardiness related objectives in single machine environments via multirecombined evolutionary algorithms

Tardiness related objectives are of utmost importance in production systems when client satisfaction is a main goal of a company. These objectives measure the system response to the client requirements and rate manager´s performance In scheduling problems with diverse single or multiple objectives and environments Evolutionary algorithms (EAs) were successfully applied. Latest improvements in EAs have been developed by means of multirecombination, a method, which allows multiple exchange of genetic material between individuals of the mating pool. These individuals can be provided by the current population or by an external source. The performance of the algorithm depends o the number of individuals in the mating pool and their mating frequency. MCMP-SRI and MCMP-SRSI are multirecombined evolutionary approaches using the concept of the stud (a breeding individual), random immigrants and/or seeds, to avoid premature convergence and adding problem-specific- knowledge. Here, both methods applied to tardiness related problems in single machine environmen are discussed and contrasted against conventional heuristics.Eje: Inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI