Parallel-Machine Scheduling Problems with Past-Sequence-Dependent Delivery Times and Aging Maintenance

We consider parallel-machine scheduling problems with past-sequence-dependent (psd) delivery times and aging maintenance. The delivery time is proportional to the waiting time in the system. Each machine has an aging maintenance activity. We develop polynomial algorithms to three versions of the problem to minimize the total absolute deviation of job completion times, the total load, and the total completion time

A multi objective volleyball premier league algorithm for green scheduling identical parallel machines with splitting jobs

Parallel machine scheduling is one of the most common studied problems in recent years, however, this classic optimization problem has to achieve two conflicting objectives, i.e. minimizing the total tardiness and minimizing the total wastes, if the scheduling is done in the context of plastic injection industry where jobs are splitting and molds are important constraints. This paper proposes a mathematical model for scheduling parallel machines with splitting jobs and resource constraints. Two minimization objectives - the total tardiness and the number of waste - are considered, simultaneously. The obtained model is a bi-objective integer linear programming model that is shown to be of NP-hard class optimization problems. In this paper, a novel Multi-Objective Volleyball Premier League (MOVPL) algorithm is presented for solving the aforementioned problem. This algorithm uses the crowding distance concept used in NSGA-II as an extension of the Volleyball Premier League (VPL) that we recently introduced. Furthermore, the results are compared with six multi-objective metaheuristic algorithms of MOPSO, NSGA-II, MOGWO, MOALO, MOEA/D, and SPEA2. Using five standard metrics and ten test problems, the performance of the Pareto-based algorithms was investigated. The results demonstrate that in general, the proposed algorithm has supremacy than the other four algorithms

Scheduling Jobs with Variable Job Processing Times on Unrelated Parallel Machines

m unrelated parallel machines scheduling problems with variable job processing times are considered, where the processing time of a job is a function of its position in a sequence, its starting time, and its resource allocation. The objective is to determine the optimal resource allocation and the optimal schedule to minimize a total cost function that dependents on the total completion (waiting) time, the total machine load, the total absolute differences in completion (waiting) times on all machines, and total resource cost. If the number of machines is a given constant number, we propose a polynomial time algorithm to solve the problem

Serial-batch scheduling – the special case of laser-cutting machines

The dissertation deals with a problem in the field of short-term production planning, namely the scheduling of laser-cutting machines. The object of decision is the grouping of production orders (batching) and the sequencing of these order groups on one or more machines (scheduling). This problem is also known in the literature as "batch scheduling problem" and belongs to the class of combinatorial optimization problems due to the interdependencies between the batching and the scheduling decisions. The concepts and methods used are mainly from production planning, operations research and machine learning

Joint optimization of production and maintenance scheduling for unrelated parallel machine using hybrid discrete spider monkey optimization algorithm

This paper considers an unrelated parallel machine scheduling problem with variable maintenance based on machine reliability to minimize the maximum completion time. To obtain the optimal solution of small-scale problems, we firstly establish a mixed integer programming model. To solve the medium and large-scale problems efficiently and effectively, we develop a hybrid discrete spider monkey optimization algorithm (HDSMO), which combines discrete spider monkey optimization (DSMO) with genetic algorithm (GA). A few additional features are embedded in the HDSMO: a three-phase constructive heuristic is proposed to generate better initial solution, and an individual updating method considering the inertia weight is used to balance the exploration and exploitation capabilities. Moreover, a problem-oriented neighborhood search method is designed to improve the search efficiency. Experiments are conducted on a set of randomly generated instances. The performance of the proposed HDSMO algorithm is investigated and compared with that of other existing algorithms. The detailed results show that the proposed HDSMO algorithm can obtain significantly better solutions than the DSMO and GA algorithms

A multi objective volleyball premier league algorithm for green scheduling identical parallel machines with splitting jobs

Parallel machine scheduling is one of the most common studied problems in recent years, however, this classic optimization problem has to achieve two conflicting objectives, i.e. minimizing the total tardiness and minimizing the total wastes, if the scheduling is done in the context of plastic injection industry where jobs are splitting and molds are important constraints. This paper proposes a mathematical model for scheduling parallel machines with splitting jobs and resource constraints. Two minimization objectives - the total tardiness and the number of waste - are considered, simultaneously. The obtained model is a bi-objective integer linear programming model that is shown to be of NP-hard class optimization problems. In this paper, a novel Multi-Objective Volleyball Premier League (MOVPL) algorithm is presented for solving the aforementioned problem. This algorithm uses the crowding distance concept used in NSGA-II as an extension of the Volleyball Premier League (VPL) that we recently introduced. Furthermore, the results are compared with six multi-objective metaheuristic algorithms of MOPSO, NSGA-II, MOGWO, MOALO, MOEA/D, and SPEA2. Using five standard metrics and ten test problems, the performance of the Pareto-based algorithms was investigated. The results demonstrate that in general, the proposed algorithm has supremacy than the other four algorithms

Minimización de la tardanza en problemas de programación de tareas en maquinas paralelas con deterioro de los recursos

En ambientes de manufactura y de servicios es frecuente encontrar diferentes tareas que son realizadas en paralelo empleando recursos heterogéneos, los cuales tienen la característica de sufrir deterioro a medida que transcurre el tiempo. Ese deterioro tiene un impacto significativo en el desempeño de dichos recursos, lo que se puede medir de diferentes formas tales, como: calidad, tiempo de proceso, entre otros. Esta investigación científica utiliza un modelo donde el deterioro de los recursos es una función de las tareas específicas previamente realizadas por el recurso. La formulación del problema se presenta por medio de un modelo de programación matemática. Este trabajo presenta dos heurísticas para resolver el problema en un tiempo razonable, donde cada heurística emplea diferentes reglas y criterios para identificar la mejor solución. Un análisis de sensibilidad, que comprende 2700 casos, es llevado a cabo para evaluar la eficacia de las heurísticas. Los resultados comprueban que las heurísticas son eficientes y generan soluciones útiles para el tomador de decisiones. In manufacturing and service environments it is common to find processes that are performed in parallel by different resources, which have the characteristic that their performance deteriorates with time. This deterioration has a significant effect on the performance of the resources that can be measured in different forms such as quality and process time. This research utilizes a model where resource deterioration is a function of the specific jobs previously completed by the resource. The problem’s formulation is presented as a mathematical program. The paper presents two heuristics to solve the problem, where each has different rules to find the best solution. A sensitivity analysis that includes 2700 cases is performed to evaluate the performance of the heuristics. The results demonstrate that the heuristics are efficient and generate useful solutions for decision makers

Minimización de la tardanza en problemas de programación de tareas en maquinas paralelas con deterioro de los recursos

En ambientes de manufactura y de servicios es frecuente encontrar diferentes tareas que son realizadas en paralelo empleando recursos heterogéneos, los cuales tienen la característica de sufrir deterioro a medida que transcurre el tiempo. Ese deterioro tiene un impacto significativo en el desempeño de dichos recursos, lo que se puede medir de diferentes formas tales, como: calidad, tiempo de proceso, entre otros. Esta investigación científica utiliza un modelo donde el deterioro de los recursos es una función de las tareas específicas previamente realizadas por el recurso. La formulación del problema se presenta por medio de un modelo de programación matemática. Este trabajo presenta dos heurísticas para resolver el problema en un tiempo razonable, donde cada heurística emplea diferentes reglas y criterios para identificar la mejor solución. Un análisis de sensibilidad, que comprende 2700 casos, es llevado a cabo para evaluar la eficacia de las heurísticas. Los resultados comprueban que las heurísticas son eficientes y generan soluciones útiles para el tomador de decisiones.In manufacturing and service environments it is common to find processes that are performed in parallel by different resources, which have the characteristic that their performance deteriorates with time. This deterioration has a significant effect on the performance of the resources that can be measured in different forms such as quality and process time. This research utilizes a model where resource deterioration is a function of the specific jobs previously completed by the resource. The problem’s formulation is presented as a mathematical program. The paper presents two heuristics to solve the problem, where each has different rules to find the best solution. A sensitivity analysis that includes 2700 cases is performed to evaluate the performance of the heuristics. The results demonstrate that the heuristics are efficient and generate useful solutions for decision makers