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

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

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

Minimizing Total Earliness and Tardiness for Common Due Date Single-Machine Scheduling with an Unavailability Interval

This paper addresses the problem of scheduling n independent jobs on a single machine with a fixed unavailability interval, where the aim is to minimize the total earliness and tardiness (TET) about a common due date. Two exact methods are proposed for solving the problem: mixed integer linear programming formulations and a dynamic programming based method. These methods are coded and tested intensively on a large data set and the results are analytically compared. Computational experiments show that the dynamic programming method is efficient in obtaining the optimal solutions and no problems due to memory requirement

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

Multi-machine preventive maintenance scheduling with imperfect interventions: A restless bandit approach

In this paper we address the problem of allocating the efforts of a collection of repairmen to a number of deteriorating machines in order to reduce operation costs and to mitigate the cost (and likelihood) of unexpected failures. Notwithstanding these preventive maintenance interventions are aimed at returning the machine to a so-called as-good-as-new state, unforeseeable factors may imply that maintenance interventions are not perfect and the machine is only returned to an earlier (uncertain) state of wear. The problem is modelled as a restless bandit problem and an index policy for the sequential allocation of maintenance tasks is proposed. A series of numerical experiments shows the strong performance of the proposed policy. Moreover, the methodology is of interest in the general context of dynamic resource allocation and restless bandit problems, as well as being useful in the particular imperfect maintenance model described

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

Competitive Two-Agent Scheduling with Learning Effect and Release Times on a Single Machine

The learning effect has gained much attention in the scheduling research recently, where many researchers have focused their problems on only one optimization. This study further addresses the scheduling problem in which two agents compete to perform their own jobs with release times on a common single machine with learning effect. The aim is to minimize the total weighted completion time of the first agent, subject to an upper bound on the maximum lateness of the second agent. We propose a branch-and-bound approach with several useful dominance properties and an effective lower bound for searching the optimal solution and three simulated-annealing algorithms for the near-optimal solutions. The computational results show that the proposed algorithms perform effectively and efficiently