Efficient Scheduling of Plantation Company Workers using Genetic Algorithm

Workers at large plantation companies have various activities. These activities include caring for plants, regularly applying fertilizers according to schedule, and crop harvesting activities. The density of worker activities must be balanced with efficient and fair work scheduling. A good schedule will minimize worker dissatisfaction while also maintaining their physical health. This study aims to optimize workers' schedules using a genetic algorithm. An efficient chromosome representation is designed to produce a good schedule in a reasonable amount of time. The mutation method is used in combination with reciprocal mutation and exchange mutation, while the type of crossover used is one cut point, and the selection method is elitism selection. A set of computational experiments is carried out to determine the best parameters’ value of the genetic algorithm. The final result is a better 30 days worker schedule compare to the previous schedule that was produced manually.

Optimización de la secuenciación con penalizaciones por adelanto y atraso con trabajos que se traslapan

The main purpose of the present research is to solve the sequencing problem with earliness penalties and quadratic tardy penalties for late job completion. Extensive research was carried out to identify a possible gap in the existing models. Due to this, it was possible to identify that the models addressed by the literature lacked the possibility of overlapping jobs. The proposal therefore originates from a pre-existing model that optimizes the penalties for early delivery by inserting idle times that cause the reduction of the earliness penalties. The proposed model starts with a heuristic method that provides an initial solution. In addition to this, the algorithm for inserting idle time generates a first optimization followed by a second optimization that overlaps the jobs so that a job can start before the initial job is completed with the objective of reducing the penalty for late completion. For the present study, the following assumptions were made idle time is allowed, the first job starts at time zero, all jobs are independent, and these can overlap up to a certain limit of compliance. The parameterization and simulations were carried out where the results obtained show that through the overlapping of jobs, the reduction of lateness penalties is achieved, which therefore leads to the validation of the proposal.El objetivo principal de la presente investigación es resolver el problema de secuenciación con penalizaciones por anticipación y penalizaciones cuadráticas por tardanza para la finalización tardía del trabajo. Se llevó a cabo una amplia investigación para identificar un faltante en los modelos existentes. Debido a esto, fue posible identificar que los modelos abordados por la literatura carecían de la posibilidad de superposición de trabajos. Por lo tanto, la propuesta parte de un modelo preexistente que optimiza las penalizaciones por entrega anticipada al insertar tiempos muertos que provocan la reducción de las penalizaciones por anticipación. El modelo propuesto parte de un método heurístico que proporciona una solución inicial. Además de esto, se plantea un algoritmo para insertar tiempo de inactividad que genera una primera optimización seguida de una segunda optimización debido a la superposición de trabajos de modo que un trabajo pueda comenzar antes de que se complete el trabajo que lo precede con el objetivo de reducir la penalización por finalización tardía. Para este estudio, se hicieron las siguientes suposiciones: se permite el tiempo de inactividad, el primer trabajo comienza en el tiempo cero, todos los trabajos son independientes y estos pueden superponerse hasta cierto límite de cumplimiento. Los resultados obtenidos al realizar la parametrización y simulaciones demuestran que a través de la superposición de trabajos se logra la reducción de las penalizaciones por tardanza, lo que lleva a la validación de la propuesta

Algorithms for job scheduling problems with distinct time windows and general earliness/tardiness penalties.

This paper addresses the single machine scheduling problem with distinct time windows and sequence- dependent setup times. The objective is to minimize the total weighted earliness and tardiness. The prob- lem involves determining the job execution sequence and the starting time for each job in the sequence. An implicit enumeration algorithm denoted IE and a general variable neighborhood search algorithm de- noted GVNS are proposed to determine the job scheduling. IE is an exact algorithm, whereas GVNS is a heuristic algorithm. In order to define the starting times, an O ( n 2 ) idle time insertion algorithm (ITIA) is proposed. IE and GVNS use the ITIA algorithm to determine the starting time for each job. However, the IE algorithm is only valid for instances with sequence-independent setup times, and takes advantage of theoretical results generated for this problem. Computational experiments show that the ITIA algo- rithm is more efficient than the only other equivalent algorithm found in the literature. The IE algorithm allows the optimal solutions of all instances with up to 15 jobs to be determined within a feasible com- putational time. For larger instances, GVNS produces better-quality solutions requiring less computational time compared with the other algorithm from the literature