Determination of flexibility of workers working time through Taguchi method approach (original) (raw)
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Improving production efficiency by modifying employee scheduling system at a palm oil industry
International Journal For Science Technology And Engineering, 2015
Employee scheduling is a common problem in most organizations, either from the service sector or industrial plants. It seeks to assign employees to tasks, work shifts or rest periods, taking into account organizational and legal rules, employees' skills and preferences, demand needs, and other applicable requirements. So it is a complex problem and a top concern for human resource management. Mostly it is done manually in several activity sectors, consuming time and resources. This paper compares the methodology followed in a palm oil industry in scheduling the employees and suggests an improved way based on the suitability of the employees towards specific tasks. The quality of work done is analyzed by calculating the quality metric value. The employees needed for each task are analyzed by the method of linear programming.
Scheduling is one of the most important issues in the planning and operation of manufacturing system, and scheduling has gained much attention increasingly in the recent years. The flexible job shop scheduling problem (JSP) is one of the most difficult problems in this area. It consists of scheduling a set of jobs on a set of machines with the objective to minimize a certain make span time. Each machine is continuously available from time zero, processing one operation at a time without preemption. Each job has a specified processing order on the machine which are fixed and known in advance. Moreover, a processing time is also fixed and known. Different researcher use different algorithms to optimize the make span time. In this paper study has been focused on the different algorithms to optimize the make span time. Now a day's different algorithms that are used are Genetic Algorithm, Artificial Neural Network, Ant Colony Optimization and Particle Swarm Optimization.
Workforce scheduling: A new model incorporating human factors
Journal of Industrial Engineering and Management, 2012
Purpose: The majority of a company's improvement comes when the right workers with the right skills, behaviors and capacities are deployed appropriately throughout a company. This paper considers a workforce scheduling model including human aspects such as skills, training, workers' personalities, workers' breaks and workers' fatigue and recovery levels. This model helps to minimize the hiring, firing, training and overtime costs, minimize the number of fired workers with high performance, minimize the break time and minimize the average worker's fatigue level. Design/methodology/approach: To achieve this objective, a multi objective mixed integer programming model is developed to determine the amount of hiring, firing, training and overtime for each worker type. Findings: The results indicate that the worker differences should be considered in workforce scheduling to generate realistic plans with minimum costs. This paper also investigates the effects of human fatigue and recovery on the performance of the production systems. Research limitations/implications: In this research, there are some assumptions that might affect the accuracy of the model such as the assumption of certainty of the demand in each period, and the linearity function of Fatigue accumulation and recovery curves. These assumptions can be relaxed in future work. Originality/value: In this research, a new model for integrating workers' differences with workforce scheduling is proposed. To the authors' knowledge, it is the first time to study the effects of different important human factors such as human personality, skills and fatigue and recovery in the workforce scheduling process. This research shows that considering both
Minimizing Labor Cost by Developing Workforce Scheduling Model: A Case Study
IAR Consortium, 2022
This research focused on improving the workforce scheduling problem at a plastic company. The research developed a computer program to solve two problems: shift scheduling and labor allocation, with the objective of minimizing labor costs, based on the constraints on the number of workers, the number of products, the suitability of each worker and other related constraints. The workforce scheduling program is built on the interface of MS. Excel software, VBA project and Solver, OpenSolver tool. The purpose of the program is to calculate the number of workers required for each working shift so as to optimize costs, then allocate workers to working shifts. The program was built and validated with data from the studied shop floor in 7 months. The results show that the program has arranged workers according to the corresponding shift and the speed of completing the shift schedule has been reduced by 8 times compared to the present, the total labor cost is saving average 15.1% per month. The program meets the set parameters: completion time is reduced, worker response level is increased and the time difference between shifts is less than 40%.
Journal of Industrial Engineering Management
Tofu factory X is one of the industries in the food sector. This factory processing soybeans into tofu. Tofu factory X is one of the tofu factories located in Depok, West Java. The problems that occur in this factory are the working hours that exceed the normal limit and do not achieve the optimal productivity levels due to the factory's inability to fulfill all demands, for this reason this research aims to calculate the workload of each worker in order to know the optimal number of workers according to the workload with Work Load Analysis and Workforce Analysis method, after that, a cost analysis is carried out to assist decision making. Based on the results of the analysis, it is found that there are 4 workers who have a workload that exceeds the normal limit, there are operator 1 with 124%, operator 4 with 116%, operator 5 with 112%, and operator 7 with 111%. The calculation of the optimal workforce using the Workforce Analysis method shows that the workers of the tofu facto...
Minimization of Makespan in Job Shop Scheduling with Heuristic and Genetic Algorithms
International Journal of Darshan Institute on Engineering Research & Emerging Technology, 2018
As the performance of manufacturing system is directly related to the optimum utilization of time and resources, optimal scheduling of work activities plays an important role. Job shop scheduling is an optimization problem, in which a set of jobs has to be processed on a set of machines such that some specific objective functions are to be minimized. Here an effort has been made to minimize the makespan and average job flow time of the job shop scheduling problem of following two natures: 5-job 3-machine problem and 10-job 5-machine problem. Data for these problems were collected from a small scale industry. This paper describes the steps in well-known heuristics such as Palmer's, Campbell Dudek and Smith (CDS) & Nawaz Enscore and Ham (NEH) and a simple algorithm for proposed Genetic Algorithm. This paper investigates the present scheduling system followed by the industry and presents the solutions for the problems considered through heuristics and proposed Genetic Algorithm. Then the comparisons were made among the results obtained through proposed genetic algorithm, heuristics and LEKIN scheduling software to suggest an optimal sequence for the problems considered.
Flexible Employee Scheduling with Multi Skill Training Program using Genetic Algorithm
This paper is the study of flexible employee scheduling with the multi skill training program of the employees in the manufacturing process. The paper starts with mathematical modeling by giving flexibility in choosing the shift types and days off assignment to the employees. An extra care is taken for not assigning night shift on the day before the day off. A robust Genetic algorithm is used to solve the mathematical model constructed. A 6hour shift for morning as well as afternoon and 12hour shift for night is assigned to the employees, which reduces the total cost function of the employees by 10.33% compared to normal 8hour shifts. And also a multi skill training program to the employees are adapted, which again reduces the total cost function of the employees by 20% compared to untrained employees.
Journal of Research in Mathematics Trends and Technology
This research has a purpose to optimize the scheduling of employees in an embroidery company for doing certain tasks using Hungarian method, as well as analyzing the sensitivity of the optimal solution if there is a reduction on the employees’ time to finish the tasks. The Hungarian method was applied on the assignment of workers in embroidery process involving 11 employees and 10 tasks. The optimal scheduling result minimizes the time of the embroidery production of the company. The optimal scheduling result found the optimal assignment of each worker to the tasks with the total work time is 13,7 hours. After the Hungarian method was applied, the company got the increasing revenue as much as 9,09 %. The sensitivity analysis was conducted by reducing the time of the employees take in embroidery the bags. The results of the sensitivity analysis are some boundaries for basis and non basis variables to maintain the optimal solution.
ANALYSIS OF SELECTION SCHEMES FOR SOLVING JOB SHOP SCHEDULING PROBLEM USING GENETIC ALGORITHM
Scheduling problems have the standard consideration in the field of manufacturing. Among the various types of scheduling problems, the job shop scheduling problem is one of the most interesting NP-hard problems. As the job shop scheduling is an optimization problem, Genetic algorithm was selected to solve it In this study. Selection scheme is one of the important operators of Genetic algorithm. The choice of selection method to be applied for solving problems has a wide role in the Genetic algorithm process. The speed of convergence towards the optimum solution for the chosen problem is largely determined by the selection mechanism used in the Genetic algorithm. Depending upon the selection scheme applied, the population fitness over the successive generations could be improved. There are various type of selection schemes in genetic algorithm are available, where each selection scheme has its own feasibility for solving a particular problem. In this study, the selection schemes namely Stochastic Universal Sampling (SUS), Roulette Wheel Selection (RWS), Rank Based Roulette Wheel Selection (RRWS) and Binary Tournament Selection (BTS) were chosen for implementation. The characteristics of chosen selection mechanisms of Genetic algorithm for solving the job shop scheduling problem were analyzed. The Genetic algorithm with four different selection schemes is tested on instances of 7 benchmark problems of different size. The result shows that the each of the four selection schemes of Genetic algorithm have been successfully applied to the job shop scheduling problems efficiently and the performance of Stochastic Universal Sampling selection method is better than all other four selection schemes.