Search results

The purpose of this paper is to propose a new generic hybrid Petri Net (PN) model combined with the lowest makespan cut (LMC) for job shop scheduling problems in mold manufacturing to minimize the makespan of the mold part manufacture schedule.

The LMC algorithm finds a solution close to the optimal solution. The searching of the LMC algorithm starts from the lowest estimated makespan (lowest makespan). Almost all of the lowest makespans (LM) are infeasible makespans. A shifting percentage (SP) is added to the LM to obtain the shifting makespan (SM). The SM is compared with the completion time computed from the reachability tree of the Petri Net (PN) model. If the completion time is greater than the SM, the corresponding branch is cut from the reachability graph, and the SM will be compared with another branch from the reachability tree. There are two scenarios. In the first scenario, there is no feasible solution resulting from the comparison of the completion time and the SM, because the SM is lower than all of the feasible solutions. Therefore, the SP is used to increase the SM. On the contrary, in the second scenario, there is a feasible solution: the SP is used to reduce the SM. In the first scenario, a makespan that is lower than the optimal makespan is found. In the second scenario, a makespan that is greater than the optimal makespan is found. After getting close to bounds of the optimal makespan, the least makespan found in the bounds is the best solution.

The integration of the Petri Net (PN) model and the LMC algorithm can help to improve the production efficiency. In a case study, the proposed algorithm is being compared with other heuristical methods which are practical examples of mold makespans based on the shortest and the longest processing times. The schedule or the sequence obtained by the proposed algorithm is 30% less than the other methods.

This research will consider scheduling multiple mold. The mold design and the mold testing phase are not considered.

The time to produce a mold is very important. Reducing the mold production time will provide more time for mold assembly and testing. The aim of LMC algorithm is minimize the makespan. The time to produce a mold is reduced by finding the best sequence of the jobs and machines.

This paper proposes the new generic hybrid Petri Net model combined with LMC for job shop scheduling problem in the case of mold making shop to optimize the makespan of mold parts scheduling.

Scheduling is defined by Baker as, “the allocation of resources over time to perform a collection of tasks”. The term facilities is often used instead of resources and the tasks to be performed may involve a variety of different operations.

The purpose of this paper is to develop an effective and efficient approach to exploit meta‐heuristic in particle swarm optimization (PSO) for the job shop scheduling problem (JSP), a class of NP‐hard optimization problems. The approach is to be built on a PSO with multiple independent swarms. PSO was inspired by bird flocking and animal social behaviors. The particles operate collectively like a swarm that flies through the hyperdimensional space to search for possible optimal solutions. The behavior of the particles is influenced by their tendency to learn from their personal past experience and from the success of their peers to adjust their flying speed and direction. Research in fusing the multiple‐swarm concept into PSO is well‐established in solving single objective optimization problems and multimodal problems.

This study examines the optimization of the JSP via a search space division scheme and use of the meta‐heuristic method of PSO by assigning each machine in a JSP an independent swarm of particles. The use of multiple swarms in PSO is motivated by the idea of “divide and conquer” to reduce the computational complexity incurred through solving a NP‐hard combinatorial optimization problem. The resulted design, JSP/PSO algorithm, fully exploits the computing power presented by the multiple‐swarm PSO.

Simulation experiments show that the proposed JSP/PSO algorithm can effectively solve the JSP problems from small to median size. If certain mechanism of information sharing between swarms can be incorporated, it is believed that the new design could offer even more computing power to tackle the large‐sized problems.

The proposed JSP/PSO algorithm is effective in solving JSPs. The proposed algorithm shows considerable promise when searching the space of non‐delay schedules. It demands relatively lower number of function evaluations compared to other state‐of‐the‐art. The drawback to the JSP/PSO is that the GT scheduling adopted is too computationally expensive. Future works will address this concern.

Previous studies approach surgery scheduling mainly from the mathematical modeling perspective which is often hard to apply in a practical environment. The aim of this study is to develop a practical scheduling system that considers the advantages of both surgery categorization and newsvendor model to surgery scheduling.

The research was carried out in a Finnish orthopaedic specialist centre that performs only joint replacement surgery. Four surgery categorization scenarios were defined and their productivity analyzed by simulation and newsvendor model.

Detailed analyses of surgery durations and the use of more accurate case categories and their combinations in scheduling improved OR productivity 11.3 percent when compared to the base case. Planning to have one OR team to work longer led to remarkable decrease in scheduling inefficiency.

In surgical services, productivity and cost‐efficiency can be improved by utilizing historical data in case scheduling and by increasing flexibility in personnel management.

The study increases the understanding of practical scheduling methods used to improve efficiency in surgical services.

Influenced by the constantly changing manufacturing environment, no single dispatching rule (SDR) can consistently obtain better scheduling results than other rules for the dynamic job-shop scheduling problem (DJSP). Although the dynamic SDR selection classifier (DSSC) mined by traditional data-mining-based scheduling method has shown some improvement in comparison to an SDR, the enhancement is not significant since the rule selected by DSSC is still an SDR.

This paper presents a novel data-mining-based scheduling method for the DJSP with machine failure aiming at minimizing the makespan. Firstly, a scheduling priority relation model (SPRM) is constructed to determine the appropriate priority relation between two operations based on the production system state and the difference between their priority values calculated using multiple SDRs. Subsequently, a training sample acquisition mechanism based on the optimal scheduling schemes is proposed to acquire training samples for the SPRM. Furthermore, feature selection and machine learning are conducted using the genetic algorithm and extreme learning machine to mine the SPRM.

Results from numerical experiments demonstrate that the SPRM, mined by the proposed method, not only achieves better scheduling results in most manufacturing environments but also maintains a higher level of stability in diverse manufacturing environments than an SDR and the DSSC.

This paper constructs a SPRM and mines it based on data mining technologies to obtain better results than an SDR and the DSSC in various manufacturing environments.

The purpose of this paper is to develop a methodology for automatically determining the optimal allocation of police officers in accordance with the division and organization of labor.

The problem is defined as the problem of the goal programming for which the mathematical model of mixed integer programming was developed. In modeling of the scheduling problem the approach police officer/scheme, based on predefined scheduling patterns, was used. The approach is applied to real data of a police station in Bosnia and Herzegovina.

This study indicates that the determination of monthly scheduling policemen is complex and challenging problem, which is usually performed without the aid of software (self-rostering), and that it can be significantly facilitated by the introduction of scheduling optimization approach.

The developed mathematical model, in its current form, can directly be applied only to the scheduling of police officers at police stations which have the same or a similar organization of work.

Optimization of scheduling significantly reduces the time to obtain a monthly schedule. In addition, it allows the police stations to experiment with different forms of organization work of police officers and to obtain an optimal schedule for each of them in a short time.

The problem of optimal scheduling of employees is often resolved in other fields. To the authors knowledge, this is the first time that the approach of goal programming is applied in the field of policing.

The paper aims to provide a simulation optimization solution to improve patient scheduling that accounts for varying ancillary service time such as x-ray to minimize patient wait time.

The two-step approach is to: identify patients' needs for ancillary services while scheduling appointments; and propose an algorithm to determine ancillary service time via simulation optimization. The main aim is to provide sufficient time between arrival at the clinic and the actual examination time for a patient to complete pre-visit activities without contributing significantly to patient wait time. Two case studies are included to demonstrate the approach.

Triaging at the appointment-scheduling time saves an average 17 minutes for physician's first consultation in a clinic day, and a 7 percent reduction on current average patient wait time for case 1. Case 2 results in a 9 percent reduction on average patient wait time. The scheduled ancillary service time depends on the frequency and the ancillary service time, and appointment slot design.

One limitation is the impact of modeling error on the account of ancillary service times and the modeling assumptions.

The proposed approach provides a studying method for clinic staff to account for ancillary services prior to physicians' visits for a better patient care. Two case studies demonstrated the practicability and promising results on reducing patient waiting.

This article presents a unique approach to considering the required ancillary services in outpatient scheduling system that minimizes patient wait times. The approach will strengthen the existing scheduling methods to allow the time for ancillary services.

In today's industry, a machine breakdown is common for a machine running a long period of time without maintenance. To avoid a sudden breakdown, periodic maintenance is usually performed in the production system. This paper aims to find a set of efficient schedules that considers both jobs and maintenance simultaneously.

This paper addresses a real‐life scheduling problem in a plastic company. An algorithm based on the variable range technique is developed to solve the problem by providing a small set of efficient schedules.

Once maintenance is performed, the job being processed must be stopped. This will result in some jobs being late or tardy and a relatively larger flow time is generated. Therefore, how to minimize these two criteria in the production system becomes an important issue in the company. Computational results show that problems with larger maintenance intervals and smaller maintaining time can produce a smaller number of efficient schedules.

It is seen that scheduling maintenance will result in some jobs being tardy and a larger flow time is generated. A decision maker can easily select a preferred schedule from the small set of efficient schedules. The proposed algorithm is appropriate not only for the studied company but also for those companies where periodic maintenance is required.

Presents an algorithm to find a small set of efficient schedules.

This study is carried out in one public and one private health-care centers based on different probabilities of patient’s no-show rate. The present study aims to determine the optimal queuing system capacity so that the expected total cost is minimized.

In this study an M/M/1/K queuing model is used for analytical properties of optimal queuing system capacity and appointment window so that total costs of these cases could be minimized. MATLAB software version R2014a is used to code the model.

In this paper, the optimal queuing system capacity is determined based on the changes in effective parameters, followed by a sensitivity analysis. Total cost in public center includes the costs of patient waiting time and rejection. However, the total cost in private center includes costs of physician idle time plus costs of public center. At the end, the results for public and private centers are compared to reach a final assessment.

Today, determining the optimal queuing system capacity is one of the most central concerns of outpatient clinics. The large capacity of the queuing system leads to an increase in the patient’s waiting-time cost, and on the other hand, a small queuing system will increase the cost of patient’s rejection. The approach suggested in this paper attempts to deal with this mentioned concern.

Describes flow‐shop scheduling problems and an interactive graphical flow‐shop manufacturing scheduling system (FSMS) developed to handle any number of jobs and machines. Outlines the methodical approach of using scheduling tools, such as lower bound, automatic generation of near‐optimal system sequences and schedule optimization in which the user is guided in determining optimal sequence, to cut scheduling time and make the scheduling system flexible. Outputs are in the form of Gantt charts. The graphical capability can be a very useful tool for decision makers such as production and operations managers who often encounter many day‐to‐day scheduling problems and challenges.