Scheduling Modeling
Ali Husseinzadeh Kashan; Saeed Afkhami; Parisa Maroofkhani
Abstract
Purpose: In this research due to the importance of the U-shaped assembly line balancing and, on the other hand, the importance of human factors and setup times, we want to develop a bi-objective mathematical model minimize the cycle time and the total cost.Methodology: Since the research problem is shown ...
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Purpose: In this research due to the importance of the U-shaped assembly line balancing and, on the other hand, the importance of human factors and setup times, we want to develop a bi-objective mathematical model minimize the cycle time and the total cost.Methodology: Since the research problem is shown to be NP-hard, NSGA-II, which is a population-based algorithm, and also SPEA-II are used to solve the problem.Findings: A mathematical model for the problem on hand is developed. We solve the problem using NSGA-II and SPEA-II. We use four criteria for analyzing the results of the mathematical model and evaluating the performance of the multi-objective evolutionary algorithms. The experimental results demonstrate that NSGA-II is superior to SPEA-II.Originality/Value: A bi-objective mathematical model for the U-shaped assembly line balancing problem considering setup-times and workers' skill is developed, and the problem is solved using two algorithms.
Scheduling Modeling
Roja Ruhbakhsh; Esmaeil Mehdizadeh; Mohammad Amin Adibi
Abstract
Purpose: Lot streaming, which has much attention in recent years, is an effective technique to increase production efficiency in a production system by splitting a job into several smaller parts in a multi-stage production system. But important assumptions that exist in the real-world scheduling environment ...
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Purpose: Lot streaming, which has much attention in recent years, is an effective technique to increase production efficiency in a production system by splitting a job into several smaller parts in a multi-stage production system. But important assumptions that exist in the real-world scheduling environment are always ignored. Hence, in this paper, these assumptions are discussed and the results are reviewed. In this paper, the aim is solving a multi objective mathematical model for solving hybrid flow shop scheduling problem with lot-streaming, setup time and transportation time.Methodology: At first, a multi objective mathematical programming model is presented for solving the problem. Then, by wighting method, the multi objective model convert to single objective model and GAMS software is used to solve the small size problems to show the performance of the mathematical mothel. Inspired by previous studies, two multi objective metaheuristic algorithms based on the genetic algorithm is used to solve the large-scale problems. To illustrate the performance of the proposed metaheuristic algorithms, the obtained results of the algorithms compared with GAMS outputs in single mode.Findings: To validate the proposed model, a sample is solved using GAMS software and compared with the genetic algorithm. The obtained results show the performance of the mathematical model. Then, two proposed algorithms are used to solve the large-scale problems. For this purpose, 30 instance problems are randomly generated and six indicators are used to compare the algorithms. After performing the experiments and comparing the algorithms with each other, the results show NRGA algorithm performs bether than NSGA-II.Originality/Value: In this paper, for solving a multi objective hybrid flow shop scheduling problem with lot-streamingm mathematical model with the aim of minimizing the makespan and total tardiness, the sequence-dependent setup time and the transportation time constraints between consecutive stages are considered. Since the problem is NP-hard, NSGA-II and NRGA algorithms were used to solve the proposed problem.
Scheduling Modeling
Parham Soofi; Mehdi Yazdani; Maghsoud Amiri; Mohammad Amin Adibi
Abstract
Purpose: One of the most important issues in the field of production scheduling, which has recently received much attention from researchers, is Dual Resource Constrained Flexible Job Shop Scheduling Problem (DRCFJSP). To deal with unexpected disruptions such as machine breakdowns, the job schedule must ...
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Purpose: One of the most important issues in the field of production scheduling, which has recently received much attention from researchers, is Dual Resource Constrained Flexible Job Shop Scheduling Problem (DRCFJSP). To deal with unexpected disruptions such as machine breakdowns, the job schedule must be robust so that in the event of a malfunction, the job schedule works properly and deviate less from the optimal solution. The purpose of this paper is to study the DRCFJSP problem with possible scenarios of machine failure or workshop disruption.Methodology: In solving the under-studied problem, the assignment of jobs and the sequence of operations on each machine should be done in such a way that under any possible scenario, the maximum completion time is minimized so that the weight combination of system performance in average mode, system performance in worst mode, the penalty for violating the time window constraints of the due dates and the variance of the objective function value is optimal according to different scenarios. For this purpose, a Robust Scenario-based Stochastic Programming (RSSP) model based on a mixed integer linear programming model has been presented for this problem and has been solved by Gams software for validation in small and medium-sized problems. Also, due to the Np-hard nature of this problem, a meta-heuristic method based on Genetic Algorithm (GA) is proposed for solving the large-sized problems. Also, the results of a case study in Alborz Yadak company related to the problem of the research are reported in the article.Findings: The results of the proposed RSSP model indicate that GAMS software is able to solve these problems up to medium sizes in an acceptable time and achieve a controlled and robust solution. Numerical results also show the proper performance of the proposed GA as an alternative to solve the RSSP model in the large-sized problems.Originality/Value: In this paper, DRCFJSP problem is studied with possible scenarios of machine failure or disruption in the workshop. Also, a RSSP model according to the mixed integer linear programming formulation and a meta-heuristic Algorithm have been presented for mentioned problem in this article.
Scheduling Modeling
Niloofar Khalili; Parisa Shahnazari-Shahrezaei; Amir Gholam Abri
Abstract
This paper deals with modeling a nurse scheduling problem considering service level in uncertain conditions. In accord with the urgent need of hospitals to provide better services to patients, it is necessary to consider nurses’ preferences in the work shift scheduling. Hence, a multi-objective ...
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This paper deals with modeling a nurse scheduling problem considering service level in uncertain conditions. In accord with the urgent need of hospitals to provide better services to patients, it is necessary to consider nurses’ preferences in the work shift scheduling. Hence, a multi-objective model is presented by regarding the rules and regulations related to the assignment of nurses to work shifts, in which the service level to patients is also considered. Due to the uncertainty of the number of patients that refer to the hospital, this parameter is taken uncertain into account. In order to evaluate the output results, two numerical examples in small and large sizes with real data of Labbafinejad Hospital with 18-person and 90-person wards are designed and Epsilon constraint method is used to solve the small-sized problem. Computational results reveal that increasing the service level to patients is directly related to increasing the number of nurses per each shift. Moreover, because of NP-Hard nature of nurse scheduling problem, the large-sized problem (90-person ward) is solved by a Gray Wolf algorithm and on the basis of designing a new chromosome. The results obtained by this method include 35 different efficient solutions for nurse scheduling in this hospital.
Scheduling Modeling
Haed Tavakkoli-Moghaddam; Seyed Hesamoddin Zegordi; Mohammad Reza Amin-Nasseri
Abstract
Purpose: This paper proposes several innovative approaches to model evaluation after obtaining the reinforcement learning model of scheduling with predictive maintenance. To train this model, its reward and loss function must be determined according to the conditions of the workshop environment.
Methodology: ...
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Purpose: This paper proposes several innovative approaches to model evaluation after obtaining the reinforcement learning model of scheduling with predictive maintenance. To train this model, its reward and loss function must be determined according to the conditions of the workshop environment.
Methodology: This learning model is examined in different modes of work entry into the workshop and the results obtained from other scheduling methods show better outputs.
Findings: The predictive maintenance model is evaluated by four learning methods and the quality of these models is examined. By selecting and adding the best machine failure model to the scheduling reinforcement learning model, the instant tasks entered into the workshop are assigned to the machines. By comparing the proposed method with the previous ones, the best performance is found and shown.
Originality/Value: One of the innovations of this paper is to provide a definition of the reward function for the issue.
Scheduling Modeling
Hamid Safarzadeh; Farhad Kianfar
Abstract
Purpose: Outsourcing is a prevalent strategy in the industry and business, which can enhance the performance of a company and rectify its constraints. This strategy can be integrated into various managerial aspects of a company, particularly the scheduling subject. In this paper, a single machine scheduling ...
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Purpose: Outsourcing is a prevalent strategy in the industry and business, which can enhance the performance of a company and rectify its constraints. This strategy can be integrated into various managerial aspects of a company, particularly the scheduling subject. In this paper, a single machine scheduling problem is investigated in which a set of jobs can be outsourced in a batch to a single subcontractor. It is supposed that the outsourcing time and cost of a job are proportional to its in-house processing time. In addition, a fixed logistics time and cost are also considered for the outsourcing batch. Moreover, the problem objective is to minimize the sum of the total completion time of the jobs and the total outsourcing cost.Methodology: To solve the problem, a set of optimal properties of the problem solution are proven via a lemma and a theorem to determine the optimal solution of the problem. Moreover, some computational experiments are also conducted at the end of the paper to examine the effect of the outsourcing strategy in the addressed problem.Findings: By the developed solution approach, the structure of the optimal solution is determined, using which the optimal solution of the problem can be chosen among a limited set of candidate solutions via some simple computations. Moreover, the computational results indicate the remarkable possible role of outsourcing in decreasing the value of the objective function of the problem.Originality/Value: In this paper, a new practical problem in the research area of scheduling with outsourcing is defined and its optimal solution is determined via a detailed analysis. Furthermore, using computational experiments it is shown that the outsourcing strategy can have a great role to attain the problem objectives.
Scheduling Modeling
Morteza Farhadi Sartangi; Ali Husseinzadeh Kashan; Hassan Haleh; Abolfazl Kazemi
Abstract
Purpose: Order picking operation is one of the most well-known labor and cost intensive internal logistics processes. Withdrawal of the order in response to customer need is defined in order to collect a set of orders from storage zone in the shortest possible time. The purpose of this research is to ...
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Purpose: Order picking operation is one of the most well-known labor and cost intensive internal logistics processes. Withdrawal of the order in response to customer need is defined in order to collect a set of orders from storage zone in the shortest possible time. The purpose of this research is to provide a scientific and practical basis considering the constraints that enforce to achieve an acceptable level of performance in order picking systems. This is done by building a Mixed Integer Linear Programming (MILP) formulation and developing an adapted solution method suited to the structure of the problemMethodology: First, by reviewing the literature in the field of order picking systems, sufficient knowledge has been obtained at the operational level, and with emphasis on warehouse management constraints, a MILP formulation is proposed by integrating order batching and picker routing. After validating the model and solving it through GAMS software, due to the nature of the problem, which is an NP-hard type, the problem is solved with an efficient algorithm, which is a grouping version of the league championship algorithm, and the results are compared. To develop the algorithm, operators are fit to the specific structure of the problem, i.e., the assignment of orders (items) to order pickers (groups)Findings: Developing a multi-period MILP formulation for multi-trip picker routing, assuming for the first time the possibility of product replenishment and limited access to pickers. For large-scale problem instances, the league championship algorithm is used. The results indicate the effective capability and efficiency of this algorithm for solving large test problem instances.Originality/Value: The issue of multi-period order picking and multi-trip routing of pickers is considered for the first time in this paper. Because of the limited number of pickers, this must be taken into account in modeling. The assumption of product replenishment is also considered for the first time in this article and its modeling has been done. In this way, orders enter the warehouse over time, during different periods, and are placed in a predetermined positions. The limited access to pickers in each period is also discussed for the first time in this paper. Finally, the objective function of minimizing the total tardiness, which is in line with the needs of the industry, is also introduced in this paper. Regarding the solution method, a league championship metaheuristic algorithm is presented which takes into account the problem structure (which corresponds to the structure of grouping problems) and solution generation operators have been developed to maintain the new solution.
Scheduling Modeling
Habibeh Nazif; Khadijeh Ghaziani
Abstract
The timetable is the problem of placing particular resources due to constraints in a limited number of times lots and space, in order to satisfy a set of goals that is used to a variety of problems. Among these problems, one can point out the University Examination Timetabling Problem (UETP), which is ...
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The timetable is the problem of placing particular resources due to constraints in a limited number of times lots and space, in order to satisfy a set of goals that is used to a variety of problems. Among these problems, one can point out the University Examination Timetabling Problem (UETP), which is the particular importance in educational problems. The university examination timetabling problem defined as the assignment of a certain set of exams to a fixed number of time slots and rooms, so that it meets all the hard constraints, also soft constraints are optimized as much as possible. This research presents a modified approach to optimize the incapacitated UETP. In this approach, a proposed Genetic Algorithm (GA) is modified by local search operators. These operators will make alterations to the timetable. This involves shifting or changing scheduled exams and thus greatly improve the ability of the algorithm to search. The efficiency of the proposed approach is compared with other techniques from literature using the Carter’s benchmark. The computational results show that this approach is quite effective and competitive in improving the solutions and is able to produce better solutions in most of the datasets compared with other algorithms.
Scheduling Modeling
Mohsen Bagheri; Neda Babaei Meybodi; Amir Hossein Enzebati
Abstract
Energy consumption considerations in production systems have recently attracted the attention of researchers. In conventional production scheduling models, the importance has more often been given to time-related rather than to energy-related performance measures. In this paper, we simultaneously consider ...
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Energy consumption considerations in production systems have recently attracted the attention of researchers. In conventional production scheduling models, the importance has more often been given to time-related rather than to energy-related performance measures. In this paper, we simultaneously consider energy consumption, completion time and tardiness in the presented Multi-Objective Mixed Integer Programming flow shop scheduling model. After validating the model by solving small-scale numerical examples with Weighted Sum and Epsilon-constraint method in GAMS, the large and medium-scale examples are solved via NSGA-II and SPEA-II metaheuristic-algorithms. The results prove the efficiency of the proposed algorithms.