فهرس المقالات Job shop

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  1 - A Job Shop Scheduling and Location of Battery Charging Storage for the Automated Guided Vehicles (AGVs)
  Saeed Dehnavi-Arani Ali Sabaghian Mehdi Fazli
  10.22094/joie.2018.543203.1511
  When the Automated Guided Vehicles (AGVs) are transferring the parts from one machine to another in a job shop environment, it is possible that AGVsstopon their guidepaths since their batteries are discharged.Consequently, it is essential to establish at least one Batte أکثر

  When the Automated Guided Vehicles (AGVs) are transferring the parts from one machine to another in a job shop environment, it is possible that AGVsstopon their guidepaths since their batteries are discharged.Consequently, it is essential to establish at least one Battery Charging Storage (BCS) to replace full batteries with empty batteries for the stopped AGVs. Due to non-predictable routes for AGVs in the manufacturing systems, to find the best place toestablish the BCS can impact performance of the system. In this paper, anintegrated mathematical modelof job shop and AGV schedulingwith respect tothe location of a BCS is proposed. The proposed nonlinear model is transformed into a linear form to beefficiently solvedin GAMS software. Finally, several numerical examplesare presented to test the validity of the proposed mathematical model.The results show that the optimal cost and location of BCS can be obtained with respect to the number of AGVs, machines, parts, and other problem parameters. In addition, it is concluded that the increasing number of AGVs in a manufacturing systemcannot be always a suitable policy for reducing the cost because in such conditions.Further to that, the conflict of AGVs may increase leading tothe increase of the makespan. In other words, following the optimal point, increasing AGVs leads to the increase in costs. تفاصيل المقالة
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  2 - Modelling and solving the job shop scheduling Problem followed by an assembly stage considering maintenance operations and access restrictions to machines
  Seyed Mohammad Hassan Hosseini
  10.22094/joie.2018.760.1484
  This paper considers job shop scheduling problem followed by an assembly stage and Lot Streaming (LS). It is supposed here that a number of products have been ordered to be produced. Each product is assembled with a set of several parts. The production system includes t أکثر

  This paper considers job shop scheduling problem followed by an assembly stage and Lot Streaming (LS). It is supposed here that a number of products have been ordered to be produced. Each product is assembled with a set of several parts. The production system includes two stages. The first stage is a job shop to produce parts. Each machine can process only one part at the same time. The second stage is an assembly shop that contains several parallel machines. Maintenance operations and access restrictions to machines in the first stage are also considered. The objective function is to minimize the completion time of all products (makespan). At first, this problem is described and modelled as a mixed integer linear programming, and GAMS software is applied to solve small-sized problems. Since this problem has been proved to be strongly NP-hard, two new algorithms based on GA and SA are developed to solve the medium- and large-sized problems. In order to verify the effectiveness of the proposed algorithms, a statistical analysis is used along with Relative Percentage Deviation (RPD) factor and well-known criterion. IMP. Various problems are solved by the proposed algorithms. Computational results reveal that both of the two proposed algorithms have good performance. However, the method based on the genetic algorithm performs better than the other proposed algorithm with respect to the objective functi تفاصيل المقالة
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  3 - Developing and solving the multi-objective flexible and sustainable job shop scheduling problem with reverse flow and job rotation considerations in uncertain situations
  Arsalan Shojaei Davood Jafari Mehran Khalag Parshang Dokohaki
  10.22094/joie.2023.1962418.1976
  Flexible job shop scheduling problem (FJSP) has received a lot of attention in recent years, but the important point is that this field of study can be subject to many assumptions and lots of innovations can be considered. One of these can be reverse flow, which has bee أکثر

  Flexible job shop scheduling problem (FJSP) has received a lot of attention in recent years, but the important point is that this field of study can be subject to many assumptions and lots of innovations can be considered. One of these can be reverse flow, which has been overlooked in many studies, while its effect on the cost and time of construction is undeniable. Other areas such as job rotation as well as issues related to sustainability can be of particular importance in this area and have not been reviewed in previous researches. Therefore, the present study seeks to provide a model to optimize the multi-objective flexible job shop scheduling problem concerning the issues of sustainability with reverse flow and job rotation considerations. For this purpose, a multi-objective mathematical scheduling model is developed, the first goal of which is to minimize the construction time and the second goal is to minimize the issues related to sustainability. To solve the model, two methods were used: Sensitivity analysis and meta-heuristic. The whale optimization algorithm (WOA) was employed in the meta-heuristic method. The results of the implementation of WOA indicate the efficiency of the proposed algorithm, while the findings of the sensitivity analysis also point to the effect of research innovations on the objective functions of the problem. تفاصيل المقالة
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  4 - A Simulated Annealing Algorithm for Multi Objective Flexible Job Shop Scheduling with Overlapping in Operations
  Mehrzad Abdi Khalife Babak Abbasi Amirhossein Kamali Dolat abadi
  In this paper, we considered solving approaches to flexible job shop problems. Makespan is not a good evaluation criterion with overlapping in operations assumption. Accordingly, in addition to makespan, we used total machine work loading time and critical machine work أکثر

  In this paper, we considered solving approaches to flexible job shop problems. Makespan is not a good evaluation criterion with overlapping in operations assumption. Accordingly, in addition to makespan, we used total machine work loading time and critical machine work loading time as evaluation criteria. As overlapping in operations is a practical assumption in chemical, petrochemical, and glass industries, we used simulated annealing algorithm for multi-objective flexible job shop scheduling problem with overlapping in operations to find a suitable solution. To evaluate performance of the algorithm, we developed a mixed integer linear programming model, and solved it with the classical method (branch and bound). The results showed that in small size problems, the solutions of the proposed algorithm and the mathematical model were so close, and in medium size problems, they only had lower and upper bounds of solution and our proposed algorithm had a suitable solution. We used an experimental design for improving the proposed algorithm. تفاصيل المقالة
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  5 - A New Approach in Job Shop Scheduling: Overlapping Operation
  parviz Fattahi mohammad Saidi Mehrabad
  In this paper, a new approach to overlapping operations in job shop scheduling is presented. In many job shops, a customer demand can be met in more than one way for each job, where demand determines the quantity of each finished job ordered by a customer. In each job, أکثر

  In this paper, a new approach to overlapping operations in job shop scheduling is presented. In many job shops, a customer demand can be met in more than one way for each job, where demand determines the quantity of each finished job ordered by a customer. In each job, embedded operations can be performed due to overlapping considerations in which each operation may be overlapped with the others because of its nature. The effects of the new approach on job shop scheduling problems are evaluated. Since the problem is well known as NP-Hard class, a simulated annealing algorithm is developed to solve large scale problems. Moreover, a mixed integer linear programming (MILP) method is applied to validate the proposed algorithm. The approach is tested on a set of random data to evaluate and study the behavior of the proposed algorithm. Computational experiments confirmed superiority of the proposed approach. To evaluate the effect of overlapping considerations on the job shop scheduling problem, the results of classical job shop scheduling with the new approach (job shop scheduling problem with overlapping operations) are compared. It is concluded that the proposed approach can improve the criteria and machines utilization measures in job shop scheduling. The proposed approach can be applied easily in real factory conditions and for large size problems. It should thus be useful to both practitioners and researchers. تفاصيل المقالة
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  6 - A Comparison of Job-shop and Group Technology Using Simulation by ARENA
  Siyavash Khaledan Hadi Shirouyehzad
  Production planning is performed through diverse methods according to the type of the system it is structured upon. One of the most important steps before production planning is to determine which system best fits the firm, and how the facilities should be designed. Bot أکثر

  Production planning is performed through diverse methods according to the type of the system it is structured upon. One of the most important steps before production planning is to determine which system best fits the firm, and how the facilities should be designed. Both job-shop and group-technology systems have their own cons and pros, each of which is suitable to a specific kind of factory. On the other hand, performance measurement is also important in terms of both productivity and queue factors. A good method to measure the performance is computer simulation by software such as ARENA. This paper utilizes the software for simulating both job-shop and group-technology systems separately for a special firm and it consequently compares the results. The results show group-technology system is better than job-shop system in both productivity and queue factors, and it is highly recommended the system be changed. تفاصيل المقالة
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  7 - A Comparison of Job-Shop and Group Technology Using Simulation by ARENA
  Siyavash Khaledan Hadi Shirouyehzad
  Production planning is performed through diverse methods according to the type of the system it is structured upon. One of the most important steps before production planning is to determine which system best fits the firm, and how the facilities should be designed. Bot أکثر

  Production planning is performed through diverse methods according to the type of the system it is structured upon. One of the most important steps before production planning is to determine which system best fits the firm, and how the facilities should be designed. Both job-shop and group-technology systems have their own pros and cons, each of which is suitable to a specific kind of factory. On the other hand, performance measurement is also important in terms of both productivity and queue factors. A good method to measure the performance is computer simulation by soft wares such as ARENA. This paper utilizes the software for separately simulating both the job-shop and group-technology systems for specific firm, and then compares the results. The results show that the group-technology system is better than the job-shop system in both productivity and queue factors, and it is highly recommended that the system should be changed. تفاصيل المقالة
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  8 - Solving Flexible Job-Shop Scheduling Problem using Hybrid Algorithm Based on Gravitational Search Algorithm and Particle Swarm Optimization
  Behnam Barzegar Homayun Motameni
  Job Shop scheduling problem has significant importance in many researching fields such as production management and programming and also combined optimizing. Job Shop scheduling problem includes two sub-problems: machine assignment and sequence operation performing. In أکثر

  Job Shop scheduling problem has significant importance in many researching fields such as production management and programming and also combined optimizing. Job Shop scheduling problem includes two sub-problems: machine assignment and sequence operation performing. In this paper combination of particle swarm optimization algorithm (PSO) and gravitational search algorithm (GSA) have been presented for solving Job Shop Scheduling problem with criteria of minimizing the maximal completion time of all the operations, which is denoted by Makespan. In this combined algorithm, first gravitational search algorithm finds best mass with minimum spent time for a job and then particle swarm Optimization algorithm is performed for optimal processing all jobs.experimental results show that proposed algorithm for solving job shop scheduling problem, especially for solving larger problem presents better efficiency. Combined proposed algorithm has been named GSPSO. تفاصيل المقالة
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  9 - Solving the flexible job shop problem by hybrid metaheuristics-based multiagent model
  Houssem Eddine Nouri Olfa Belkahla Driss Khaled Ghe´dira
  The flexible job shop scheduling problem (FJSP) is a generalization of the classical job shop scheduling problem that allows to process operations on one machine out of a set of alternative machines. The FJSP is an NP-hard problem consisting of two sub-problems, which a أکثر

  The flexible job shop scheduling problem (FJSP) is a generalization of the classical job shop scheduling problem that allows to process operations on one machine out of a set of alternative machines. The FJSP is an NP-hard problem consisting of two sub-problems, which are the assignment and the scheduling problems. In this paper, we propose how to solve the FJSP by hybrid metaheuristics-based clustered holonic multiagent model. First, a neighborhood-based genetic algorithm (NGA) is applied by a scheduler agent for a global exploration of the search space. Second, a local search technique is used by a set of cluster agents to guide the research in promising regions of the search space and to improve the quality of the NGA final population. The efficiency of our approach is explained by the flexible selection of the promising parts of the search space by the clustering operator after the genetic algorithm process, and by applying the intensification technique of the tabu search allowing to restart the search from a set of elite solutions to attain new dominant scheduling solutions. Computational results are presented using four sets of well-known benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach. تفاصيل المقالة
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  10 - An algorithm for multi-objective job shop scheduling problem
  Parviz Fattahi Mohammad Saidi Mehrabad Mir B. Aryanezhad
  Scheduling for job shop is very important in both fields of production management and combinatorial op-timization. However, it is quite difficult to achieve an optimal solution to this problem with traditional opti-mization approaches owing to the high computational com أکثر

  Scheduling for job shop is very important in both fields of production management and combinatorial op-timization. However, it is quite difficult to achieve an optimal solution to this problem with traditional opti-mization approaches owing to the high computational complexity. The combination of several optimization criteria induces additional complexity and new problems. In this paper, we propose a Pareto approach to solve multi-objective job shop scheduling. The objective considered is to minimize the overall completion time (makespan) and total weighted tardiness (TWT). An effective simulated annealing algorithm based on proposed approach is presented to solve multi-objective job shop scheduling problems. An external memory of non-dominated solutions is considered to save and update the non-dominated solutions during the problem solving process. The parameters in the proposed algorithm are determined after conducting a pilot study. Numerical examples are used to evaluate and study the performance of the proposed algorithm. تفاصيل المقالة
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  11 - Flexible job shop scheduling under availability constraints
  M.T Taghavi-Fard H.R Dehnar Saidy
  In this paper, an exact geometric algorithm is presented for solving two-job sequencing and scheduling problems in flexible flow shop and job shop environments while the resources are (un)available in some time periods and processors (un)availability is the same in all أکثر

  In this paper, an exact geometric algorithm is presented for solving two-job sequencing and scheduling problems in flexible flow shop and job shop environments while the resources are (un)available in some time periods and processors (un)availability is the same in all work centers. This study seems utterly new and it is applicable to any performance measure based on the completion time. The investigated models are very close to the actual scheduling problems, because they envisage the flexible job shop environments, heads, set-up times, arbitrary number of unavailability periods on all resources, arbitrary number of work-centers, any kind of cross-ability, any kind of resume-ability and several types of performance measures. The proposed model is presented to solve two-job problems because it is a graphical approach. However, it is concluded that the idea can be extended to n-dimensional problems as well. تفاصيل المقالة
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  12 - An integrated approach for scheduling flexible job-shop using teaching–learning-based optimization method
  Raviteja Buddala Siba Sankar Mahapatra
  In this paper, teaching–learning-based optimization (TLBO) is proposed to solve flexible job shop scheduling problem (FJSP) based on the integrated approach with an objective to minimize makespan. An FJSP is an extension of basic job-shop scheduling problem. There أکثر

  In this paper, teaching–learning-based optimization (TLBO) is proposed to solve flexible job shop scheduling problem (FJSP) based on the integrated approach with an objective to minimize makespan. An FJSP is an extension of basic job-shop scheduling problem. There are two sub problems in FJSP. They are routing problem and sequencing problem. If both the sub problems are solved simultaneously, then the FJSP comes under integrated approach. Otherwise, it becomes a hierarchical approach. Very less research has been done in the past on FJSP problem as it is an NP-hard (non-deterministic polynomial time hard) problem and very difficult to solve till date. Further, very less focus has been given to solve the FJSP using an integrated approach. So an attempt has been made to solve FJSP based on integrated approach using TLBO. Teaching–learning-based optimization is a meta-heuristic algorithm which does not have any algorithm-specific parameters that are to be tuned in comparison to other meta-heuristics. Therefore, it can be considered as an efficient algorithm. As best student of the class is considered as teacher, after few iterations all the students learn and reach the same knowledge level, due to which there is a loss in diversity in the population. So, like many meta-heuristics, TLBO also has a tendency to get trapped at the local optimum. To avoid this limitation, a new local search technique followed by a mutation strategy (from genetic algorithm) is incorporated to TLBO to improve the quality of the solution and to maintain diversity, respectively, in the population. Tests have been carried out on all Kacem’s instances and Brandimarte's data instances to calculate makespan. Results show that TLBO outperformed many other algorithms and can be a competitive method for solving the FJSP. تفاصيل المقالة