جستجوی مقالات مرتبط با کلیدواژه "زمان بندی" در نشریات گروه "مدیریت"

This paper aims to propose a flow shop scheduling problem for equipment overhaul. This problem consists of three stages, the separation of the components of an equipment is done in the first stage. Repairs and overhaul operations are carried out on the separated parts of the first stage in the second stage. Finally, the overhauled parts of the previous stage are mounted on each other in the third stage. In the third stage, operations are performed in parallel workshops. The objective function of the problem is the maximum time to complete jobs, and the sequence of processing jobs should be done in such a way that the value of the objective function is minimized.

Design/methodology/approach: 

To solve the problem, a mixed integer programming model has been proposed for small size, which determines the processing sequence of jobs based on the position of each job. A genetic algorithm has been used to solve the problem in large dimensions. By increasing the size of the problem and in different sizes, the results have been examined and analyzed, which shows the efficiency of the model and the proposed algorithm.

To check the accuracy of the model's performance and also the effect of the presence of parallel machines in the third stage, an example was presented in this paper. Accordingly, while the accuracy of the model's performance was checked, the effect of the presence of more machines was determined in the third stage. As the number of machines increased in the third stage, the value of the objective function did not deteriorate. The performance of GAMS in solving the problem in a small size was investigated. Considering that solving the problem for large dimensions is not possible in a reasonable time and the problem is NP-hard, then solving the problem in large dimensions was done using a genetic algorithm. Therefore, solving the problem on a large dimension has been done using the genetic algorithm. According to the obtained results, the efficiency of the genetic algorithm was shown. Due to its low average value, it indicated the convergence of the genetic algorithm.

Research limitations/implications:

 Considering that there are not many published papers in the field of equipment overhaul, it is difficult to access related models and papers. Therefore, in this paper, the model and solution method have been written with many reviews. Also, to check and reduce costs, the number of third-stage machines has been determined using sensitivity analysis.

Practical implications:

 The problem of equipment overhaul is used in many fields in reality. For example, the operations related to the maintenance, repair and overhaul of the aircraft engine have been investigated. Since the planning of maintenance and maintenance operations is difficult, the focus of research has been on improving maintenance operations by finding suitable scheduling for job shop operations in maintenance operations. They emphasized that scheduling can improve maintenance operations and presented a simulation model.

Social implications:

 The purpose of creating a space to start an activity is to reduce costs, earn money and achieve profit. To examine the cost-effectiveness of the equipment overhaul issue, we can refer to the number of third-stage machines. According to the amount of equipment to perform an overhaul on them, the number of third-stage machines can be determined. Therefore, extra machines can be removed to reduce the cost. On the other hand, if the related equipment to the customers is different, to reduce the storage costs or increase customer satisfaction, different goals should be considered. Here, the objective function of maximum completion time is considered for this purpose. If the equipment must be available at a certain time, goals such as the total time to complete the job can be considered. In line with the application of the social implications in the investigated problem in this paper and considering that the investment costs, as well as the ability to respond to the applicants' requests, are related to the number of third-stage machines, the value of the objective function is analyzed based on the number of third-stage machines and analysis has been done.

Originality/value: 

In this paper, a three-stage flow shop scheduling problem in the overhaul industry was studied. Accordingly, a new mathematical model based on the job processing position was proposed, which dealt with the exact solution of the problem in small dimensions. According to the type of problem in the overhaul environment, the combined flow shop problem for equipment overhaul was investigated. Also, the use of parallel machines in the third stage of the equipment overhaul problem is one of the new issues under investigation.

The scheduling of parallel machines and preventive maintenance is one of the key issues in the field of production processes, and has always been a topic of interest for researchers. This research aims to design an integrated model for scheduling and preventive maintenance for parallel machines considering the probability of disruption in facilities and uncertainty in parameters of the model. In this regard, a mathematical scheduling model has been proposed with two objective functions of minimizing the weighted completion time of products and maximizing the reliability of the production line. The NP-hard nature of the studied problem from a computational perspective, meta-heuristic algorithms such as NSGA-II and MOPSO were utilized to solve numerical problems in medium and large scales. Therefore, numerical problems were designed in different size and solved by the proposed algorithms. The results showed that the NSGA-II compared to the MOPSO algorithm provide better solutions. However, MOPSO has better efficiency than NSGA-II in term of computation time, this superiority is not considerable and it can not be considered as a definitive basis for comparing two algorithms.

In this paper a new two-phase method is presented for solving the curriculum based university course timetabling problem. In both phases of the new present method a new metaheuristic approach is used.

Methodology:

 In the first phase of the new method, a feasible high quality solution is computed. To this end, at first the hard constraints relating to the time periods are considered and a solution is computed that satisfies these hard constraints. In the next step, a new method is introduced for the assignment of rooms to courses, after application of which on the solution that satisfies the time period hard constraints, a feasible solution is computed. In the second phase, at first several new neighborhood functions are used to improve the quality of computed feasible solution. While the fitness function of the first phase is based on the violation of hard constraints, the fitness function of the second phase is based on the penalty of the feasible solution.

The numerical results indicate that the required computing time increases with the size of instances and the algorithm tends to converge towards the optimal solution after a few minutes.

Originality/Value: 

The presented algorithm enables us to deal with large university course timetabling problems in practice. Moreover, it provides us with an efficient way to obtain feasible solutions to such real-world instances and try to improve their quality.

Project scheduling with certain data has a long history of research. Meanwhile, the use of uncertain parameters is limited to PERT technique and some innovative algorithms for calculating project completion time, which are not capable of considering all types of uncertainties. In this research, we developed some mathematical models to calculate the critical path and the activities’ float under normal conditions and time-cost trade-off.; In addition to validity check of the models, we developed an algorithm to generate extreme scenarios based on the interval values of the input parameters. The developed algorithm is able to generate the optimal interval for the values of the decision variables in the project schedule. Solving the scheduling problem of a real construction project involving more than 80 activities showed that if the input parameters of the problem were interval numbers, the values of the output decision variables of the models would often be interval numbers with a less degree of uncertainty. The spread of uncertainty and risks in the project is often linear. This means that with an optimal plan for the project schedule, most of the estimated errors and risks in the project would cause predictable behavior in uncertain decision variables especially, activities’ float.

 Scheduling of batch production and machine disruption are the two main challenges in manufacturing organizations. Due to the complexity of production processes, many industries try to group jobs according to family criteria and use a common setup time to process every family. Also, machine breakdown is an influential factor in the planning of production systems. In this paper, the problem of scheduling a single machine with family setup times and breakdowns is studied. It is assumed that there is a breakdown with an uncertain start time and duration based on the specified probability distribution functions during the planning horizon. The objective function of this problem is the sum of the expected maximum earliness and maximum tardiness.

Design/methodology/approach:

 For the problem under study, a new mixed integer linear programming model has been developed. Due to the NP-hardness of the problem, a new branch and bound algorithm with the dominance rules and an efficient lower bound is presented for its optimal solving, which uses a new heuristic approach to achieve the upper bound.

 To evaluate the performance of the introduced algorithms, 2520 instances were designed and solved with the presented algorithms. The computational results indicated that 98% of the instances were optimally solved in the specified time limitation by the branch and bound algorithm, and the average percentage of deviation from the optimal solution in the proposed heuristic approach was less than 30%. The results demonstrated the efficiency of the proposed algorithms.

Research limitations/implications:

 Considering the newness of the problem investigated in this paper, the proposed instances and algorithms can be used as a basis for evaluating other solution methodologies in future research studies. Also, considering other modes of machine failures such as scenario-based failures or re-scheduling the jobs to minimize the deviations of the actual schedule from the planned program, situations with more than one machine such as parallel machines, flow shops, and job shops, other objective functions related to scheduling such as maximum completion time or total completion time, as well as the development of other exact, heuristic or meta-heuristic algorithms are suggested as subjects for future study.

Practical implications:

 The problem studied in this paper can be attractive and practical for manufacturing organizations. Industries such as automotive, ship and aircraft manufacturing, steel, telecommunication power supply manufacturing, electronic, computer processors, and all industries and systems that somehow deal with the batch production process and unexpected machine breakdowns, can benefit from the results of this research.

Social implications:

 Because in this study, the starting and finishing times of machine breakdowns were predicted, by applying the results of this research, the production of defective products will be prevented when the machine breaks down, and this leads to the reduction of waste in the environment. Also, according to the objective function defined in the problem investigated in this article, the implication of the results of this research in production environments leads to the reduction of earliness and tardiness costs, which in turn increases the work efficiency of human resources and as a result, increases the job satisfaction.

Originality/value: 

It seems no study has been conducted on the single machine scheduling problem with batch production, random breakdown, and the objective function of minimizing the sum of the expected maximum earliness and maximum tardiness of the jobs. Particularly, innovations of this paper are threefold: i) a new mixed integer linear programming model was developed for the problem; ii) a novel heuristic approach was proposed to solve the problem, based on hill climbing (PHC); and iii) a new branch and bound algorithm with the dominance rules and an efficient lower bound was presented to solve the problem optimally, which used the PHC heuristic approach to achieve the upper bound.

This paper sought to develop a food supply chain model that integrates the operational decisions (vehicle routing and scheduling) with strategic decisions (cross-docking centers locating) in a hub network, considering life - real constraints and the perishable nature.

In this research, an integer Goal programming model for location, timing, and vehicle routing problems is proposed with the possibility of split demand for fresh items in which the impact of perishability is considered as the second objective besides the total cost. Accordingly, an augmented -constraint method was used to generate a Pareto optimal for these conflicting objectives. This model was implemented in CPLEX software, 20.1 version.

Previous studies neither considered the perishable nature of the items in cross-docking locations nor the split delivery vehicle routing scheduling models. The most important aspect of innovation in this research was that the characteristics of split demand in improving the timing of vehicles were used and in addition to improving the cost function, the value of the second objective function (network accountability) was also increased dramatically. The results of sensitivity analysis on some parameters such as shelf life of products (SL), quality reduction point (QRP), and capacity of vehicles (Q), showed the efficiency of the proposed model.

Finally, the proposed model was utilized in random data and numerical results, and some managerial insights were provided. Comparing the results of the proposed model with the benchmark model in equal experimental conditions, the efficiency of the proposed model was confirmed. Cross-docking is nowadays used by many companies and industries and the provided model by this study can be applied especially for time-sensitive products.

The present study was carried out in order to evaluate and rank the indicators of urban transport sustainability using descriptive-analytical methodology and Delphi methods, best-worst and best priority. In order to achieve the indicators of sustainable transportation in three economic, social and environmental dimensions, the criteria were ranked based on library studies, and the opinion of experts, so as to finalize the criteria of great importance on the research issue to enter the Delphi method, and be used as the evaluation criteria to save time and the number of trips. The final criteria were entered into the weighting and ranking process using the best-worst method and the best priority method. The results of the two methods of best-worst and best priority were compared using statistical methods.Considering that the ranking of the criteria was the same in both methods and the Spearman correlation coefficient was +1, so there is a strong correlation between the criteria.Also, the Pearson correlation coefficient between weights was 0.989 and the significance level was zero, which demonstrate a strong relationship between variables. Therefore, ranking and weighting obtained from the two methods were accepted.

Cellular production is one of the important applications of group technology in production. With the development of modern industrial technology, many manufacturers use it as a solution to implement complex and realistic scenarios that increase the productivity and flexibility of a production system. Cellular production includes cell formation, cellular and intracellular arrangement, operation scheduling, and resource allocation. The process of formation and grouping of machines to produce families of parts to minimize the cost of moving materials among cells is called cell formation. In other words, cell formation in cell production systems and assignment of machine groups and family of parts to these cells is done to minimize the total cost and increase flexibility and productivity in production. The layout design is also related to the position of the cells relative to each other and the position of the machines in each cell relative to each other. In some production units, the placement of cells in relation to each other and even the placement of devices in each cell is not done properly, which increases the movement of materials, semi-finished parts, and consequently, production costs. On the other hand, with changes in customer needs and demand and competitive market conditions, the combination of existing cells and their arrangement in one period may not be appropriate for another period, and it is necessary to make changes to reply to customer needs and remain competitive. The possibility of making changes in cells combination, placement inside and between cells is called dynamic cell formation. In other words, dynamic cell formation involves changing the position of the cells relative to each other and the proper placement of the machines in one cell so that it is possible to move the machines to a new position or another cell and increase or decrease them.

Operation scheduling and assigning human resources incurring a notable proportion of expenses in the cell formation. These issues seem more important when financial resources are limited. In this research, dynamic cell formation problems based on scheduling, allocation of workers, and constraints of financial resources on machines and workers are simultaneously investigated, Accordingly, the present study seeks to minimize the total costs, including the costs of machines, workers, and transportation of parts. At first, a mathematical model was presented. The model was then linearized and validated. After that, a genetic algorithm was proposed to solve the problem where the parameters were adjusted and selected by using the Taguchi method. Sensitivity analysis was also performed based on the related parameters in constraints of financial resources of machines and workers.

The results showed the accuracy of the model and its validation. It was also shown that the proposed algorithm is highly efficient and can be used for medium and large-sized problems where it is not impossible to find the optimal solution.

Sensitivity analysis showed that the constraints of financial resources for purchasing machines have a greater impact on the objective function than workers' financial constraints, which is of high importance.

This paper deals with the problem of two-stage assembly flow shop scheduling with considering sequence-independent setup times. The objective is to minimize total completion times of all orders. In this problem, there are several orders for one type of product. Each ordered product is formed of several different parts. At first, the parts are manufactured in a flow shop stage with some different machines and then they are assembled into a final product on a single machine. This paper presents three meta-heuristic algorithms, namely Parallel Variable Neighborhood Search (PVNS), Artificial Immune Algorithm (AIA) and Simulated Annealing (SA), for solving under studied problem. The Taguchi experimental design method as an optimization technique is employed to tune different parameters and operators of presented algorithms. Also, Numerical experiments are used to evaluate the performance of the proposed algorithms. The results show that the PVNS algorithm performs better than the other algorithms.

In today's complex world and in order to increase competitiveness, planners in the manufacturing systems have focused on product distribution and collection of used products. In this paper, the closed-loop supply chain scheduling problem is investigated for the first time. A comprehensive and integrated model is presented for production scheduling, delivering products to retailers using limited-capacity vehicles, and pick-upping end of life products in order to recycle and reuse in supply chain. The aim of this problem is to minimize maximum tardiness. Due to the fact that this problem is NP-hard, a genetic algorithm is presented to solve the large-size instances by obtaining near-optimal solutions. To illustrate the importance of the problem under consideration, a case study of the motor oil supply chain is presented.

As nobody can prevent disasters like earthquake, tsunamis, hurricanes, floods and etc. and because of huge harmful effects of the events, in recent years, scientists mostly try to find the best way to control undesirable consequences of these events. According that Emergency decision making EDM is a way of emergency management to control the bad effects of these disasers. EDM used for rescue units that their mission is going to critical places and save the lives and properties, must be found location and allocated to places that crisis occur there. This paper presents two scenarios that each one contain multi objective decision support model to find a location to establishment of critical bases and allocate rescue units of primary nodes to each crisis. During this action three main objectives of these kind of problems considered. First goal of the (MILP) models for each scenarios is minimizing sum of all severity of crisis delays of rescue unit to reach the critical places. The second goal of the problem minimizes total arrival time rescue units at critical places. As the third goal the model minimizes total costs of this transportation. These three objectives ranked by priority. The difference between two scenarios is that first scenario crisis is predected and dosen't occured but in second scenario crisis existed by raid. Then to illustrate the efficiency of the model a numerical example solved by GAMS and results is showed.

In this paper, the problem of scheduling jobs with non-identical sizes has been studied on a single-batch processing machine, in order to minimize the makespan. Using new lower bounds, a branch and bound algorithm has been proposed to solve the problem. In this algorithm, two new methods have been used to generate lower bounds and results have been compared with the existing lower bound in literature. In order to evaluate the performance of the proposed method, test problems have been randomly generated and branch and bound algorithm has been tested with different lower bounds on these cases. Findings indicated that when the size of the jobs is large compared to the capacity of the machine, the branch and bound algorithm with the new lower bound has the best performance. When the size of the jobs is small compared to the capacity of the machine (up to half the capacity of the machine), the algorithm with existing lower bound has better performance. In addition, when the size of the jobs is neither large nor small, the lower bounds provide the best performance.

Based on predictions, services are a key component of the growth of the global economy in future (Arnold et al. 2011). Acording to Jane and Kumar (2012), services play a critical role in a supply chain. Also, according to Wang et al. (2015), a "product" or "service" must exist in each supply chain which is produced by the upstream sectors and delivered to downstream. Recently due to increasing customer expectations, companies’ competition has been replaced by the supply chains competition and as a result, competition has been increased in the simultaneous supply of products and services. This has led to challenges in integrating companies and in coordinating the materials, information and financial flow that were previously overlooked. Accordingly, a new managerial philosophy has been developed known as Product-Service Supply Chain (PSSC) (Stanley & Wisner, 2002). This study seeks to develop a performance evaluation model for the product-service supply chain in the home appliance industry, which is finally solved using Adaptive Neuro-Fuzzy Inference System (ANFIS).

In this paper, performance evaluation constructs and criteria of service supply chain are identified by reviewing the literature and exploratory and confirmatory factor analysis and then, the performance evaluation of service supply chains in Iran's home appliance industry has been performed using these constructs, criteria and ANFIS. Findings and

Based on the findings, ten main extracted constructs can be suggested for the performance evaluation of the supply chain. They include "Operational Performance (OP)", "Strategic Performance (SP)", "Financial Performance (FP)", "Performance of Information and Communication Technology (PICT)", “Return Performance” (REP), “Risk Performance (RIP)”, “Logistic Performance (LP)”, “Market Performance (MP)”, “Internal Structure Performance (PIS)” and “Growth and Innovation Performance (PGI)”, among which, the Strategic Performance (SP) and Return Performance (REP) are the most important and the least important constructs, respectively.

Based on the findings, the following practical recommendations are suggested to the companies: Enhancing the demand forecasts performance and utilizing more appropriate methods and software to improve forecasts in demand and order management areas. Improving the return management status by increased attention and more investment in return management processes. Effective investment in service development management to enhance the R&D services performance. Utilizing risk management approaches and methods to identify and take preventive actions on the risks in the companies’ service supply chain.

One of the most important issues in disaster response phase is to supply the relief items which is needed by affected areas. The uncertainty of this demand causes many problems. This paper presents a novel robust possibilistic programming model for a routing and scheduling problem in a relief commodities distribution network under demand uncertainty. In relief commodities distribution operations, the possibility of servicing each affected area by multiple vehicles and time window constraint have been considered. The objective of the proposed model is to reduce the total time required by the relief vehicles to reach the affected areas. The fourth region of Tehran city as a case study is provided to illustrate the performance and applicability of the proposed model. Finally, to assess the robustness of the solutions obtained by the novel robust optimization model, they are compared to those generated by the deterministic mixed-integer linear programming model in a number of realizations under different test problems.

In petrochemical companies, the existence of various, long job shifts can endanger the physical and mental health of employees, while having a proper timetable, in accordance with labor laws and policies, can help reduce the consequences of the disruption of their work shifts. The purpose of this research is to develop a model for timing the manpower of petrochemical companies in such a way that, by meeting the requirements of the company and the optimal number of employees in each shift, it can minimize the company's payment costs, and maximize the performance and preferences of the employees. The researchers used integer programming model. Because of the complexity of the issue and the multi-objective nature of the model, to solve the mathematical model, NSGA-II algorithm has been used. In order to obtain a better performance of the algorithm, its parameters were tuned using the Taguchi calibration method. Based on the findings from various scenarios, the 21-day working model is better than the 16-day working model. Since the model is multi-objective and is solved using the Pareto's approach, the decision maker can, according to the circumstances, choose one of the optimal Pareto solutions. Petrochemicals can apply scientific and optimum operation research approaches and its applications, in order to set up employee work schedules, create work-life balance, reduce work-related fatigue, decrease job burnout and improve their performance and productivity. Although scheduling and selecting the appropriate working model is complicated for petrochemical companies, NSGA-II algorithm can be used as an apt and powerful tool in decision making over choosing best working model.

The purpose of this research was to apply various flexibilities including device, tools, direction toward accessing the device (TAD) flexibilities, and considering the qualitative parameters based on the fuzzy inference system for integrated optimization of process planning and scheduling using the Constraint Programmingapproach. There are many approaches to solving IPPS problems. In this research, because of the multitude of existing variables and the complexity of the solution space, limited planning has been used to solve the problem. At first, the qualitative parameters of the model are calculated based on the fuzzy inferencing system and after providing other inputs and solving the problem using limited planning, an optimal answer will be obtained. To evaluate the efficiency of the integrated model, an example in the literature considering three states of short, medium and long due date time, has been solved using IBM ILOG Cplex optimization studio software. The results indicated the proper functioning of the limited planning method to obtain optimal solutions in a limited time. In fact, the results of the numerical experiments showed that the proposed model has acceptable performance and the proposed algorithm can efficiently solve IPPS. Finally, we can conclude that it is a very suitable method for integrated optimization of multiple objectives.

The success in crisis management is reached when organizations consider preparedness as well as prevention.
This necessitates the organizations to adopt enhanced management principles, specifically project management
principles. It is well known that the implementation of preventive programs and projects of crisis management
are among the most vital measures to reduce crisis occurrence and its consequences. However, on the other
hand, resources of organizations and responsible authorities are limited and rational management seeks maximum
exploitation of these limited resources to counteract crisis. In this paper, the aim is feasibility study and optimization
of programs and projects of crisis management is such a way that not only allocated resources are sufficient,
but also the goals of crisis management are met satisfactorily. To this end, the optimization approach based on
mathematical programming is used. Two optimization models of crisis management including (1) optimization
of a combination of crisis management projects and (2) optimization of scheduling of crisis management projects
are proposed. The first model seeks selection of the best combination of crisis management projects from a list of
potential projects. While the second model suggests the best schedule of projects implementation. Both models
act in such a way that constraints on required resources and the logical relationship among projects are met, and
moreover, the cost of projects are minimized and goals of crisis management are reached. These models, as a consulting
system, help managers to adopt the best decision concerning crisis projects. Furthermore, the suggested
models are executed on a case study and the results are analyzed

Timing of curriculum planning for students and faculty could be done using diverse methods. The present research concerns with curriculum planning for professors considering the student's opinions. In doing so, the courses and the timing are determined based on the professor's common timetable, the professor's intensive courses timing and the class limitations. To achieve this goal, the genetic algorithm methodology was used in two steps. In the first stage, single-point cutting operator was used and in the second stage of the algorithm, a new intelligent operator called cyclic reverse list (RIL) was used provided that gold, silver and bronze time types were used for different courses. The advantages of this algorithm are using a new appropriate function (hot rolled), as well as new criteria and a new operator (RIL). Unlike conventional methods, in this method the appropriateness is considered in proportion with the whole population and we try to remove the impossible solutions. The optimal solution is chosen from among a multitude of provided responses. Therefore, it was found that we can reach the optimal solutions with regard to a better appropriateness.