scheduling

Production planning, scheduling, and sequencing form the core functions of manufacturing companies. The evolving and fluctuating market demands have turned production into a challenge, as companies must deliver high-quality products using minimal resources while responding to uncertain market demands. Therefore, the need for efficient production planning, scheduling, and sequencing has become a crucial research area for both companies and researchers in recent decades. This paper addresses the modeling and solution of a production planning and scheduling problem related to human-robot collaboration under fuzzy conditions. The proposed model aims to determine optimal decisions such as production quantity, human-robot allocation for product manufacturing on each line, processing time, and product production scheduling. To achieve integrated decisions for production planning and scheduling in human-robot collaboration, three objective functions are considered: maximizing the net present value, minimizing the maximum completion time of product manufacturing, and minimizing the total early and tardy times.

Since demand quantity and processing time are considered uncertain parameters in this problem, a pessimistic fuzzy programming approach is used to handle these parameters. To solve the three-objective model, the epsilon-constraint method, the Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-objective Particle Swarm Optimization (MOPSO), and Multi-objective Whale Optimization Algorithm (MOWOA) are applied. Thus, for solving the problem in small sizes and performing sensitivity analysis of the mathematical model, the epsilon-constraint method is used, while for solving larger-sized problems, metaheuristic algorithms are employed.

The analysis of the mathematical model under uncertainty reveals that reducing the maximum completion time of product manufacturing decreases both the net present value and the total early and tardy times. Controlling the model using fuzzy programming and the uncertainty rate also shows that increasing this parameter leads to a reduction in net present value and an increase in the maximum completion time of product manufacturing. Furthermore, analyzing various numerical examples of different sizes indicates that the solution quality of the MOWOA, NSGA-II, and MOPSO algorithms is superior to that of the epsilon-constraint method. Among these algorithms, MOWOA achieves the highest number of efficient solutions with the smallest branch distance metric and the shortest distance from the ideal point.

The analyses indicate that the highest total early and tardy times occur when the uncertainty rate is set at 0.5. Additionally, sensitivity analysis of the bank interest rate shows that a 4% increase in the interest rate results in a 15.68% reduction in the net present value. The bank interest rate has no impact on the maximum completion time of product manufacturing or the total early and tardy times. The analysis of numerical examples with various sizes also demonstrates that the epsilon-constraint method is incapable of solving larger numerical examples, and the quality of the results obtained from metaheuristic algorithms is superior to that of the exact method. Moreover, the number of efficient solutions, the widest spread, and the solution time are better in the metaheuristic algorithms than in the epsilon-constraint method. Among the metaheuristic algorithms, MOWOA exhibits superior performance compared to other solution methods.

In this research, a model has been proposed to optimize the transportation route. By considering different customers, different demands, considering different distribution points, considering different products and, different production process time of products, the current model has been able to examine the topic of vehicle routing from different aspects. These items were not available in previous studies, and other models have only generally increased profits. After solving the model by two methods, the results have shown that the planning done for vehicle routing has made a significant contribution to reducing operating costs, the route traveled, and at the same time reducing the consumption of fossil fuels. that the delivery of the consignment to the customers was within the time limit requested by the customers and exactly according to the time limit, and the products were delivered to the customers before the end of the expected deadline. Therefore, customer demand has been delivered to customers in the shortest possible way, at the lowest cost, with the lowest emissions and without shortages. Also, the results of the exact and meta-heuristic solution algorithms have shown the high adaptation of the modeling and the limitations of the model; In such a way that all the limitations of the proposed system have become feasible and the problem has been able to achieve an optimal answer in the desired period of time.

The issue of scheduling flow shop is always an important issue in all industries and factories, which undergoes fundamental changes with the emergence of different paradigms. This article aimed to analyze the problem of scheduling and determining the sequence of tasks in production systems with flow shop according to the components of the industry4.0. For this purpose, a data-driven model and its integration with meta-heuristic hybrid algorithms are presented to solve the problem. In the first step the problem model is designed and to deal with uncertainty the data-driven robust optimization approach has been used for the first time in flow shop problems. The important parameters of the model were estimated using SARIMA and SVR algorithms, and then the problem model was solved using hybrid algorithms, and the findings showed that LP-GA-SA algorithm has the best performance. The main innovation of this article is to present a data-driven optimization approach and use the SVR algorithm in parameter estimation and investigate the impact of industry4.0 on flow shop optimization. The findings show that the use of robotics and AI from Industry 4.0 in the flow shop will improve the execution time and costs in the long run. The two main issues that Industry 4.0 directly affects the workshop flow are the learning coefficient and the deterioration rate. The increase in the learning coefficient that is obtained due to the use of Industry 4.0 technologies improves all the target functions. It also minimizes the deterioration effect, which again improves the target functions.

There is a direct relationship between the number of machinery in any industrial problem, their costs of purchase,  and the space they occupy. Accordingly, due considerations about the constraints are required in space and budget. Workshop production problems under uncertainty conditions including more than two pieces of machinery are regarded as NP-hard. Therefore, using common research in operation techniques, one cannot embark upon modelling and finding the optimal answer for such problems. Hence, this study aims to propose a conceptual mathematical model for the problem of industrial production. The research problem involves cost limitations while working out the solution through simulation studies.

Design/methodology/approach: 

First a mathematical model has been proposed for workshop production with cost and space constraints. Then by enlisting the help of Arena software, 14 simulation models have been designed for machine ‘m’ and work ‘n’ of different production sequences. Finally, by solving a numerical model example, the problem of nearing the optimal condition of the problem, considering minimizing the objective function of the tardiness and earliness costs, has been resolved. This measure has been adopted to determine the optimal number of machinery, considering work prioritization laws in work cells by executing the model and scatter search algorithm and designing relevant experiments.

Solving a numerical example considering the default number of machinery and the LPT optimization method resulted in the best answer when the total tardiness and earliness costs were intended. In contrast, in the case of increasing the number of machineries, both the LPT (longest process time) and CR (critical ratio) methods led to the best answers with the help of dispersed search algorithms in simulation studies. The results included the cost and space constraints.

Research implications:

 One of the prominent features of simulation is that with the minimum distance between the problem and the real world (not removing the limitations and complexities for solving the problem, which is usually done by removing the facts in other problem-solving methods, to reduce the complexity and the possibility of solving the problem) problem-solving and decision-making scenarios are applied. This helps managers in decision-making. Most of the time, the physical study of systems is very difficult and costly, or even impossible; therefore, an alternative model should be used to study the system.

Originality/value: 

A careful investigation of previous research and relevant literature reveals that none of the studies in the literature have simultaneously dealt with the problem of the number of machinery in conditions of cost and space limitations. Therefore, the present study incorporated the mentioned constraints in the scheduling problems and adopted simulation problem-solving methods. It resulted in delving and enriching the subject further.

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.

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.

Cross Dock operation an execution tactic to deliver cargoes with an aggregation-consolidation approach in such a way that the logistics system faces minimal inventory accumulation or lack of inventory execution. Accordingly in order to increase agility at the supply chain level, logistics engineers are designing and implementing cross docking at the supply chain level. Logistics executives are always striving to find solutions that improve speed and improve supply chain safety. Observing all of these indicators can provide a lean supply chain. Cross Dock as an intermediary warehouse can play an important and influential role in improving the flow of raw materials, products and logistics services. The main purpose of this paper is to present a mathematical model of a trailer scheduling approach with a stochastic planning approach to provide the optimal solution to reduce the expected total logistics operation time and thus reduce logistics costs. These start from the stage of receiving pallets (suppliers) from suppliers or warehouses of other logistics cluster sites at the supply chain level and, following internal logistics processes performed on inlet docks, by reloading in outlet docks. Dock will be completed to fulfill the orders of consumers of these consignments. The mathematical model used is integer programming and genetic algorithm and MATLAB software was used to analyze data.

 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.

Home Healthcare provides a wide range of home care services from prevention to rehabilitation and primary care to professional nursing services. This research presents a two-objective mathematical model based on the mixed integer linear programming approach for the home healthcare routing and scheduling problem (HHCRSP) with the objectives of minimization the nurses’ travel costs and the maximal working time difference among nurses. Considering the multimodal transportation, several initial health centers and one final health center and patient and nurse time windows are important features of the studied mathematical model. Small-sized problems have solved by using the Ɛ-constraint method on GAMS software. Also, due to the NP-hardness of the problem, MOPSO and NSGA-II algorithms have used to solve the medium and large-sized problems. The statistical results showed that the NSGA-II performed better than the MOPSO for medium and large sizes of problems in both MID and NOS performance metrics. Overall, Results of the performance metrics on different sizes of problems indicate the efficient and effective performance of NSGA-II in solving the understudied problem.

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.