Parallel Production Lines Research Articles

Developing a tactical decision-making framework for a sustainable egg supply chain considering switchable parallel machines

ABSTRACT The increasing world population has increased food supply needs in recent years. Despite the numerous studies in the field of food supply chains, there are few articles on optimizing a sustainable egg supply chain. A fuzzy multi-objective mathematical model is presented to optimize a sustainable egg supply chain in a tactical decision structure. Switchable green packaging and labeling machines in parallel production lines, suppliers with different reliability rates and the provision of raw materials with different qualities have been studied in this article. Despite the significant reduction of the total costs while using switchable machines, the majority of the costs are still related to raw materials so that an increase of 2 to 4 times in the amount of input materials can change the values of objective functions by 23% to 60%. By using the unused capacity of the packaging and labeling machines, the total costs can be reduced significantly.

A digital twin-driven flexible scheduling method in a human-machine collaborative workshop based on hierarchical reinforcement learning.

Under the influence of the global COVID-19 pandemic, the demand for medical equipment and epidemic prevention materials has increased significantly, but the existing production lines are not flexible and efficient enough to dynamically adapt to market demand. The human-machine collaboration system combines the advantages of humans and machines, and provides feasibility for implementing different manufacturing tasks. With dynamic adjustment of robots and operators in the production line, the flexibility of the human-machine collaborative production line can be further improved. Therefore, a parallel production line is set up as a parallel community, and the digital twin community model of the intelligent workshop is constructed. The fusion and interaction between the production communities enhance the production flexibility of the manufacturing shop. Aiming at the overall production efficiency and load balancing state, a digital twin-driven intra-community process optimization algorithm based on hierarchical reinforcement learning is proposed, and as a key framework to improve the production performance of production communities, which is used to optimize the proportion of human and machine involvement in work. Finally, taking the assembly process of ventilators as an example, it is proved that the intelligent scheduling strategy proposed in this paper shows stronger adjustment ability in response to dynamic demand as well as production line changes.

Collaborative scheduling of machining-assembly in complex multiple parallel production lines environment considering kitting constraints

In multi-stage machining-assembly production, collaborative scheduling for multiple production lines can effectively improve the execution efficiency of production planning and increase the effective output of the production system. In this paper, a production scheduling mathematical model was constructed for the collaborative scheduling problem of machining-assembly multi-production lines with kitting constraints, with the optimization objectives of minimizing assembly completion time and tardiness time. For the scheduling model, the product assembly process is constrained by the machining sequence of the jobs on the machining lines. Only by collaborating on the production scheduling schemes of the machine line and the assembly line as a whole can the output efficiency of the product on the assembly line be improved. An improved hybrid multi-objective optimization algorithm named SMOEA/D is designed to solve this scheduling model. The algorithm uses adaptive parents’ selection and mutation rate strategies and integrates the Tabu search strategy for the search process in the solution space when the solution of the sub-problem has not been improved after specified search generations, to improve the local search ability and search accuracy of MOEA/D algorithm. To verify the performance of the SMOEA/D algorithm in solving machining-assembly collaborative scheduling problems in production systems with different resource configurations and scales, two sets of numerical experiments were designed, corresponding to situations where the number of operations on each production line is equal or unequal. The running results of the proposed algorithm were compared with three other well-known multi-objective algorithms. The comparison results indicate that the SMOEA/D algorithm is effective and superior for solving such problems.

A multi‐objective hybrid evolutionary search algorithm for parallel production line balancing problem including disassembly and assembly tasks

AbstractThis paper proposes a multi‐objective hybrid evolutionary search algorithm to simultaneously optimize the number of workstations, the idle index and the quantity of the production equipment required for the parallel production line balancing problem including disassembly and assembly tasks. The proposed algorithm uses the crossover operation and the mutation operation to generate neighborhood individuals of the current evolutionary population. Based on the mixed population composed of the current evolutionary population and the newly generated neighborhood population, a novel population update method is developed. The individuals of the new generation population are composed of Pareto dominant individuals filtered from the mixed population and several individuals randomly selected from the dominated individuals. Then the local search strategy based on simulated annealing operation is applied to the new generation of population individuals to overcome the defect of premature convergence of the proposed algorithm. By solving test examples of different scales and comparing them with a variety of classical algorithms, the advantages in the aspects of the diversity, the convergence, and the distribution of the solution results of the proposed algorithm are proved. Finally, the established mathematical model and the developed algorithm are applied to the parallel production line designed for the bogie remanufacturing production line of a railway freight car maintenance company. The advantages of the parallel production line in improving the production efficiency and reducing the production cost are identified.

Agent-based modelling in capacitated lot sizing problem with sequence dependent setup time

Setups are indispensable in production, but consume substantial amount of productive time. It is vital to consider sequence dependent setup times for determining production lots to satisfy demand for diversified product types on parallel production lines. Optimisation is the most used technique to generate detailed schedules for such sequencing problems. But the feasibility of the solution is not guaranteed under uncertainty conditions. Thus, evaluating the solution configurations under uncertainty is necessary to confirm feasibility. In this paper, an agent-based, discrete event simulation technique is used to develop a flow-shop model which faces demand for multiple product varieties, and has sequence dependent setup time. The simulation aims at evaluating various results in the solution space of an optimisation model to check their feasibility under various uncertain conditions in the form of setup time, processing time, and demand. Based on the results obtained from this optimisation-simulation model, it was observed that uncertainty influences cost parameters such as overtime cost, holding cost, and lost sales.

Scheduling Multiple Groups of Jobs for a Multi-Line Steel Hot Rolling Mill

This work addresses the optimal scheduling for a multi-line steel hot rolling mill with two parallel production lines. Groups of products are manufactured at one of these lines in an alternate sequence. An optimal schedule minimizes the sum of the required setup times between all products and optimally fills up retooling times necessary at one line by processing a group of products at the other line. This requires both sequence optimization and, for some groups, the optimal selection of jobs.The optimization problem is a combination of traveling salesman and orienteering problems and considers all groups simultaneously. To iteratively solve the combined problem, an algorithm consisting of a heuristic simulated annealing and a local search procedure is employed. The effectiveness of the algorithm is analyzed in a case study.

Modeling and Optimization for Multi-Objective Nonidentical Parallel Machining Line Scheduling with a Jumping Process Operation Constraint

This paper investigates the nonidentical parallel production line scheduling problem derived from an axle housing machining workshop of an axle manufacturer. The characteristics of axle housing machining lines are analyzed, and a nonidentical parallel line scheduling model with a jumping process operation (NPPLS-JP), which considers mixed model production, machine eligibility constraints, and fuzzy due dates, is established so as to minimize the makespan and earliness/tardiness penalty cost. While the physical structures of the parallel lines in the NPPLS-JP model are symmetric, the production capacities and process capabilities are asymmetric for different models. Different from the general parallel line scheduling problem, NPPLS-JP allows for a job to transfer to another production line to complete the subsequent operations (i.e., jumping process operations), and the transfer is unidirectional. The significance of the NPPLS-JP model is that it meets the demands of multivariety mixed model production and makes full use of the capacities of parallel production lines. Aiming to solve the NPPLS-JP problem, we propose a hybrid algorithm named the multi-objective grey wolf optimizer based on decomposition (MOGWO/D). This new algorithm combines the GWO with the multi-objective evolutionary algorithm based on decomposition (MOEA/D) to balance the exploration and exploitation abilities of the original MOEA/D. Furthermore, coding and decoding rules are developed according to the features of the NPPLS-JP problem. To evaluate the effectiveness of the proposed MOGWO/D algorithm, a set of instances with different job scales, job types, and production scenarios is designed, and the results are compared with those of three other famous multi-objective optimization algorithms. The experimental results show that the proposed MOGWO/D algorithm exhibits superiority in most instances.

From Single Batch to Mass Production-Automated Platform Design Concept for a Phase II Clinical Trial Tissue Engineered Cartilage Product.

Advanced Therapy Medicinal Products (ATMP) provide promising treatment options particularly for unmet clinical needs, such as progressive and chronic diseases where currently no satisfying treatment exists. Especially from the ATMP subclass of Tissue Engineered Products (TEPs), only a few have yet been translated from an academic setting to clinic and beyond. A reason for low numbers of TEPs in current clinical trials and one main key hurdle for TEPs is the cost and labor-intensive manufacturing process. Manual production steps require experienced personnel, are challenging to standardize and to scale up. Automated manufacturing has the potential to overcome these challenges, toward an increasing cost-effectiveness. One major obstacle for automation is the control and risk prevention of cross contaminations, especially when handling parallel production lines of different patient material. These critical steps necessitate validated effective and efficient cleaning procedures in an automated system. In this perspective, possible technologies, concepts and solutions to existing ATMP manufacturing hurdles are discussed on the example of a late clinical phase II trial TEP. In compliance to Good Manufacturing Practice (GMP) guidelines, we propose a dual arm robot based isolator approach. Our novel concept enables complete process automation for adherent cell culture, and the translation of all manual process steps with standard laboratory equipment. Moreover, we discuss novel solutions for automated cleaning, without the need for human intervention. Consequently, our automation concept offers the unique chance to scale up production while becoming more cost-effective, which will ultimately increase TEP availability to a broader number of patients.

Efficiencies of KREBS15 and GMAX15 hydrocyclones used in desliming at Samarco Mining S/A

ABSTRACT This study used mathematical models to compare the classification efficiency of two types of hydrocyclones – GMAX and KREBS – both of which have cylindrical sections with a diameter of 15″ (0.381 m). These devices were installed at the desliming stage and positioned on two parallel production lines at the Samarco Mining Industrial Facility. Samples were collected at the feed, underflow, and overflow streams. Lynch and Rao’s model was used to calibrate the partition curve. To determine the efficiencies of these classifiers, the total area under the curve until the d 50 point and above the curve beyond d 50 was calculated. Lynch and Rao’s alpha (α) parameter was also determined. Results show that GMAX15 performed better than KREBS15, with a lower area (−22.4%) and a finer cut point, d50c (0.0183 mm). The KREBS15 and GMAX15 hydrocyclones, operating under similar conditions, presented the same α value of 1.85.

Optimization models for a lot sizing and scheduling problem on parallel production lines that share scarce resources

The purpose of this paper is to propose mathematical models to represent a lot sizing and scheduling problem on multiple production lines that share scarce resources and to investigate the computational performance of the proposed models. The main feature that differentiates this problem from others in the literature is that the decision on which lines to organize should be taken considering the availability of the necessary resources. The optimization criterion is the minimization of the costs incurred in the production process (inventory, backlogging, organization of production lines, and sequence-dependent setup costs). Nine mixed integer optimization models to represent the problem are given and, also, the results of an extensive computational study carried out using a set of instances from the literature. The computational study indicates that an efficient formulation, able to provide high quality solutions for large sized instances, can be obtained from a classical model by making the binary production variables explicit, using the facility location reformulation as well as the single commodity flow constraints to eliminate subsequences. Moreover, from the results, it is also clear that the consideration of scarce resources makes the problem significantly more difficult than the traditional one.

Capacity planning of a mixed-model assembly line for prefabricated housebuilding elements

Abstract This paper deals with new opportunities of Industry 4.0 applications for capacity planning on mixed-model assembly lines for industrialized housebuilding. We formulate a linear programming model for capacity planning on several parallel production lines and show how this model is fed with accurate data. The solution of the linear program in turn generates a detailed allocation of personnel and workload assignment for a given planning period. We discuss the planning approach and describe the implementation on a real world environment.

Minimizing the cycle time in the distributed flow shop scheduling problem

Abstract We deal with the problem of cyclic scheduling tasks in the distributed manufacturing system with factories having the flow shop layout. The optimization aim is to minimize the cycle time. We have proposed the proprietary model and introduced some special properties of the problem, which allow us to find the cycle time for each given particular solution quickly. Since the formulated problem is NP-hard, we propose a metaheuristic algorithm based on the tabu search approach. The quality of the algorithm has been verified experimentally on common benchmarks and compared to other methods known in the literature, namely the scatter search and list scheduling. The problem also has a relation with the problem of balancing workloads of identical parallel production lines.

An economic evaluation of operational decisions – an application in scheduling evaluation in fertilizer plants

Operational management is based on decisions that are related to the way resources are used in the short run. Reviewing the choice between several alternative operational decisions requires proper economic evaluation. The information provided by traditional cost accounting is not suitable for direct use at the operational decision-making process. We present an approach that requires identifying the physical consequences of the decisions being assessed, identifying relevant activity driver measures, establishing the material balance and estimating the differential cost by using relevant cost information. We illustrate our proposed approach with a real-life example of fertilizer production order scheduling on parallel and heterogeneous production lines in OCP’s integrated supply chain. The results show that the use of management accounting outputs leads to a biased evaluation of the financial consequences of the decisions. Finally, we point out the relevance of the use of our economic evaluation approach in a decision support system context.

Intelligent scheduling of discrete automated production line via deep reinforcement learning

The reinforcement learning (RL) is being used for scheduling to improve the adaptability and flexibility of an automated production line. However, the existing methods only consider processing time certain and known and ignore production line layouts and transfer unit, such as robots. This paper introduces deep RL to schedule an automated production line, avoiding manually extracted features and overcoming the lack of structured data sets. Firstly, we present a state modelling method in discrete automated production lines, which is suitable for linear, parallel and re-entrant production lines of multiple processing units. Secondly, we propose an intelligent scheduling algorithm based on deep RL for scheduling automated production lines. The algorithm establishes a discrete-event simulation environment for deep RL, solving the confliction of advancing transferring time and the most recent event time. Finally, we apply the intelligent scheduling algorithm into scheduling linear, parallel and re-entrant automated production lines. The experiment shows that our scheduling strategy can achieve competitive performance to the heuristic scheduling methods and maintains stable convergence and robustness under processing time randomness.

An heuristic algorithm for fair strategic personnel assignment in continuous operation

This article introduces an heuristic initialise-and-refine algorithm for fair strategic (long-term) personnel assignment in continuous operation. Here, continuous operation refers to stable, reoccurring parallel work processes, with known tasks and constant demand - an example of which is given by university tuition on a degree program across many years. The proposed method attempts to address both the employees' competency-preference matching and the workload balancing aspects of fairness simultaneously, in situations where the task schedule across the parallel production lines is known beforehand. As an application, the proposed method is successfully used for optimising the module assignment of teachers in a Mechanical Engineering degree program at a Finnish university.

An heuristic algorithm for fair strategic personnel assignment in continuous operation

This article introduces an heuristic initialise-and-refine algorithm for fair strategic (long-term) personnel assignment in continuous operation. Here, continuous operation refers to stable, reoccurring parallel work processes, with known tasks and constant demand - an example of which is given by university tuition on a degree program across many years. The proposed method attempts to address both the employees' competency-preference matching and the workload balancing aspects of fairness simultaneously, in situations where the task schedule across the parallel production lines is known beforehand. As an application, the proposed method is successfully used for optimising the module assignment of teachers in a Mechanical Engineering degree program at a Finnish university.

Decomposition approaches for the design and scheduling of multiproduct multistage batch plants with parallel lines

Multiproduct chemical batch plants, often equipped with parallel production lines, are facilities where similar products are produced by sharing available equipment and time. Hence, when designing such plants, the operational use should be considered simultaneously. In this paper, the design and scheduling problem of a batch plant with parallel lines is addressed, using the Resource-Task Network formulation. The proposed mixed-integer linear programming model aims at minimising total costs through the optimisation of the number of lines to install, their design and the number, size and timings of the batches of every product on the installed lines. Besides an integral (exact) approach, we applied three decomposition approaches to tackle the computational complexity associated with this problem: line decomposition, bottleneck decomposition and a combination of both. Through several examples, it is shown that all three decomposition approaches reduce the computation times significantly compared to the integral approach, while obtaining (near) optimal solutions.

Modeling and solving a sugarcane harvest front scheduling problem

Sugarcane harvest front scheduling is a complex process that affects the main activities of sugarmill supply logistics and it should simultaneously consider the available harvest and transport capacities, which are sometimes neglected. This paper models this scheduling problem, exploring an analogy to the well-known general lotsizing and scheduling problem with parallel production lines, and presents a heuristic method to solve it, involving an aggregation process, a constructive MIP heuristic based on relax-and-fix and an improving MIP heuristic based on fix-and-optimize. This optimization approach is applied to solve a large-sized real problem instance of a Brazilian mill whole season. To validate the approach, a validation process of the literature is applied by consulting a selected group of Brazilian harvest planning specialists. The results highlight the potential of the approach and how it can change the current harvest planning practices, enabling plans that consider the balance of harvest and transport capacities without using sectorization.

Organizational specificity of batch production enterprises of processing industry

This article deals with the approaches to the management of batch production in enterprises of processing industries. The advantages of batch production in comparison with piece production and continuous production are proved in the text. The authors show the mechanism of parallel or consecutive production line management. The comparison of key parameters of batch production in foreign and Russian enterprises is carried out in the article. The authors describe similarity and difference of batch production at the enterprises which are characterized by the discontinuity of production cycle with an exact calculation of temporal parameters of the stages of direct labor process. This comparison is made according to the methodology of similarity determination of the elements of production management in its various types and primarily in batch and piece production. Three variants of productive process modernization of the batch production are analyzed in the text of the article. Their integrating efficiency is assessed using such parameters as annual production volume of output, production cycle continuity, level of prime equipment load.

Multi objective lotsizing and scheduling with material constraints in flexible parallel lines using a Pareto based guided artificial bee colony algorithm

Flexible production lines are significantly used in recent years for production of different variety of products in large quantity. These production lines usually produce mixed model products in lots which can improve the production efficiency and satisfy the different customer’s demand. However, it is very challenging for factory planners to make decision on the optimal size of lots for each product model in production lines. Therefore, current research investigates lotsizing and mixed model scheduling problem in flexible parallel production lines considering sequence dependent setup times between mixed model products with aims to minimize makespan of production lines, balance the workload among lines and maximize the net profit, simultaneously. Additionally, a new constraint of material availability is introduced to the problem. A novel Pareto based guided artificial bee colony algorithm (PGABC) with three different guided mechanisms is designed for the current problem to obtain near optimal Pareto solutions. Taguchi method is adopted to tune the effective parameters of the proposed PGABC algorithm. Furthermore, nine different sets of instances including 90 problems are generated and tested using PGABC. The performance of PGABC is compared with other three famous methods used for multi objective optimization in literature including, multi objective artificial bee colony algorithm, non-dominated sorting genetic algorithm III (NSGAIII) and improved strength Pareto evolutionary algorithm (SPEA2). Computational results indicate that proposed PGABC outperforms the other considered algorithms both in terms of solution diversity and quality based on the considered test problem instances. Finally, an industrial case problem from a bus and coach company is solved by the proposed approach for mixed-model lotsizing and scheduling in parallel production lines.