Journal articles: 'Production planning and scheduling problem'

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Bhosale, Kailash Changdeorao, and Padmakar Jagannath Pawar. "Production planning and scheduling problem of continuous parallel lines with demand uncertainty and different production capacities." Journal of Computational Design and Engineering 7, no. 6 (July 14, 2020): 761–74. http://dx.doi.org/10.1093/jcde/qwaa055.

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Abstract Production planning and scheduling problems are highly interdependent as scheduling provides optimum allocation of resources and planning is an optimum utilization of these allocated resources to serve multiple customers. Researchers have solved production planning and scheduling problems by the sequential method. But, in this case, the solution obtained by the production planning problem may not be feasible for scheduling method. Hence, production planning and scheduling problems must be solved simultaneously. Therefore, in this work, a mathematical model is developed to integrate production planning and scheduling problems. The solution to this integrated planning and scheduling problem is attempted by using a discrete artificial bee colony (DABC) algorithm. To speed up the DABC algorithm, a k-means clustering algorithm is used in the initial population generation phase. This k-means clustering algorithm will help to converge the algorithm in lesser time. A real-life case study of a soap manufacturing industry is presented to demonstrate the effectiveness of the proposed approach. An objective function to minimize overall cost, which comprises the processing cost, material cost, utility cost, and changeover cost, is considered. The results obtained by using DABC algorithm are compared with those obtained by CPLEX software. There is a saving of ₹2 23 324 for weeks 1–4 in overall cost compared with the results obtained by using CPLEX software.

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Hassani, Zineb Ibn Majdoub, Abdellah El Barkany, Abdelouahhab Jabri, and Ikram El Abbassi. "Models for Solving Integrated Planning and Scheduling Problem: Computational Comparison." International Journal of Engineering Research in Africa 34 (January 2018): 161–70. http://dx.doi.org/10.4028/www.scientific.net/jera.34.161.

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This article concerns the integration of planning and scheduling production system. Planning and scheduling are usually treated separately because of their complexity. Scheduling largely depends on the production quantities computed at the production planning level. However, ignoring scheduling constraints in the tactical level leads to inconsistent decisions. So, it is important to integrate planning and scheduling to efficiently manage operations and to determine a realistic production plan for a given sequence of jobs on each machine. In this paper, we present some approaches proposed to solve the problem and we realize a comparison between the two most interesting ones, using the standard solver CPLEX.

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De Antón, J., J. Senovilla, J. M. González, and F. Acebes. "Production planning in 3D printing factories." International Journal of Production Management and Engineering 8, no. 2 (July 18, 2020): 75. http://dx.doi.org/10.4995/ijpme.2020.12944.

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<p>Production planning in 3D printing factories brings new challenges among which the scheduling of parts to be produced stands out. A main issue is to increase the efficiency of the plant and 3D printers productivity. Planning, scheduling, and nesting in 3D printing are recurrent problems in the search for new techniques to promote the development of this technology. In this work, we address the problem for the suppliers that have to schedule their daily production. This problem is part of the LONJA3D model, a managed 3D printing market where the parts ordered by the customers are reorganized into new batches so that suppliers can optimize their production capacity. In this paper, we propose a method derived from the design of combinatorial auctions to solve the nesting problem in 3D printing. First, we propose the use of a heuristic to create potential manufacturing batches. Then, we compute the expected return for each batch. The selected batch should generate the highest income. Several experiments have been tested to validate the process. This method is a first approach to the planning problem in 3D printing and further research is proposed to improve the procedure.</p>

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Stawowy, A., and J. Duda. "Coordinated Production Planning and Scheduling Problem in a Foundry." Archives of Foundry Engineering 17, no. 3 (September 1, 2017): 133–38. http://dx.doi.org/10.1515/afe-2017-0105.

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Abstract In the paper, we present a coordinated production planning and scheduling problem for three major shops in a typical alloy casting foundry, i.e. a melting shop, molding shop with automatic line and a core shop. The castings, prepared from different metal, have different weight and different number of cores. Although core preparation does not required as strict coordination with molding plan as metal preparation in furnaces, some cores may have limited shelf life, depending on the material used, or at least it is usually not the best organizational practice to prepare them long in advance. Core shop have limited capacity, so the cores for castings that require multiple cores should be prepared earlier. We present a mixed integer programming model for the coordinated production planning and scheduling problem of the shops. Then we propose a simple Lagrangian relaxation heuristic and evolutionary based heuristic to solve the coordinated problem. The applicability of the proposed solution in industrial practice is verified on large instances of the problem with the data simulating actual production parameters in one of the medium size foundry.

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Burkarda, Rainer E., Mihály Hujterb, Bettina Klinz, Rüdiger Rudolf, and Marc Wennink. "A process scheduling problem arising from chemical production planning." Optimization Methods and Software 10, no. 2 (January 1998): 175–96. http://dx.doi.org/10.1080/10556789808805710.

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Zhang, Zhi Cong, Kai Shun Hu, Hui Yu Huang, and Shuai Li. "Production Planning Considering Transfer Lot Size." Applied Mechanics and Materials 44-47 (December 2010): 552–56. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.552.

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Traditional methods conduct production planning and scheduling separately and solve transfer lot sizing problem between these two steps. Unfortunately, this may result in infeasibility in planning and scheduling. We take into account transfer lot size in production planning to obtain the consistency and to eliminate the gap between planning and real production. We present the detailed Transfer Lot-Based Model with mixed integer programming. Experiments show that performance measures of a production plan change remarkably with increasing of transfer lot size.

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Hassani, Zineb Ibn Majdoub, Abdellah El Barkany, Abdelouahhab Jabri, Ikram El Abbassi, and Abdel Moumen Darcherif. "New Approach to Integrate Planning and Scheduling of Production System: Heuristic Resolution." International Journal of Engineering Research in Africa 39 (November 2018): 156–69. http://dx.doi.org/10.4028/www.scientific.net/jera.39.156.

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In general, planning and scheduling of production are treated separately under the hierarchical strategy. Then, over the time, the iterative strategy appeared which partially considers the scheduling constraints during planning, except that the latter remains unsatisfactory because there is no guarantee that these constraints are taken into account. For this, is born the integrated strategy which integrates planning and scheduling and aims to solve the problem and define a feasible production plan. Since capacity constraints don’t reflect reality in terms of resource availability, and they are not always considered, capacity becomes aggregated. To remedy this problem, it is necessary to integrate more precise constraints of scheduling at the planning level. Based on this observation, we propose in this article a new model that integrates planning and scheduling and considers the constraint of resource availability. In our model, the objective function optimizes the total cost of production for a mono-level job-shop problem. To solve this N-P difficult problem we use a stochastic approached method as genetic algorithm (GA).

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Semenkina, O. E., and E. A. Popov. "Nature-Inspired Algorithms for Solving a Hierarchical Scheduling Problem in Short-Term Production Planning." Herald of the Bauman Moscow State Technical University. Series Instrument Engineering, no. 3 (126) (June 2019): 46–63. http://dx.doi.org/10.18698/0236-3933-2019-3-46-63.

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The paper deals with the scheduling problem relevant in many fields, such as project management, lesson scheduling or production scheduling. In practice, using optimisation methods to solve the scheduling problem is considerably restricted by the fact that in the real world, problem statement involves high dimensionality, high production process complexity and many nontrivial constraints. These specifics mean that even merely searching for a feasible solution becomes a difficult task. Consequently, in order to solve the problem in a reasonable amount of time it is necessary to use problem-oriented heuristics. Ensuring manufacturing process stability involves respecting all constraints, but at the same time, short-term production planning demands fast solutions whenever there is a change of state. We propose to implement a hierarchical problem structure that puts the travelling salesman problem at the top and replaces the nested resource-constrained project scheduling problem with a simulation model. The paper considers using such algorithms as the Lin --- Kernighan heuristic, the genetic algorithm and the ant colony optimization. We study the efficiency of employing the algorithms mentioned above to solve the scheduling problem in the statement proposed.

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Li, Ruiqiu, and Huimin Ma. "Integrating Preventive Maintenance Planning and Production Scheduling under Reentrant Job Shop." Mathematical Problems in Engineering 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/6758147.

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This paper focuses on a preventive maintenance plan and production scheduling problem under reentrant Job Shop in semiconductor production. Previous researches discussed production scheduling and preventive maintenance plan independently, especially on reentrant Job Shop. Due to reentrancy, reentrant Job Shop scheduling is more complex than the standard Job Shop which belongs to NP-hard problems. Reentrancy is a typical characteristic of semiconductor production. What is more, the equipment of semiconductor production is very expensive. Equipment failure will affect the normal production plan. It is necessary to maintain it regularly. So, we establish an integrated and optimal mathematical model. In this paper, we use the hybrid particle swarm optimization algorithm to solve the problem for it is highly nonlinear and discrete. The proposed model is evaluated through some simple simulation experiments and the results show that the model works better than the independent decision-making model in terms of minimizing maximum completion time.

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Tang, Peng, and Hong Bin Yu. "Planning and Scheduling Models for EMAS Productive Process." Applied Mechanics and Materials 365-366 (August 2013): 553–56. http://dx.doi.org/10.4028/www.scientific.net/amm.365-366.553.

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This paper addresses the problems of EMAS (an engineer material to arresting aircraft on the ground) production operation that the intensity of each product was uneven and cellular concrete was not completely crumple.In order to solve the problem the development of a nonlinear planning model for EMAS production was presented. The model is based on the company it could be helpful in guiding production. The second part of the work were optimization the model and get theoretical optimal value rely on both continuous and discrete time representations.

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Ibn Majdoub Hassani, Zineb, Abdellah El Barkany, Abdelouahhab Jabri, Ikram El Abbassi, and Abdel Moumen Darcherif. "Hybrid approach for solving the integrated planning and scheduling production problem." Journal of Engineering, Design and Technology 18, no. 1 (August 28, 2019): 172–89. http://dx.doi.org/10.1108/jedt-11-2018-0198.

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Purpose This paper aims to present a new model for solving the integrated production planning and scheduling. Usually, the two decision levels are treated sequentially because of their complexity. Scheduling depends on the lot sizes calculated at the tactical level and ignoring scheduling constraints generates unrealistic and inconsistent decisions. Therefore, integrating more detail scheduling constraint in production planning is important for managing efficiently operations. Therefore, an integrated model was developed, and two evolutionary optimization approaches were suggested for solving it, namely, genetic algorithm (GA) and the hybridization of simulated annealing (SA) with GA HSAGA. The proposed algorithms have some parameters that must be adjusted using Taguchi method. Therefore, to evaluate the proposed algorithm, the authors compared the results given by GA and the hybridization. The SA-based local search is embedded into a GA search mechanism to move the GA away from being closed within local optima. The analysis shows that the combination of simulated annealing with GA gives better solutions and minimizes the total production costs. Design/methodology/approach The paper opted for an approached resolution method particularly GA and simulated annealing. The study represents a comparison between the results found using GA and the hybridization of simulated annealing and GA. A total of 45 instances were studied to evaluate job-shop problems of different sizes. Findings The results illustrate that for 36 instances of 45, the hybridization of simulated annealing and GA HSAGA has provided best production costs. The efficiency demonstrated by HSAGA approach is related to the combination between the exploration ability of GA and the capacity to escape local optimum of simulated annealing. Originality/value This study provides a new resolution approach to the integration of planning and scheduling while considering a new operational constrain. The model suggested aims to control the available capacity of the resources and guaranties that the resources to be consumed do not exceed the real availability to avoid the blocking that results from the unavailability of resources. Furthermore, to solve the MILP model, a GA is proposed and then it is combined to simulated annealing.

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Tabucanon, Mario T., and Surat Petchratanaporn. "Tyre Manufacturing: Simplifying a Complex Production Planning and Scheduling Problem." Logistics Information Management 4, no. 3 (March 1991): 10–14. http://dx.doi.org/10.1108/eum0000000002878.

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Dvorak, Filip, Maxwell Micali, and Mathias Mathieug. "Planning and Scheduling in Additive Manufacturing." Inteligencia Artificial 21, no. 62 (September 7, 2018): 40. http://dx.doi.org/10.4114/intartif.vol21iss62pp40-52.

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Recent advances in additive manufacturing (AM) and 3D printing technologies have led to significant growth in the use of additive manufacturing in industry, which allows for the physical realization of previously difficult to manufacture designs. However, in certain cases AM can also involve higher production costs and unique in-process physical complications, motivating the need to solve new optimization challenges. Optimization for additive manufacturing is relevant for and involves multiple fields including mechanical engineering, materials science, operations research, and production engineering, and interdisciplinary interactions must be accounted for in the optimization framework. In this paper we investigate a problem in which a set of parts with unique configurations and deadlines must be printed by a set of machines while minimizing time and satisfying deadlines, bringing together bin packing, nesting (two-dimensional bin packing), job shop scheduling, and constraints satisfaction. We first describe the real-world industrial motivation for solving the problem. Subsequently, we encapsulate the problem within constraints and graph theory, create a formal model of the problem, discuss nesting as a subproblem, and describe the search algorithm. Finally, we present the datasets, the experimental approach, and the preliminary results.

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Gao, Yan, Xin Zhang, and Jian Zhong Xu. "An Improved Production Scheduling Algorithm Based on Resource Constraints." Applied Mechanics and Materials 455 (November 2013): 619–24. http://dx.doi.org/10.4028/www.scientific.net/amm.455.619.

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For resource-constrained project scheduling problems, with aircraft assembly as its background, we established its mathematics model as constraint satisfaction problem. An improved critical path scheduling algorithm is proposed, considering the constraints of precedence relations, resource constraints and space constraints, through the two stages of planning, reaching for aircraft assembly task scheduling optimization objectives. Through the given numerical example results show that, when the objective consists in minimizing the project duration, the algorithm has better performance.

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LIANG, HELAN, and SUJIAN LI. "CC-DHCR PLANNING AND SCHEDULING METHOD BASED ON SLAB CLUSTER." Journal of Advanced Manufacturing Systems 07, no. 02 (December 2008): 249–52. http://dx.doi.org/10.1142/s0219686708001437.

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Focusing on the limitations of the traditional Continuous Casting-Direct Hot Charge Rolling (CC-DHCR) planning and scheduling methods that rarely consider dynamic scheduling problems, a new method is put forward. The key idea is to make out clusters and integrated plans in the planning layer, and then to adjust the rolling sequences according to the slab cluster-based strategy in the dynamic scheduling layer. Results of the test with data from practical production process show that the method can effectively solve the CC-DHCR planning and scheduling problem and increase the DHCR ratio.

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Kalinowski, Krzysztof, Cezary Grabowik, Grzegorz Ćwikła, and Witold Janik. "The Graph of Operations Planning Sequence of a Production Order for Scheduling with Mixed Planning Strategies and Alternatives." Applied Mechanics and Materials 809-810 (November 2015): 1420–25. http://dx.doi.org/10.4028/www.scientific.net/amm.809-810.1420.

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The paper presents the most important issues related to the scheduling of production orders in real manufacturing systems. In the elaborated method an and/or type graph of operations planning sequence of a production order is proposed for modelling the production system load. In a single structure the graph takes into account alternative routes of a production order realisation and the precedence constraints in presence of complex, hierarchical structures of processes. Two modelling ways of that process using the "operation on the edge" or "operation on the node" notation are also presented. In the developed method scheduling strategies, which have a major impact on the order of placing operations in the schedule and handling of production lots are also considered. By a state space graph representation of scheduling problem, using graph theory, it can be possible to analyze the structure and complexity of both the modelling problem and the graph search techniques.

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Stawowy, A., and J. Duda. "Production Scheduling for the Furnace - Casting Line System." Archives of Foundry Engineering 13, no. 3 (September 1, 2013): 84–87. http://dx.doi.org/10.2478/afe-2013-0065.

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Abstract The problem considered in the paper is motivated by production planning in a foundry equipped with the furnace and casting line, which provides a variety of castings in various grades of cast iron/steel for a large number of customers. The quantity of molten metal does not exceed the capacity of the furnace, the load is a particular type of metal from which the products are made in the automatic casting lines. The goal is to create the order of the melted metal loads to prevent delays in delivery of goods to customers. This problem is generally considered as a lot-sizing and scheduling problem. The paper describes two computational intelligence algorithms for simultaneous grouping and scheduling tasks and presents the results achieved by these algorithms for example test problems.

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Zhang, Chi, Zhen He, and Yuan Peng Ruan. "Production Planning Optimization in F Company: A Scheduling Theory Case Study." Advanced Materials Research 655-657 (January 2013): 1646–49. http://dx.doi.org/10.4028/www.scientific.net/amr.655-657.1646.

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Scheduling is an effective optimization methodology which has been widely used for production planning. This paper presents a scheduling model to optimize the output of an assembly line in F Semiconductor Company in Tianjin, China. The authors formulate the optimization problem as linear programming. The model and its implementation are described in detail in this article. The optimum production allocations have been founded by the scheduling model and the output has been increased.

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Zhang, Tao, Yue Wang, Xin Jin, and Shan Lu. "Integration of Production Planning and Scheduling Based on RTN Representation under Uncertainties." Algorithms 12, no. 6 (June 10, 2019): 120. http://dx.doi.org/10.3390/a12060120.

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Production planning and scheduling are important bases for production decisions. Concerning the traditional modeling of production planning and scheduling based on Resource-Task Network (RTN) representation, uncertain factors such as utilities are rarely considered as constraints. For the production planning and scheduling problem based on RTN representation in an uncertain environment, this paper formulates the multi-period bi-level integrated model of planning and scheduling, and introduces the uncertainties of demand and utility in planning and scheduling layers respectively. Rolling horizon optimization strategy is utilized to solve the bi-level integrated model iteratively. The simulation results show that the proposed model and algorithm are feasible and effective, can calculate the consumption of utility in every period, decrease the effects of uncertain factors on optimization results, more accurately describe the uncertain factors, and reflect the actual production process.

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Sugimura, Nobuhiro, Koji Iwamura, and Tomohiko Maeda. "Special Issue on Production Planning and Scheduling." International Journal of Automation Technology 9, no. 3 (May 5, 2015): 209. http://dx.doi.org/10.20965/ijat.2015.p0209.

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This issue focuses on production planning and scheduling for production system and the related problems that have arisen in these areas in the last half century as digital computer systems developed. These problems relate to production management, production planning, shop floor control, product design and process planning. In the first stage of production planning and scheduling systems R&D, optimization is a key issue that has been widely discussed and many theories and optimization algorithms proposed. Rule-based methods are discussed as potential solutions to these problems. With rapid advances in computer and information processing technologies and performance, tremendous progress has been made in the areas of production systems such as production planning, production scheduling, advances production systems (APS), enterprise resource planning (ERP), just-in time (JIT) processes, the theory of constraint (TOC), product data management (PDM) and computer-aided design / manufacturing / engineering (CAD/CAM/CAE). This special issue addresses the latest research advances, applications, and case studies in production planning and scheduling covering such as decentralized and autonomous production systems, distributed simulation models, robust capacity planning models, wireless sensor networks for production systems and applications to automotive component and steel production. We hope that learning about these advances will enable readers to share their own experience and knowledge in technology, new developments and the potential applications of production planning and scheduling methods and solutions.

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da Silva, Felipe Augusto Moreira, Antonio Carlos Moretti, and Anibal Tavares de Azevedo. "A Scheduling Problem in the Baking Industry." Journal of Applied Mathematics 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/964120.

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This paper addresses a scheduling problem in an actual industrial environment of a baking industry where production rates have been growing every year and the need for optimized planning becomes increasingly important in order to address all the features presented by the problem. This problem contains relevant aspects of production, such as parallel production, setup time, batch production, and delivery date. We will also consider several aspects pertaining to transportation, such as the transportation capacity with different vehicles and sales production with several customers. This approach studies an atypical problem compared to those that have already been studied in literature. In order to solve the problem, we suggest two approaches: using the greedy heuristic and the genetic algorithm, which will be compared to small problems with the optimal solution solved as an integer linear programming problem, and we will present results for a real example compared with its upper bounds. The work provides us with a new mathematical formulation of scheduling problem that is not based on traveling salesman problem. It considers delivery date and the profit maximization and not the makespan minimization. And it also provides an analysis of the algorithms runtime.

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Chen, C., R. Racine, and F. Swift. "A Practical Approach to the Apparel Production‐planning and Scheduling Problem." International Journal of Clothing Science and Technology 4, no. 2/3 (February 1992): 9–17. http://dx.doi.org/10.1108/eb002988.

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Arkhipov, Dmitry I., Olga Battaïa, and Alexander A. Lazarev. "Long-term production planning problem: scheduling, makespan estimation and bottleneck analysis." IFAC-PapersOnLine 50, no. 1 (July 2017): 7970–74. http://dx.doi.org/10.1016/j.ifacol.2017.08.991.

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Liang, Jingran, Yuyan Wang, Zhi-Hai Zhang, and Yiqi Sun. "Energy efficient production planning and scheduling problem with processing technology selection." Computers & Industrial Engineering 132 (June 2019): 260–70. http://dx.doi.org/10.1016/j.cie.2019.04.042.

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Kolus, Ahmet, Ahmed El-Khalifa, Umar M. Al-Turki, and Salih Osman Duffuaa. "An integrated mathematical model for production scheduling and preventive maintenance planning." International Journal of Quality & Reliability Management 37, no. 6/7 (April 21, 2020): 925–37. http://dx.doi.org/10.1108/ijqrm-10-2019-0335.

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PurposeThe integration between production scheduling and maintenance planning is attracting the attention of planners in the manufacturing sector with the increase in global competitiveness. Researchers have developed various methodologies to optimize integrated decisions in planning and scheduling, including mathematical modeling under different conditions. This paper considers the simultaneous scheduling of production and maintenance activities with the objective of minimizing the expected total tardiness cost on a single machine (production line).Design/methodology/approachScheduling in these two types of activities, production and maintenance, are traditionally done independently, causing conflicts between the two functional areas. To eliminate or at least reduce conflicts, the scheduling of both activities can be done simultaneously with the objective of meeting due dates and maintaining maximum machine availability. In this paper, a mathematical model for an integrated problem is developed and demonstrated by an example.FindingsThe proposed integrated model shows a high potential for significant improvements in performance with respect to the cost of tardiness in delivery and machine availability. This is demonstrated by an example showing an average savings of approximately 40%.Originality/valueThis substantial saving is owed to the integration of two important decision-making processes in manufacturing systems. Although the integrated problem is complex and difficult to solve, the fact that it is savings driven makes the problem of interest to researchers and practitioners in manufacturing.

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Rivera Letelier, Orlando, Daniel Espinoza, Marcos Goycoolea, Eduardo Moreno, and Gonzalo Muñoz. "Production Scheduling for Strategic Open Pit Mine Planning: A Mixed-Integer Programming Approach." Operations Research 68, no. 5 (September 2020): 1425–44. http://dx.doi.org/10.1287/opre.2019.1965.

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Production scheduling is a large-scale optimization problem that must be solved on a yearly basis by every open pit mining project throughout the world. Surprisingly, however, this problem has only recently started to receive much attention from the operations research community. In this article, O. Rivera, D. Espinoza, M. Goycoolea, E. Moreno, and G. Muñoz propose an integer programming methodology for tackling this problem that combines new classes of preprocessing schemes, cutting planes, heuristics, and branching mechanisms. This methodology is shown to compute near-optimal solutions on a number of real-world planning problems whose complexity is beyond the capabilities of preexisting approaches.

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Tanizaki, Takashi, Hideki Katagiri, and António Oliveira Nzinga René. "Scheduling Algorithms Using Metaheuristics for Production Processes with Crane Interference." International Journal of Automation Technology 12, no. 3 (May 1, 2018): 297–307. http://dx.doi.org/10.20965/ijat.2018.p0297.

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This paper proposes scheduling algorithms using metaheuristics for production processes in which cranes can interfere with each other. There are many production processes that involve cranes in manufacturing industry, such as in the steel industry, so a general purpose algorithm for this problem can be of practical use. The scheduling problem for this process is very complicated and difficult to solve because the cranes must avoid interfering with each other plus each machine has its own operational constraints. Although several algorithms have been proposed for a specific problem or small-scale problem, general purpose algorithms that can be solved in real time (about 30 minutes or less) in the company’s production planning work have not been developed for large-scale problems. This paper develops some metaheuristic algorithms to obtain suboptimal solutions in a short time, and it confirms their effectiveness through computer experiments.

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Ba, Li, Yan Li, Mingshun Yang, Xueliang Wu, Yong Liu, Xinqin Gao, and Zhihong Miao. "A Mathematical Model for Multiworkshop IPPS Problem in Batch Production." Mathematical Problems in Engineering 2018 (2018): 1–16. http://dx.doi.org/10.1155/2018/7948693.

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Integrated Process Planning and Scheduling (IPPS) problem is an important issue in production scheduling. Actually, there exit many factors affecting scheduling results. Many types of workpieces are commonly manufactured in batch production. Moreover, due to differences among process methods, all processes of a workpiece may not be performed in the same workshop or even in the same factory. For making IPPS problem more in line with practical manufacturing, this paper addresses an IPPS problem with batches and limited vehicles (BV-IPPS). An equal batch splitting strategy is adopted. A model for BV-IPPS problem is established. Makespan is the objective to be minimized. For solving the complex problem, a particle swarm optimization (PSO) with a multilayer encoding structure is proposed. Each module of the algorithm is designed. Finally, case studies have been conducted to validate the model and algorithm.

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Kaczmarczyk, Waldemar. "Extended Model Formulation of the Proportional Lot-Sizing and Scheduling Problem with Lost Demand Costs." Decision Making in Manufacturing and Services 5, no. 1 (October 3, 2011): 49–56. http://dx.doi.org/10.7494/dmms.2011.5.1.49.

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We consider mixed-integer linear programming (MIP) models of production planning problems known as the small bucket lot-sizing and scheduling problems. We present an application of a class of valid inequalities to the case with lost demand (stock-out) costs. Presented results of numerical experiments made for the the Proportional Lot-sizing and Scheduling Problem (PLSP) confirm benefits of such extended model formulation.

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Duda, J., and A. Stawowy. "Application of Interval Arithmetic to Production Planning in a Foundry." Archives of Foundry Engineering 17, no. 1 (March 1, 2017): 41–44. http://dx.doi.org/10.1515/afe-2017-0008.

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Abstract A novel approach for treating the uncertainty about the real levels of finished products during production planning and scheduling process is presented in the paper. Interval arithmetic is used to describe uncertainty concerning the production that was planned to cover potential defective products, but meets customer’s quality requirement and can be delivered as fully valuable products. Interval lot sizing and scheduling model to solve this problem is proposed, then a dedicated version of genetic algorithm that is able to deal with interval arithmetic is used to solve the test problems taken from a real-world example described in the literature. The achieved results are compared with a standard approach in which no uncertainty about real production of valuable castings is considered. It has been shown that interval arithmetic can be a valuable method for modeling uncertainty, and proposed approach can provide more accurate information to the planners allowing them to take more tailored decisions.

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Shim, Sang-Oh, KyungBae Park, and SungYong Choi. "Sustainable Production Scheduling in Open Innovation Perspective under the Fourth Industrial Revolution." Journal of Open Innovation: Technology, Market, and Complexity 4, no. 4 (September 21, 2018): 42. http://dx.doi.org/10.3390/joitmc4040042.

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This research addresses a specific issue in the field of operation scheduling. Even though there are lots of researches on the field of planning and scheduling, a specific scheduling problem is introduced here. We focus on the operation scheduling requirements that the Fourth Industrial Revolution has brought currently. From the point of view of open innovation, operation scheduling is known as the one that is using the Internet of Things, Cloud Computing, Big Data, and Mobile technology. To build proper operation systems under the Fourth Industrial Revolution, it is very essential to devise effective and efficient scheduling methodology to improve product quality, customer delivery, manufacturing flexibility, cost saving, and market competence. A scheduling problem on designated parallel equipments, where some equipments are grouped according to the recipe of lots, is considered. This implies that a lot associated with a specific recipe is preferred to be processed on an equipment among predetermined (designated) ones regardless of parallel ones. A setup operation occurs between different recipes of lots. In order to minimize completion time of the last lot, a scheduling algorithm is proposed. We conducted a simulation study with randomly generated problems, and the proposed algorithm has shown desirable and better performance that can be applied in real-time scheduling.

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Li, Y., W. H. Ip, and D. W. Wang. "Genetic algorithm approach to earliness and tardiness production scheduling and planning problem." International Journal of Production Economics 54, no. 1 (January 1998): 65–76. http://dx.doi.org/10.1016/s0925-5273(97)00124-2.

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Duda, J., A. Stawowy, and R. Basiura. "Mathematical Programming for Lot Sizing and Production Scheduling in Foundries." Archives of Foundry Engineering 14, no. 3 (August 8, 2014): 17–20. http://dx.doi.org/10.2478/afe-2014-0053.

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Abstract The problem considered in the paper is motivated by production planning in a foundry equipped with the furnace and casting line, which provides a variety of castings in various grades of cast iron/steel for a large number of customers. The quantity of molten metal does not exceed the capacity of the furnace, the load is a particular type of metal from which the products are made. The goal is to create the order of the melted metal loads to prevent delays in delivery of goods to customers. This problem is generally considered as a lot-sizing and scheduling problem. The paper describes a mathematical programming model that formally defines the optimization problem and its relaxed version that is based on the conception of rolling-horizon planning.

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Geiger, Martin Josef, Lucas Kletzander, and Nysret Musliu. "Solving the Torpedo Scheduling Problem." Journal of Artificial Intelligence Research 66 (September 2, 2019): 1–32. http://dx.doi.org/10.1613/jair.1.11303.

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The article presents a solution approach for the Torpedo Scheduling Problem, an operational planning problem found in steel production. The problem consists of the integrated scheduling and routing of torpedo cars, i. e. steel transporting vehicles, from a blast furnace to steel converters. In the continuous metallurgic transformation of iron into steel, the discrete transportation step of molten iron must be planned with considerable care in order to ensure a continuous material flow. The problem is solved by a Simulated Annealing algorithm, coupled with an approach of reducing the set of feasible material assignments. The latter is based on logical reductions and lower bound calculations on the number of torpedo cars. Experimental investigations are performed on a larger number of problem instances, which stem from the 2016 implementation challenge of the Association of Constraint Programming (ACP). Our approach was ranked first (joint first place) in the 2016 ACP challenge and found optimal solutions for all used instances in this challenge.

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Wang, Zhong, Gang Zhang, and Guo Wei Zhao. "The Advanced Production Planning of Automated Scheduling Problems in Irradiation Industry." Advanced Materials Research 139-141 (October 2010): 1688–91. http://dx.doi.org/10.4028/www.scientific.net/amr.139-141.1688.

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Because of the continuity and discreteness during production, Irradiation industry becomes one of an especial industry. Various product type, uncertain lots and complex processes cycle increase the difficulty of production planning and scheduling. The implementation of CIMS/ERP systems during product processes in the enterprise can not fulfill the special requirements especially in product automation monitoring and quality information monitoring. Research and application of scheduling method and optimization technology effect crucially for enterprise to improve its production efficiency and reduce its production cost. So, more and more scholars pay their attention to this research field. For this reason, the paper presents a mathematical production scheduling model in irradiation workshop based on the research of irradiation industry development and shop scheduling in China and abroad. The author also design an application of the irradiation shop scheduling Optimization with this model in detailed, based on described the concept, principle of genetic algorithm and its method.

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Dziurzanski, Piotr, Shuai Zhao, Sebastian Scholze, Albert Zilverberg, Karl Krone, and Leandro Soares Indrusiak. "Process planning and scheduling optimisation with alternative recipes." at - Automatisierungstechnik 68, no. 2 (February 25, 2020): 140–47. http://dx.doi.org/10.1515/auto-2019-0104.

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AbstractThis paper considers an application of a new variant of a multi-objective flexible job-shop scheduling problem, featuring multisubset selection of manufactured recipes, to a real-world chemical plant. The problem is optimised using a multi-objective genetic algorithm with customised mutation and elitism operators that minimises both the total production time and the produced commodity surplus. The algorithm evaluation is performed with both random and historic manufacturing orders. The latter demonstrated that the proposed system can lead to more than 10 % makespan improvements in comparison with human operators.

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Wang, Meng Lan, and Wen Bin Liu. "An Improved Tabu Search Algorithm for a Type of Single Machine Sequencing Problem." Advanced Materials Research 756-759 (September 2013): 3997–4001. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3997.

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Machine scheduling is a central task in production planning. In general it means the problem of scheduling job operations on a given number of available machines. In this paper we consider a machine scheduling problem with one machine, or the Single Machine Total Tardiness Problem. To solve this NP-hard problem, we develop an improved Tabu Search Algorithm, which is tested to have the ability to find good results by an example.

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Alvandi, Samira, Wen Li, and Sami Kara. "An Integrated Simulation Optimisation Decision Support Tool for Multi-Product Production Systems." Modern Applied Science 11, no. 6 (April 23, 2017): 56. http://dx.doi.org/10.5539/mas.v11n6p56.

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Over the past decades, the rising energy prices and imposing environmental regulations have motivated manufacturers to improve the energy efficiency of their manufacturing processes. Manufacturers need to also consider energy efficiency in addition to classical performance measures. The additional performance dimension (energy-related indicators) significantly increases the complexity of classical production planning problems (e.g. scheduling), already known as NP-hard problem).To overcome the inherited complexity, an integrated simulation-optimization framework is proposed. The proposed approach tackles scheduling problem in a multi-product/multi-machine manufacturing environment and optimizes several production objectives simultaneously. A case study is presented to demonstrate the applicability of the proposed approach in a real-life industrial facility.

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Noori, S., M. Bagherpour, F. Zorriassatine, A. Makui, and R. Parkin. "A new project scheduling approach for improving multi-product multi-period production planning problems." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 222, no. 11 (November 1, 2008): 1517–27. http://dx.doi.org/10.1243/09544054jem1081.

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The problem of matching production levels for individual products to demand fluctuations during multiple periods is known in the production planning literature as the multi-product multi-period (MPMP) problem. Linear programming (LP)-based solutions have been extensively reported in this respect. MPMP problems are commonly solved by using either analytic or simulation methods. More recently, hybrid solutions consisting of both analytical models and simulation analysis have been proposed where some operational criteria, e.g. the order of visit to machining centres, are taken into account. In this paper, results related to some of the literature based on hybrid solutions are used as the initial feasible solutions and then examined in the context of project scheduling by considering the influences of resource constraints. After converting the MPMP to a project network problem and assigning resources to activities and consequently levelling the resource profiles, it is discovered that machine utilization can be further improved by applying unused machine capacities. A LP model is therefore developed in order to maximize feasible production rates over all the production planning periods. The proposed approach results in improvements on the results of earlier hybrid solutions reported in the literature. Finally, three different planning problems are suggested for further applications of the proposed approach in the context of manufacturing environments.

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Duda, J., and A. Stawowy. "Production Scheduling under Fuzziness for the Furnace - Casting Line System." Archives of Foundry Engineering 15, no. 4 (December 1, 2015): 29–32. http://dx.doi.org/10.1515/afe-2015-0074.

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Abstract The problem considered in the paper is motivated by production planning in a foundry equipped with a furnace and a casting line, which provides a variety of castings in various grades of cast iron/steel for a large number of customers. The goal is to create the order of the melted metal loads to prevent delays in delivery of goods to customers. This problem is generally considered as a lot-sizing and scheduling problem. However, contrary to the classic approach, we assumed the fuzzy nature of the demand set for a given day. The paper describes a genetic algorithm adapted to take into account the fuzzy parameters of simultaneous grouping and scheduling tasks and presents the results achieved by the algorithm for example test problem.

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Li, Hai Tao, Su Jian Li, Di Wu, Fang Han, and Fang Wang. "Hot Rolling Batch Planning Problem Model Based on Genetic Algorithm." Advanced Materials Research 433-440 (January 2012): 2042–46. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.2042.

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To solve the hot rolling batch planning problem in production scheduling of iron and steel enterprises, a hot rolling batch planning model is formulated based on multiple travelling salesmen problem(MTSP) model. The objective is to minimize the total limit penalty value of adjacent stripped steels in width, thickness and hardness. The main constraints include jumps in width, thickness and hardness between adjacent stripped steels, which are essential for steel production process. An improved genetic algorithm is designed to solve the model. A simulation example shows the reasonability of the model and validity of the algorithm.

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Srsen, Saso, and Marjan Mernik. "A jssp solution for production planning optimization combining industrial engineering and evolutionary algorithms." Computer Science and Information Systems, no. 00 (2020): 58. http://dx.doi.org/10.2298/csis201009058s.

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A Job Shop Scheduling Problem (JSSP), where p processes and n jobs should be processed on m machines so that the total completion time is minimal, is a well-known problem with many industrial applications. Many researchers focus on the JSSP classification and algorithms that address the different JSSP classes. In this research work, the production times, a very well-known theme covered in Industrial Engineering (IE), are integrated into an Evolutionary Algorithm (EA) to solve real-world JSSP problems. Since a drawback of classical IE is a manual determination of the technological times, an Internet of Things (IoT) architecture is proposed as a possible solution.

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Simroth, Axel, Denise Holfeld, and Renè Brunsch. "Job Shop Production Planning under Uncertainty: A Monte Carlo Rollout Approach." Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 3 (June 16, 2015): 175. http://dx.doi.org/10.17770/etr2015vol3.617.

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<p>The Monte Carlo Rollout method (MCR) is a novel approach to solve combinatorial optimization problems with uncertainties approximatively. It combines ideas from Rollout algorithms for combinatorial optimization and the Monte Carlo Tree Search in game theory. In this paper the results of an investigation of applying the MCR to a Scheduling Problem are shown. The quality of the MCR method depends on the model parameters, search depth and search width, which are strong linked to process parameters. These dependencies are analyzed by different simulations. The paper also deals with the question whether the Lookahead Pathology occurs. </p>

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Klement, Nathalie, Mohamed Amine Abdeljaouad, Leonardo Porto, and Cristóvão Silva. "Lot-Sizing and Scheduling for the Plastic Injection Molding Industry—A Hybrid Optimization Approach." Applied Sciences 11, no. 3 (January 28, 2021): 1202. http://dx.doi.org/10.3390/app11031202.

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The management of industrial systems is done through different levels, ranging from strategic (designing the system), to tactical (planning the activities and assigning the resources) and operational (scheduling the activities). In this paper, we focus on the latter level by considering a real-world scheduling problem from a plastic injection company, where the production process combines parallel machines and a set of resources. We present a scheduling algorithm that combines a metaheuristic and a list algorithm. Two metaheuristics are tested and compared when used in the proposed scheduling approach: the stochastic descent and the simulated annealing. The method’s performances are analyzed through an experimental study and the obtained results show that its outcomes outperform those of the scheduling policy conducted in a case-study company. Moreover, besides being able to solve large real-world problems in a reasonable amount of time, the proposed approach has a structure that makes it flexible and easily adaptable to several different planning and scheduling problems. Indeed, since it is composed by a reusable generic part, the metaheuristic, it is only required to develop a list algorithm adapted to the objective function and constraints of the new problem to be solved.

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Simeonov, S., and J. Simeonovová. "Simulation scheduling in food industry application." Czech Journal of Food Sciences 20, No. 1 (November 18, 2011): 31–37. http://dx.doi.org/10.17221/3506-cjfs.

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Nowadays manufacturers are facing rapid and fundamental changes in the ways business is done. Producers are looking for simulation systems increasing throughput and profit, reducing cycle time, improving due-date performance, reducing WIP, providing plant-wide synchronization, etc. Planning and scheduling of coffee production is important for the manufacturer to synchronize production capacity and material inputs to meet the delivery date promised to the customer. A simulation model of coffee production was compiled. It includes roasting, grinding and packaging processes. Using this model the basic features of the coffee production system are obtained. An optimization module of the simulation SW is used for improving the current structure of the production system. Gantt charts and reports are applied for scheduling. Capacity planning problems related to coffee production are discussed.  

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Takahashi, Keita, Masahiko Onosato, and Fumiki Tanaka. "A comprehensive approach for managing feasible solutions in production planning by an interacting network of Zero-Suppressed Binary Decision Diagrams." Journal of Computational Design and Engineering 2, no. 2 (January 7, 2015): 105–12. http://dx.doi.org/10.1016/j.jcde.2014.12.005.

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Abstract Product Lifecycle Management (PLM) ranges from design concepts of products to disposal. In this paper, we focus on the production planning phase in PLM, which is related to process planning and production scheduling and so on. In this study, key decisions for the creation of production plans are defined as production-planning attributes. Production-planning attributes correlate complexly in production-planning problems. Traditionally, the production-planning problem splits sub-problems based on experiences, because of the complexity. In addition, the orders in which to solve each sub-problem are determined by priorities between sub-problems. However, such approaches make solution space over-restricted and make it difficult to find a better solution. We have proposed a representation of combinations of alternatives in production-planning attributes by using Zero-Suppressed Binary Decision Diagrams. The ZDD represents only feasible combinations of alternatives that satisfy constraints in the production planning. Moreover, we have developed a solution search method that solves production-planning problems with ZDDs. In this paper, we propose an approach for managing solution candidates by ZDDs' network for addressing larger production-planning problems. The network can be created by linkages of ZDDs that express constraints in individual sub-problems and between sub-problems. The benefit of this approach is that it represents solution space, satisfying whole constraints in the production planning. This case study shows that the validity of the proposed approach.

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Micieta, Branislav, Jolanta Staszewska, Matej Kovalsky, Martin Krajcovic, Vladimira Binasova, Ladislav Papanek, and Ivan Antoniuk. "Innovative System for Scheduling Production Using a Combination of Parametric Simulation Models." Sustainability 13, no. 17 (August 24, 2021): 9518. http://dx.doi.org/10.3390/su13179518.

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The article deals with the design of an innovative system for scheduling piece and small series discrete production using a combination of parametric simulation models and selected optimization methods. An innovative system for solving production scheduling problems is created based on data from a real production system at the workshop level. The methodology of the innovative system using simulation and optimization methods deals with the sequential scheduling problem due to its versatility, which includes several production systems and due to the fact that in practice, several modifications to production scheduling problems are encountered. Proposals of individual modules of the innovative system with the proposed communication channels have been presented, which connect the individual elements of the created library of objects for solving problems of sequential production scheduling. With the help of created communication channels, it is possible to apply individual parameters of a real production system directly to the assembled simulation model. In this system, an initial set of optimization methods is deployed, which can be applied to solve the sequential problem of production scheduling. The benefit of the solution is an innovative system that defines the content of the necessary data for working with the innovative system and the design of output reports that the proposed system provides for production planning for the production shopfloor level. The DPSS system works with several optimization methods (CR—Critical Ratio, S/RO—Slack/Remaining Operations, FDD—Flow Due Date, MWKR—Most Work Remaining, WSL—Waiting Slack, OPFSLK/PK—Operational Flow Slack per Processing Time) and the simulation experiments prove that the most suitable solution for the FT10 problem is the critical ratio method in which the replaceability of the equipment was not considered. The total length of finding all solutions by the DPSS system was 1.68 min. The main benefit of the DPSS system is the combination of two effectively used techniques not only in practice, but also in research; the mentioned techniques are production scheduling and discrete computer simulation. By combining techniques, it is possible to generate a dynamically and interactively changing simulated production program. Subsequently, it is possible to decide in the emerging conditions of certainty, uncertainty, but also risk. To determine the conditions, models of production systems are used, which represent physical production systems with their complex internal processes. Another benefit of combining techniques is the ability to evaluate a production system with a number of emerging problem modifications.

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Chen, Chen, Thomas Phang, and Lee Kong Tiong. "Planning semi-automated precast production using GA." International Journal of Industrialized Construction 1, no. 1 (July 27, 2020): 48–63. http://dx.doi.org/10.29173/ijic215.

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Although fully automated production systems have been developed and used in some industry leaders, most of the precast factories have yet to be developed to that stage. Semi-automated production lines are still popularly used. As production productivity can be maximally improved within the physical constraints by applying a sound production plan, this paper tends to propose a production planning method for the semi-automated precast production line using genetic algorithm (GA). The production planning problem is formulated into a flexible job shop scheduling problem (FJSSP) model and solved using an integrated approach. Thanks to the development of new technologies such as building information modeling (BIM) platform and radio frequency identification (RFID), implementation of a just-in-time (JIT) schedule in the semi-automated precast production line becomes practicable on the grounds of risk mitigation and enhanced demand forecast capability. In this regard, the optimization objectives are minimum makespan, station idle time, and earliness and tardiness penalty. An example was applied to validate the integrated GA approach. The experimental results show that the developed GA approach is a useful and effective method for solving the problem that it can return high-quality solutions. This paper thus contributes to the body of knowledge new precast production planning method for practical usage.

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Redutskiy, Yury. "Integration of oilfield planning problems: infrastructure design, development planning and production scheduling." Journal of Petroleum Science and Engineering 158 (September 2017): 585–602. http://dx.doi.org/10.1016/j.petrol.2017.08.066.

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Smutnicki, Czesław. "Scheduling with High Variety of Customized Compound Products." Decision Making in Manufacturing and Services 1, no. 2 (October 11, 2007): 91–110. http://dx.doi.org/10.7494/dmms.2007.1.2.91.

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Domestic appliance is an instance of manufacturing various products on clients demand with frequent changes of production. Although the technological process for each individual product is relatively simply, the variety of products, mixed orders, frequent machines changeovers, machines with unusual service policy, lack or limited storage, etc., generates quite nontrivial planning, batching and scheduling problems and furthermore of a huge size. In this paper, we present speci c real process of production of refrigerators, mathematical and graph models of the problem and an outline of solution algorithm, based the on local search approach.

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Journal articles on the topic 'Production planning and scheduling problem'

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