Integrated Production Planning Research Articles

Integrated production planning and scheduling for a mixed batch job-shop based on alternant iterative genetic algorithm

An integrated optimization production planning and scheduling based on alternant iterative genetic algorithm is proposed here. The operation constraints to ensure batch production successively are determined in the first place. Then an integrated production planning and scheduling model is formulated based on non-linear mixed integer programming. An alternant iterative method by hybrid genetic algorithm (AIHGA) is employed to solve it, which operates by the following steps: a plan is given to find a schedule by hybrid genetic algorithm; in turn, a schedule is given to find a new plan using another hybrid genetic algorithm. Two hybrid genetic algorithms are alternately run to optimize the plan and schedule simultaneously. Finally a comparison is made between AIHGA and a monolithic optimization method based on hybrid genetic algorithm (MOHGA). Computational results show that AIHGA is of higher convergence speed and better performance than MOHGA. And the objective values of the former are an average of 12.2% less than those of the latter in the same running time.

A Lagrangian heuristic for an integrated lot-sizing and fixed scheduling problem

This paper presents a novel approach for solving an integrated production planning and scheduling problem. In theory as well as in practice, because of their complexity, these two decision levels are most of the time treated sequentially. Scheduling largely depends on the production quantities (lot sizes) computed at the production planning level and ignoring scheduling constraints in planning leads to inconsistent decisions. Integrating production planning and scheduling is therefore important for efficiently managing operations. An integrated model and an iterative solution procedure were proposed in earlier research papers: The approach has limitations, in particular when solving the planning problem. In this paper, a new formulation is proposed to determine a feasible optimal production plan, i.e. lot sizes, for a fixed sequence of operations on the machines when setup costs and times are taken into account. Capacity constraints correspond to paths of the conjunctive graph associated to the sequence. An original Lagrangian relaxation approach is proposed to solve this NP-hard problem. A lower bound is derived and an upper bound is calculated using a novel constructive heuristic. The quality of the approach is tested on numerous problem instances.

Integrated Location-Production-Distribution Planning in a Multiproducts Supply Chain Network Design Model

This paper proposes integrated location, production, and distribution planning for the supply chain network design which focuses on selecting the appropriate locations to build a new plant and distribution center while deciding the production and distribution of the product. We examine a multiechelon supply chain that includes suppliers, plants, and distribution centers and develop a mathematical model that aims at minimizing the total cost of the supply chain. In particular, the mathematical model considers the decision of how many plants and distribution centers to open and where to open them, as well as the allocation in each echelon. The LINGO software is used to solve the model for some problem cases. The study conducts various numerical experiments to illustrate the applicability of the developed model. Results show that, in small and medium size of problem, the optimal solution can be found using this solver. Sensitivity analysis is also conducted and shows that customer demand parameter has the greatest impact on the optimal solution.

Hybrid genetic algorithm to solve a joint production maintenance model

This paper proposes a solution approach based on heuristic methods for a model integrating production planning and maintenance scheduling in imperfect production systems. Performance of population-based heuristics is considerably influenced by the population management tools that are generally aimed to keep the diversity and to force the search process to examine unvisited areas within the solution space. We use genetic algorithm hybridize with a standard tabu search as well as population management strategies to efficiently solve the complicated nonlinear model. Exploiting population data in order to define new solutions from unvisited sections of response space, learning from the population to extract the specifications of promising solutions and maintaining the population diversity are the most important particularities of the proposed approach. Comparing the algorithm with conventional implementation of GA and tabu search, demonstrates its surpassing performance in terms of solution time and quality.

An integrated production and distribution planning model for perishable products

In this paper, a new formulation for integrating production planning and distribution planning of perishable products through lot sizing and inventory routing problem is presented. Some assumptions about the problem are as follows; there is a production facility which produces a single product, then delivers them directly using limited number of capacitated vehicles to geographically dispersed distribution centres. Also, the product is perishable, i.e., it is storable only for predetermined periods. Because of the computational complexity of the problem, using the exact methods is not feasible especially when the problem size is large. So, we have developed an efficient heuristic algorithm which is able to find good quality solutions in a reasonable time. Efficiency of the algorithm is proved through number of randomly generated test problems. The algorithm performance is compared to the LINGO commercial optimiser.

Integrated production planning and preventive maintenance in deteriorating production systems

The traditional production planning model based upon the famous linear programming formulation has been well documented. However, the integration of preventive maintenance planning in the same model is a recent problem. This paper proposes an extended linear programming model as a hybrid approach for computing the optimum production plan with minimum total cost. The dual objective problem of production planning and maintenance is treated into a mixed integer linear program. This program is not only considering cases of multi-lines, multi-periods and multi-items but also taking into account the deterioration of the lines. This deterioration is represented in the model as a reduction of production lines capacities in function of the time evolution. Maintenance operations are supposed to provide lines in an operational state as good as new, i.e. with a maximum capacity. Through the study of the models limits, it is shown that the proposed approach can deal with a broader range of problems than that of Aghezzaf and Najid (2008) [3]. An optimal relaxation technique based on the polyhedral theory is developed to improve the computational time and expand the limits of the proposed model. Also, a “Fix and Relax heuristic” is developed for complex problems. Their computation time and their difference are computed referring to the same lower bound and the same considerations as those presented by Aghezzaf and Najid. It is proved through more than 880 several simulations for each model with different capacities and different setup costs, that this approach can solve large size problems with moderate computational time and gap.

Integrating production planning and maintenance: an iterative method

Purpose – Making decisions on production and maintenance separately, as is often done in practice and research literature, may not result in overall optimization. This paper aims to propose a joint method that better integrates production planning and maintenance at the tactical level. The potential of improving the performance of the classic planning method is also explored. Design/methodology/approach – An integrated production planning and maintenance model is proposed. The production capacity losses resulted from both preventive and corrective maintenance activities are considered. Meanwhile, the reliability deterioration of the machine is considered to be operation dependent. An iterative approach is presented to find a solution for the nonlinear model through iteratively solving a sequence of mixed integer linear programming instances, accompanied by modification of some parameters prior to each iteration. Computational experiments are conducted to evaluate the performance of the proposed method compared with three other methods, including two methods based on separate planning and one integrated model. Findings – The superiority of the proposed method compared with all the other three methods is demonstrated. Thus, the values of both integrated planning and considering operation-dependent failures are testified. The advantage of the proposed method is highlighted in the cases of high capacity utilization, long maintenance durations and low maintenance costs. The performance of the two methods based on separate planning is sensitive to the system utilization, and when utilization is high, the one with an availability-sensitive objective function defined for the maintenance problem performs better. Originality/value – Few studies have been carried out to integrate decisions on production lot and maintenance. Their considerations are either incomplete or not realistic enough. A more comprehensive and realistic integrated model is proposed in this paper, along with an iterative solution algorithm for it. A potential way to improve the performance of the classic planning method with its simplicity preserved is also presented.

Multi-choice multi-objective mathematical programming model for integrated production planning: a case study

This article develops a multi-choice multi-objective linear programming model in order to solve an integrated production planning problem of a steel plant. The aim of the integrated production planning problem is to integrate the planning sub-functions into a single planning operation. The sub-functions are formulated by considering the capacity of different units of the plant, cost of raw materials from various territories, demands of customers in different geographical locations, time constraint for delivery the products, production cost and production rate at different stages of production process. Departure cost is also considered in the formulation of mathematical programming model. Some of the parameters are decided from a set of possible choices, therefore such parameters are considered as multi-choice type. Multi-choice mathematical programming problem cannot be solved directly. Therefore an equivalent multi-objective mathematical programming model is established in order to find the optimal solution of the problem. Computation of the mathematical programming model is performed with the practical production data of a plant to study the methodology.

Information support technologies of integrated production planning and control for OEM driven networked manufacturing

PurposeThe purpose of this paper is to study the information support technologies of integrated production planning control for OEM (original equipment manufacturer) driven networked manufacturing systems, and offer implications to firms for implementing networked manufacturing.Design/methodology/approachOEM driven networked manufacturing and its operations modes and support technologies are first discussed. Then, integration framework of production planning and control is proposed and relative technologies are discussed. Finally, a case of the application of information support technologies in networked manufacturing is illustrated.FindingsBoth theory analysis and case experience show that information integration and sharing are critical for effective operations of OEM driven networked manufacturing and an integrated production planning and control system can benefit firms for successfully operating a networked manufacturing system.Practical implicationsIt is valuable to develop and apply integrated production planning and control systems in OEM driven networked manufacturing, Firms should pay more attention to information sharing and communication with partners and utilize advanced information technologies to synchronize the operations of partners.Originality/valueIntegration framework of production planning and control proposed in this paper has originality and the technology strategies are also practical. Managerial ideas, technology framework and application strategies of integrated production planning and control are helpful for firms to implement OEM driven networked manufacturing.

Integrated production planning and control: A multi-objective optimization model

Purpose: Production planning and control has crucial impact on the production and business activities of enterprise. Enterprise Resource Planning (ERP) is the most popular resources planning and management system, however there are some shortcomings and deficiencies in the production planning and control because its core component is still the Material Requirements Planning (MRP) . For the defects of ERP system, many local improvement and optimization schemes have been proposed , and improve the feasibility and practicality of the plan in some extent, but study considering the whole planning system optimization in the multiple performance management objectives and achieving better application performance is less. The purpose of this paper is to propose a multi-objective production planning optimization model Based on the point of view of the integration of production planning and control, in order to achieve optimization and control of enterprise manufacturing management. Design/methodology/approach: On the analysis of ERP planning system’s defects and disadvantages, and related research and literature, a multi-objective production planning optimization model is proposed, in addition to net demand and capacity, multiple performance management objectives, such as on-time delivery, production balance, inventory, overtime production, are considered incorporating into the examination scope of the model, so that the manufacturing process could be management and controlled Optimally between multiple objectives. T he validity and practicability of the model will be verified by the instance in the last part of the paper . Findings: The main finding is that production planning management of manufacturing enterprise considers not only the capacity and materials, but also a variety of performance management objectives in the production process , and building a multi-objective optimization model can effectively optimize the management and control of enterprise manufacturing . Practical implications: By setting cost parameters for all kinds of production objectives, mangers can maintain the balances among multiple objectives, and achieve the optimized management and control in the manufacturing process. Originality/value: This paper propose a multi-objective production planning optimization model, which can consider multiple performance management objectives of manufacturing process, and can achieve effective planning and control of production process of enterprise.

합금철기업의 SCM 모델 구현

This research identified approachable management innovation technique from SCM point of view, and analyzed sales/logistics, integrated production planning, phenomenon of cooperation and suggested an innovation model over this for strengthening competitiveness of a case company. Process innovation model with an enterprise viewpoint is designed and suggested to solve the previous ares for improvement, and suggested various models of an ordering management model, available delivery management model, logistics operation model based on planning and S&OP model.

O problema combinado de planejamento da produção e corte de estoque sob incertezas: aplicação em fábricas de móveis de pequeno porte

Esse trabalho investiga um problema combinado de planejamento da produção e corte de estoque comum em fábricas de móveis de pequeno porte, onde os custos de produção envolvidos no processo de manufatura e as demandas dos produtos não são conhecidos com precisão. Para lidar com essas incertezas, foram propostos modelos de otimização robusta que permitem controlar o conservadorismo da solução de acordo com a atitude do decisor em relação ao risco. Experimentos computacionais baseados em dados reais de uma planta moveleira mostraram que é possível obter soluções robustas sem sacrificar em demasia o custo total. Além disso, observou-se que estratégias tipicamente utilizadas na prática para lidar com as incertezas podem gerar planos de produção com custos totais proibitivos.

Production planning and worker training in dynamic manufacturing systems

Production planning is a vital activity in any manufacturing system, and naturally implies assigning the available resources to the required operations. This paper develops and analyzes a comprehensive mathematical model for dynamic manufacturing systems. The proposed model integrates production planning and worker training considering machine and worker time availability, operation sequence and multi-period planning horizon. The objective is to minimize machine maintenance and overhead, system reconfiguration, backorder and inventory holding, training and salary of worker costs. Computational results are presented to verify the proposed model.

Integrated production planning and order acceptance under uncertainty: A robust optimization approach

The aim of this paper is to formulate a model that integrates production planning and order acceptance decisions while taking into account demand uncertainty and capturing the effects of congestion. Orders/customers are classified into classes based on their marginal revenue and their level of variability in order quantity (demand variance). The proposed integrated model provides the flexibility to decide on the fraction of demand to be satisfied from each customer class, giving the planner the choice of selecting among the highly profitable yet risky orders or less profitable but possibly more stable orders. Furthermore, when the production stage exceeds a critical utilization level, it suffers the consequences of congestion via elongated lead-times which results in backorders and erodes the firm’s revenue. Through order acceptance decisions, the planner can maintain a reasonable level of utilization and hence avoid increasing delays in production lead times. A robust optimization (RO) approach is adapted to model demand uncertainty and non-linear clearing functions characterize the relationship between throughput and workload to reflect the effects of congestion on production lead times. Illustrative simulation and numerical experiments show characteristics of the integrated model, the effects of congestion and variability, and the value of integrating production planning and order acceptance decisions.

An integrated production planning and scheduling framework for make-to-order environment

A capacity constrained integrated production planning and scheduling framework for a manufacturing firm operating in make-to-order environment, has been formalized. It primarily aims at the generation of shop floor schedules that are feasible with production plans generated at a higher level, practical and efficient at the shop floor. The formalized framework integrates capacity planning, loading and scheduling activities of a job-shop with consideration of process plans flexibility. The performance of framework is assessed through two case studies. Experimental investigation reveals that shop utilization level considered at capacity planning level affects master production schedule and system performance. Further, presence of multiple process plans improves system performance; especially, they assist in reducing the number of tardy part-types. Moreover, shortest processing time dispatching rule performs better than earliest due date during scheduling.

Research on Design and Development for Integrated Production Planning and Scheduling System of Rare Metal Manufacturing Based on WEB Platform

Aiming at the design and development of integrated production planning and scheduling system(PPSS) of rare metal manufacturing, the goals and main functions of the integrated production planning and scheduling system were analyzed and designed based on the analysis of rare metal manufacturing enterprise’s main characteristics and management status. Through analysis of the present situation and trend of production planning and scheduling, an integrated system architecture was proposed. Key technologies of system integration and system developing were delivered, and some key functions of production planning and scheduling system which include annual plans management, monthly plans management, MRP management, production scheduling management and production Kanban management were developed based on JSP technology and WEB platform. The practical applications in some rare metal manufacturing enterprises were proved that the integrated production planning and scheduling system could enhance the manufacturing management level effectively.

Integrating Production Planning and Control Business Processes

Organizations have production planning and control (PPC) processes supported by systems that execute, mainly, repetitive calculations. Based on these calculation results, decisions are taken by production managers. These decision processes make the connection between different levels of aggregation of information and could benefit from the increment of the level of automation. An increased level of application of business process modelling languages is proposed in order to contribute to increment the level of process automation and the detail of business analysis. Thus being, concepts of integration of production management processes, specifically of production planning and control processes are presented. These concepts, the application of business process modelling language (BPML) and some solutions of PPC integration compose the core content of this work. Additionally, criteria for evaluation of these processes of integration are identified and discussed. Finally, the presentation of an industrial case will be supported by BPML model.

Integrated production planning and scheduling optimization of multisite, multiproduct process industry

The current manufacturing environment for process industry has changed from a traditional single-site, single market to a more integrated global production mode where multiple sites are serving a global market. In this paper, the integrated planning and scheduling problem for the multisite, multiproduct batch plants is considered. The major challenge for addressing this problem is that the corresponding optimization problem becomes computationally intractable as the number of production sites, markets, and products increases in the supply chain network. To effectively deal with the increasing complexity, the block angular structure of the constraints matrix is exploited by relaxing the inventory constraints between adjoining time periods using the augmented Lagrangian decomposition method. To resolve the issues of non-separable cross-product terms in the augmented Lagrangian function, we apply diagonal approximation method. Several examples have been studied to demonstrate that the proposed approach yields significant computational savings compared to the full-scale integrated model.

A cutting plane approach for integrated planning and scheduling

In this paper we propose a branch-and-cut algorithm for solving an integrated production planning and scheduling problem in a parallel machine environment. The planning problem consists of assigning each job to a week over the planning horizon, whereas in the scheduling problem those jobs assigned to a given week have to be scheduled in a parallel machine environment such that all jobs are finished within the week. We solve this problem in two ways: (1) as a monolithic mathematical program and (2) using a hierarchical decomposition approach in which only the planning decisions are modeled explicitly, and the existence of a feasible schedule for each week is verified by using cutting planes. The two approaches are compared with extensive computational testing.

A tabu search algorithm for determining the economic design parameters of an integrated production planning, quality control and preventive maintenance policy

In this work, an integrated model of economic production quantity, economic design of an -control chart, and preventive maintenance are investigated under non-uniform quality control parameters. The effect on the expected total cost and quality control cost is investigated with three different assumptions of the quality control parameters. Using a tabu seach algorithm, the optimal values of quality control parameters, for different PM levels, are found. A non-uniform scheme for sampling frequency, sample size and control limit co-efficient provides lower cost as compared to schemes where only sampling frequency is taken as non-uniform.