Manufacturing Planning Systems Research Articles

Enterprise-wide modeling & optimization—An overview of emerging research challenges and opportunities

The process systems engineering (PSE) as well as the operations research and management science (ORMS) literature has hitherto focused on disparate processes and functions within the enterprise. These themes have included upstream R&D pipeline management, planning and scheduling in batch and continuous manufacturing systems and more recently supply chain optimization under uncertainty. In reality, the modern process enterprise functions as a cohesive entity involving several degrees of cross-functional co-ordination across enterprise planning and process functions. The complex organizational structures underlying horizontally and vertically integrated process enterprises challenge our understanding of cross-functional co-ordination and its business impact. This article looks at the impact of enterprise-wide cross-functional coordination on enterprise performance, sustainability and growth prospects. Cross-functional coordination is defined as the integration of strategic and tactical decision-making processes involving the control of financial and inventory flows (both internal and external) as well as resource deployments. Initially, we demonstrate the existence of cross-functional decision-making dependencies using an enterprise network model. Subsequently, we discuss interactions between enterprise planning decisions involving project financing, debt-equity balancing, R&D portfolio selection, risk hedging with real derivative instruments, supply chain asset creation and marketing contracts which influence decision-making at the activity/process level. Several case studies are included to re-enforce the point that planning and process decisions need to be integrated.

Heuristic modeling for a dynamic and goal programming in production planning of continuous manufacturing systems

At the first sight it seems that advanced operation research is not used enough in continuous production systems as comparison with mass production,batch production and job shop systems,but really in a comprehensive evaluation the advanced operation research techniques can be used in continuous production systems in developing countries very widely,because of initial inadequate plant layout,stage by stage development of production lines,the purchnse of second hand machineries from various countries,plurality of customers.A case of production system planning is proposed for a chemical company in which the above mentioned conditions arc almost presented.The goals and constraints in this issue are as follows:①Minimizing deviation of customer's requirements.②Maximizing the profit.③Minimizing the frequencies of changes in formula production.④Minimizing the inventory of final products.⑤Balancing the production sections with regard to rate in production.⑥Limitation in inventory of raw material.The present situation is in such a way that various techniques such as goal programming,linear programming and dynamic programming can be used.But dynamic production programming issues are divided into two categories,at first one with limitation in production capacity and another with unlimited production capacity.For the first category,a systematic and acceptable solution has not been presented yet. Therefore an innovative method is used to convert the dynamic situation to a zero-one model.At last this issue is changed to a goal programming model with non-linear limitations with the use of GRG algorithm and that's how it is solved.

Optimal (s, S) production policies with delivery time guarantees and breakdowns

This paper studies optimal (s, S) policies for production planning in stochastic manufacturing systems with failures. For our models, we consider a one-machine system which produces one type of product. We consider two cases. The first one considers failure in the production process and the second one considers a failure in the distribution process. Parameters characterising the system are stochastic and they are assumed to be exponentially distributed. The analytical form of the steady state probability distribution for the inventory levels is obtained using Markov processes. The average profit of the system can be written in terms of the resulting probability distribution. We include numerical examples to illustrate the determination of optimal strategies. Hence, the optimal (s, S) policies are obtained using two methods depending on the failure type. [Received 12 February 2007; Accepted 26 April 2007]

Optimizing production smoothing decisions via batch selection for mixed-model just-in-time manufacturing systems with arbitrary setup and processing times

This paper is concerned with the production smoothing problem that arises in the context of just-in-time manufacturing systems. The production smoothing problem can be solved by employing a two-phase solution methodology, where optimal batch sizes for the products and a sequence for these batches are specified in the first and second phases, respectively. In this paper, we focus on the problem of selecting optimal batch sizes for the products. We propose a dynamic programming (DP) algorithm for the exact solution of the problem. Our computational experiments demonstrate that the DP approach requires significant computational effort, rendering its use in a real environment impractical. We develop three meta-heuristics for the near-optimal solution of the problem, namely strategic oscillation, scatter search and path relinking. The efficiency and efficacy of the methods are tested via a computational study. The computational results show that the meta-heuristic methods considered in this paper provide near-optimal solutions for the problem within several minutes. In particular, the path relinking method can be used for the planning of mixed-model manufacturing systems in real time with its negligible computational requirement and high solution quality.

A real-option approach to flexibility planning in reconfigurable manufacturing systems

Reconfigurable manufacturing systems (RMS) are a new class of manufacturing systems aiming at combining the high throughput of dedicated manufacturing lines with the flexibility of flexible manufacturing systems. An RMS can simultaneously manufacture a large variety of product types in unpredictable quantities while maintaining mass production efficiency. Flexibility typically comes at a price and is only valuable as a hedge against environmental uncertainty. It is important to determine an appropriate level of flexibility in the reconfiguration of production systems while considering the tradeoffs between its costs and benefits. This paper develops a real-option theoretical model that provides insights into flexibility planning in an RMS. A practical method is presented to assist the justification of an RMS in deciding how to influence its operating environment and choose right reconfiguration technologies in order to maximize the performance measure of profitability. The real-option approach surmounts traditional discounted cash-flow-analysis-based valuation methods that tend to ignore the upside potentials of an investment from management flexibility. The proposed model is tested with a real-life RMS installed for the Printed Circuit Board (PCB) assembly of mobile phones with a high level of variety.

Preface: Introduction to Special Issue

Demands for flexibility, greater variety of products, and advanced production processes complicate the logistics and planning of manufacturing systems; this also increases the number of alternatives that should be formalized in a model. The evaluation of all the possible alternatives of the planning problem is often considered an NP-hard combinatorial problem. Practical problems often contain nonlinearities, combinatorial relationships, and uncertainties that cannot be easily formalized under mathematical predefined relationships, binding the use of most commercial optimization toolboxes. Despite the fact that simulation models have proved to be useful for examining the performance of alternative system configurations and/or alternative operating procedures, several limitations arise when simulation techniques are applied to improve logistic systems performance due to its inability to evaluate more than a fraction of the immense range of options available. This special issue is the second of two issues that focus on the use of simulation techniques to improve manufacturing and logistic systems performance. Papers published in the first issue (Volume 80, Number 3) were mainly related to new modeling and simulation methodologies to improve logistic and manufacturing systems performance. Papers and technical notes in this second issue are focused on illustrating the use of experimental models to improve the performance of manufacturing and logistic systems by means of case studies. The article “A Petri Nets–Based Scheduling Methodology for Multipurpose Batch Plants’’ presents an optimization methodology of batch production processes assembled by shared resources that rely on a mapping of state events into time events, allowing in this way the straightforward use of well-consolidated scheduling policies developed for

A novel approach to production planning of flexible manufacturing systems using an efficient multi-objective genetic algorithm

In this paper, a novel approach using an efficient multi-objective genetic algorithm EMOGA is proposed to solve the problems of production planning of flexible manufacturing systems (FMSs) having four objectives: minimizing total flow time, machine workload unbalance, greatest machine workload and total tool cost. EMOGA makes use of Pareto dominance relationship to solve the problems without using relative preferences of multiple objectives. High efficiency of EMOGA arises from that multiple objectives can be optimized simultaneously without using heuristics and a set of good non-dominated solutions can be obtained providing additional degrees of freedom for the exploitation of resources of FMSs. Experimental results demonstrate effectiveness of the proposed approach using EMOGA for production planning of FMSs.

Virtual Automation System Standards

Computer based models have become an indispensable means for the planning, optimisation and operation of industrial manufacturing systems. The usability and significance of the models strongly depends on their accuracy, availability and functional extent. Today, suitable models for components of automation systems are available only for some types of components. Functionality for simulation purposes is often limited and integration costs are often still considerable. These problems were widely solved for industrial robots in the 'Realistic Robot Simulation' (RRS) projects. There, world-wide standards for Virtual Robot Controllers (VRCs) were created. Now, this success should be transferred to other components of automation systems like programmable logic controllers (PLCs), human-machine interface (HMI) systems, bus systems and effector sensor systems.

Coordinating production planning in cellular manufacturing environment using Tabu search

In this paper, an integrated model for production planning in cellular manufacturing (CM) systems is proposed. The model considers general CM features such as quadratic terms for inter-cell material handling and decisions for manufacturing cell construction. In addition, the solution of the model will provide production planning decisions such as times to start part processing and levels of finished part inventory. The model is then transformed to equivalent simpler models and is solved by Tabu search based procedure. Results from testing numerical examples show encouraging behaviors of the developed model and algorithm.

A heuristic algorithm to batching and loading problems in a flexible manufacturing system

Part type selection and machine loading are two major problems in the production planning of flexible manufacturing systems (FMS). The two problems are viewed as selecting subsets from the jobs of part types in a planning horizon and allocating jobs of the subsets among machines. In this paper, in order to develop a practical and efficient approach to solving FMS production planning problems, a heuristic algorithm is suggested that develops heuristic rules with the objective of minimisation of the number of tool changes and minimisation of the imbalance in per machine. To compare the proposed algorithm, a series of computational experiments is done on randomly generated test problems and the results show that the developed algorithm is very simple and efficient.

A new framework for MRP systems to be effective in engineered-to-order environments

Present day manufacturing resource planning (MRP) systems do not perform well in engineered-to-order (ETO) environments. A study of the use of MRP systems by ETO companies has led to the development of a new framework for manufacturing planning systems. New features allow better coordination of activities in the areas of engineering, material acquisition, production, and delivery. The new framework especially addresses the problem of planning with uncertain information which is characteristic of an ETO environment.

An intelligent hierarchical workstation control model for FMS

Hierarchical planning, scheduling and control in flexible manufacturing systems (FMS) provide a systematic way to effectively allocate resources along different time horizons. This paper describes the design and development of an intelligent hierarchical control model based on a proposed tool management method. The control model consists of four levels: the process plan selection, the master scheduling, the job sequencing and the control level. The model is developed to optimize the machine utilization and balance tool magazine capacity of a flexible machining workstation (FMW) in a tool-sharing environment. Problems are identified and modeled in the level of process plan selection, master scheduling, and job sequencing. A genetic-based algorithm was developed to solve the problem domains throughout the hierarchical planning and scheduling model. Fuzzy logic technique could also be incorporated into the master production schedule (MPS) level to allow for a more realistic result in the presence of uncertainty and impreciseness in order to fit the realistic nature of actual industrial environments.

An architecture to support the manufacturing system design and planning

This paper presents research work that is aiming to extend an existing architecture that integrates Virtual Reality (VR) capabilities with simulation and engineering tools for remote collaboration. Based on the work performed at an Engineering Centre, closely linked to industry, a couple of examples that are developed in real manufacturing contexts are described and discussed. The discussion justifies a step forward for an advanced engineering platform targeted to typical small- and medium-sized companies, supporting collaborative work, sharing experience and best practices between several geographically dispersed engineering teams. For companies with a lack of advanced engineering expertise and resources, the expected links that the engineering platform will promote have a significant impact upon the continuous improvement process.

Framework for a lean manufacturing planning system

This paper presents a planning system for lean manufacturing and is applicable to a variety of manual and automated manufacturing operations. By defining a standard set of lean manufacturing metrics, our research sets up a framework for performance measurement and benchmarking. A financial cost of waste measure is developed from data on current performance levels, and the planning framework develops a lean scorecard that establishes the gap between current performance and desired stretch performance targets, to facilitate planning for closure of the performance gap. For planning, users select one or more of 14 standard lean manufacturing strategies ranging from Kanban or pull systems, to manufacturing cells. Each strategy can be implemented at a basic or comprehensive level. In addition, projected performance improvements are used to estimate the performance improvements for each of the metrics. Finally, a lean manufacturing cash flow summary is developed to show cost of waste, cost of lean implementation, and lean savings over a five-year period. Together, these cash flows make it possible to compute a Return on Investment (ROI) evaluation of the lean manufacturing expenditures.

Implementation of STEP Application Protocol 224 in an automated manufacturing planning system

This paper reports the results of an on-going research project at Cranfield University in collaboration with a leading consultant company. A major objective of the research to develop a new generation process planning system using the latest tools and technologies, and to fully comply with the international standard for the exchange of product model data (STEP) Application Protocol 224 (AP224) for process planning using machining features. Although AP224 has been accepted as an international standard, there is a surprising lack of compatible software packages in the market. The developed prototype STEP-enabled manufacturing planning system (SMPS) can generate process plans and associated documents from AP224 files automatically, without any user interaction. The system consists of decision logic stored in an external database that makes it generic and compatible with any manufacturing application. A graphical tool is provided for knowledge/logic capture. The system can also accept a feature tree generated using the feature model editor (FME), which has been developed as an add-on to the system. The FME is intended to be a tool for conceptual design of simple components. The FME feature tree is used in the early design stage while the AP224 file generated from the detailed design model is used in the later stages. The prototype system is intended for single-piece machined (prismatic) parts and has been tested with case studies provided by the project collaborators.

GA-based integrated approach to FMS part type selection and machine-loading problem

Part type selection and machine loading are two interrelated subproblems in production planning of flexible manufacturing systems. The total solution requires a simultaneously combined approach to avoid the possible conflicts between the two sets of individually obtained solutions. A strict mixed-integer programming (MIP) model that integrates part type selection and machine loading together is formulated. The MIP takes into account the constraints such as magazine capacity, tool life, available machine time, etc. The objective is to minimize the difference between maximum and minimum workloads of all the machine resources in each batch. A genetic algorithm-based method is developed to obtain the solution of the problem effectively. Concepts of virtual job and virtual operation are introduced in the encoding scheme, and a chromosome is composed of both these strings. Among each chromosome, the partition symbol list is mainly used to handle the part type selection problem, while the virtual job list mainly used to cope with the loading problem. Special crossover and mutation operators are designed to adapt to the problem. Our approach can simultaneously balance the workloads in different batches. At last, illustrative examples are presented, and a comparison between standard MIP algorithm and a genetic algorithm method is given.

Strategic Manufacturing Planning Systems and Their Linkage to Planning System Success

ABSTRACTAcademics and practitioners alike are focusing more attention on manufacturing strategy after having recognized the important role it plays in shaping the success of industrial firms. Even though research in this area has increased in the last decade, the focus of much of that work has been on the content rather than the process of the manufacturing strategy. Consequently, this study attempts to understand the important elements of the strategic manufacturing planning process and its effectiveness. Borrowing from the extant literature in the fields of strategic management and information systems, we propose a research model that relates strategic manufacturing planning system design to planning system success. Using structured questionnaires, empirical data is collected from over 200 manufacturing executives to test the model hypotheses. Planning process in manufacturing was found to be a bottom‐up approach from a corporate or business perspective, which differs from the top‐down planning process prevalent in strategic information systems planning process. Findings also indicate that greater planning system success in manufacturing is associated with a planning system that combines some “rational” elements (formality, comprehensiveness, control focus, longer horizon) with others that lend adaptability (wider participation and more intense interaction). But the strategic manufacturing planning system is more than just a collection of independent planning characteristics. Instead, it can be viewed as a gestalt planning system whereby planning characteristics move together in affecting overall planning system success.

A model for integrated production planning in cellular manufacturing systems

Inventory control models deal with production planning in order to minimize inventory and shortage cost, while cellular manufacturing analysis mainly addresses how machines should be grouped and parts be produced. A mathematical programming model is developed using an integrated approach for production and inventory planning in a cellular manufacturing environment. The mathematical programming model minimizes inter‐cell material handling cost, finished‐good inventory cost and system set‐up cost. The non‐linear mixed integer programming model cannot be directly solved for real size practical problems due to its NP‐complexity. A decomposition‐based heuristic algorithm was then developed to efficiently solve the integrated planning and control problem. Numerical examples are provided to test and illustrate the model and the solution method presented in this paper.

Random-Direction Optimization Algorithms with Applications to Threshold Controls

This work develops a class of stochastic optimization algorithms. It aims to provide numerical procedures for solving threshold-type optimal control problems. The main motivation stems from applications involving optimal or suboptimal hedging policies, for example, production planning of manufacturing systems including random demand and stochastic machine capacity. The proposed algorithm is a constrained stochastic approximation procedure that uses random-direction finite-difference gradient estimates. Under fairly general conditions, the convergence of the algorithm is established and the rate of convergence is also derived. A numerical example is reported to demonstrate the performance of the algorithm.

Optimal ( s, S) policies with delivery time guarantees for manufacturing systems with early set-up

We consider optimal ( s, S) policies with delivery time guarantees for production planning in one-machine manufacturing systems with early set-up. The machine produces one type of product and delivery time guarantee is offered to the customers for each unit of ordered product. The inter-arrival time of the demand and the processing time for one unit of product are assumed to be exponentially distributed. In a ( s, S) policy, the machine will shut down when an inventory level of S is attained and once the inventory level drops to s, the machine will re-start. A set-up time is required for the machine. We model the set-up by the exponential distribution. We obtained an analytical form of the steady state probability distribution for the inventory levels derived. The average profit of the system can be written in terms of this probability distribution. Hence the optimal ( s, S) policy can be obtained by varying different possible values of s and S.