Production planning of mixed-model assembly lines: a heuristic mixed integer programming based approach

Process technology capabilities are becoming increasingly important as flexible manufacturing continues to be more prevalent, and as competition compels companies to provide expanded variety, at ever lower cost, so introducing plant and processes technological constraints. Model flexibility can also benefit from an appropriate production planning process, especially concerning mixed-model assembly lines, since it can facilitate master scheduling and line balancing activities, which are essential aspects of flexibility. Robust and practical planning approaches have to take into account two different aspects: the first consists in ensuring that the elaborated aggregate plan can be disaggregated into at least one detailed feasible plan for the realised demand, whereas the second in ensuring that this detailed plan is feasible at the operational level. This article faces the model flexibility challenge, reviewing and discussing the planning problem of a real world assembly manufacturing system, producing high volume and a variety of agricultural tractors and machines, analysing and resolving some important issues related to technological, organisational and managerial constraints. This article illustrates the implementation of an Advanced Planning System integrated with a mixed integer-programming model, which is solved by a new iterative heuristic approach capable of achieving interesting planning improvements for model-flexibility management.     

An Empirical Investigation of the Contribution of Strategic Sourcing to Manufacturing Flexibilities and Performance*

Empirical studies for achieving manufacturing flexibility goals have centered around the uses and advantages of advanced technology. Few studies have examined alternative ways of attaining flexibility objectives. Strategic sourcing finds mention in the literature as a potential route to manufacturing flexibility. However, little empirical evidence exists to validate this view. Literature also describes manufacturing flexibility as a multidimensional concept but fails to adequately examine the synergies among the different dimensions. This research investigates the influence of strategic sourcing and advanced manufacturing technologies on specific manufacturing flexibilities, and examines relationships among different flexibilities. The results are tied to manufacturing cost reduction. The findings suggest that strategic sourcing can assist in the achievement of modification flexibilities. Volume and modification flexibility are found to influence new product flexibility. Modification flexibility is found to influence manufacturing cost reduction. The results show that strategic sourcing can be used to target specific manufacturing flexibilities and that interflexibility synergies need to be considered while formulating flexibility-based manufacturing strategies.     

Interpretive structural modeling-based framework for mass customisation enablers: an Indian footwear case

Abstract

In the epoch of open economy and with the emergence of availability of individualised products over the Internet, Indian manufacturing industries are facing an enormous pressure to become more flexible and responsive, to accomplish customer’s varied and increasing demands. Mass customisation (MC) is about developing a customised product on demand for a particular customer after reception of a real order and producing it with the similar operational efficiency as one would anticipate from a mass-produced product. MC takes into account the merits of both the earlier systems of production, i.e. mass production and craft production. The craft production satisfies the personalised demands of customers and the mass production produces a limited variety of products at lower cost. Industries in developing countries such as India confront pressure from several perspectives to adopt MC. This study has been presented in the context of Indian manufacturing industries, and particularly for footwear industries to examine the enablers of MC. Achieving MC, however, require certain enabling technologies and processes in place. Several such enablers have been identified from the research literature. The objective of this study it to key out significantly important enablers for MC using interpretive structural modelling (ISM), and develop a hierarchy of these enablers for the Indian footwear units. ISM results show that modularity-based practices, digital manufacturing practices and supply chain integration are the most important MC enablers. Enhanced flexibility and responsiveness in the footwear production system can be achieved through modular and reconfigurable production system.     

Fuzzy multi-attribute decision-making for evaluating manufacturing flexibility

Evaluation of manufacturing flexibility in operations management is important to determine the competitiveness of manufacturing system, and is being increasing discussed in the literature on manufacturing system. The objective of this research is to develop a multi-attribute decision-making model based on fuzzy set theory to improving the manufacturing flexibility evaluation process. Since most information available in this stage is not numerical, fuzzy linguistic assessment is used to represent the performance rating of all flexibility metrics under flexibility dimensions and importance grade of all flexibility dimensions. This paper proposes a modified LOWA operator, which uses the maximum entropy weights, and uses it to direct computation on linguistic terms. The developed model evaluates the degree of manufacturing flexibility that can assist decision-makers, when making capital investment decisions and measuring performance, in finding the need for improving manufacturing flexibility, and in determining the dimensions of flexibility as the best directions to improvement. Examples using a case study of leading Taiwan firm in the bicycle industry are used to illustrate the concept developed. In addition, sensitivity analysis can be performed to examine the solution reasonability.     

Investment in Facility Changeover Flexibility for Early Entry into High‐Tech Markets

A model is introduced to analyze the manufacturing‐marketing interface for a firm in a high‐tech industry that produces a series of high‐volume products with short product life cycles on a single facility. The one‐time strategic decision regarding the firm's investment in changeover flexibility establishes the link between market opportunities and manufacturing capabilities. Specifically, the optimal changeover flexibility decision is determined in the context of the firm's market entry strategy for successive product generations, the changeover cost between generations, and the production efficiency of the facility. Moreover, the dynamic pricing policy for each product generation is obtained as a function of the firm's market entry strategy and manufacturing efficiency. Our findings provide insights linking internal manufacturing capabilities with external market forces for the high‐tech and high‐volume manufacturer of products with short life cycles. We show the impact of manufacturing efficiency and a firm's ability to benefit from volume‐based learning on the dynamic pricing policy for each product generation. The results demonstrate the benefits realized by a firm that works with its manufacturing equipment suppliers to develop more efficient and flexible technology. In addition, we explore how opportunities afforded by pioneer advantage enable a firm operating a less efficient facility to realize long term competitive advantage by deploying an earlier market entry strategy.     

Aggregate production planning using spreadsheet software

Firms can benefit by developing and using aggregate production plans. However, reports of ongoing production planning applications are rare. The complexity of production planning models has been cited as one of the reasons that firms do not develop formal production plans. This study was conducted to determine if a simple model, trial-and-error on a spreadsheet, could be used to produce cost-effective production planning solutions. Results indicate that good solutions can be obtained using this method. Furthermore, solutions tend to improve as the knowledge of the production planner increases and as time spent developing solutions increases. In addition, experience at producing spreadsheet-based solutions can translate into lower cost solutions for more complex problems while using less time for analysis. These findings indicate that spreadsheets can be effective decision aids for developing production planning solutions. By coupling the power and simplicity of a spreadsheet modelling package with the trial-and-error approach to production planning, model complexity should no longer be cited as a reason for not developing and using formal aggregate production plans.     

Forecasting Product Returns for Remanufacturing Operations

Driven by legislative pressures, an increasing number of manufacturing companies have been implementing comprehensive recycling and remanufacturing programs. The accurate forecasting of product returns is important for procurement decisions, production planning, and inventory and disposal management in such remanufacturing operations. In this study, we consider a manufacturer that also acts as a remanufacturer, and develop a generalized forecasting approach to determine the distribution of the returns of used products, as well as integrate it with an inventory model to enable production planning and control. We compare our forecasting approach to previous models and show that our approach is more consistent with continuous time, provides accurate estimates when the return lags are exponential in nature, and results in fewer units being held in inventory on average. The analysis revealed that these gains in accuracy resulted in the most cost savings when demand volumes for remanufactured products were high compared to the volume of returned products. Such situations require the frequent acquisition of cores to meet demand. The results show that significant cost savings can be achieved by using the proposed approach for sourcing product returns.     

A survey of simulation modeling techniques in production planning and control (PPC)

The manufacturing sector as a whole has undergone remarkable changes in terms of scale, complexity and technology over the past decades and this applies across most modern high-technology manufacturing such as electronics, semiconductor, aerospace and automotive industries. In order to remain competitive, manufacturers have to produce high-quality products at low cost, and at the same time retain sufficient flexibility and to meet rapidly changing customer demands. Production planning and control (PPC) is a key role which enables the manufacturer to gain visibility and control over all aspects of manufacturing activities. PPC in itself forms a subject of study, within which simulation techniques have proven themselves to be one of the most practical methodologies available to investigate and evaluate manufacturing issues. In this review paper, we focus on state-of-the art applications of simulation techniques in PPC to demonstrate their applicability to modern manufacturing issues. The review reports on academic publications on simulation applications in manufacturing from 2002 to 2014, incorporating surveys of peer-reviewed literature. The review covers three types of simulation techniques (system dynamic, discrete event simulation and agent-based simulation) and eight PPC issues (facility resource planning, capacity planning, job planning, process planning, scheduling, inventory management, production and process design, purchase and supply management). Literature survey is analysed on the basis of simulation application to PPC problems which can give a guideline for simulation technique selection and also can help for simulation modelling in PPC problemsWould you consider changing the term “modeling” to “modelling” in the title. Please check, and correct if necessary.     

Redesigning a Cellular Manufacturing System to Handle Long-Term Demand Changes: A Methodology and Investigation*

Cellular manufacturing systems have been proposed as an alternative to the job shop since they provide some of the operational benefits of a flow line production process, while retaining to some extent the flexibility of job shops. However, this must be balanced against the possibility of additional initial investments in equipment to form the cells and a certain loss in manufacturing flexibility, particularly in terms of the ability to deal with long-term demand changes. This paper presents a model-based heuristic cell system redesign methodology to deal with such demand changes. The methodology is validated and applied to system designs generated from several data sets published in the literature. Results show that different kinds of demand changes incur distinct kinds of costs. Further, characteristics of cell designs that can handle long-term demand changes at least cost are identified.     

Concurrent consideration of product design,process planning and production planning activities

In manufacturing engineering, product design, process planning and production planning activities are often considered independently. However, in order to effectively respond to changes in business situations, such as changes in demand forecast, product mix and technology, it is desirable to consider them concurrently. For this purpose, a large-scale linear programming model has been developed. The model considers minimization of the sum of processing cost, late shipment cost and inventory holding cost as the objective, and concurrently selects product designs, and generates process plans and production plans. The number of columns in the formulation can be large and, hence, an efficient column generation scheme is developed to solve the model. The model and solution procedure are illustrated with examples.     

Cloud manufacturing system for sheet metal processing

Abstract

Cloud computing is changing the way industries and enterprises run their businesses. Cloud manufacturing is emerging as an approach to transform the traditional manufacturing business model, while helping the manufacturer to align production efficiency with its business strategy, and creating intelligent factory networks that enable collaboration across the whole enterprise. Many production planning and control (PPC) problems are essentially optimisation problems, where the objective is to develop a plan that meets the demand at minimum cost or maximum profit. Because the underlying optimisation problem will vary in the different business and operation phases, it is important to think about optimisation in a dynamic mechanism and in a number of interlinked sub-problems at the same time. Cloud manufacturing has the potential to offer decision support as a service and medium of communication in PPC. To solve these problems and produce collaboration across the supply chain, this paper provides an overview of the state of the art in cloud manufacturing and presents a model of cloud-based production planning and production system for sheet metal processing.     

Multistage Stochastic Optimization for Production‐Inventory Planning with Intermittent Renewable Energy

A growing number of companies install wind and solar generators in their energy‐intensive facilities to attain low‐carbon manufacturing operations. However, there is a lack of methodological studies on operating large manufacturing facilities with intermittent power. This study presents a multi‐period, production‐inventory planning model in a multi‐plant manufacturing system powered with onsite and grid renewable energy. Our goal is to determine the production quantity, the stock level, and the renewable energy supply in each period such that the aggregate production cost (including energy) is minimized. We tackle this complex decision problem in three steps. First, we present a deterministic planning model to attain the desired green energy penetration level. Next, the deterministic model is extended to a multistage stochastic optimization model taking into account the uncertainties of renewables. Finally, we develop an efficient modified Benders decomposition algorithm to search for the optimal production schedule using a scenario tree. Numerical experiments are carried out to verify and validate the model integrity, and the potential of realizing high‐level renewables penetration in large manufacturing system is discussed and justified.     

考虑故障停机的生产控制与维修计划联合决策模型

通过生产控制与维修计划协同决策,降低生产成本。首先描述生产过程,分析各项费用。其次,建立了考虑生产过程失控、故障率和故障停时间的生产过程控制、生产计划优化和维修管理联合优化决策的模型。通过模型求解,联合制定出生产过程检查策略、生产计划（经济生产批量、生产批次）以及维修计划（PM间隔期）,实现单位时间内总费用最低的目标。再次,案例研究,分析生产过程失控、故障率和故障停机时间对于经济生产批量、生产过程检查策略和生产系统维修计划的影响。该模型从理论上解决了生产过程控制、生产计划优化和维修管理联合优化决策难题,对于指导企业制定生产计划和生产系统的检修计划,进而提高产品质量、降低生产成本、确保准时交货都具有指导意义和实用价值。     

A modified hierarchical production planning system integrated with MRP: A case study

In this paper, a hierarchical planning system is proposed which integrates aggregate capacity planning with MRP. This system is to be implemented in a metal box manufacturing company which multi-user MRP system covering manufacturing activities as well as procurement sales order processing and accounting systems. The hierarchical planning system includes a medium-range aggregate planning model adapted to the firm's requirements and strategies. The model consists of a mathematical formulation which covers labour capacity has already installed a constraints and includes certain cost estimations in the objective function. The planning horizon of the medium range planning is taken as twelve months in order to cover sales seasonality. The aggregate production quantities resulting from the optimized medium-range planning model are disaggregated according to procedures already found in the literature. Furthermore, the theoretical infeasibilities pertaining to the disaggregation procedures are also resolved in an heuristic manner. Using the latter modified disaggregation procedure, a feasible disaggregated plan is generated for the whole planning horizon. The proposed plan is compared with the current production policy of the firm and it is observed that the proposed plan leads to backorder reduction.     

Deciding manufacturing priorities: Flexibility, cost, quality and service

Traditional manufacturing planning focuses first on understanding the realities of the product marketplace and then aligning the production processes to match those realities. By conforming to that independent-market dependent-production model and simply implementing the determined tactics, manufacturing misses the opportunity to create competitive advantage by adopting a strategy that enhances the firm's position. To illustrate how manufacturing can incorporate strategic objectives into its planning, we add a third dimension to the traditional two-dimensional form of the product—process matrix. Along the new axis we show how four key competitive thrusts relate to the stages of the product—process life cycles. The thrusts of flexibility, cost, quality, and service are made explicit by plotting them, in turn, against the stages in the evolution of the product and the process. Each plot reveals new ways for manufacturing to plan for and realize competitive advantage through the application of computer-integrated manufacturing principles.     

Implementation of holonic scheduling and control in flow-line manufacturing systems: die-casting case study

The success of a flow-line manufacturing system depends on effective production scheduling and control. However, it has been found that current flow-line manufacturing scheduling and control algorithms lack the flexibility to handle interruptions or resource breakdowns; hence, system performance drops automatically and rapidly when interruptions occur. The objective of this research is to investigate if the performance of a flow-line manufacturing system can be improved by integrating agent-based, holonic scheduling and production control. A holonic manufacturing scheduling model has been developed and implemented into a die-casting manufacturing flow line throughout a simulation model. The analysis takes into account the comparisons of overall performances of the system models with the holonic scheduling and conventional scheduling approaches. Simulation results indicate that the holonic manufacturing scheduling and control can significantly increase the uptime efficiency and the production rate of the flow-line manufacturing system.     

Scenario aggregation in single-resource production planning models with uncertain demand

In this paper we present an application of the scenario aggregation approach proposed by Rockafellar and Wets to a simple standard multi-product multi-period production planning problem with uncertain demand and setup cost modelled by logical zero-one variables. The uncertainty in demand is expressed by a number of demand scenarios. As compared with more traditional approaches that require distributional assumptions and/or estimates of parameters from historical demand data, the scenario approach offers greater flexibility and makes it possible to take subjective information into account. The scenario aggregation principle and the corresponding progressive hedging algorithm offer a theoretically sound basis for generating consistent solutions for production planning models with uncertain demand. Since the production planning problem studied in this paper is of mixed-integer type the original scenario aggregation approach cannot be applied directly. However, since the integer variables in the production planning model are indirectly coupled to the continuous production decisions an alternative method in which only the production quantities are used to couple the different realizations can be used. This paper is a first attempt to perform this form of coupling. We illustrate the ideas on a small example and use this example to demonstrate how the solution can be evaluated in terms of flexibility measures.     

Flexible reasoning using sensible agent-based systems: A case study in job flow scheduling

Planning and control systems for highly dynamic and uncertain manufacturing environments require adaptive flexibility and decision-making capabilities. Modern distributed manufacturing systems assess the utility of planning and executing solutions for both system goals (e.g. minimize manufacturing production time for all parts or minimize WIP) and local goals (e.g. expedite part A production schedule or maximize machine X utilization). Sensible Agents have the ability to alter their autonomy levels to choose among a set of decision models in order to handle the differences between local and system goals. In this paper, Sensible Agents are applied to a production planning and control problem in the context of job shop scheduling and decision model theory. Sensible Agents provide for trade-off reasoning mechanisms among system and local utilities that are flexible and responsive to an agent's abilities, situational context and position in the organizational structure of the system.     

A DISCRETE PRODUCTION SWITCHING RULE FOR AGGREGATE PLANNING

Aggregate planning (AP) is a necessary activity for manufacturing and services alike. A shift toward high-volume batch and continuous flow processes within American manufacturing has given rise to increasing numbers of crew-loaded facilities. A majority of AP approaches incorporate continuous decision variables and require frequent adjustments to both production and work-force settings. Despite the availability and diversity of these approaches, few significant applications have been reported. This paper presents the detailed development of a discrete AP switching rule that can be applied to a variety of cost environments. Inventory costs are estimated using an interval approach rather than traditional point estimates. The model allows incorporation of overtime options and is interactive in nature. Decision variables from the model can be disaggregated and linked directly to lower-level planning activities. Actual results of model implementation are reported. An overview of the model's incorporation into the larger context of hierarchical production planning is found in [21].     

Integration of CAD/CAM and production control in sheet metal manufacturing — an application area of operations research

Abstract

This paper describes a process planning system for sheet metal parts. This system has capabilities to deliver process planning data just in time. The change of tools, processes and even machines for manufacturing of the parts is possible at the shop door after design has been finished.

The sequence of the production planning steps carried out can be changed. In this way the system can be adapted to the requirements of different sheet metal manufacturing companies.

More detailed information is given about the methods used for nesting of the workpieces on the sheets to be produced.