Production planning system for a combination of make-to-stock and make-to-order products

This paper deals with the problem of designing a production planning system for a combination of make-to-stock (MTS) and make-to-order (MTO) products where the production facility produces both types of products. Important management points in such production systems are to design an efficient production planning system which can shorten the manufacturing time of MTO products as well controlling the unfilled rate of MTS products to the market demand at a low level. A hierarchical production planning model is introduced in order to design the production planning system. The buffer capacity is set as a design variable for determining production capacity at a higher planning level, and the rule for allocating the production capacity to types of products is adopted as a design variable at a lower level. First, we analyse how these two design variables affect the unfilled rate of MTS products and the average manufacturing time of MTO products. Second, we clarify the relationship of the buffer capacity with the manufacturing time of MTO products and with the required buffer inventory level of MTS products which can maintain the unfilled rate of MTS products to the market demand within an acceptable limit. Third, we show an example of the range of design variables which can control the unfilled rate of MTS products and the manufacturing time of MTO products within their acceptable limits, if the individual limits of the performance measure is given to respond to customer orders quickly.     

Modeling and analysis of the product assignment problem in single stage electronic assembly systems

Consider the production of an evolving family of similar products, each having a well-defined life cycle. The fundamental production resources are inherently flexible, i.e., reconfigurable and reprogrammable. Two distinct strategies can be followed in configuring production facilities: (1) focused facilities, where a facility is dedicated to one product at a time, but may be reassigned; and (2) nonfocused facilities, where setup operations permit a variety of products to be produced during a given planning period. When focused facilities are used, which is a common strategy in some electronics companies, products must be assigned to specific facilities. If facilities are not identical, and capacity is limited, then changing production requirements may force reassignment of products from one facility to another. Thus, the product assignment/reassignment decision may have a significant impact on the production capacity required. This paper concentrates on the product assignment/reassignment decision when a pure focused facility strategy is used. This problem is analyzed and a number of insights are developed. Based on this analysis, the problem is reformulated and an optimal solution procedure based on a multi-commodity network flow model is presented and tested for the product assignment/reassignment decision     

Distributed product and facility prototyping in extended manufacturing enterprises

In order to reduce the cost at the early product development stages, the planners need methodologies and tools that would allow them to judge upon the implications of the product design on the required manufacturing processes and facilities for their production. This paper reports on a new theoretical platform and a pilot implementation of a decision-making environment for distributed product and facility prototyping in an extended enterprise. The approach is based on an exchange of requests and information between collaborative autonomous agents that support the design, manufacturing planning and facility formation activities. The decision-making is formalized as iterative matching of design, process and facility attributes using multilevel resource capability representation within the extended enterprise. The system is implemented as an XML/CORBA-based environment for conveying design and manufacture messages across traditional technology boundaries. The reported research aims to provide the designers with a rapid manufacturing feasibility assessment tool to be used at different design and planning stages in extended manufacturing enterprises.     

Facility layout design in a changing environment

Increasing global competition, rapid changes in technology and the necessity to respond quickly to a cost and quality conscious customer have changed the dynamics of facilities planning. Today's manufacturing facility needs to be responsive to the frequent changes in product mix and demand while minimizing material handling and machine relocation costs. In this paper, we present a framework for the design of a dynamic facility which can respond effectively to the changes in product design, mix and volume in a continuously evolving work environment. A genetic algorithm-based heuristic is used for solving the design problem and two test cases are presented to illustrate the use of the methodology.     

Operational planning models for distribution networks

The design and optimisation of a logistic network deals with a wide set of decisions, e.g. the determination of the best location and capacity of the different logistic facilities (production plants, distribution centres, transit points, wholesalers, etc.), the allocation of the product demand coming from customers in presence (or absence) of fractionable flows of material, the determination of the best transportation mode (truck, rail, etc.) as well as loading and routing of vehicles. These decisions involve multiple stages of a distribution network: customers-regional distribution centres (RDC), RDCs-central distribution centres (CDC) and CDCs-production plants and sources, in presence of multiple products and the variable time (i.e. time-dependent product demand and flows of material). This paper presents a top-down methodology that joins the strategic planning, the tactical planning and the operational planning of distribution networks with a special focus on the development of effective heuristic methods to face the vehicle routing problem. Original models and heuristic algorithms for the operational planning are illustrated. The impact of the strategic and tactical decisions on the performance of the operational planning is evaluated by the application of the proposed hierarchical approach to two realistic case studies. Obtained results are illustrated in a what-if experimental analysis conducted on multiple problem settings and realistic scenarios.     

Optimising resource portfolio planning for capital-intensive industries under process-technology progress

This paper addresses the problem of resource portfolio planning of firms in high-tech, capital-intensive manufacturing industries. In light of the strategic importance of resource portfolio planning in these industries, we offer an alternative approach to modelling capacity planning and allocation problems that improves the deficiencies of prior models in dealing with three salient features of these industries, i.e. fast technological obsolescence, volatile market demand, and high capital expenditure. This paper first discusses the characteristics of resource portfolio planning problems including capacity adjustment and allocation. Next, we propose a new mathematical programming formulation that simultaneously optimises capacity planning and task assignment. For solution efficiency, a constraint-satisfied genetic algorithm (CSGA) is developed to solve the proposed mathematical programming problem on a real-time basis. The proposed modelling scheme is employed in the context of a semiconductor testing facility. Experimental results show that our approach can solve the resource portfolio planning problem more efficiently than a conventional optimisation solver. The overall contribution is an analytical tool that can be employed by decision makers responding to the dynamic technological progress and new product introduction at the strategic resource planning level.     

Meeting demand variation using flexible U-shaped assembly lines

Nowadays, companies must be able to provide a higher degree of product customisation to fulfil the needs of the increasingly sophisticated customer demand. This can only be achieved by having flexible production systems, able to cope with extended product ranges and with the uncertainty and variability of demand in the current market environment. The purpose of this paper is to present a contribution related to facilities design that accounts for this issue, by presenting flexible U-shaped line configurations for an assembly system. In this type of line, whenever the production volume or product mix changes, the only modification in the line will be the number of operators working in the line, as the physical workstations remain fixed. The relevance of the problem is stated and a heuristic procedure, based on ant colony algorithms, developed to address this problem is described. The results of the application of the proposed procedure to an assembly line of a major manufacturer of electronic security systems are reported.     

Using a multi-agent system to optimise resource utilisation in multi-site manufacturing facilities

Due to cost economies and better serving the global market, many enterprises expanded their manufacturing environment from a localised, single-site facility to more globalised, multi-site facilities. In order to take advantage of operating multi-site facilities, it is vital to make optimisation decisions of resource utilisation as if these facilities situated across different geographical locations are one integrated facility and take into account of the extended multi-site constraints and variables. This paper proposes a multi-agent system, using its characteristics of autonomy and intelligence, to integrate process planning and production scheduling across different facilities, so as to secure the most efficient and cost-effective plan and schedule to meet the demand. A currency-based agent iterative bidding mechanism is developed to facilitate the co-ordination of agents to achieve the goal. A genetic algorithm is employed to tune the currency values for agent bidding. In this paper, a case study is used for simulation in order to demonstrate the effectiveness and performance of the proposed agent system.     

New Tabu search heuristics for the dynamic facility layout problem

A manufacturing facility is a dynamic system that constantly evolves due to changes such as changes in product demands, product designs, or replacement of production equipment. As a result, the dynamic facility layout problem (DFLP) considers these changes and is defined as the problem of assigning departments to locations during a multi-period planning horizon such that the sum of the material handling and re-arrangement costs is minimised. In this paper, three tabu search (TS) heuristics are presented for this problem. The first heuristic is a simple TS heuristic. The second heuristic adds diversification and intensification strategies to the first, and the third heuristic is a probabilistic TS heuristic. To test the performances of the heuristics, two sets of test problems from the literature are used in the analysis. The results show that the second heuristic out-performs the other proposed heuristics and the heuristics available in the literature.     

A new heuristic algorithm for capacity planning in a manufacturing facility under learning

In this paper, a new and efficient heuristic algorithm is presented to determine the production capacity in a new facility under the effect of learning. Different demand patterns are studied while deciding the capacity requirements. Further, the learning effects due to human-machine interaction often found in a manufacturing system are considered during the development of the model. A computer program is developed and interfaced with XMP software on a Sun 3/60 workstation. Several numerical examples are presented to illustrate the heuristic algorithm.     

Synchronized Production Policies for Deteriorating Items in a Declining Market

This paper considers a production-inventory system for a line of product in which the items are deteriorating at a constant rate, the demand rate decreases negative exponentially, and no shortages are allowed. We consider a finite planning horizon, and assume that the replenishments will occur through internal production at fixed intervals of time, but that flexible rates of production are available for each period. Mathematical models are developed to yield the optimal information on the production runs under various explicit time and cost implications of in-house production. Computational results are provided, and comparison of policies made with respect to the instantaneous replenishment situation. The model has important ramifications for just-in-time policies, flexible manufacturing environments, and make-buy strategic decisions.     

Computer-aided production management issues in the engineer-to-order production of complex capital goods explored using a simulation approach

This paper describes the exploration of manufacturing planning and control issues in the capital goods industry using a simulation approach. The companies produce products which have deep and complex product structures and are produced in low volume on an engineer- or make-to-order basis (ETO, MTO). The work reported here draws on the results of surveys of companies involved in the manufacture of capital goods which identified their characteristics of ETO and MTO capital goods companies and their strategic issues. The planning and control approaches adopted in the manufacturing facilities and the difficulties experienced in the application of computer-aided production management (CAPM) systems were also examined. The simulation model developed enables complex manufacturing systems to be modelled and was configured to represent a typical ETO/MTO facility using industrial data. A series of full factorial experiments were performed to explore a number of production management problems identified in surveys including capacity planning, assembly planning and scheduling strategies. Conclusions are drawn on the effects on performance and capacity of: applying minimum set-up and processing times for both major and minor activities; using different data update periods and assembly lead times; and adopting various scheduling and despatching approaches. These results are compared with those obtained by other workers who used survey techniques alone, and have implications for the manufacturers of capital goods.     

Layout design in fractal organizations

Today's complex, unpredictable and unstable marketplace requires flexible manufacturing systems capable of cost-effective high variety–low volume production in frequently changing product demand and mix. In fractal organizations, system flexibility and responsiveness are achieved by allocating all manufacturing resources into multifunctional cells that are capable of processing a wide variety of products. In this paper, various fractal cell configuration methods for different system design objectives and constraints are proposed. These parameters determine the level of interaction between the cells, the distribution of different product types among the cells and the similarity of cell capabilities. A tabu-search-based method is proposed to optimize the product distribution to the cells and the arrangement of machines and cells on the shop floor. This optimization is performed for different fractal cell configuration methods and cell quantities. The quality of the resulting shop floor layouts is measured in terms of resource requirements and material movements. The results indicate that in fractal layouts, a trade-off is required between machine quantities and material travelling distance. It was generally possible to reduce travelling distances by increasing the degree of optimization on machine layout and product distribution for a specific product demand and mix.     

PRODUCTION SCHEDULING IN A FLEXIBLE MANUFACTURING SYSTEM WITH SETUPS

A model is presented for the planning and scheduling of production batches in a flexible manufacturing system environment in which setup costs and times are nonnegligible and alternate routings are possible. The formulation is an integer program with a multicommodity flow network structure. A heuristic procedure based on price-directive decompostion using column generation is used to obtain solutions. Numerical experimentation is performed to assess die quality of the heuristic versus optimal solutions, and to determine the impact of routing flexibility on total cost, inventory levels, bottlenecks, capacity utilization, throughput time, number of setups and split lots. Important cost-benefit trade-off implications are shown for the design of flexible manufacturing systems.     

Grouping and selecting products: the design key of Reconfigurable Manufacturing Systems (RMSs)

A Reconfigurable Manufacturing System (RMS) is a new paradigm that focuses on manufacturing a high variety of products at the same system. Having specified a design strategy for an RMS as the first design step at the tactical level, products must be grouped to identify and allocate corresponding manufacturing facilities. An interface between market and manufacturing called reconfiguration link is presented to specify and arrange products for manufacturing. The reconfiguration link incorporates the tasks of determining the products in the production range, grouping them into families and selecting the appropriate family at each configuration stage. The proposed approach of (re)configuring products before manufacturing facilitates assigning product families to the required manufacturing facilities in terms of (re)configuring manufacturing systems. This paper contributes an overall approach of grouping products into families based on operational similarities, when machines are still not identified. Since the problem of product family selection consists of quantitative and qualitative objectives, the Analytical Hierarchical Process (AHP) is then used while considering both market and manufacturing requirements. The AHP model is verified in an industrial case study through using Expert Choice software. The solutions take advantage of monitoring sensitivity analysis while changing the priorities of manufacturing and/or market criteria. The concept of the proposed model is generic in structure and applicable to many firms. However, the model must be adapted according to the specific nature of the company under study. For instance, product family choices may differ from one company to another because of the available technology and the volume and type of existing products in the production range.     

Dynamic control in automated manufacturing: a knowledge integrated approach†

Flexibly automated facilities permit a wider variety of products as well as objectives for making those products—thus requiring manufacturing control strategies to face an environment of ever present change. To operate in this environment, a system composed of hard automation, flexible automation and humans, which can be responsive to product and process requirements, machine breakdowns and delays, engineering changes and improvement opportunities, is needed. Such a system does not fall into the realm of any current manufacturing solution techniques. Something more than exact optimization, heuristic algorithms or stochastic estimates must be utilized. The research discussed herein describes a dynamic solution strategy to operate in this changing environment with adaptive self-improving characteristics. The proposed methodology for optimizing the control of an automated manufacturing facility is an integrated approach utilizing real-time feedback from the operating facility, direct feedback from a simulation of the facility and guidance from a historical knowledge base. This system is being implemented in a knowledge based environment called CAYENE. CAYENE is a hybrid artificial intelligence system, written in Lisp, based on the idea of using object oriented programming as a unifying principle for functional, frame and rule-based programming.     

Design for global manufacturing and assembly

In this paper the author presents and tests new concurrent engineering strategies that focus on manufacturing and assembly operations with a global perspective. Specifically, the focus is upon new design for manufacturability and assembly (DFMA) strategies to support multi-facility, global operations. These DFMA strategies are more holistic than most published techniques in that they focus on total system approaches that explicitly consider product mix, process configuration, and capital procurement strategies, as well as tooling, design, and set-up costs associated with manufacture and assembly. Furthermore, the DFMA strategies presented here consider the cost of transportation logistics in multi-facility, global manufacturing, assembly, and distribution networks., A general mathematical formulation is presented and tested under realistic conditions. It is demonstrated that this model can be used for capacity planning and product sourcing for multiple part types in many facilities. Similarly, it can be used to examine the sensitivity of solutions to changes in various costs, productivity levels, or product configuration and mix assumptions at each facility. Test results demonstrate the efficacy of use of the formulation in general terms. Finally, a research agenda is posited for the future development of the strategies.     

Incentives and yield management in improving productivity of manufacturing facilities

Financial incentives are used to improve productivity and quality in manufacturing and service facilities. This improvement in productivity would normally release part of the facility's productive capacity. Without stimulating additional demand to consume this released capacity, the facility would Be unable to tap the full benefits of the improvements in productivity. Hence, yield management is introduced in an attempt to entice more demand and increase revenues. In this paper, we develop a Non Linear Programming (NLP) model to jointly determine the optimum financial incentives and price discount levels for each rate class. The model aims at maximizing net revenues. It includes nonlinear relationships representing the impact of incentives on productivity and quality improvements as well as the effect of price discounts on customer demand in each market segment. The generic nature of our NLP model makes it applicable to all multi-product manufacturing facilities covering sales, production and delivery. The model is applied to determine the optimum incentive and price discount levels for perishable products in a multi-product ready-mix concrete plant. It is demonstrated that the model is useful in maximizing net revenues through productivity improvements and an increase in customer demand.     

Optimal configuration of remanufacturing supply network with return quality decision

This research studies the configuration problem of a remanufacturing production network together with the decision for return quality thresholds, in which, the manufacturer has multiple remanufacturing facilities to satisfy different market demands. Quality of returns is stochastic, while demand for remanufactured products is either stochastic or deterministic. The problem we considered is to determine facilities to operate, minimum quality to accept into each operating facility, return quantity and demand allocation simultaneously so that the system’s profit is maximised. The problem is formulated as a mixed integer non-linear programming model. Through the use of a numerical example, the impact of quantity of returns, total spending, quality uncertainty, demand uncertainty and transportation cost on the remanufacturing system is analysed.     

Optimal and Heuristic Facility Phase-out Strategies

This paper presents an efficient branch and bound algorithm and near optimal heuristic algorithms for solving the problem of withdrawing inventory and/or service facilities for a good or service whose overall demand is declining over time. In particular, this paper models the problem faced by a manager who must consider closing up to M initially open and operating support facilities as demand shifts and declines over a T period planning horizon. The criterion is minimization of total estimated discounted costs. The costs considered are the variable operating cost at each facility, the transportation costs between facilities and demand centers, and the costs to operate and close each facility. Computational results are presented for both the optimum finding and heuristic algorithms.