Decision architectures for production planning in multi-stage multi-product manufacturing systems

Recent trends in automated manufacturing call for hierarchical decision architectures for production planning, suitable for integration with part flow controls. Different design approaches are currently adopted for implementing production planning architectures, depending either on the objective of defining a centralized production plan for the whole manufacturing system (as in the case of MRP and OPT), or on the desire of coordinating local plans for the component work cells (as for JIT). The paper analyzes such approaches by use of a unifying mathematical formulation of the production plan optimization problem, to recognize the main features of the existing planning approaches, and compare their usefulness in different manufacturing processes.     

Earliness–tardiness production planning for just-in-time manufacturing: A unifying approach by goal programming

With popularity of the just-in-time (JIT) philosophy, researchers have started to seek the integration of Manufacturing Resource Planning (MRP-II) and JIT methodologies. This paper deals with the master production planning problem for a mass manufacturing system in the JIT environment, an earliness–tardiness production planning (ETPP) problem. The objective is to determine the optimum production rate for each product so that the total penalties imposed on the early and tardy production for all production periods be minimized. A goal programming (GP) approach is proposed to formulate the ETPP problem in a more generalized form, which includes several existing models in one unifying model. Moreover, the proposed GP algorithm ensures a global optimum solution, while the existing ones did not. In addition, it also possesses the advantages over others, such as easier to comprehend, easier to solve, and easier to extend it to the problem of multiple goals.     

Control methods for dynamic time-based manufacturing under customized product lead times

For manufacturers, the integration of high performance manufacturing with customer-oriented practices plays an important role in improving the performance of their business system. The benefits from such integration can only be maximized when the two parts are designed to work cooperatively. Though previous research has contributed much to manufacturing control algorithms and customer service practices, there has been little consideration of the two parts as a whole; consequently, the methods proposed may not be well supported by the other practices adopted in the system. This study develops production control methods that support a customer-oriented lead time policy, and aims to increase the performance of both manufacturing and customer service. The control methods are proposed for hybrid flow shops handling orders arriving dynamically. Computer simulations are conducted on a large number of problem instances, and the results show that the designed distributed feedback and decision-making functions enable the proposed methods to significantly outperform existing methods in achieving just-in-time (JIT) job completion under customized product lead times. Even taking into account the possible tradeoff between JIT job completion and flow time length, the proposed methods still deliver competitive performance.     

A mathematical programming model for integrating production and procurement transport decisions

In this paper, we propose a new mathematical programming model for integrating production and procurement transport planning decisions in manufacturing systems in a unique optimization model. This problem was introduced conceptually and dubbed as MRP IV by Díaz-Madroñero et al. (2012) to extend the current MRP (material requirement planning) systems. This proposal simultaneously considers material, production resources capacities and procurement transport planning decisions with different shipping modes (such as full-truckload, less-than-truckload and milk-run) in the supply chain to avoid suboptimal results, which are usually generated due to sequential and independent plans. We considered an industrial automobile company to validate the proposed model using real world data. The results obtained by the MRP IV proposed model, in terms of total planning costs and transport efficiency indicators, are better than those obtained in the current heuristic procedures followed in the company under study.     

Equipment Selection Problems in Just-in-Time Manufacturing Systems

This paper is concerned with the development of a mathematical model for solving the problem of equipment selection in just-in-time (JIT) manufacturing systems. Equipment selection issues are important, particularly during the initial stages of implementing JIT. In this paper a mathematical model is proposed for capacity planning and for selecting equipment in a multi-stage JIT production system. An example situation is considered to explain the application and behaviour of the model.     

Optimized material requirements planning for semiconductor manufacturing

This paper describes a custom operational research algorithm, which is run nightly by IBM to create a material requirements plan for its semiconductor fabrication facility in Vermont, USA. To model alternative manufacturing processes and part substitutions, this application interweaves linear programming and heuristic methods to reap the benefits of each decision technology. At each level of the bills of materials supply chain with complex decision choices to be made, parallel linear programmes are invoked and their results are fed into a material requirements planning (MRP) heuristic, which processes parts through multiple iterations. The results from processing one level of the bills of materials supply chain are exploded to create demand for the next level and the interweaving of the two decision technologies continues. The algorithm creates recommended manufacturing releases and work-in-process priorities. These outputs point out opportunities for improvement in order to satisfy all demands on time. The output can be interpreted with well-known MRP assumptions.     

A reduced variable neighborhood search algorithm for uncapacitated multilevel lot-sizing problems

Multilevel lot-sizing (MLLS) problems, which involve complicated product structures with interdependence among the items, play an important role in the material requirement planning (MRP) system of modern manufacturing/assembling lines. In this paper, we present a reduced variable neighborhood search (RVNS) algorithm and several implemental techniques for solving uncapacitated MLLS problems. Computational experiments are carried out on three classes of benchmark instances under different scales (small, medium, and large). Compared with the existing literature, RVNS shows good performance and robustness on a total of 176 tested instances. For the 96 small-sized instances, the RVNS algorithm can find 100% of the optimal solutions in less computational time; for the 40 medium-sized and the 40 large-sized instances, the RVNS algorithm is competitive against other methods, enjoying good effectiveness as well as high computational efficiency. In the calculations, RVNS updated 7 (17.5%) best known solutions for the medium-sized instances and 16 (40%) best known solutions for the large-sized instances.     

A multi-class multi-level capacitated lot sizing model

When demand loading is higher than available capacity, it takes a great deal of effort for a traditional MRP system to obtain a capacity-feasible production plan. Also, the separation of lot sizing decisions and capacity requirement planning makes the setup decisions more difficult. In a practical application, a production planning system should prioritize demands when allocating manufacturing resources. This study proposes a planning model that integrates all MRP computation modules. The model not only includes multi-level capacitated lot sizing problems but also considers multiple demand classes. Each demand class corresponds to a mixed integer programming (MIP) problem. By sequentially solving the MIP problems according to their demand class priorities, this proposed approach allocates finite manufacturing resources and generates feasible production plans. In this paper we experiment with three heuristic search algorithms: (1) tabu search; (2) simulated annealing, and (3) genetic algorithm, to solve the MIP problems. Experimental designs and statistical methods are used to evaluate and analyse the performance of these three algorithms. The results show that tabu search and simulated annealing perform best in the confirmed order demand class and forecast demand class, respectively.     

Scheduling just-in-time part supply for mixed-model assembly lines

With increasing cost competition and product variety, providing an efficient just-in-time (JIT) supply has become one of the greatest challenges in the use of mixed-model assembly line production systems. In the present paper, therefore, we propose a new approach for scheduling JIT part supply from a central storage center. Usually, materials are stored in boxes that are allotted to the consumptive stations of the line by a forklift. For such a real-world problem, a new model, a complexity proof as well as different exact and heuristic solution procedures are provided. Furthermore, a direct comparison with a simple two-bin kanban system is provided. Such a system is currently applied in the real-world industrial process that motivates our research. It becomes obvious that this policy is considerably outperformed according to the resulting inventory- and α-service levels. Moreover, at the interface between logistics and assembly operations, strategic management implications are obtained. Specifically, based on the new approach, it is the first time a statistical analysis is being made as to whether widespread Level Scheduling policies, which are well-known from the Toyota Production System, indeed facilitate material supply. Note that in the literature it is frequently claimed that this causality exists.     

Optimal models for a multi-stage supply chain system controlled by kanban under just-in-time philosophy

This research studies a multi-stage supply chain system that operates under a JIT (just-in-time) delivery policy. Kanbans play an important role in the information and material flows in a supply chain system. Thus, a kanban mechanism is employed to assist in linking different production processes in a supply chain system to implement the scope of JIT philosophy. For a multi-stage supply chain system, a mixed-integer nonlinear programming (MINLP) problem is formulated from the perspective of JIT delivery policy where a kanban may reflect to a transporter such as a truck or a fork-lifter. The number of kanbans, the batch size, the number of batches and the total quantity over one period are determined optimally. It is solved optimally by branch and bound method. A greedy heuristic to avoid the large computational time in branch-and-bound algorithm is developed for solving a large MINLP. Coupled with plant-wide efforts for cost control and management commitment, a logistic system for controlling the production as well as the supply chain is built, which results in minimizing the total cost of the supply chain system. The results show that the improvements in reduction of inventory, wasted labor and customer service in a supply chain are significantly accomplished through the kanban mechanism.     

Focusing material requirements planning (MRP) towards performance

This paper looks at the successes and disappointments of MRP. It studies numerous articles to determine what the key shortcomings of MRP are. Next, it investigates if these failures are correctable, and what the consequences of not correcting these deficiencies means. This article considers alternatives that have been discussed in the current literature. Last of all this article discusses whether the improvements these alternatives suggest are sufficient to make MRP worth salvaging, or whether MRP is a system that needs to be discarded in favor of systems such as JIT (Just-in-Time), Optimized Production Technology (OPT), Theory of Constraints (TOC), and Bottleneck Allocation Methodology (BAM).     

Collaborative production planning of supply chain under price and demand uncertainty

This research is motivated by an automobile manufacturing supply chain network. It involves a multi-echelon production system with material supply, component fabrication, manufacturing, and final product distribution activities. We address the production planning issue by considering bill of materials and the trade-offs between inventories, production costs and customer service level. Due to its complexity, an integrated solution framework which combines scatter evolutionary algorithm, fuzzy programming and stochastic chance-constrained programming are combined to jointly take up the issue. We conduct a computational study to evaluate the model. Numerical results using the proposed algorithm confirm the advantage of the integrated planning approach. Compared with other solution methodologies, the supply chain profits from the proposed approach consistently outperform, in some cases up to 13% better. The impacts of uncertainty in demand, material price, and other parameters on the performance of the supply chain are studied through sensitivity analysis. We found the proposed model is effective in developing robust production plans under various market conditions.     

The effect of material flow and workload on the performance of machine location heuristics

Depending on the problem structure and routing strategies a machine location problem plays an important role in controlling the material flow of work-in-process in discrete product manufacturing environment. In this paper we investigate the effect of material flow and workload on the performance of heuristics for solving an important design problem for job routing and material flow in a manufacturing system. In this research we first develop a model for workload or traffic intensity between machines in a shop floor and then identify different structures of the problems, especially the data. This measure is then used to evaluate the effect of workload on efficiency of the heuristics to solve machine location problems. Some concluding remarks are made on to the effect of the workload or the traffic intensity of materials within the machine cell on the performance of some known heuristics. Conclusions are also made on the performance measures such as makespan, transporter utilization and machine utilization, depending on the problem and data structures.     

Applications of mathematical programming models

A sizeable proportion of manufacturing expenses can be attributed to facility layout and material handling. Facility layout decisions involve designing the arrangement of elements in manufacturing systems. Among the most critical material handling decisions in this area are the arrangement and design of material flow patterns. This survey article reviews loop based facility planning and material handling decisions for trip based material handling equipment with an emphasis on unit load automated guided vehicles. The article examines issues related with facility design, material handling design, and fleet sizing and operating.     

Just-in-time replenishment decisions for assembly manufacturing with investor-supplied finance

While JIT ideas have been enthusiastically embraced by manufacturing practitioners, the small replenishment batch sizes advocated are difficult to reconcile with the standard management science cost trade-off approach. The difficulty is diagnosed as being due to the standard assumption that capital for inventory is borrowed and hence boundless. We present a new analysis of inventory reduction decisions, such as adopting JIT replenishment or component substitution, using a deterministic batch sizing model which assumes that inventory is financed by the investors in the company and is thus finite. As a consequence, the investment level is treated as an additional variable of the decision analysis. Using the well established technique of constrained optimisation it is shown that for investor-financed operations the effective value of money invested in inventory is the marginal return on investment of the company, and increases with the degree of constraint. Thus, JIT policy options are especially favourable when low levels of inventory investment are sought, even without setup cost reduction, because the capital formerly invested in stock holdings of the JIT components can be reinvested in the inventory of other components to make their replenishment more efficient using larger batch sizes. The analysis is illustrated using an actual case study of a small manufacturing enterprise seeking to reduce inventory and increase return on investment. The analysis has interesting practical implications for inventory managers including a proposed simple method for identifying candidate components for JIT replenishment.     

A coordination system for seasonal demand problems in the supply chain

This article considers a single product coordination system using a periodic review policy, participants of the system including a supplier and one or more heterogeneous buyers over a discrete time planning horizon in a manufacturing supply chain. In the coordination system, the demand of buyer in each period is deterministic, the supplier replenishes all the buyers, and all participants agree to plan replenishment to minimize total system costs. To achieve the objective of the coordination system, we make use of small lot sizing and frequent delivery policies (JIT philosophy) to transport inventory between supplier and buyers. Moreover, demand variations of buyers are allowed in the coordination system to suit real-world situations, especially for hi-tech industries. Furthermore, according to the mechanisms of minimizing the total relevant costs, the proposed method can obtain the optimal number of deliveries, shipping points and shipping quantities in each order for all participants in the coordination system.     

A Lot-sizing Model for Just-in-time Manufacturing

We consider the problem of determining lot sizes of multiple items that are manufactured by a single capacitated facility. The manufacturing facility may represent a bottleneck processing activity on the shop floor or a storeroom that provides components to the shop floor. Items flow from the facility to a downstream facility, where they are assembled according to a specified mix. Just-in-time (JIT) manufacturing requires a balanced flow of items, in the proper mix, between successive facilities. Our model determines lot sizes of the various items based on available capacity and four attributes of each item: demand rate, holding cost, set-up time and processing time. Holding costs for each item accrue until the appropriate mix of items is available for shipment downstream. We develop a lot-sizing heuristic that minimizes total holding cost per time unit over all items, subject to capacity availability and the required mix of items.     

A note on a generalized network flow model for manufacturing process

Manufacturing network flow （MNF） is a generalized network model that overcomes the limitation of an ordinary network flow in modeling more complicated manufacturing scenarios, in particular the synthesis of different materials into one product and/or the distilling of one type of material into many different products. Though a network simplex method for solving a simplified version of MNF has been outlined in the literature, more research work is still needed to give a complete answer whether some classical duality and optimality results of the classical network flow problem can be extended in MNF. In this paper, we propose an algorithmic method for obtaining an initial basic feasible solution to start the existing network simplex algorithm, and present a network-based approach to checking the dual feasibility conditions. These results are an extension of those of the ordinary network flow problem.     

A just-in-time three-level integrated manufacturing system for linearly time-varying demand process

This paper considers a just-in-time (JIT) manufacturing system in which a single manufacturer procures raw materials from a single supplier, process them to produce finished products, and then deliver the products to a single-buyer. The customer demand rate is assumed to be linearly decreasing time-varying. In the JIT system, in order to minimize the suppliers as well as the buyers holding costs, the supply of raw materials and the delivery of finished products are made in small quantities. In this case, both the supply and the delivery may require multiple installments for a single production lot. We develop a mathematical model for this problem, propose a simple methodology for solving the model, and illustrate the effectiveness of the method with numerical examples.     

Concurrent engineering: a competitive strategy for process industries

Concurrent Engineering (CE) is a systematic approach to the integrated, simultaneous design of both products and their related processes, including production. A number of research reports are available in the literature to deal with CE and its applications in manufacturing industries. However, the application of CE in Process Industries (PI) has not received enough attention from researchers and practitioners for improving productivity and quality. To use CE effectively in process industries requires accuracy of data and a common architecture. In PI, the products are processed with minimal interruptions in any one production run or between production runs of products that exhibit process characteristics, such as liquids, fibres, powders and gases. The general characteristics of this industry add value to materials by mixing, separating, forming, or chemical reactions. PI often initiate their flow with only a few raw materials and subsequently process a variety of blending and resplitting operations. Nevertheless, there are many common characteristics such as material and information flow between manufacturing and PI. This indicates the application areas of CE in PI and hence to improve the competitiveness of PI in terms of flexibility and responsiveness. An attempt has been made in this paper to study how CE can be utilized to design products, production system, and production planning and control (PPC) system for improving productivity and quality in PI.