A stochastic production planning problem in hybrid manufacturing and remanufacturing systems with resource capacity planning

DIRECT is derivative-free global-search algorithm has been found to perform robustly across a wide variety of low-dimensional test problems. The reason DIRECT’s robustness is its lack of algorithmic parameters that need be “tuned” to make the algorithm perform well. In particular, there is no parameter that determines the relative emphasis on global versus local search. Unfortunately, the same algorithmic features that enable DIRECT to perform so robustly have a negative side effect. In particular, DIRECT is usually quick to get close to the global minimum, but very slow to refine the solution to high accuracy. This is what we call DIRECT’s “eventually inefficient behavior.” In this paper, we outline two root causes for this undesirable behavior and propose modifications to eliminate it. The paper builds upon our previously published “MrDIRECT” algorithm, which we can now show only addressed the first root cause of the “eventually inefficient behavior.” The key contribution of the current paper is a further enhancement that allows MrDIRECT to address the second root cause as well. To demonstrate the effectiveness of the enhanced MrDIRECT, we have identified a set of test functions that highlight different situations in which DIRECT has convergence issues. Extensive numerical work with this test suite demonstrates that the enhanced version of MrDIRECT does indeed improve the convergence rate of DIRECT.     

Production planning and inventory control with remanufacturing and disposal

In this paper we consider a stochastic inventory system with production, remanufacturing, and disposal operations. Customer demands must either be fulfilled from the production of new products or by the remanufacturing of used products. Used products are either remanufactured or disposed of. To coordinate production, remanufacturing and disposal operations efficiently, we extend the PUSH and PULL strategies that Van der Laan et al. developed to control a system in which all returned products are remanufactured and no planned disposals occur. The other contributions of this paper are to indicate when and why planned disposals are economically beneficial, and to compare the PUSH-disposal strategy to the PULL-disposal strategy. In addition, we investigate the robustness of the control parameters of the PUSH- and PULL-disposal strategy over the different stages of a product life-cycle.     

Optimal acquisition and production policy in a hybrid manufacturing/remanufacturing system with core acquisition at different quality levels

We study the acquisition and production planning problem for a hybrid manufacturing/remanufacturing system with core acquisition at two (high and low) quality conditions. We model the problem as a stochastic dynamic programming, derive the optimal dynamic acquisition pricing and production policy, and analyze the influences of system parameters on the acquisition prices and production quantities. The production cost differences among remanufacturing high- and low-quality cores and manufacturing new products are found to be critical for the optimal production and acquisition pricing policy: the acquisition price of high-quality cores is increasing in manufacturing and remanufacturing cost differences, while the acquisition price of low-quality cores is decreasing in the remanufacturing cost difference between high- and low-quality cores and increasing in manufacturing and remanufacturing cost differences; the optimal remanufacturing/manufacturing policy follows a base-on-stock pattern, which is characterized by some crucial parameters dependent on these cost differences.     

Production capacity planning and control in multi-stage manufacturing

This paper develops a mathematical model for determining the optimum lot-sizes for a set of products and the capacity required to produce them in a multi-stage production system. The purpose of the modelling is to support capacity planning at the production function level and the basic criterion considered for the optimisation is the minimisation of the total system cost (TSC) per unit time. The TSC consists of (i) set-up cost, (ii) cost due to the quenching of batches, and (iii) hiring cost of the machines. An example is presented to explain the model.     

Production planning under uncertainty in textile manufacturing

Textile manufacturing consists of yarn production, fabric formation, and finishing and dyeing stages. The subject of this paper is the yarn production planning problem, although the approach is directly applicable to the fabric production planning problem due to similarities in the respective models. Our experience at an international textile manufacturer indicates that demand uncertainty is a major challenge in developing yarn production plans. We develop a stochastic programming model that explicitly includes uncertainty in the form of discrete demand scenarios. This results in a large-scale mixed integer model that is difficult to solve with off-the-shelf commercial solvers. We develop a two-step preprocessing algorithm that improves the linear programming relaxation of the model and reduces its size, consequently improving the computational requirements. We illustrate the benefits of a stochastic programming approach over a deterministic model and share our initial application experience.     

Heuristics with guaranteed performance bounds for a manufacturing system with product recovery

We consider a manufacturing system with product recovery. The system manufactures a new product as well as remanufactures the product from old, returned items. The items remanufactured with the returned products are as good as new and satisfy the same demand as the new item. The demand rate for the new item and the return rate for the old item are deterministic and constant. The relevant costs are the holding costs for the new item and the returned item, and the fixed setup costs for both manufacturing and remanufacturing. The objective is to determine the lot sizes and production schedule for manufacturing and remanufacturing so as to minimize the long-run average cost per unit time. We first develop a lower bound among all classes of policies for the problem. We then show that the optimal integer ratio policy for the problem obtains a solution whose cost is at most 1.5% more than the lower bound.     

Multi-product economic lot scheduling problem with separate production lines for manufacturing and remanufacturing

We study the economic lot scheduling problem with two production sources, manufacturing and remanufacturing, for which operations are performed on separate, dedicated lines. We develop an exact algorithm for finding the optimal common-cycle-time policy. The algorithm combines a search for the optimal cycle time with a mixed integer programming (MIP) formulation of the problem given a fixed cycle time. Using case study data from an auto part producer, we perform a sensitivity study on the effects of key problem parameters such as demand rates and return fractions. Furthermore, by comparing to results in Tang and Teunter [Tang, O., Teunter, R.H., 2006. Economic lot scheduling problem with returns. Production and Operations Management] for the situation where all operations are performed on the same line, we analyze the cost benefits of using dedicated lines.     

Reverse logistics network design for product recovery and remanufacturing

Due to environmental concerns, reverse logistics now is becoming an important strategy to increase customer satisfaction. This research develops a generic mixed integer nonlinear programming model (MINLP) for reverse logistics network design. This is a multi-echelon reverse logistics model. It maximizes total profit by handling products returned for repair, remanufacturing, recycling, reuse, or incineration/landfill. A hybrid genetic algorithm (GA) is proposed to solve the problem. The designed model is validated and tested by using a real-life example of recycling bulk waste in Taoyuan City, Taiwan. Sensitivity analyses are conducted on various parameters to illustrate the capabilities of the proposed model. Post-optimality analysis and comparison show that the proposed model performs better than current reverse logistic operations and the proposed hybrid GA demonstrates the efficiency of solving the complex reverse logistics problem.     

Supply planning for industrial ecology and remanufacturing under uncertainty: a numerical study of leaded-waste recovery from television disposal

The use of waste as a raw material for manufacturing is hampered by the uncertainty associated with the availability of supply. Technological change and obsolescence further complicates the ability of decision makers to consider discarded durable products as a potential source of raw materials. This uncertainty complicates remanufacturing and industrial ecology. A problem since remanufacturing and industrial ecology need to be (and can be) profitable as well as environmentally desirable if they are to be encouraged. To address this problem the modelling of the waste flow of durable goods is considered. The disposal of televisions in the United States is used to illustrate the challenges and requirements for forecasting in an environment with supply uncertainty. This example is timely since the diposal of cathode ray tubes (CRTs) in municipal landfills is being banned and an alternate technology trajectory for televisions exists—the flat panel display and phase-out of analogue broadcasting in the US. This paper estimates the waste stream resulting from three different scenarios of CRT leaded-waste disposal patterns. The reuse of lead-containing CRT glass is found to offer potential. The elimination of this controversial waste stream, as a result of replacement by the adoption of flat panel television technology, is still decades away. The findings in this study indicate the range of the quantity of waste that will require an alternative infrastructure as it is displaced from municipal landfills. This study provides important information for both developing a collection infrastructure and processing alternatives to extract the residual value of the disposed of televisions.     

Optimal policy and holding cost stability regions in a periodic review inventory system with manufacturing and remanufacturing options

In this paper a periodic review inventory model with finite horizon and remanufacturing, manufacturing options is studied. It is assumed that demand and cost parameters are constant and a sufficiently large quantity of used products is available at the beginning of the horizon. The model is studied within the class of policies with given remanufacturing and manufacturing set up and the optimal policy is obtained within this class. The policy specifies the period of switching from remanufacturing to manufacturing (switching period), the periods where remanufacturing and manufacturing activities take place and the corresponding lot sizes. An explicit formula for the cost function and some of its properties are established. Based on these, an algorithm which partitions the set of holding cost parameters into subsets, computes the optimal policy and constructs its corresponding stability regions on every such subset is proposed.     

Integrated cellular manufacturing systems design with production planning and dynamic system reconfiguration

This paper presents and analyzes a comprehensive model for the design of cellular manufacturing systems (CMS). A recurring theme in research is a piecemeal approach when formulating CMS models. In this paper, the proposed model, to the best of the authors’ knowledge, is the most comprehensive one to date with a more integrated approach to CMS design, where production planning and system reconfiguration decisions are incorporated. Such a CMS model has not been proposed before and it features the presence of alternate process routings, operation sequence, duplicate machines, machine capacity and lot splitting. The developed model is a mixed integer non-linear program. Linearization procedures are proposed to convert it into a linearized mixed integer programming formulation. Computational results are presented by solving some numerical examples, extracted from the existing literature, with the linearized formulation.     

Real-time production planning and control system for job-shop manufacturing: A system dynamics analysis

Much attention has been paid to production planning and control (PPC) in job-shop manufacturing systems. However, there is a remaining gap between theory and practice, in the ability of PPC systems to capture the dynamic disturbances in manufacturing process. Since most job-shop manufacturing systems operate in a stochastic environment, the need for sound PPC systems has emerged, to identify the discrepancy between planned and actual activities in real-time and also to provide corrective measures. By integrating production ordering and batch sizing control mechanisms into a dynamic model, we propose a comprehensive real-time PPC system for arbitrary capacitated job-shop manufacturing. We adopt a system dynamics (SD) approach which is proved to be appropriate for studying the dynamic behavior of complex manufacturing systems. We study the system’s response, under different arrival patterns for customer orders and the existence of various types real-time events related to customer orders and machine failures. We determine the near-optimal values of control variables, which improve the shop performance in terms of average backlogged orders, work in process inventories and tardy jobs. The results of extensive numerical investigation are statistically examined by using analysis of variance (ANOVA). The examination reveals an insensitivity of near-optimal values to real-time events and to arrival pattern and variability of customer orders. In addition, it reveals a positive impact of the proposed real-time PPC system on the shop performance. The efficiency of PPC system is further examined by implementing data from a real-world manufacturer.     

Flexible long-term capacity planning in closed-loop supply chains with remanufacturing

We deal with long-term demand-driven capacity planning policies in the reverse channel of closed-loop supply chains (CLSCs) with remanufacturing, under high capacity acquisition cost coupled with uncertainty in actual demand, sales patterns, quality and timing of end-of-use product returns. The objective is to facilitate the decision-making when the management faces the dilemma of implementing either a strategy of early large-scale investments to benefit from economies of scale and capacity readiness, or a flexible strategy of low volume but more frequent capacity expansions. We consider a CLSC with two sequential product-types. We study the system’s response in terms of transient flows, actual/desired capacity level, capacity expansions/contractions and total supply chain profit, employing a simulation-based system dynamics optimization approach. Extensive numerical investigation covers a broad range of real-world remanufacturable products under alternative scenarios in relation to the market preference over product-types. The key findings propose flexible policies as improved alternatives to large-scale capacity expansions/contractions in terms of adaptability to the actual pattern of end-of-use product returns and involved risk in the investments’ turnover. Flexible policies are also proposed as practices to avoid overcapacity phenomena in collection and remanufacturing capacity and as robust policies to product demand. Their implementation is revealed to be even more important for the case of remanufacturing, when a high capacity acquisition unit-cost ratio (remanufacturing/collection) is coupled with strong economies of scale. Finally, results under different information sharing structures show changes in remanufacturing policies, thus justifying the importance of coordination between the decision-maker and the distributor.     

On the optimal control of manufacturing and remanufacturing activities with a single shared server

We consider a single-product make-to-stock manufacturing–remanufacturing system. Returned products require remanufacturing before they can be sold. The manufacturing and remanufacturing operations are executed by the same single server, where switching from one activity to another does not involve time or cost and can be done at an arbitrary moment in time. Customer demand can be fulfilled by either newly manufactured or remanufactured products. The times for manufacturing and remanufacturing a product are exponentially distributed. Demand and used products arrive via mutually independent Poisson processes. Disposal of products is not allowed and all used products that are returned have to be accepted. Using Markov decision processes, we investigate the optimal manufacture–remanufacture policy that minimizes holding, backorder, manufacturing and remanufacturing costs per unit of time over an infinite horizon. For a subset of system parameter values we are able to completely characterize the optimal continuous-review dynamic preemptive policy. We provide an efficient algorithm based on quasi-birth–death processes to compute the optimal policy parameter values. For other sets of system parameter values, we present some structural properties and insights related to the optimal policy and the performance of some simple threshold policies.     

Expert systems for planning and scheduling manufacturing systems

In this paper, research and applications of expert systems in production planning and scheduling are reviewed. Components of expert systems are briefly discussed. Relationship between expert system and operations research approaches are presented. Integration of operations research and expert system techniques is explored.     

Tool planning models for flexible manufacturing systems

Although the tool loading problem for Flexible Manufacturing Systems (FMSs) has been analyzed in the past, the tool planning problem, the basis of tool management, has largely been ignored. In this paper, the interface between tool planning and the FMS loading and routing decisions is analyzed. It is shown that tool policy has a pronounced effect on the flexibility and the planned makespan of an FMS. A tool planning model is developed and integrated into an overall FMS detailed tool loading and part routing procedure. This model while considerably reducing the number of tools required (by 55%) matches the performance of a policy that equips each machine with all tools in terms makespan, routing flexibility, and tool productivity.     

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.     

Production and replacement policies for a deteriorating manufacturing system under random demand and quality

This work investigates the production planning of an unreliable deteriorating manufacturing system under uncertainties. The effect of the deterioration phenomenon on the machine is mainly observed in its availability and the quality of the parts produced, with the rates of failure and defectives increasing with the age of the machine. The option to replace the machine should be considered to mitigate the effect of deterioration in order to ensure long-term satisfaction of demand. The objective of this paper is to find the production rate and the replacement policy that minimize the total discounted cost, which includes inventory, backlog, production, repair and replacement costs, over an infinite planning horizon. We formulate the stochastic control problem in the framework of a semi-Markov decision process to consider the machine's history. The integration of random demand and quality behaviour led us to propose a new modeling approach by developing optimality conditions in terms of a second-order approximation of Hamilton–Jacobi–Bellman (HJB) equations. Numerical methods are used to obtain the optimal control policies. Finally, a numerical example and a sensitivity analysis are presented in order to illustrate and confirm the structure of the optimal solution obtained.     

A reliability-based manufacturing process planning method for the components of a complex mechatronic system

Uncertainties in the values of the parameters of a system can originate from the manufacturing tolerances of the system components, which can produce a degree of unreliability in the performance of the system. A systematic framework for realistic reliability assessment of an electro-hydraulic servo system has been presented in this paper with the objective of providing adequate information for the selection of the best manufacturing process for each of the servo valve components. Monte Carlo simulation has been employed to evaluate the effect of these uncertainties of the servo valve parameters on the statistical performance of the system. Possible manufacturing processes have been introduced for each component and the justifiability of using each one has been discussed based on the estimated reliability of the system.     

Capacity planning in manufacturing networks with discrete options

We consider multiproduct manufacturing systems modeled by open networks of queues with general distributions for arrival patterns and service times. Since exact solutions are not available for measuring mean number of jobs in these systems, we rely on approximate analyses based on the decomposition approach developed, among others, by Reiser and Kobayashi [16], Kuehn [14], Shanthikumar and Buzacott [19], Whitt [29], and extensions by Bitran and Tirupati [2]. The targeting problem (TP) presented in this paper addresses capacity planning issues in multiproduct manufacturing systems. Since TP is a nonlinear integer program that is not easy to solve, we present a heuristic to obtain an approximate solution. We also provide bounds on the performance of this heuristic and illustrate our approach by means of a numerical example.