Vladimir Polotski

Set-up and production planning in hybrid manufacturing–remanufacturing systems with large returns

Hybrid systems that use both raw materials and returned products in the production process are considered. The system contains one facility, and undergoes set-ups each time it switches between two production modes. In particular, we address systems engaged mainly in remanufacturing and having a large percentage of return. This situation is encountered in companies with mature remanufacturing channels. The targeted application area is comprised of hybrid systems that uses leasing as a business model with manufacturing serving to attenuate return uncertainty. To evaluate the system performance, we take into account manufacturing and remanufacturing costs, holding costs in serviceable and return inventories, backlog and set-up costs. Our analysis of hybrid systems with high return levels reveals features that are peculiar to such systems and that differentiate them from systems with lower return rates. We first present analytical solutions for optimal production and set-up schedule, and determine the possible cycle shapes for reliable systems. Optimal policies contain intervals of manufacturing and remanufacturing at maximal rate, and intervals of on-demand and on-return remanufacturing. Failure-prone systems are studied next, using the formalism of stochastic dynamic programming. Optimal policies give rise to the trajectories converging to the patterns similar to the analytically calculated cycles.

Production and setup policy optimization for hybrid manufacturing–remanufacturing systems

Hybrid systems that use both raw materials (manufacturing mode) and returned products (remanufacturing mode) as a supply for their production process are considered. The system studied consists of one facility that necessitates setup for switching from one production mode to another. As in industrial practice, the flow rate of returned products is usually below the market demand, switching from one mode to another is unavoidable for meeting the demand. Therefore, determining the optimal production and setup policies is critical for effectively planning production process and reducing the manufacturing cost. Evaluating system performance, we take into account production costs in manufacturing and remanufacturing modes, serviceable and return inventory costs, backlog and setup costs. We present first an analytical solution for optimal production and setup schedule along the production cycles, considering the case of reliable systems. These cycles are shown to contain intervals of production at maximal rates as well as on-demand manufacturing and on-return remanufacturing. Next, for failure-prone systems, the optimality conditions in the form of Hamilton–Jacobi–Bellman (HJB) equations are developed. Solving HJB equations numerically, the optimal production and setup policies are calculated, and it is demonstrated that the optimal trajectories converge to the production cycles) of the type determined analytically beforehand. The sensitivity analysis of the obtained solutions (both analytical and numerical) over system parameters is presented to validate the proposed approach and demonstrate the robustness of the results.

Age-dependent production and replacement strategies in failure-prone manufacturing systems

A failure-prone manufacturing system that consists of one machine producing one type of product is studied. The random phenomena examined are machine breakdowns and repairs. We assume that the machine undergoes a progressive deterioration while in operation and that the machine failure rate is a function of its age. The aging of the machine (the dynamics of the machine age) is assumed to be an increasing function of its production rate. Corrective maintenance activities are imperfect and restore the age of the machine to as-bad-as-old conditions. When a failure occurs, the machine can be repaired, and during production, the machine can be replaced, depending on its age. When the replacement action is selected, the machine is replaced by a new and identical one. The decision variables are the production rate and the replacement policy. The objective of this article is to address the simultaneous production and replacement policy optimization problem in the context of manufacturing with deterioration and imperfect repairs satisfying the customer demand and minimizing the total cost, which includes costs associated with inventory, backlog, production, repair and replacement, over an infinite planning horizon. We thoroughly explore the impact of the machine aging on the production and replacement policies. Particular attention is paid to the verification of underlying mathematical results that guarantee the existence of optimal solutions and the convergence of numerical methods. Due to imperfect repairs, the dynamics of the system is affected by the system history, and semi-Markov processes have to be used for modeling. Optimality conditions in the form of the Hamilton–Jacobi–Bellman equations are developed, and numerical methods are used to obtain the optimal control policies (production (rate) and replacement policies). A numerical example is given to illustrate the proposed approach, and an extensive sensitivity analysis is presented to confirm the structure of the obtained control policies.

NAVIGATION OF AUTONOMOUS MOBILE ROBOT WITH GATE RECOGNITION AND CROSSING

Abstract This paper presents the problem of navigation, gate recognition, and gate crossing for a real size outdoor mobile robot. The vehicle localization and general navigation is based on Global Positioning System (GPS) and Inertial Navigation System (INS). These sensors are aided by a Laser Measuring System (LMS). The laser scanner sensor gives information about the gate exact location, its shape, and vehicle attitude with respect to the gate. This necessitates the transition from GPS/INS based guidance in the open field to the range based, guidance system in proximity of the gate. A recognition procedure based on the concept of the gate signature has been developed and shown. It is followed by novel map-matching and localization procedures respectively. The developed navigation controller makes use of the polar coordinate representation and discontinuous feedback adapted for real time applications.

Optimal utilisation level for lean product development in a multitasking context

Flow of information is of utmost importance during product development (PD) endeavours with timely feedback supporting the resolution of higher risk elements. PD task size, multitasking and resource utilisation levels of the PD system influence information flow and the value ultimately realised from the investment in PD. In this paper, a model incorporating a methodology developed using queuing theory, and in particular, results obtained for Jackson networks are extended to help engineering management to improve PD task flow and consequently become more ‘lean’. Considered factors include: optimal PD task size and multitasking (focus) level as well as the utilisation level of PD resources. Empirical data were collected from a case study company and compared to optimal values. The benefits of the proposed model and approaches are discussed.

Production Planning of a Failure-Prone Manufacturing System under Different Setup Scenarios

This paper presents a control problem for the optimization of the production and setup activities of an industrial system operating in an uncertain environment. This system is subject to random disturbances (breakdowns and repairs). These disturbances can engender stock shortages. The considered industrial system represents a well-known production context in industry and consists of a machine producing two types of products. In order to switch production from one product type to another, a time factor and a reconfiguration cost for the machine are associated with the setup activities. The parts production rates and the setup strategies are the decision variables which influence the inventory and the capacity of the system. The objective of the study is to find the production and setup policies which minimize the setup and inventory costs, as well as those associated with shortages. A modeling approach based on stochastic optimal control theory and a numerical algorithm used to solve the obtained optimality conditions are presented. The contribution of the paper, for industrial systems not studied in the literature, is illustrated through a numerical example and a comparative study.

Maintenance and setup planning in manufacturing systems under uncertainties

Purpose The purpose of this paper is to find the optimal production and setup policies for a manufacturing system that produces two different types of parts. The manufacturing system consists of one machine subject to random failures and repairs. Reconfiguring the machine to switch production from one type of product to another generates a non-production time and a significant cost. Design/methodology/approach This paper proposes an approach based on the development of optimal production and setup policies, taking into account the possibilities of undertaking the setup for all modes of the machine, and covering them at the end of setup. New optimality conditions are developed in terms of modified Hamilton-Jacobi-Bellman (HJB) equations and recursive numerical methods are applied to solve such equations. Findings The proposed approach led to determine more realistic production rates of both parts and setup sequences for the different modes of the machine that significantly influence the inventory and the system capacity. A numerical example and sensitivity analysis are used to determine the structure of the optimal policies and to show the helpfulness and robustness of the results obtained. Practical implications Following the steps of the proposed approach will provide the control policies for industrial manufacturing systems with setup permitted at all modes of the machine, and when the setup does not necessarily restore the machine to its operational mode. The proposed optimal policy takes into account the stochastic nature of the machine mode at the end of setup and we show that ignoring it leads to non-natural policies and underestimates significantly the safety stock thresholds. Originality/value Considering the assumptions presented in this paper leads to a new structure of the control laws for the production planning of manufacturing systems with setup.

Combining predetermined and measured assembly time techniques: Parameter estimation, regression and case study of fenestration industry

Time estimation is an important element of the effort evaluation process, which is indispensable along many phases of business development from bidding for the competitive contract to design and production phases. In particular, the time estimates are useful in the resource planning process, especially when the precision of the provided estimates is quantitatively characterised. We propose in this work an approach that combines the techniques developed within predetermined time methods (such as MODAPTS and MINIMOST) with the statistical techniques that use the real data (collected along the stopwatch time measurements). Our approach allows to obtain not only time estimates themselves, but also the confidence intervals for them. This information helps the practitioner to decide whether provided time estimates (coupled with accuracy parameters) meet his criteria. The proposed approach can be used in time estimations for the project containing several operations of different nature, and the application of our methodology is discussed in detail for the case of fenestration industry.

Optimizing production while reducing machinery lockout/tagout circumvention possibilities

Purpose – The optimization of production imposes a review of facility maintenance policies. Accidents during maintenance activities are frequent, sometimes fatal and often associated with deficient or absent machinery lockout/tagout. Lockout/tagout is often circumvented in order to avoid what may be viewed as unnecessary delays and increased production costs. To reduce the dangers inherent in such practice, the purpose of this paper is to propose a production strategy that provides for machinery lockout/tagout while maximizing manufacturing system availability and minimizing costs. Design/methodology/approach – The joint optimization problem of production planning, maintenance and safety planning is formulated and studied using a stochastic optimal control methodology. Hamilton-Jacobi-Bellman equations are developed and studied numerically using the Kushner approach based on finite difference approximation and an iterative policy improvement technique. Findings – The analysis leads to a solution that sugg...

Manufacturing-Remanufacturing Failure-Prone Systems under Random Demands

Abstract The problem of production control for a hybrid manufacturing/remanufacturing system under uncertainties is analyzed. Two sources of uncertainties are considered: machines random breakdowns and repairs are modeled as a Poisson process, and demand level variations are modeled as a diffusion type process. The solution to optimal control problem is determined numerically by solving the Hamilton-Jacobi-Bellman (HJB) equations which are shown to be second order partial differential equations (PDEs). The optimal policy for manufacturing is of hedging-point type, while for remanufacturing it is a three-value function. We illustrate obtained results by simulation examples and analyze how the optimal production policy and optimal cost evolve while the statistical characteristics of the random demand vary.