Integrated production scheduling in large-scale flexible electronic parts manufacturing

This study reports the development of a production scheduling system for the integrated management of production in large-scale, high-volume electronic assembly lines. The development of the system incorporates control and planning considerations by addressing the interaction of various subsystems. Stochastic and deterministic aspects of the problem environment are appropriately handled via relevant simulation and analytic models. By effecting a hierarchical breakdown of the problem environment, the system produces information used in practical decision making for production planning and scheduling. Procedures used encompass and address considerations for management of work-in-process, optimization of the various subsystems' performance, minimization of setup time effect, and inventory carrying costs.     

Inventory decisions for a finite horizon problem with product substitution options and time varying demand

The inventory control of substitutable products has been recognized as a problem worthy of study in the operations management literature. Product substitution provides flexibility in supply chain management and enhances response time in production control. This paper proposes a finite horizon inventory control problem for two substitutable products, which are ordered jointly in each replenishment epoch. Demand for the products are assumed to be time–varying. In case of a stock–out for one of the products, its demand is satisfied by using the stock of the other product. The optimal ordering schedule, for both products, that minimizes the total cost over a finite planning horizon is derived. Numerical examples along with sensitivity analyses are also presented.     

Ergodic control problems for optimal stochastic production planning with production constraints

We study the ergodic control problem related to stochastic production planning in a single product manufacturing system with production constraints. The existence of a solution to the corresponding Hamilton-Jacobi-Bellman equation and its properties are shown. Furthermore, the optimal control for the ergodic control problem and an example are given.     

Analysis of deteriorating inventory/production systems using a linear quadratic regulator

We consider an inventory-production system where items deteriorate at a constant rate. The objective is to develop an optimal production policy that minimizes the cost associated with inventory and production rate. The inventory problem is first modeled as a linear optimal control problem. Then linear quadratic regulator (LQR) technique is applied to the control problem in order to determine the optimal production policy. Examples are solved for three different demand functions. Sensitivity analysis is then conducted to study the effect of changing the cost parameters on the objective function.     

Über eine optimale Feedback-Kontrolle unter der Verwendung von ARMA-Modellen

Zusammenfassung Es wird ein dynamisches System betrachtet, dessen Ausgang sich additiv aus einer Kontroll- und einer Störgröße ergibt. Kontroll- und Störgröße entstehen dabei selbst als Ausgänge von ARMA-Filtern, deren Eingänge die Steuerung des Systems bzw. weißes Rauschen sind. Es sind dabei Kosten zu minimieren, die durch die Abweichung des Systemausgangs von einer Zielgröße bzw. durch die Steuerung des Systems entstehen. Die Fragestellung führt auf ein dynamisches Optimierungsproblem, das mit dem Verfahren der rückschreitenden Induktion gelöst wird. In einigen speziellen Fällen kann man auch explizite Formeln für die Berechnung der optimalen Systemsteuerung angeben.

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Summary The aim of this paper is the solution of a feedback control problem. We concern a dynamic system whose output is the sum of the outputs of two ARMA-filters. The inputs of these filters are the control of the system and white noise respectively. We have to minimize costs arising from the deviation of the system-output from a fixed target and by the control of the system. This leads to a problem of dynamic optimization, which is solved by the method of backward induction. In some special cases the computation of optimal system controls is possible by means of explicit formulas.

     

Analysis of an Optimal Control Problem Related to the Anaerobic Digestion Process

Our aim in this work is to synthesize optimal feeding strategies that maximize, over a time period, the biogas production in a continuously filled bioreactor controlled by its dilution rate. Such an anaerobic process is described by a four-dimensional dynamical system. Instead of modeling the optimization of the biogas production as a Lagrange-type optimal control problem, we propose a slightly different optimal control approach in this paper: We study the minimal time control problem to reach a target point, which is chosen in such a way that it maximizes the biogas production at steady state. Thanks to the Pontryagin maximum principle and the geometric control theory, we provide an optimal feedback control for the minimal time control problem, when the initial conditions are taken within the invariant and attractive manifold of the system. The optimal synthesis exhibits turnpike and anti-turnpike singular arcs and a cut locus.     

An assembly-type supply chain system controlled by kanbans under a just-in-time delivery policy

This research studies the assembly-type supply chain system controlled by kanban mechanism. First, a supply chain system is modeled as a mixed-integer nonlinear programming (MINLP) problem. A composite formulation of the assembly-type supply chain system is developed by appropriately aggregating the individual branch models as a whole system. The batch size, the number of batches, and the total quantity over one period in the mainline and each branch line are determined. The small size MINLP problems are solved optimally by a branch-and-bound method. For the large size MINLP problems, a heuristic is developed which divides the ATSCS into several small size problems, and then conquers them individually. Next, the kanban operation between two adjacent plants is developed to schedule the loading and unloading, and transportation. Coupled with plant-wide efforts for cost control and management commitment, a logistics system is built for controlling the production as well as the supply chain system, which results in minimizing the total cost of the supply chain system. Numerical examples are presented to illustrate the two heuristic procedures and a better solution is obtained for the ATSCS problem.     

Some Facts about the Ramsey Model

In modeling the dynamics of capital, the Ramsey equation coupled with the Cobb–Douglas production function is reduced to a linear differential equation by means of the Bernoulli substitution. This equation is used in the optimal growth problem with logarithmic preferences. The study deals with solving the corresponding infinite horizon optimal control problem. We consider a vector field of the Hamiltonian system in the Pontryagin maximum principle, taking into account control constraints. We prove the existence of two alternative steady states, depending on the constraints. This result enriches our understanding of the model analysis in the optimal control framework.     

Optimal control of a bioreactor

This paper is concerned with the analysis of a control problem related to the optimal management of a bioreactor. This real-world problem is formulated as a state-control constrained optimal control problem. We analyze the state system (a complex system of partial differential equations modelling the eutrophication processes for non-smooth velocities), and we prove that the control problem admits, at least, a solution. Finally, we present a detailed derivation of a first order optimality condition - involving a suitable adjoint system - in order to characterize these optimal solutions, and some computational results.     

Production control and replenishment strategy with multiple suppliers

This paper considers the joint supplier selection, replenishment and manufacturing control problem in a dynamic stochastic context. This problem is characterized by conflicting interests between suppliers, the manufacturer, and clients, which raise the need for coordination and information sharing. This paper contributes to the discourse mainly by developing and resolving an integrated mathematical model leading to information sharing strategies for supplier selection, replenishments and production activities. This is an optimal control problem with state constraints and hybrid dynamics. A dynamic stochastic model is thus proposed, and the optimality conditions obtained are then solved numerically. It is shown that the problem considered leads to a modified state-dependent multi-level (s, S) policy for the supplier selection and replenishment strategy and a base-stock policy for the production activities. The fact that these control policies are coupled confirms the necessity of considering the interactions present in the system in an integrated model. The obtained results show clearly that it is always profitable to consider multiple suppliers to make replenishment and production decisions. Moreover, it is shown that the availability rates of the supply chain actors and the replenishment lead time are important parameters to consider when choosing the best supplier.     

Uniqueness of solution of production control problem in a manufacturing system with degenerate demand

This paper studies the production inventory problem of minimizing the expected discounted present value of production cost control in a manufacturing system with degenerate stochastic demand. We establish the existence of a unique solution of the Hamilton-Jacobi-Bellman (HJB) equation associated with this problem. The optimal control is given by a solution to the corresponding HJB equation.     

The optimal control of just-in-time-based production and distribution systems and performance comparisons with optimized pull systems

In just-in-time (JIT) production systems, there is both input stock in the form of parts and output stock in the form of product at each stage. These activities are controlled by production-ordering and withdrawal kanbans. This paper discusses a discrete-time optimal control problem in a multistage JIT-based production and distribution system with stochastic demand and capacity, developed to minimize the expected total cost per unit of time. The problem can be formulated as an undiscounted Markov decision process (UMDP); however, the curse of dimensionality makes it very difficult to find an exact solution. The author proposes a new neuro-dynamic programming (NDP) algorithm, the simulation-based modified policy iteration method (SBMPIM), to solve the optimal control problem. The existing NDP algorithms and SBMPIM are numerically compared with a traditional UMDP algorithm for a single-stage JIT production system. It is shown that all NDP algorithms except the SBMPIM fail to converge to an optimal control.Additionally, a new algorithm for finding the optimal parameters of pull systems is proposed. Numerical comparisons between near-optimal controls computed using the SBMPIM and optimized pull systems are conducted for three-stage JIT-based production and distribution systems. UMDPs with 42 million states are solved using the SBMPIM. The pull systems discussed are the kanban, base stock, CONWIP, hybrid and extended kanban.     

Spieltheorie zur optimalen Produktionssteuerung in Bauunternehmen

Zusammenfassung Es wird das Problem der Steuerung von Produktionsgröße und Produktauswahl in Fertigteilwerken der Bauindustrie in Abhängigkeit der Baustellennachfrage dargestellt. Das Kernproblem wird als Matrixspiel formuliert. Die erste Strategie verfolgt die Bestimmung der optimalen Produktionsgröße in Bezug auf die Art der Fertigteüelemente. Die zweite Strategie dient dem optimalen Transport, dargestellt als duales Transportproblem. Diese Problemlösung — in Annäherung an die Dekomposition der Aufgabe in weniger schwierige Teilprobleme — vermindert den EDV-Aufwand beträchtlich.

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Summary The paper deals with a problem of control of production volume and its assortment within the prefabricated units taking into account the demand of building sites. The essential problem is formulated as a matrix game in which a first strategy allows to set the optimal volume and kind of products. A second strategy allows to establish the optimal transport plan for these products and it is considered as a dual transport problem. This approach to the problem causes a considerable decrease of computer calculation range because of the decomposition of the problem to less complicated optimization subproblems.

     

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.     

A single-stage supply chain system controlled by kanban under just-in-time philosophy

The production system using kanban was pioneered by Toyota Motor Company in Japan and subsequently it was adopted by numerous other Japanese and US companies for applying the just-in-time manufacturing principles. This research studies a single-stage supply chain system that is controlled by kanban mechanism. The supply chain system is modelled as a mixed-integer nonlinear programming (MINLP) problem. It is solved optimally by branch-and-bound method to determine the number of kanbans, batch size, number of batches, and the total quantity over one period. Meanwhile, the kanban operation between two adjacent plants is worked out considering the factors of loading and unloading time, and transport time. Coupled with plant-wide efforts for cost control and management commitment to enhance other measures of performance, a logistics system for controlling the production as well as the supply chain system is developed, which results in minimizing the total cost of the supply chain system. The results show that the improvements in reduction of inventory, wasted labour, and customer service in a supply chain are accomplished through the kanban mechanism.     

Optimal production control of a dynamic two-product manufacturing system with setup costs and setup times

This paper deals with the optimal control of a one-machine two-product manufacturing system with setup changes, operating in a continuous time dynamic environment. The system is deterministic. When production is switched from one product to the other, a known constant setup time and a setup cost are incurred. Each product has specified constant processing time and constant demand rate, as well as an infinite supply of raw material. The problem is formulated as a feedback control problem. The objective is to minimize the total backlog, inventory and setup costs incurred over a finite horizon. The optimal solution provides the optimal production rate and setup switching epochs as a function of the state of the system (backlog and inventory levels). For the steady state, the optimal cyclic schedule is determined. To solve the transient case, the system's state space is partitioned into mutually exclusive regions such that with each region, the optimal control policy is determined analytically.     

Optimal Production Planning in a Stochastic Manufacturing System with Long-Run Average Cost

This paper is concerned with the optimal production planning in a dynamic stochastic manufacturing system consisting of a single machine that is failure prone and facing a constant demand. The objective is to choose the rate of production over time in order to minimize the long-run average cost of production and surplus. The analysis proceeds with a study of the corresponding problem with a discounted cost. It is shown using the vanishing discount approach that the Hamilton–Jacobi–Bellman equation for the average cost problem has a solution giving rise to the minimal average cost and the so-called potential function. The result helps in establishing a verification theorem. Finally, the optimal control policy is specified in terms of the potential function.     

Proportional economic growth under conditions of limited natural resources

The paper is devoted to economic growth models in which the dynamics of production factors satisfy proportionality conditions. One of the main production factors in the problem of optimizing the productivity of natural resources is the current level of resource consumption, which is characterized by a sharp increase in the prices of resources compared with the price of capital. Investments in production factors play the role of control parameters in the model and are used to maintain proportional economic development. To solve the problem, we propose a two-level optimization structure. At the lower level, proportions are adapted to the changing economic environment according to the optimization mechanism of the production level under fixed cost constraints. At the upper level, the problem of optimal control of investments for an aggregate economic growth model is solved by means of the Pontryagin maximum principle. The application of optimal proportional constructions leads to a system of nonlinear differential equations, whose steady states can be considered as equilibrium states of the economy. We prove that the steady state is not stable, and the system tends to collapse (the production level declines to zero) if the initial point does not coincide with the steady state. We study qualitative properties of the trajectories generated by the proportional development dynamics and indicate the regions of production growth and decay. The parameters of the model are identified by econometric methods on the basis of China’s economic data.     

Application of Mathematical Programming to a Large-scale Agricultural Production and Distribution System

This paper addresses an actual production planning problem for a large seed corn production company. Various scenarios are included in different mathematical programming models in order to help the management make production decisions. A linear programming package and a mixed-integer programming package are combined by a designed heuristic program to solve the problem. The solutions obtained and an accompanying sensitivity analysis provide the management with insight into the system's operation and potentials of cost savings.     

Bernoulli substitution in the Ramsey model: Optimal trajectories under control constraints

We consider a neoclassical (economic) growth model. A nonlinear Ramsey equation, modeling capital dynamics, in the case of Cobb-Douglas production function is reduced to the linear differential equation via a Bernoulli substitution. This considerably facilitates the search for a solution to the optimal growth problem with logarithmic preferences. The study deals with solving the corresponding infinite horizon optimal control problem. We consider a vector field of the Hamiltonian system in the Pontryagin maximum principle, taking into account control constraints. We prove the existence of two alternative steady states, depending on the constraints. A proposed algorithm for constructing growth trajectories combines methods of open-loop control and closed-loop regulatory control. For some levels of constraints and initial conditions, a closed-form solution is obtained. We also demonstrate the impact of technological change on the economic equilibrium dynamics. Results are supported by computer calculations.