Allocating flexible servers in serial systems with switching costs

Consider a firm that operates a make-to-order serial production system and employs a cross-trained workforce. We model such a firm as a tandem queuing system in which flexible servers can be allocated across stations, and assume that a switching cost is charged when servers move between stations. We show that even in the two-station two-server case the optimal policy follows a complex state-dependent structure that may be difficult to implement in practice. We propose three alternate heuristic policies and assess their performance. We show that a simpler policy which only moves one server can achieve close to optimal results.     

Real-time production scheduling of an automated cardline

In this paper, we describe a flow model of an automated-printed circuit card assembly line at the International Business Machines Corporation (IBM) plant at Tucson, Arizona. We use a simulation based on this model as a test bed to discuss the performance of a hierarchical scheduling policy described in [3]. We compare this with other policies for loading parts into a flexible manufacturing system. We demonstrate that the hierarchical strategy is effective in meeting production requirements (both total volume and balance among part types) while limiting average work-in-process (WIP). This is a consequence of the feedback nature of the policy. Hedging (i.e. building up buffer stock) compensates for machine failures, thus resulting in high production percentages. The work-in-process (WIP) is low, as the policy reduces internal queues by respecting the capacity constraints of the system at every instant.     

Joint management of capacity and inventory in make-to-stock production systems with multi-class demand

We evaluate the benefits of coordinating capacity and inventory decisions in a make-to-stock production environment. We consider a firm that faces multi-class demand and has additional capacity options that are temporary and randomly available. We formulate the model as a Markov decision process (MDP) and prove that a solution to the optimal joint control problem exists. For several special cases we characterize the structure of the optimal policy. For the general case, however, we show that the optimal policy is state-dependent, and in many instances non-monotone and difficult to implement. Therefore, we consider three pragmatic heuristic policies and assess their performance. We show that the majority of the savings originate from the ability to dynamically adjust capacity, and that a simple heuristic that can adjust production capacity (based on workload fluctuation) but uses a static production/rationing policy can result in significant savings.     

Optimal integrated production and inventory control of an assemble-to-order system with multiple non-unitary demand classes

We study a pure assemble-to-order system subject to multiple demand classes where customer orders arrive according to a compound Poisson process. The finished product is assembled from m different components that are produced on m distinct production facilities in a make-to-stock fashion. We show that the optimal production policy of each component is a state-dependent base-stock policy and the optimal inventory allocation policy is a multi-level state-dependent rationing policy. Using numerical experimentation, we first study the system behavior as a function of order size variability and order size. We show that the optimal average cost rate is more sensitive to order size variability than to order size. We also compare the optimal policy to the first-come first-serve policy and show that there is great benefit to inventory rationing. We also propose two simple heuristics and show that these can effectively mimic the optimal policy which is generally much more difficult to determine and, especially, to implement.     

Monotonicity properties of user equilibrium policies for parallel batch systems

We study a simple network with two parallel batch-service queues, where service at a queue commences when the batch is full and each queue is served by infinitely many servers. A stream of general arrivals observe the current state of the system on arrival and choose which queue to join to minimize their own expected transit time. We show that for each set of parameter values there exists a unique user equilibrium policy and that it possesses various monotonicity properties. User equilibrium policies for probabilistic routing are also discussed and compared with the state-dependent setting.     

Comparison among three pull control policies: kanban, base stock, and generalized kanban

This paper is concerned with maketostock pull control policies. A classical policy is the kanban policy. Another policy, very easy to implement, is the base stock policy. These two policies contain one design parameter per stage. A general control policy, known as the generalized kanban policy, can also be used to implement the pull mechanism. The generalized kanban policy includes, as special cases, the kanban and the base stock policies. This policy uses two parameters for each stage of the production system. The aim of this paper is to provide qualitative and quantitative comparisons of these three policies. The results of our study will help to choose the policy to implement in order to control a production system. We give practical rules. We also show that if there is no delay in filling orders, all three policies have similar costs. However, for the systems studied, we show that, if there is a delay in filling orders, generalized kanban systems and base stock systems yield close to optimal costs, which are lower than costs of kanban systems for the same service quality.     

Cyclic scheduling for F.M.S.: Modelling and evolutionary solving approach

This paper concerns the domain of flexible manufacturing systems (FMS) and focuses on the scheduling problems encountered in these systems. We have chosen the cyclic behaviour to study this problem, to reduce its complexity. This cyclic scheduling problem, whose complexity is NP-hard in the general case, aims to minimise the work in process (WIP) to satisfy economic constraints. We first recall and discuss the best known cyclic scheduling heuristics. Then, we present a two-step resolution approach. In the first step, a performance analysis is carried out; it is based on the Petri net modelling of the production process. This analysis resolves some indeterminism due to the system’s flexibility and allows a lower bound of the WIP to be obtained. In the second step, after a formal model of the scheduling problem has been given, we describe a genetic algorithm approach to find a schedule which can reach the optimal production speed while minimizing the WIP. Finally, our genetic approach is validated and compared with known heuristics on a set of test problems.     

Synchronizing production and air transportation scheduling using mathematical programming models

Traditional scheduling problems assume that there are always infinitely many resources for delivering finished jobs to their destinations, and no time is needed for their transportation, so that finished products can be transported to customers without delay. So, for coordination of these two different activities in the implementation of a supply chain solution, we studied the problem of synchronizing production and air transportation scheduling using mathematical programming models. The overall problem is decomposed into two sub-problems, which consists of air transportation allocation problem and a single machine scheduling problem which they are considered together. We have taken into consideration different constraints and assumptions in our modeling such as special flights, delivery tardiness and no delivery tardiness. For these purposes, a variety of models have been proposed to minimize supply chain total cost which encompass transportation, makespan, delivery earliness tardiness and departure time earliness tardiness costs.     

A total standard WIP estimation method for wafer fabrication

The standard work-in-process (WIP) level in a wafer fabrication factory is an important parameter which can be properly used to trigger the decision of when to release specific wafer lots. There are many WIP-based release control policies which have been proven to be effective for wafer fabrication manufacturing, few methods have been proposed to find the suitable WIP-level as a parameter for these release policies. This paper proposes a queueing network-based algorithm to determine the total standard WIP level so that the Fixed-WIP release algorithm to determine the total standard WIP level so that the Fixed-WIP release control policy can apply. A numerical example is provided to elaborate the algorithm. A simulation model of a real-world wafer fabrication factory in Taiwan is built and analyzed. Results of simulation experiment indicate that under the Fixed-WIP control policy, the total standard WIP level estimated from this study achieves a target throughput rate while keeping the corresponding cycle time relatively low. Results also demonstrate that the queueing network-based algorithm is a very useful method to determine the standard WIP level efficiently.     

A two-level hedging point policy for controlling a manufacturing system with time-delay,demand uncertainty and extra capacity

This paper focuses on the production control of a manufacturing system with time-delay, demand uncertainty and extra capacity. Time-delay is a typical feature of networked manufacturing systems (NMS), because an NMS is composed of many manufacturing systems with transportation channels among them and the transportation of materials needs time. Besides this, for a manufacturing system in an NMS, the uncertainty of the demand from its downstream manufacturing system is considered; and it is assumed that there exist two-levels of demand rates, i.e., the normal one and the higher one, and that the time between the switching of demand rates are exponentially distributed. To avoid the backlog of demands, it is also assumed that extra production capacity can be used when the work-in-process (WIP) cannot buffer the high-level demands rate. For such a manufacturing system with time-delay, demand uncertainty and extra capacity, the mathematical model for its production control problem is established, with the objective of minimizing the mean costs for WIP inventory and occupation of extra production capacity. To solve the problem, a two-level hedging point policy is proposed. By analyzing the probability distribution of system states, optimal values of the two hedging levels are obtained. Finally, numerical experiments are done to verify the effectiveness of the control policy and the optimality of the hedging levels.     

Sequencing and loading of products on a flowline

One of the main objectives of balancing a flowline is to achieve maximum throughput with minimum WIP. To that end, various methods have been proposed to control the loading of products (flow of material) on a flowline. However, the sequence in which the products are released is an issue that has rarely been addressed in conjunction with the material flow control mechanisms. In this paper, we address the problem of determining the optimal cyclic sequence in which to load different products on a flowline in order to achieve optimum throughput at minimum WIP. A lower bound on the WIP level is developed which is then used to determine the desired WIP level and the sequence in which to load the products so as to achieve optimum throughput. A new product release policy is also developed. Comparative results of the new product release policy and the sequencing procedure with other product release policies and sequencing heuristics are presented and they indicate superiority of the proposed methods.     

混流无等待流水线批次批量生产计划方法

在两阶段混流无等待流水装配环境下,为解决任务组批生产导致的订单按期交付能力弱,以及产品需求与部件供应无法准确衔接的问题,以最小化在制品库存成本和产品提前拖期惩罚为目标,建立了两阶段批次批量生产计划数学模型。设计了双目标蚁群求解算法,构造了每只蚂蚁对不同目标的偏重算子,以及基于批次的可行解生成方法和信息素更新机制,提高了解的局部和全局搜索能力。通过与NSGA-II进行对比,验证了本算法在同等时间内计算精度优于后者,为提高两阶段多条生产线组批生产计划的可执行性提供方法支持。     

Optimal inspection and replacement policies for multi-state deteriorating systems

In this paper, a phase-type approach is proposed to derive optimal inspection and replacement policies for semi-Markovian deteriorating systems. In this approach, the general sojourn time distributions of a semi-Markovian maintenance model are approximated by acyclic phase-type distributions. Using the approximation, a semi-Markovian maintenance model can be transformed into a Markovian maintenance model such that the analytical tractability of Markov processes can be preserved. Based on the Markovian model, algorithms are provided to derive the optimal state-dependent and state-age-dependent inspection and replacement policies such that the expected long-run cost rate is minimized. Furthermore, procedures are developed to implement the optimal policies on semi-Markovian deteriorating systems. The implementation of the optimal policies are illustrated by numerical examples.     

Models for concurrent product and process design

We propose procedures to address product design and manufacturing process configurations concurrently in environments characterized by large degrees of product proliferation. Exploiting the intrinsic flexibility of product and process design, we present two approaches that synchronize production flows through the manufacturing system. These approaches integrate product and manufacturing system design decisions with operational concerns and provide powerful means for managing production in environments characterized by a proliferation of products. Experimental results show that the proposed methods can substantially reduce manufacturing lead times, work in process (WIP), and overall system complexity.     

Newsvendor equations for production networks

We consider production networks with stochastic activity leadtimes. When activities finish early holding costs are incurred and when end products are delivered late penalty costs are incurred. Objective is to find the activity start and finish times that minimize the total cost. We introduce the concept of a tardy path and derive the optimality equations for each node in the network. We show that under the optimal solution, for a set of nodes the tardiness probability satisfies the Newsvendor equations.     

Cost-effective inventory control in a value-added manufacturing system

This paper considers the cost-effective inventory control of work-in-process (WIP) and finished products in a two-stage distributed manufacturing system. The first stage produces a common WIP, and the second stage consists of several production sites that produce differentiated products with different capacity and service level requirements. The unit inventory holding cost is higher at the second stage. This paper first uses a network of inventory-queue model to evaluate the inventory cost and service level achievable for given inventory control policy, and then derives a very simple algorithm to find the optimal inventory control policy that minimizes the overall inventory holding cost and satisfies the given service level requirements. Some managerial insights are obtained through numerical examples.     

Control Policy for a Manufacturing System with Random Yield and Rework

We develop a production policy that controls work-in-process (WIP) levels and satisfies demand in a multistage manufacturing system with significant uncertainty in yield, rework, and demand. The problem addressed in this paper is more general than those in the literature in three aspects: (i) multiple products are processed at multiple workstations, and the capacity of each workstation is limited and shared by multiple operations; (ii) the behavior of a production policy is investigated over an infinite-time horizon, and thus the system stability can be evaluated; (iii) the representation of yield and rework uncertainty is generalized. Generalizing both the system structure and the nature of uncertainty requires a new mathematical development in the theory of infinite-horizon stochastic dynamic programming. The theoretical contributions of this paper are the existence proofs of the optimal stationary control for a stochastic dynamic programming problem and the finite covariances of WIP and production levels under the general expression of uncertainty. We develop a simple and explicit sufficient condition that guarantees the existence of both the optimal stationary control and the system stability. We describe how a production policy can be constructed for the manufacturing system based on the propositions derived.     

Adaptive policies for multi-mode project scheduling under uncertainty

In this paper we propose an adaptive model for multi-mode project scheduling under uncertainty. We assume that there is a due date for concluding the project and a tardiness penalty for failing to meet this due date, and that several distinct modes may be used to undertake each activity. We define scheduling policies based on a set of thresholds. The starting time of the activity is compared with those thresholds in order to define the execution mode.We propose a procedure, based on the electromagnetism heuristic, for choosing a scheduling policy. In computational tests, we conclude that the adaptive scheduling policy found by using the model and the heuristic solution procedure is consistently better than the optimal non-adaptive policy. When the different modes have very different characteristics and there is a reasonable difference between the average duration of the project and the due date, the cost advantage of the adaptive policy becomes very significant.     

A hybrid genetic algorithm for an identical parallel-machine problem with maintenance activity

The scheduling of maintenance activities has been extensively studied, with most studies focusing on single-machine problems. In real-world applications, however, multiple machines or assembly lines process numerous jobs simultaneously. In this paper, we study a parallel-machine scheduling problem in which the objective is to minimize the total tardiness given that there is a maintenance activity on each machine. We develop a branch-and-bound algorithm to solve the problem with a small problem size. In addition, we propose a hybrid genetic algorithm to obtain the approximate solutions when the number of jobs is large. The performance of the proposed algorithms is evaluated based mainly on computational results.     

A two-bottleneck system with binomial yields and rigid demand

This study considers multistage production systems where production is in lots and only two stages have non-zero setup costs. Yields are binomial and demand, needing to be satisfied in its entirety, is “rigid”. We refer to a stage with non-zero setup cost as a “bottleneck” (BN) and thus to the system as “a two-bottleneck system” (2-BNS). A close examination of the simplest 2-BNS reveals that costs corresponding to a particular level of work in process (WIP) depend upon costs for higher levels of WIP, making it impossible to formulate a recursive solution.For each possible configuration of intermediate inventories a production policy must specify at which stage to produce next and the number of units to be processed. We prove that any arbitrarily “fixed” production policy gives rise to a finite set of linear equations, and develop algorithms to solve the two-stage problem. We also show how the general 2-BNS can be reduced to a three-stage problem, where the middle stage is a non-BN, and that the algorithms developed can be modified to solve this problem.