An operation scheme for make-to-order job-shop production systems

The purpose of this paper is to solve multi-job order release problem whose objective is to minimize the sum of mean shop floor flow times (SFFTs) of orders subject to due-date constraint. A heuristic algorithm is proposed for the problem by decomposing the load profile of the shop. The load profile is divided into two parts. One is for the planning orders (new orders and unreleased orders) and the other is for the current orders being processed. The advantage of decomposing the load profile is to reduce computational complexity in the estimation step of order completion time. A job dispatching rule is also suggested to synchronize part flows in the shop floor. The experimental results show that work-in-process (WIP) in both bottleneck stations and assembly station, the SFFT of orders, and the percentage of tardy orders are significantly reduced.     

钢铁企业按订单生产模式下合同池优化

基于对钢铁企业按订单生产模式下合同月计划编制策略、约束条件和优化目标的研究,建立了合同月计划优化模型.模型以最大化编入计划的加权合同重量为优化目标,综合考虑了设备能力和合同月计划中各种产品规格、品种比例限制等约束.采用惩罚函数法将约束最优化模型转换为无约束最优化模型.设计了求解模型的向导局域搜索算法.以某钢铁企业的实际合同月计划问题作为实例,对模型和算法进行了系统地验证,计算结果表明模型和算法在优化效果和运算效率方面均优于该企业现有合同计划系统.     

Joint admission,production sequencing,and production rate control for a two-class make-to-order manufacturing system

Today's manufacturing industry faces a number of challenges related to the rapid delivery of products with a high degree of variety. Striking a balance between the effectiveness in capacity utilization and the rapidness in order-fulfillment is a substantial challenge for manufacturing companies. This work aims to provide a theoretical basis from which to address this practical question. To this end, we address the problem of coordinating the admission, production sequencing, and production rate controls for a two-class make-to-order manufacturing system. Formulating the problem as a Markov decision process model, we identify the structural properties of optimal control policies under both discounted and average profit criteria. We show that the cμ rule is optimal for production sequencing and the optimal admission and production rate control policies can be characterized by the state-dependent threshold levels, provided that the production times are not associated with customer class. We also show that the optimal production rates are monotone in the system state, as in the case of a single class queueing system, and that the lower priority class can be preferred to the higher priority class in order admission under a certain condition on the system parameters. Our numerical study demonstrates that a considerable economic benefit can be achieved if the production rate is dynamically controlled between the minimum and maximum rates rather than fixed to the mean rate of these values. Finally, we present a heuristic policy that is described by linear switching functions for the control of order admission and a selection rule for the control of production rate. We compare the performance of our heuristic to the optimal policy using a numerical experiment, revealing that the heuristic provides near optimal solutions to test example problems and is robust to the system parameters.     

Collaborative production planning with production time windows and order splitting in make-to-order manufacturing

In this paper, we study a generalized production planning problem, that simultaneously investigates the two decisions that play critical roles in most firms, namely, production planning and order splitting and assignment. The problem takes into consideration the production time windows and capacities. We formulate the integrated problem as a linear mixed-integer program with a minimized total cost. A particle swarm optimization-based approach is developed to address the problem. Extensive computational experiments show that the proposed approach outperforms a commercial optimization package. Some managerial insights are also explored and reported. Finally, concluding remarks and future research directions are provided.     

A two-stage hybrid manufacturing model with controllable make-to-order production rates

As an effective strategy to facilitate delivering customized products within short lead time, hybrid manufacturing via a two-stage process has received attention from academia and industry. In this paper, we study a two-stage hybrid manufacturing system in which semifinished products are manufactured in a make-to-stock fashion in the first stage and end-products are produced from semifinished goods in a make-to-order (MTO) mode in the second stage. The rate of MTO production can be controlled within given limits, depending on the status of the system. The primary goal of this paper is to study a policy for coordinating order admission, MTO production rate, and inventory replenishment controls. Formulating the problem as a Markov decision process model, we characterize the structure of optimal control policies to maximize the long-run average profit. Using a numerical experiment, we study how the flexibility in MTO production rate affects the optimal policy and the optimal profit. We also examine the effect of the number of alternative MTO production rates on the optimal profit. We propose three heuristic policies implementable for general cases. The first heuristic describes two linear switching functions for admission and production controls and a selection rule for MTO production rate control. The second heuristic specifies fixed thresholds for the control decisions using the local information. The third heuristic presents linear switching functions that approximate the optimal threshold curves. Unlike second and third heuristics, the first heuristic does not require a grid search to determine the control parameters. We implement numerical studies to examine the marginal impact of system parameters and the effect of the number of alternative MTO production rates on the performance of the heuristics. Compared to the optimal policy, the average percentage performance of the first and third heuristics is less than 1% for both numerical studies. On the other hand, the average percentage performance of the second heuristic is larger than 3%, and it exceeds 10% for a set of particular problem examples.     

A hybrid scheduling decision support model for minimizing job tardiness in a make-to-order based mould manufacturing environment

In the make-to-order (MTO) mode of manufacturing, the specification of each product is unique such that production processes vary from one product to another making the production schedule complex. In order to achieve high level productivity, the production flow is not arranged in sequence; instead, the job schedule of different production jobs is adjusted to fit in with the multiple-job shop environment. A poor scheduling of jobs leads to high production cost, long production time and tardiness in job performance. The existing of tardiness in the production schedule significantly affects the harmony among the multiple jobs on the shops floor. In order to provide a complete solution for solving MTO scheduling problems with job shifting and minimizing job tardiness, a hybrid scheduling decision support model (SDSM) is introduced. The model is combined by a Genetic Algorithm (GA) and an optimisation module. GA is adopted to solve the complex scheduling problem taking into consideration of the wide variety of processes while the optimisation module is suggested for tackling tardiness in doing the jobs in a cost effective way. The simulation results reveal that the model shortens the generation time of production schedules and reduces the production cost in MTO-based production projects.     

Factors for choosing production control systems in make-to-order shops: a systematic literature review

Journal of Intelligent Manufacturing - Production control systems (PCSs) control the flow of jobs in a production system. The selection of a suitable PCS in the context of make-to-order (MTO) is...     

求解炼钢连铸生产调度问题的改进算法

将炼钢连铸生产调度问题抽象为混和流水车间调度,建立了0-1型混合整数线性规划模型,并提出了一种遗传和线性规划相结合的求解方法。该模型通过优化钢水传搁时间来满足钢水的温度要求,通过最小化浇次开浇提前/拖期惩罚来协调连铸与热轧间的生产节奏。在算法设计中,给出了一种染色体编码来表示炉次设备指派与炉次在设备上的加工顺序方案,并探讨了相应的遗传操作。最后,仿真实验的结果表明了该算法的有效性。     

Deadlock-free scheduling strategy for automated production cell

Deadlock must be avoided in a manufacturing system. In this paper, an efficient algorithm for finding a good deadlock-free schedule in a manufacturing system with enough (sufficient) or limited buffer is presented. This algorithm is based on the effective genetic algorithm (GA) search method. A formal Petri net structure is introduced, and the token player is used to assure deadlock freeness. In order to make the scheduling strategy generated by GA meet the required deadlock-free constraint, a Petri net is involved in checking the implementation of a manufacturing system during the job-scheduling process. The effectiveness and efficiency of the proposed approach is illustrated by several examples.