Scheduling tool changes and special jobs on a single machine to minimize makespan

This paper considers a variation of the classical single machine scheduling problem with tool changes. In the variation, two sets of jobs, namely special jobs and normal jobs, are considered. By special jobs, we mean that each special job must be processed within the first prefixed time units of a tool life. To solve the scheduling problem with small size and moderate size, we propose two mathematical programming models. To solve the scheduling problem with large size, we propose three sets of algorithms and focus on the performance of six algorithms based on the studies of a new bin packing problem. Worst-case analysis is conducted. Numerical experiment shows that each of the six algorithms can solve instances with up to 5000 jobs in about 0.5 s with an average relative error less than 4%.     

Minimizing tardiness on a single processor with sequence-dependent setup times: a simulated annealing approach

In today's fast-paced Just-In-Time and mass customization manufacturing in a sequence-dependent setup environment, the challenge of making production schedules to meet due-date requirements is becoming a more complex problem. Unfortunately, much of the research on operations scheduling problems has either ignored setup times or assumed that setup times on each machine are independent of the job sequence. This paper considers the problem of minimizing tardiness, a common measure of due-date performance, in a sequence-dependent setup environment. Simulated annealing was used to solve the sequencing problem, and its performance was compared with random search. Our experimental results show that the algorithm can find a good solution fairly quickly, and thus can rework schedules frequently to react to variations in the schedule. The algorithm is invaluable for ‘on-line’ production scheduling and ‘last-minute’ changes to production schedule. The results of this research also suggest ways in which more complex and realistic job shop environments, such as multiple machines with a higher number of jobs in the sequence, and other scheduling objectives can be modeled. This research also investigates computational aspects of simulated annealing in solving complex scheduling problems.     

Lot Splitting in Unbalanced Production Systems*

This research investigates the impact of lot splitting in unbalanced production systems, under a variety of experimental conditions. Scheduling policies specifically designed for use in the presence of a long-term bottleneck, a condition frequently encountered in practice, are developed and tested. Results indicate that when steps are taken at nonbottleneck work centers to capitalize on capacity imbalances through increasing the number of setups and, hence, the variety of products produced, shop effectiveness is improved. The results also indicate that scheduling policies that tend to increase the size of the average process batch retard the overlapping of operations, which is critical to the success of the lot-splitting methodology in reducing flow time. Finally, it is shown that increasing capacity at nonbottleneck work centers along with implementation of effectiveness-oriented scheduling polices leads to improved shop performance.     

考虑交货因素的热轧无缝钢管订单排程模型与算法

无缝钢管的市场需求具有多品种、小批量的特点，为了在满足客户需求的同时保证高效连续化生产，文章在满足生产工艺特征的基础上将配送地址和交货期等合同因素引入热轧无缝钢管订单排程问题中，建立了以适期交货、订单集中生产配送和最小化机器设备调整为优化目标的订单排程优化模型，并设计了两阶段求解算法：首先，以订单交货期与配送地址差异最小为目标，基于凝聚策略设计了订单聚类算法，将具有相同工艺约束、相似合同要求的订单进行聚类，并形成初始轧制计划；然后，以设备调整和提前/拖期最小为目标，设计混合变邻域搜索算法，对初始轧制批次进行排程优化。基于实际订单数据的实验结果表明，模型和算法对问题的描述和求解是可行有效的。     

Coordinated scheduling of production and delivery stages with stage-dependent inventory holding costs

This paper considers a problem of integrated decision-making for job scheduling and delivery batching wherein different inventory holding costs between production and delivery stages are allowed. In the problem, jobs are processed on a facility at a production stage and then delivered at the subsequent delivery stage by a capacitated vehicle. The objective is to find the coordinated schedule of production and delivery that minimizes the total cost of the associated WIP inventory, finished product inventory and delivery, where both the inventory costs are characterized in terms of the weighted flow-time and the delivery cost is proportional to the required number of delivery batches. It is proved that the problem is NP-hard in the strong sense. Thereupon, three heuristic algorithms are derived. Some restricted cases are also characterized as being solvable in polynomial time. Numerical experiments are conducted to evaluate the performance of the derived heuristic algorithms.     

Satellite downlink scheduling problem: A case study

The synthetic aperture radar (SAR) technology enables satellites to efficiently acquire high quality images of the Earth surface. This generates significant communication traffic from the satellite to the ground stations, and, thus, image downlinking often becomes the bottleneck in the efficiency of the whole system. In this paper we address the downlink scheduling problem for Canada׳s Earth observing SAR satellite, RADARSAT-2. Being an applied problem, downlink scheduling is characterised with a number of constraints that make it difficult not only to optimise the schedule but even to produce a feasible solution. We propose a fast schedule generation procedure that abstracts the problem specific constraints and provides a simple interface to optimisation algorithms. By comparing empirically several standard meta-heuristics applied to the problem, we select the most suitable one and show that it is clearly superior to the approach currently in use.     

Scheduling a single semi-continuous batching machine

This paper addresses a new problem, called semi-continuous batch scheduling, which arises in the heating-operation of tube-billets in the steel industry. Each heating furnace can be regarded as a semi-continuous batching machine, which can handle up to C jobs simultaneously. The jobs in the same batch enter and leave the machine semi-continuously, which differs from the traditional batching machine scheduling where the jobs in same batch have a starting time and a finishing time. In this paper the processing time of a batch depends on the capacity of the semi-continuous batching machine, the longest processing time of jobs in the batch and its size. The objectives are to schedule jobs on the machine so that the makespan and the total completion time are minimized. A schedule for a semi-continuous batching machine consists of a batching and sequencing for the batches. We propose the optimal properties of two different objective functions and present the different dynamic programming algorithms with a running time of O(n²), respectively.     

Performance of Selected Part-Machine Grouping Techniques for Data Sets of Wide Ranging Sizes and Imperfection

This study addresses the part-machine grouping problem in group technology, and evaluates die performance of several cell formation methods for a wide range of data set sizes. Algorithms belonging to four classes are evaluated: (1) array-based methods: bond energy algorithm (BEA), direct clustering analysis (DCA) and improved rank order clustering algorithm (ROC2); (2) non-hierarchical clustering method: ZODIAC; (3) augmented machine matrix methods: augmented p-median method (APM) and augmented linear clustering algorithm (ALC); and (4) neural network algorithms: ART1 and variants: ART1/KS, ART1/KSC, and Fuzzy ART. The experimental design is based on a mixture-model approach, utilizing replicated clustering. The performance measures include Rand Index and bond energy recovery ratio, as well as computational requirements for various algorithms. Experimental factors include problem size, degree of data imperfection, and algorithm tested. The results show that, among the algorithms applicable for large, industry-size data sets, ALC and neural networks are superior to ZODIAC, which in turn is generally superior to array-based methods of ROC2 and DCA.     

Scheduling algorithms for computer-aided line balancing in printed circuit board assembly

Generalized flexible flow line (GFFL) is a scheduling environment comprising several machine banks which the products visit in the same order but can skip some machine banks. The type of machines in a bank can differ but they are suitable for performing the same manufacturing tasks. To change one product to another demands a set-up operation of the machine. This paper describes several scheduling algorithms for the GFFL problem. The overall structure of these algorithms is similar, consisting of machine allocation and sequencing phases. The algorithms have been integrated into an interactive production scheduling system for electronics assembly. Sample cases are used to illustrate the operation of the system in practice.     

A harmony search-based memetic optimization model for integrated production and transportation scheduling in MTO manufacturing

This paper investigates an integrated production and transportation scheduling problem in an MTO supply chain. A harmony search-based memetic optimization model is developed to handle this problem, in which certain heuristic procedures are proposed to convert the investigated problem into an order assignment problem. A novel improvisation process is also proposed to improve the optimum-seeking performance. The effectiveness of the proposed model is validated by numerical experiments. The experimental results show that (1) the proposed model can solve the investigated problem effectively and that (2) the proposed memetic optimization process exhibits better optimum-seeking performance than genetic algorithm-based and traditional memetic optimization processes.     

Lot scheduling problems of multiple items in the shop with both receiving and delivery just in time

Abstract. This paper deals with lot scheduling problems of multiple items processed in the shop with either a single machine or heterogeneous machines, under the condition that parts to be processed must arrive at the production line at the right times in the right quantities, and that completed parts must be delivered at their due date. Each of the problems is divided into subproblems of lot sizing and scheduling the resulting lots. Solution procedures solving the subproblems are separately optimal. However, since one of the subproblems is not independent of the other, the proposed algorithms adopting these procedures are to be considered heuristic. An iterative procedure is provided to improve solutions. The performance measure used is that of minimizing the total actual flow time considering both receiving just in time and delivery just in time. Numerical examples are presented to show the implementation of the algorithms.     

Scheduling in an assembly-type production chain with batch transfer

This paper addresses a three-machine assembly-type flowshop scheduling problem, which frequently arises from manufacturing process management as well as from supply chain management. Machines one and two are arranged in parallel for producing component parts individually, and machine three is an assembly line arranged as the second stage of a flowshop for processing the component parts in batches. Whenever a batch is formed on the second-stage machine, a constant setup time is required. The objective is to minimize the makespan. In this study we establish the strong NP-hardness of the problem for the case where all the jobs have the same processing time on the second-stage machine. We then explore a useful property, based upon which a special case can be optimally solved in polynomial time. We also study several heuristic algorithms to generate quality approximate solutions for the general problem. Computational experiments are conducted to evaluate the effectiveness of the algorithms.     

Dynamic scheduling of manufacturing job shops using extreme value theory

Most job shop scheduling approaches reported in the literature assume that the scheduling problem is static (i.e. job arrivals and the breakdowns of machines are neglected) and in addition, these scheduling approaches may not address multiple criteria scheduling or accommodate alternate resources to process a job operation. In this paper, a scheduling method based on extreme value theory (SEVAT) is developed and addresses all the shortcomings mentioned above. The SEVAT approach creates a statistical profile of schedules through random sampling, and predicts the quality or 'potential' of a feasible schedule. A dynamic scheduling problem was designed to reflect a real job shop scheduling environment closely. Two performance measures, viz. mean job tardiness and mean job cost, were used to demonstrate multiple criteria scheduling. Three factors were identified, and varied between two levels each, thereby spanning a varied job shop environment. The results of this extensive simulation study show that the SEVAT scheduling approach produces a better performance compared to several common dispatching rules.     

基于改进遗传算法的置换装配线调度问题研究

企业的置换装配线调度问题（Permutation Assembly-line Scheduling Problem,PASP）是一类典型的NP-hard型生产调度问题,是现代集成制造系统CIMS极为关心的问题。该问题可以具体描述为n个工件要在m台机器上加工,每个工件需要经过m道工序,每道工序要求不同的机器,这n个工件通过m台机器的顺序相同,它们在每台机器上的加工顺序也相同,问题的主要目标是找到n个工件在每台机器上的最优加工顺序,使得最大完工时间最小。由于PASP问题的NP-hard性质,本文使用遗传算法对其进行求解。尽管遗传算法常用以求解调度问题,但其选择与交叉机制易导致局部最优及收敛慢。因此,本文提出基于区块挖掘与重组的改进遗传算法用于求解置换装配线调度问题。首先通过关联规则挖掘出不同的优秀基因,然后将具有较优结果的基因组合为优势区块,产生具优势的人工解,并引入高收敛性的局部搜索方法,提高搜索到最优解的机会与收敛效率。本文以OR-Library中Taillard标准测试例来验证改进遗传算法的求解质量与效率,结果证明：本文所提算法与其它求解调度问题的现有5种知名算法相比,不仅收敛速度较快,同时求解质量优于它们。     

A near-optimal heuristic for the sequencing problem in multiple-batch flow-shops with small equal sublots

In this paper, we consider the lot-streaming problem of sequencing a set of batches, to be processed in equal sublots, in a flow-shop, so as to minimize makespan. A new heuristic procedure, called the bottleneck minimal idleness heuristic, is developed. Results of an experimental study are presented. It is shown that the proposed procedure generates solutions that are very close to the optimal solutions, and that the solutions generated are better than those obtained by using the fast insertion heuristic, considered to be a good heuristic for solving the flow-shop scheduling problem, when applied to the problem on hand.     

The Effects of Multitasking on Operations Scheduling

This study considers a typical scheduling environment that is influenced by the behavioral phenomenon of multitasking. Under multitasking, the processing of a selected job suffers from interruption by other jobs that are available but unfinished. This situation arises in a wide variety of applications; for example, administration, manufacturing, and process and project management. Several classical solution methods for scheduling problems no longer apply in the presence of multitasking. The solvability of any scheduling problem under multitasking is no easier than that of the corresponding classical problem. We develop optimal algorithms for some fundamental and practical single machine scheduling problems with multitasking. For other problems, we show that they are computationally intractable, even though in some cases the corresponding problem in classical scheduling is efficiently solvable. We also study the cost increase and value gained due to multitasking. This analysis informs companies about how much it would be worthwhile to invest in measures to reduce or encourage multitasking.     

A Tighter Extra-Resource Analysis of Online Deadline Scheduling

This paper is concerned with online algorithms for scheduling jobs with deadlines on a single processor. It has been known for long that unless the system is underloaded, no online scheduling algorithm can be 1-competitive, i.e., matching the performance of the optimal offline algorithm. Nevertheless, recent work has revealed that some online algorithms using a moderately faster processor (or extra processors) can guarantee very competitive performance Kalyanasundaram and Pruhs, 2000 or even be 1-competitive Koo et al., 2002; Lam and To, 2001. This paper takes a further step to investigate online scheduling algorithms with an even higher performance guarantee (i.e., better than 1-competitive algorithms) and in particular, presents an extra-resource analysis of the earliest-deadline-first strategy (EDF) with respect to such a higher performance guarantee.A preliminary version of this paper has been accepted by The Australian Theory Symposium on Computing, 2004.This research was supported in part by Hong Kong RGC Grant HKU-7024/01E.     

Single-machine batch delivery scheduling with an assignable common due window

This paper addresses a batch delivery single-machine scheduling problem in which jobs have an assignable common due window. Each job will incur an early (tardy) penalty if it is early (tardy) with respect to the common due window under a given schedule. There is no capacity limit on each delivery batch, and the cost per batch delivery is fixed and independent of the number of jobs in the batch. The objective is to find the optimal size and location of the window, the optimal dispatch date for each job, as well as an optimal job sequence to minimize a cost function based on earliness, tardiness, holding time, window location, window size, and batch delivery. We show that the problem can be optimally solved in O(n⁸)$O(n^8)$ time by a dynamic programming algorithm under a reasonable assumption on the relationships among the cost parameters. A computational experiment is also conducted to evaluate the performance of the proposed algorithm. We also show that some special cases of the problem can be optimally solved by lower order algorithms.     

Real-time scheduling and control of one-of-a kind production

Due to the great diversity of product types in one-of-a-kind production (OKP), the production scheduling and control in OKP is much more difficult than production scheduling and control of other production systems, e.g. mass production and batch production. Hence, the production efficiency in OKP companies is relatively lower. To improve productivity in OKP, a dynamic hierarchy production control system is presented. Using this control structure, a production system in an OKP company can be flexibly organized or re-organized according to the structure of a customized product (or an OKP product). Production synchronizing and scheduling algorithms for OKP shop floor production control are presented. Using these algorithms, a cybernetic model can be developed for shop floor control in OKP. The algorithms are applied to two alternate production scheduling goals in OKP, namely ASAP (as soon as possible) production and JIT (just in time) production.     

Hospital resident scheduling problem

This paper addresses the resident scheduling problem (RSP) at hospitals concerned with prescribing work-nights for residents while considering departmental staffing and skill requirements as well as residents' preferences. Three scenarios that represent most situations and account for various departmental requirements and needs are described. Although similar scheduling problems are considered in the literature, no analysis exists that adequately deals with the speciffic nature of this problem. The problem is modeled as a mixed-integer program and heuristic solution procedures are developed for the different identified scheduling scenarios. These procedures exploit the inherent network structure of the problem which is an important feature that enhances problem solvability. For the sake of comparison, the problem is also solved exactly via the CPLEX-MIP (version 6.0) package. The contribution of this work is important since many hospitals are still utilizing manual techniques in preparing their own schedules, expending considerable effort and time and yet contending with limited scheduling flexibility.