Scheduling : Theory, Algorithms, and Systems / M. Pinedo (original) (raw)

Journal of Systems Science and Systems Engineering, 2003

There seems to be a significant gap between the theoretical and the practical aspects of scheduling problems in the job shop environment. Theoretically, scheduling systems are designed on the basis of an optimum approach to the scheduling model. However in the practice, the optimum that is built into the scheduling applications seems to face some challenges when dealing with the dynamic character of a scheduling system, for instance machine breakdown or change of orders. Scheduling systems have become quite complex in the past few years. Competitive business environments and shorter product life cycles are the imminent challenges being faced by many companies these days. These challenges push companies to anticipate a demand driven supply chain in their business environment. A demand-driven supply chain incorporates the customer view into the supply chain processes. As a consequence of this, scheduling as a core process of the demand-driven supply chain must also reflect the customer view. In addition, other approaches to solving scheduling problems, for instance approaches based on human factors, prefer the scheduling system to be more flexible in both design and implementation. After discussion of these factors, the authors propose the integration of a different set of criteria for the development of scheduling systems which not only appears to have a better flexibility but also increased customer-focus.

Scheduling Computer and Manufacturing Processes (Second Edition). Jacek B?azewicz, Klaus H. Ecker, Erwin Pesch, G�nter Schmidt and Jan W??glarz, Springer, Berlin, ISBN 3-540-41931-4

Journal of Scheduling, 2002

Throughout this book we are concerned with scheduling computer and manufacturing processes. Despite the fact that we deal with two different areas of applications, the same model could be applied. This is because the above processes consist of complex activities to be scheduled, which can be modeled by means of tasks (or jobs), relations among them, processors, sometimes additional resources (and their operational functions), and parameters describing all these items in greater detail. The purpose of the modeling is to find optimal or sub-optimal schedules in the sense of a given criterion, by applying best suited algorithms. These schedules are then used for the original setting to carry out the various activities. In this chapter we introduce basic notions used for such a modeling of computer and manufacturing processes. 3.1 Definition of Scheduling Problems In general, scheduling problems considered in this book are characterized by three sets: set

Some Common Performance Measures in Scheduling Problems: Review Article

2009

In this Study, we discussed 29 different scheduling criteria. We developed mathematical expressions for all the criteria considered. Each of the criteria was expressed as a function of either the completion time of job or the given parameters. This will assist researchers to easily compute the value of any of the scheduling criteria considered in this study.

The Problem of Scheduling in Parallel Machines: A Case Study

Lecture Notes in Engineering and Computer …, 2010

In this paper, a problem of scheduling tasks in uniform parallel machines with sequence-dependent setup times is presented. The problem under analysis contains a particular set of constraints, including equipment capacity, task precedences, lot sizing and task delivery plan. The complexity of the mathematical programming model developed for this problem hasn't permitted to find one solution using optimising methods and so the authors have developed a heuristic based on the simulated annealing algorithm which allows obtaining nearly-optimal solutions.

Modified Algorithm for Scheduling Problem

Kirkuk University Journal-Scientific Studies, 2010

The problem of scheduling n jobs on a single machine is considered, where the jobs are divided into two classes and a machine set up is necessary between jobs of different classes. Jobs i (i= 1,…, n) becomes available for processing at time zero, requires a positive processing time i P. Disjoint subsets N 1 and N 2 define the partition of jobs into two classes. If two jobs in the same class are sequenced in adjacent positions, then no set up time between these jobs in necessary. We address the bicriterion (multi objective) scheduling problem, the two criteria are the minimization of flow time ( N i i c) and the minimization maximum Tardiness (max T). We characterized the set of all efficient points and the optimal solution. A modified algorithm presented to find efficient solutions for the problem with set up times. A relation found between number of efficient solutions and range of 'tardiness of shortest processing time (SPT T), tardiness of early due date (EDD T)'. This algorithm treats with a case that the set up time in  SPT rule is in increasing order. A counter example presented to show that the algorithm will fail if the set up time in  SPT rule is in decreasing order. Our task is to present the decision makers with all possible solutions and let them make the final selection. The decision maker has two objectives in mind ( N i i c) , (max T) and some solutions (efficient), we will choose the best one from the efficient solutions depending on his experiences.

Some common performance measures in scheduling problems

Research Journal of Applied Sciences, Engineering …, 2009

A heuristic algorithm for the n job, m machine sequencing problem

Management Science

The search for a solution to the problem of finding an optimal or near optimal se- quence of jobs being scheduled in a flow shop type situation has given consideration to both exact and approximate techniques of solution. Exact techniques, which usually require an electronic computer, ...

Deterministic Job-Shop Scheduling: Past, Present and Future

European Journal of Operational Research, 1998

Due to the stubborn nature of the deterministic job-shop scheduling problem many solutions proposed are of hybrid construction cutting across the traditional disciplines. The problem has been investigated from a variety of perspectives resulting in several analytical techniques combining generic as well as problem specific strategies. We seek to assess a subclass of this problem in which the objective is minimising makespan, by providing an overview of the history, the techniques used and the researchers involved. The sense and direction of their work is evaluated by assessing the reported results of their techniques on the available benchmark problems. From these results the current situation and pointers for future work are provided. of manufacturing it involves finding a sequential allocation of competing resources that optimises a particular objective function. The deterministic job-shop scheduling problem, hereinafter referred to as Π J , is the most general of the classical scheduling problems. Π J consists of a finite set J of n jobs { } J i i n =1 to be processed on a finite set M of m machines { } . M k k m =1 Each job J i must be processed on every machine and consists of a chain or complex of m i operations O i1 , O i2 , …, O im i , which have to be scheduled in a predetermined given order (precedence constraint). There are N operations in total, N = m i i n .

Multiprocessor Scheduling

2007

Scheduling theory is concerned with the optimal allocation of scarce resources (for instance, machines, processors, robots, operators, etc.) to activities over time, with the objective of optimizing one or several performance measures. The study of scheduling started about fifty years ago, being initiated by seminal papers by and Bellman (1956). Since then machine scheduling theory have received considerable development. As a result, a great diversity of scheduling models and optimization techniques have been developed that found wide applications in industry, transport and communications. Today, scheduling theory is an integral, generally recognized and rapidly evolving branch of operations research, fruitfully contributing to computer science, artificial intelligence, and industrial engineering and management. The interested reader can find many nice pearls of scheduling theory in textbooks, monographs and handbooks by Tanaev et al. (1994a,b), Pinedo (2001), Leung (2001), Brucker (2007, and Blazewicz et al. (2007). This book is the result of an initiative launched by Prof. Vedran Kordic, a major goal of which is to continue a good tradition -to bring together reputable researchers from different countries in order to provide a comprehensive coverage of advanced and modern topics in scheduling not yet reflected by other books. The virtual consortium of the authors has been created by using electronic exchanges; it comprises 50 authors from 18 different countries who have submitted 23 contributions to this collective product. In this sense, the volume in your hands can be added to a bookshelf with similar collective publications in scheduling, started by Coffman (1976) and successfully continued by Chretienne et al. (1995), Gutin and Punnen (2002), and Leung (2004). This volume contains four major parts that cover the following directions: the state of the art in theory and algorithms for classical and non-standard scheduling problems; new exact optimization algorithms, approximation algorithms with performance guarantees, heuristics and metaheuristics; novel models and approaches to scheduling; and, last but least, several real-life applications and case studies. The brief outline of the volume is as follows. Part I presents tutorials, surveys and comparative studies of several new trends and modern tools in scheduling theory. Chapter 1 is a tutorial on theory of cyclic scheduling. It is included for those readers who are unfamiliar with this area of scheduling theory. Cyclic scheduling models are traditionally used to control repetitive industrial processes and enhance the performance of robotic lines in many industries. A brief overview of cyclic scheduling models arising in manufacturing systems served by robots is presented, started with a discussion of early works appeared in the 1960s. Although the considered scheduling problems are, in general, NP-hard, a graph approach presented in this chapter permits to reduce some special cases to the parametric critical path problem in a graph and solve them in polynomial time. Chapter 2 describes the so-called multi-agent scheduling models applied to the situations in which the resource allocation process involves different stakeholders ("agents"), each having his/her own set of jobs and interests, and there is no central authority which can VI solve possible conflicts in resource usage over time. In this case, standard scheduling models become invalid, since rather than computing "optimal solutions", the model is asked to provide useful elements for the negotiation process, which eventually should lead to a stable and acceptable resource allocation. The chapter does not review the whole scope in detail, but rather concentrates on combinatorial models and their applications. Two major mechanisms for generating schedules, auctions and bargaining models, corresponding to different information exchange scenarios, are considered. Known results are reviewed and venues for future research are pointed out. Chapter 3 considers a class of scheduling problems under unavailability constraints associated, for example, with breakdown periods, maintenance durations and/or setup times. Such problems can be met in different industrial environments in numerous real-life applications. Recent algorithmic approaches proposed to solve these problems are presented, and their complexity and worst-case performance characteristics are discussed. The main attention is devoted to the flow-time minimization in the weighted and unweighted cases, for single-machine and parallel machine scheduling problems. Chapter 4 is devoted to the analysis of scheduling problems with communication delays. With the increasing importance of parallel computing, the question of how to schedule a set of precedence-constrained tasks on a given computer architecture, with communication delays taken into account, becomes critical. The chapter presents the principal results related to complexity, approximability and non-approximability of scheduling problems in presence of communication delays. Part II comprising eight chapters is devoted to the design of scheduling algorithms. Here the reader can find a wide variety of algorithms: exact, approximate with performance guarantees, heuristics and meta-heuristics; most algorithms are supplied by the complexity analysis and/or tested computationally. Chapter 5 deals with a batch version of the single-processor scheduling problem with batch setup times and batch delivery costs, the objective being to find a schedule which minimizes the sum of the weighted number of late jobs and the delivery costs. A new dynamic programming (DP) algorithm which runs in pseudo-polynomial time is proposed. By combining the techniques of binary range search and static interval partitioning, the DP algorithm is converted into a fully polynomial time approximation scheme for the general case. The DP algorithm becomes polynomial for the special cases when jobs have equal weights or equal processing times. Chapter 6 studies on-line approximation algorithms with performance guarantees for an important class of scheduling problems defined on identical machines, for jobs with arbitrary release times. Chapter 7 presents a new hybrid metaheuristic for solving the jobshop scheduling problem that combines augmented-neural-networks with genetic algorithm based search. In Chapter 8 heuristics based on a combination of the guided search and tabu search are considered to minimize the maximum completion time and maximum tardiness in the parallel-machine scheduling problems. Computational characteristics of the proposed heuristics are evaluated through extensive experiments. Chapter 9 presents a hybrid meta-heuristics based on a combination of the genetic algorithm and the local search aimed to solve the re-entrant flowshop scheduling problems. The hybrid method is compared with the optimal solutions generated by the integer programming technique, and the near optimal solutions generated by a pure genetic algorithm. Computational experiments are performed to illustrate the effectiveness and efficiency of the proposed algorithm.

Scheduling : Theory, Algorithms, and Systems / M. Pinedo (original) (raw)

Related papers