Showing papers on "Job shop scheduling published in 2001"
TL;DR: This work considers algorithmic problems in a distributed setting where the participants cannot be assumed to follow the algorithm but rather their own self-interest, and suggests a framework for studying such algorithms.
1,301 citations
Journal Article•
TL;DR: The behaviour of several different hyperheuristic approaches for a real-world personnel scheduling problem is analysed and the effectiveness of this approach is shown and wider applicability of hyper heuristic approaches to other problems of scheduling and combinatorial optimisation is suggested.
Abstract: The concept of a hyperheuristic is introduced as an approach that operates at a higher lever of abstraction than current metaheuristic approaches. The hyperheuristic manages the choice of which lower-level heuristic method should be applied at any given time, depending upon the characteristics of the region of the solution space currently under exploration. We analyse the behaviour of several different hyperheuristic approaches for a real-world personnel scheduling problem. Results obtained show the effectiveness of our approach for this problem and suggest wider applicability of hyperheuristic approaches to other problems of scheduling and combinatorial optimisation.
502 citations
TL;DR: In this paper, the authors survey the vast literature in this area with a perspective that integrates models, data, and optimal and heuristic algorithms, for the major classes of project scheduling problems.
Abstract: There have been many survey papers in the area of project scheduling in recent years. These papers have primarily emphasized modeling and algorithmic contributions for specific classes of project scheduling problems, such as net present value (NPV) maximization and makespan minimization, with and without resource constraints. Paralleling these developments has been the research in the area of project scheduling decision support, with its emphasis on data sets, data generation methods, and so on, that are essential to benchmark, evaluate, and compare the new models, algorithms and heuristic techniques. These investigations have extended the frontiers of research and application in all areas of project scheduling and management. In this paper, we survey the vast literature in this area with a perspective that integrates models, data, and optimal and heuristic algorithms, for the major classes of project scheduling problems. We also include recent surveys that have compared commercial project scheduling systems. Finally, we present an overview of web-based decision support systems and discuss the potential of this technology in enabling and facilitating researchers and practitioners in identifying new areas of inquiry and application.
437 citations
Posted Content•
TL;DR: This paper surveys the vast literature in this area with a perspective that integrates models, data, and optimal and heuristic algorithms, for the major classes of project scheduling problems, and includes recent surveys that have compared commercial project scheduling systems.
Abstract: There have been many survey papers in the area of project scheduling in recent years. These papers have primarily emphasized modeling and algorithmic contributions for specific classes of project scheduling problems, such as net present value (NPV) maximization and makespan minimization, with and without resource constraints. Paralleling these developments has been the research in the area of project scheduling decision support, with its emphasis on data sets, data generation methods, and so on, that are essential to benchmark, evaluate, and compare the new models, algorithms and heuristic techniques. These investigations have extended the frontiers of research and application in all areas of project scheduling and management. In this paper, we survey the vast literature in this area with a perspective that integrates models, data, and optimal and heuristic algorithms, for the major classes of project scheduling problems. We also include recent surveys that have compared commercial project scheduling systems. Finally, we present an overview of web-based decision support systems and discuss the potential of this technology in enabling and facilitating researchers and practitioners in identifying new areas of inquiry and application.
423 citations
TL;DR: It is shown in several examples that although the optimal schedule may be very different from that of the classical version of the problem, and the computational effort becomes significantly greater, polynomial-time solutions still exist.
353 citations
TL;DR: It appears that the 50% rule for buffer sizing may lead to a serious overestimation of the required buffer protection, and regularly updating the baseline schedule and the critical chain provides the best intermediate estimates of the final project duration and yields the smallestfinal project duration.
341 citations
TL;DR: In this paper, a new genetic algorithm approach is proposed to solve the resource-constrained project scheduling problem with multiple execution modes for each activity and makespan minimization as objective.
Abstract: In this paper we consider the resource-constrained project scheduling problem with multiple execution modes for each activity and makespan minimization as objective. We present a new genetic algorithm approach to solve this problem. The genetic encoding is based on a precedence feasible list of activities and a mode assignment. After defining the related crossover, mutation, and selection operators, we describe a local search extension which is employed to improve the schedules found by the basic genetic algorithm. Finally, we present the results of our thorough computational study. We determine the best among several different variants of our genetic algorithm and compare it to four other heuristics that have recently been proposed in the literature. The results that have been obtained using a standard set of instances show that the new genetic algorithm outperforms the other heuristic procedures with regard to a lower average deviation from the optimal makespan.
307 citations
TL;DR: A parallel and easily implemented hybrid optimization framework is presented, which reasonably combines genetic algorithm with simulated annealing, and applies it to job-shop scheduling problems.
271 citations
Book•
01 Jan 2001
TL;DR: This paper presents a meta-modelling framework for solving the scheduling problem of deciding when to execute each activity, so that both temporal constraints and resource constraints are satisfied.
Abstract: Given a set of resources with given capacities, a set of activities with given processing times and resource requirements, and a set of temporal constraints between activities, a “pure” scheduling problem consists of deciding when to execute each activity, so that both temporal constraints and resource constraints are satisfied. Most scheduling problems can easily be represented as instances of the constraint satisfaction problem (Kumar, 1992): given a set of variables, a set of possible values (domain) for each variable, and a set of constraints between the variables, assign a value to each variable, so that all the constraints are satisfied.
235 citations
TL;DR: This paper develops a tabu search algorithm which integrates some important features including an efficient neighborhood, a dynamic tabu tenure mechanism, techniques for constraint handling, intensification and diversification, and large numbers of binary and ternary “logical” constraints.
Abstract: The daily photograph scheduling problem of earth observation satellites such as Spot 5 consists of scheduling a subset of mono or stereo photographs from a given set of candidates to different cameras. The scheduling must maximize a profit function while satisfying a large number of constraints. In this paper, we first present a formulation of the problem as a generalized version of the well-known knapsack model, which includes large numbers of binary and ternary “logical” constraints. We then develop a tabu search algorithm which integrates some important features including an efficient neighborhood, a dynamic tabu tenure mechanism, techniques for constraint handling, intensification and diversification. Extensive experiments on a set of large and realistic benchmark instances show the effectiveness of this approach.
219 citations
TL;DR: In this article, a new constructive heuristic procedure is proposed to solve the problem of permutation flow shop scheduling with the criterion of minimising the total flow time, which is flexible in the computational effort required, as it can be adjusted to the requirements of the problem.
TL;DR: A computational study shows that this approach out-performs the previous methods found in the literature for a set of randomly generated instances and a heuristic version of the solution approach is also proposed and tested on larger instances.
Abstract: This paper presents an exact approach for solving the simultaneous vehicle and crew scheduling problem in urban mass transit systems. We consider the single depot case with a homogeneous fleet of vehicles. This approach relies on a set partitioning formulation for the driver scheduling problem that incorporates side constraints for the bus itineraries. The proposed solution approach consists of a column generation process (only for the crew schedules) integrated into a branch-and-bound scheme. The side constraints on buses guarantee that an optimal vehicle assignment can be derived afterwards in polynomial time. A computational study shows that this approach out-performs the previous methods found in the literature for a set of randomly generated instances. A heuristic version of the solution approach is also proposed and tested on larger instances.
TL;DR: This paper considers a real ship scheduling problem that can be considered as a multi-ship pickup and delivery problem with soft time windows (m-PDPSTW) and proposes an optimisation based approach based on a set partitioning formulation to solve the problem.
TL;DR: The resource-constrained project scheduling problem with multiple execution modes for each activity and the makespan as the minimization criterion is considered and a simulated annealing approach to solve this problem is presented.
Abstract: In this paper the resource-constrained project scheduling problem with multiple execution modes for each activity and the makespan as the minimization criterion is considered. A simulated annealing approach to solve this problem is presented. The feasible solution representation is based on a precedence feasible list of activities and a mode assignment. A comprehensive computational experiment is described, performed on a set of standard test problems constructed by the ProGen project generator. The results are analyzed and discussed and some final remarks are included.
TL;DR: In this article, an ant colony system (ACS) based optimization approach is proposed for the enhancement of hydroelectric generation scheduling, where the search space of multi-stage scheduling is first determined.
Abstract: In this paper, an ant colony system (ACS) based optimization approach is proposed for the enhancement of hydroelectric generation scheduling. To apply the method to solve this problem, the search space of multi-stage scheduling is first determined. Through a collection of cooperative agents called ants, the near-optimal solution to the scheduling problem can be effectively achieved. In the algorithm, the state transition rule, local pheromone-updating rule, and global pheromone-updating rule are all added to facilitate the computation. Because this method can operate the population of agents simultaneously, the process stagnation can be better prevented. The optimization capability can be thus significantly enhanced. The proposed approach has been tested on Taiwan Power System (Taipower) through the utility data. Test results demonstrated the feasibility and effectiveness of the method for the application considered.
TL;DR: Using the scheduling problems of Charles-Lemoyne Hospital and the Jewish General Hospital, it is shown how to modify a hospital's existing scheduling rules to develop techniques which produce better schedules and reduce the time needed to build them.
Abstract: This paper introduces the problem of scheduling emergency room physicians. We interviewed physicians from six hospitals in the greater Montreal, Canada area, in order to understand the emergency room scheduling problem. Extracting the real scheduling problem is difficult because physician working conditions are based on informal mutual cooperation which is usually not documented. We present the characteristics of the scheduling problem and the scheduling techniques currently used in the six emergency rooms we analyzed. Using the scheduling problems of Charles-Lemoyne Hospital and the Jewish General Hospital, we show how to modify a hospital's existing scheduling rules to develop techniques which produce better schedules and reduce the time needed to build them.
22 Jun 2001
TL;DR: A battery lifespan evaluation metric is developed which is aware of the shape of the discharge power profile and shows that the battery lifespan can be increased by up to 29% by optimizing the discharged power file alone.
Abstract: This paper addresses battery-aware static scheduling in battery-powered distributed real-time embedded systems. As suggested by previous work, reducing the discharge current level and shaping its distribution are essential for extending the battery lifespan. We propose two battery-aware static scheduling schemes. The first one optimizes the discharge power profile in order to maximize the utilization of the battery capacity. The second one targets distributed systems composed of voltage-scalable processing elements (PEs). It performs variable-voltage scheduling via efficient slack time re-allocation, which helps reduce the average discharge power consumption as well as flatten the discharge power profile. Both schemes guarantee the hard real-time constraints and precedence relationships in the real-time distributed embedded system specification. Based on previous work, we develop a battery lifespan evaluation metric which is aware of the shape of the discharge power profile. Our experimental results show that the battery lifespan can be increased by up to 29% by optimizing the discharge power file alone. Our variable-voltage scheme increases the battery lifespan by up to 76% over the non-voltage-scalable scheme and by up to 56% over the variable-voltage scheme without slack-time re-allocation.
Proceedings Article•
21 Jan 2001TL;DR: This paper presents a constraint-based approach to solving the Earth Observing Satellites (EOS) scheduling problem, and proposes a stochastic heuristic search method for solving it.
Abstract: We address the problem of scheduling observations for a collection of earth observing satellites. This scheduling task is a difficult optimization problem, potentially involving many satellites, hundreds of requests, constraints on when and how to service each request, and resources such as instruments, recording devices, transmitters, and ground stations. High-fidelity models are required to ensure the validity of schedules; at the same time, the size and complexity of the problem makes it unlikely that systematic optimization search methods will be able to solve them in a reasonable time. This paper presents a constraint-based approach to solving the Earth Observing Satellites (EOS) scheduling problem, and proposes a stochastic heuristic search method for solving it.
TL;DR: It is proved that the problem of minimizing maximum lateness is NP-hard and also provided a pseudo-polynomial time algorithm to solve it optimally.
TL;DR: An integer programming formulation for the problem of batching and scheduling of certain kinds of batch processors, generates a lower bound from a partial LP relaxation, provides a polynomial algorithm to solve a special case, and tests a set of heuristics on the general problem.
Abstract: This paper discusses the problem of batching and scheduling of certain kinds of batch processors. Examples of these processors include heat treatment facilities, particularly in the steel and ceramics industries, as well as a variety of operations in the manufacture of integrated circuits. In general, for our problem there is a set of jobs waiting to be processed. Each job is associated with a given family and has a weight or delay cost and a volume. The scheduler must organize jobs into batches in which each batch consists of jobs from a single family and in which the total volume of jobs in a batch does not exceed the capacity of the processor. The scheduler must then sequence all the batches. The processing time for a batch depends only on the family and not on the number or the volume of jobs in the batch. The objective is to minimize the mean weighted flow time.The paper presents an integer programming formulation for this problem, generates a lower bound from a partial LP relaxation, provides a polynomial algorithm to solve a special case, and tests a set of heuristics on the general problem. The ability to pack jobs into batches is the key to efficient solutions and is the basis of the different solution procedures in this paper. The heuristics include a greedy heuristic, a successive knapsack heuristic, and a generalized assignment heuristic. Optimal solutions are obtained by complete enumeration for small problems.The conclusions of the computational study show that the successive knapsack and generalized assignment heuristics perform better than the greedy. The generalized assignment heuristic does slightly better than the successive knapsack heuristic in some cases, but the latter is substantially faster and more robust. For problems with few jobs, the generalized assignment heuristic and the knapsack heuristic almost always provide optimal solutions. For problems with more jobs, we compare the heruistic solutions' values to lower bounds; the computational work suggests that the heuristics continue to provide solutions that are optimal or close to the optimal. The study also shows that the volume of the job relative to the capacity of the facility and the number of jobs in a family affect the performance of the heuristics, whereas the number of families does not. Finally, we give a worst-case analysis of the greedy heuristic.
TL;DR: This paper addresses the reward-based scheduling problem for periodic tasks and proves the optimality of Rate Monotonic Scheduling (with harmonic periods), Earliest Deadline First, and Least Laxity First policies for the case of uniprocessors when used with the optimal service times.
Abstract: Reward-based scheduling refers to the problem in which there is a reward associated with the execution of a task. In our framework, each real-time task comprises a mandatory and an optional part. The mandatory part must complete before the task's deadline, while a nondecreasing reward function is associated with the execution of the optional part, which can be interrupted at any time. Imprecise computation and Increased-Reward-with-Increased-Service models fall within the scope of this-framework. In this paper, we address the reward-based scheduling problem for periodic tasks. An optimal schedule is one where mandatory-parts complete in a timely manner and the weighted average reward is maximized. For linear and concave reward functions, which are most common, we 1) show the existence of an optimal schedule where the optional service time of a task is constant at every instance and 2) show how to efficiently compute this service time. We also prove the optimality of Rate Monotonic Scheduling (with harmonic periods), Earliest Deadline First, and Least Laxity First policies for the case of uniprocessors when used with the optimal service times we computed. Moreover, we extend our result by showing that any policy which can fully utilize all the processors is also optimal for the multiprocessor periodic reward-based scheduling. To show-that our optimal solution is pushing the limits of reward-based scheduling, we further prove that, when the reward functions are convex, the problem becomes NP-Hard. Our static optimal solution, besides providing considerable reward improvements over the previous suboptimal strategies, also has a major practical benefit. Run-time overhead is eliminated and existing scheduling disciplines may be used without modification with the computed optimal service times.
TL;DR: In this paper, the authors studied the dial-a-ride problem with a single server and gave a 2.5-competitive algorithm for the case of infinite capacity and a 1.5competitive algorithm with finite capacity.
TL;DR: This work derives optimal and suboptimal beam scheduling algorithms for electronically scanned array tracking systems as a multiarm bandit problem involving hidden Markov models (HMMs) and presents a finite-dimensional optimal solution.
Abstract: We derive optimal and suboptimal beam scheduling algorithms for electronically scanned array tracking systems. We formulate the scheduling problem as a multiarm bandit problem involving hidden Markov models (HMMs). A finite-dimensional optimal solution to this multiarm bandit problem is presented. The key to solving any multiarm bandit problem is to compute the Gittins (1989) index. We present a finite-dimensional algorithm that computes the Gittins index. Suboptimal algorithms for computing the Gittins index are also presented. Numerical examples are presented to illustrate the algorithms.
TL;DR: Computational results indicate that a genetic algorithm with optimized parameters for controlling the evolution of solutions consistently performs significantly better than the heuristic for blocking #owshops developed in a recent Ph.D. thesis by Abadi.
TL;DR: Through an extensive computational study, results show that with the parallel scheduling generation scheme and the multi-project approach the project manager can obtain a good multiproject schedule with the time criterion selected: minimising mean project delay or minimising multipro project duration increase.
Abstract: Frequently, the availability of resources assigned to a project is limited and not sufficient to execute all the concurrent activities. In this situation, decision making about their schedule is necessary. Many times this schedule supposes an increase in the project completion time. Additionally, companies commonly manage various projects simultaneously, sharing a pool of renewable resources. Given these resource constraints, we often can only apply heuristic methods to solve the scheduling problem. In this work the effect of the schedule generation schemes – serial or parallel – and priority rules – MINLFT, MINSLK, MAXTWK, SASP or FCFS – with two approaches – multi-project and single-project – are analysed. The time criteria considered are the mean project delay and the multiproject duration increase. Through an extensive computational study, results show that with the parallel scheduling generation scheme and the multi-project approach the project manager can obtain a good multiproject schedule with the time criterion selected: minimising mean project delay or minimising multiproject duration increase. New heuristics – based on priority rules with a two-phase approach – that outperform classical ones are proposed to minimise mean project delay with a multi-project approach. Finally, the best heuristics analysed are evaluated together with a representative sample of commercial project management software.
TL;DR: A depth-first branch-and-bound algorithm that makes use of extra precedence relations to resolve a number of resource conflicts and a fast recursive search algorithm for the max- npv problem to compute upper bounds are introduced.
Abstract: In this paper we study the resource-constrained project-scheduling problem with discounted cash flows. Each activity of this resource-constrained project-scheduling problem has certain resource requirements and a known deterministic cash flow that can be either positive or negative. Deterministic cash flows are assumed to occur over the duration of the activities. Progress payments and cash outflows occur at the completion of activities. The objective is to schedule the activities subject to a fixed deadline to maximize the net present value subject to the precedence and resource constraints. With these features the financial aspects of project management are taken into account.We introduce a depth-first branch-and-bound algorithm that makes use of extra precedence relations to resolve a number of resource conflicts and a fast recursive search algorithm for the max- npv problem to compute upper bounds. The recursive search algorithm exploits the idea that positive cash flows should be scheduled as early as possible while negative cash flows should be scheduled as late as possible within the precedence constraints. The procedure has been coded in Visual C++, Version 4.0 under Windows NT, and has been validated on two problem sets.
03 Jan 2001
TL;DR: This work designs a simple tabu search meta-heuristic that exploits the special properties of different types of neighborhood moves, and creates highly effective candidate list strategies to solve an airport gate assignment problem that dynamically assigns airport gates to scheduled flights.
Abstract: Considers an airport gate assignment problem that dynamically assigns airport gates to scheduled flights based on passengers' daily origin and destination flow data. The objective of the problem is to minimize the overall connection times during which passengers walk to catch their connection flights. We formulate this problem as a mixed 0-1 quadratic integer programming problem and then reformulate it as a mixed 0-1 integer problem with a linear objective function and constraints. We design a simple tabu search meta-heuristic to solve the problem. The algorithm exploits the special properties of different types of neighborhood moves, and create highly effective candidate list strategies. We also address issues of tabu short-term memory, dynamic tabu tenure, aspiration rules, and various intensification and diversification strategies. Preliminary computational experiments are conducted, and the results are presented and analyzed.
TL;DR: A solution to the problems of resource allocation and scheduling of loading and unloading operations in a container terminal is presented and it is shown that the optimized lists reduce the number of crane conflicts on the yard and the average length of the truck queues in the terminal.
Abstract: A solution to the problems of resource allocation and scheduling of loading and unloading operations in a container terminal is presented. The two problems are formulated and solved hierarchically. First, the solution of the resource allocation problem returns, over a number of work shifts, a set of quay cranes used to load and unload containers from the moored ships and the set of yard cranes to store those containers on the yard. Then, a scheduling problem is formulated to compute the loading and unloading lists of containers for each allocated crane. The feasibility of the solution is verified against a detailed, discrete-event based, simulation model of the terminal. The simulation results show that the optimized resource allocation, which reduces the costs by [frac13], can be effectively adopted in combination with the optimized loading and unloading list. Moreover, the simulation shows that the optimized lists reduce the number of crane conflicts on the yard and the average length of the truck queues in the terminal.
TL;DR: The results of this extensive simulation study indicate that the genetic algorithms scheduling approach produces better scheduling performance in comparison to several common dispatching rules.
Abstract: Most job shop scheduling methods reported in the literature usually address the static scheduling problem. These methods do not consider multiple criteria, nor do they accommodate alternate resources to process a job operation. In this paper, a scheduling method based on genetic algorithms is developed and it addresses all the shortcomings mentioned above. The genetic algorithms approach is a schedule permutation approach that systematically permutes an initial pool of randomly generated schedules to return the best schedule found to date. A dynamic scheduling problem was designed to closely reflect a real job shop scheduling environment. Two performance measures, namely mean job tardiness and mean job cost, were used to demonstrate multiple criteria scheduling. To span a varied job shop environment, three factors were identified and varied between two levels each. The results of this extensive simulation study indicate that the genetic algorithms scheduling approach produces better scheduling performance in comparison to several common dispatching rules.
29 Mar 2001
TL;DR: An integrated framework that addresses several important test scheduling problems in system-on-a-chip test automation is presented and a new algorithm that uses preemption to obtain optimal test schedules in polynomial time is presented.
Abstract: Test scheduling is a major problem in system-on-a-chip (SOC) test automation. We present an integrated framework that addresses several important test scheduling problems. We first present efficient techniques to determine optimal SOC test schedules with precedence constraints, i.e., schedules that preserve desirable orderings among tests. We then present a new algorithm that uses preemption to obtain optimal test schedules in polynomial time. Finally, we present a new method for determining optimal power-constrained schedules. Experimental results for a representative SOC show that test schedules can be obtained in reasonable CPU time for all cases.