Showing papers on "Job shop scheduling published in 2000"
TL;DR: The model, solution method, and system developed and implemented for hot rolling production scheduling in Shanghai Baoshan Iron & Steel Complex shows 20% improvement over the previous manual based system.
317 citations
TL;DR: This paper examines recent developments in the field of evolutionary computation for manufacturing optimization with a wide range of problems, from job shop and flow shop scheduling, to process planning and assembly line balancing.
Abstract: The use of intelligent techniques in the manufacturing field has been growing the last decades due to the fact that most manufacturing optimization problems are combinatorial and NP hard. This paper examines recent developments in the field of evolutionary computation for manufacturing optimization. Significant papers in various areas are highlighted, and comparisons of results are given wherever data are available. A wide range of problems is covered, from job shop and flow shop scheduling, to process planning and assembly line balancing.
264 citations
TL;DR: A fast and simple priority dispatch method is described and shown to produce acceptable schedules most of the time and a look ahead algorithm is introduced that outperforms the dispatcher by about 12% with only a small increase in run time.
Abstract: This paper describes three approaches to assigning tasks to earth observing satellites EOS. A fast and simple priority dispatch method is described and shown to produce acceptable schedules most of the time. A look ahead algorithm is then introduced that outperforms the dispatcher by about 12% with only a small increase in run time. These algorithms set the stage for the introduction of a genetic algorithm that uses job permutations as the population. The genetic approach presented here is novel in that it uses two additional binary variables, one to allow the dispatcher to occasionally skip a job in the queue and another to allow the dispatcher to occasionally allocate the worst position to the job. These variables are included in the recombination step in a natural way. The resulting schedules improve on the look ahead by as much as 15% at times and 3% on average. We define and use the "window-constrained packing" problem to model the bare bones of the EOS scheduling problem.
251 citations
TL;DR: An intelligent agent based dynamic scheduling system that selects the most appropriate priority rule according to the shop conditions in real time, while simulated environment performs scheduling activities using the rule selected by the agent.
234 citations
TL;DR: The several scheduling policies under machine breakdowns in a classical job shop system are tested and a partial scheduling scheme under both deterministic and stochastic environments for several system configurations are investigated.
229 citations
TL;DR: The development and application of a hybrid genetic algorithm (HGA) that incorporates a local improvement procedure based on tabu search (TS) into a basic genetic algorithms (GA) and significantly outperforms the other methods in terms of solution quality.
223 citations
TL;DR: The proposed local search method is based on a tabu search technique and on the shifting bottleneck procedure used to generate the initial solution and to refine the next-current solutions.
220 citations
TL;DR: This problem can be modelled as a hybrid flow shop scheduling problem with mixed no-wait/no-store constraints and mixed bottleneck/non-bottleneck machines and an approximation algorithm based on the tabu search approach is proposed.
220 citations
TL;DR: In this article, the authors considered a version of multiprocessor scheduling with the special feature that jobs may be rejected at a certain penalty, and the main result was a $1+\phi\approx 2.618$ competitive algorithm for the on-line version of the problem.
Abstract: We consider a version of multiprocessor scheduling with the special feature that jobs may be rejected at a certain penalty. An instance of the problem is given by $m$ identical parallel machines and a set of $n$ jobs, with each job characterized by a processing time and a penalty. In the on-line version the jobs become available one by one and we have to schedule or reject a job before we have any information about future jobs. The objective is to minimize the makespan of the schedule for accepted jobs plus the sum of the penalties of rejected jobs.
The main result is a $1+\phi\approx 2.618$ competitive algorithm for the on-line version of the problem, where $\phi$ is the golden ratio. A matching lower bound shows that this is the best possible algorithm working for all $m$. For fixed $m$ we give improved bounds; in particular, for $m=2$ we give a $\phi\approx 1.618$ competitive algorithm, which is best possible.
For the off-line problem we present a fully polynomial approximation scheme for fixed $m$ and a polynomial approximation scheme for arbitrary $m$. Moreover, we present an approximation algorithm which runs in time $O(n\log n)$ for arbitrary $m$ and guarantees a $2-\frac{1}{m}$ approximation ratio.
211 citations
16 Aug 2000
TL;DR: In this paper, the concept of hyperheuristic is introduced as an approach that operates at a higher lever of abstraction than current metaheuristic approaches and 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.
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.
210 citations
TL;DR: In this article, the authors extend the framework of simple temporal problems studied originally by Dechter, Meiri and Pearl to consider constraints of the form x 1 −y 1 ≤r 1 ∨⋯∨ x n −y n ≤r n, where x 1,…,x n,y 1,…,y n are variables ranging over the real numbers, r 1,..,r n are real constants, and n≥1.
TL;DR: A measure of schedule robustness that explicitly considers the risk of poor system performance over all potential realizations of job processing times is presented and results indicate that robust schedules provide effective hedges against processing time uncertainty while maintaining excellent expected makespan performance.
Abstract: This paper focuses on manufacturing environments where job processing times are uncertain. In these settings, scheduling decision makers are exposed to the risk that an optimal schedule with respect to a deterministic or stochastic model will perform poorly when evaluated relative to actual processing times. Since the quality of scheduling decisions is frequently judged as if processing times were known a priori, robust scheduling, i.e., determining a schedule whose performance (compared to the associated optimal schedule) is relatively insensitive to the potential realizations of job processing times, provides a reasonable mechanism for hedging against the prevailing processing time uncertainty. In this paper we focus on a two-machine flow shop environment in which the processing times of jobs are uncertain and the performance measure of interest is system makespan. We present a measure of schedule robustness that explicitly considers the risk of poor system performance over all potential realizations of...
TL;DR: It is shown that the test scheduling decision problem is equivalent to the m-processor open shop scheduling problem and is therefore NP-complete and a commonly encountered instance of this problem (m=2) can be solved in polynomial time.
Abstract: We present optimal solutions to the test scheduling problem for core-based systems. Given a set of tasks (test sets for the cores), a set of test resources (e.g., test buses, BIST hardware) and a test access architecture, we determine start times for the tasks such that the total test application time is minimized. We show that the test scheduling decision problem is equivalent to the m-processor open shop scheduling problem and is therefore NP-complete. However a commonly encountered instance of this problem (m=2) can be solved in polynomial time. For the general case (m>2), we present a mixed-integer linear programming (MILP) model for optimal scheduling and apply it to a representative core-based system using an MILP solver available in the public domain. We also extend the MILP model to allow optimal test set selection from a set of alternatives. Finally, we present an efficient heuristic algorithm for handling larger systems for which the MILP model may be infeasible.
TL;DR: In this paper, a price/profit-based unit commitment (UC) algorithm is proposed to schedule generation units in a manner that minimizes costs while meeting all demand, which considers the softer demand constraint and allocates fixed and transitional costs to the scheduled hours.
Abstract: As the electrical industry restructures, many of the traditional algorithms for controlling generating units need modification or replacement, previously utilized to schedule generation units in a manner that minimizes costs while meeting all demand, the unit commitment (UC) algorithm must be updated A UC algorithm that maximizes profit will play an essential role in developing successful bidding strategies for the competitive generator Simply bidding to win contracts is insufficient; bidding strategies must result in contracts that, on average, cover the total generation costs No longer guaranteed to be the only electricity supplier, a generation company's share of the demand will be more difficult to predict than in the past Removing the obligation to serve softens the demand constraint In this paper the authors provide a price/profit-based UC formulation which considers the softer demand constraint and allocates fixed and transitional costs to the scheduled hours The authors describe a genetic algorithm solution to this new UC problem and present results for an illustrative example
TL;DR: A review of the reported research in this area is presented, the extent of applicability of various approaches is discussed, and directions for future research are suggested.
Abstract: In recent years, a few researchers have addressed the need for the integration of process planning and scheduling functions in order to achieve superior overall system performance. Many of these researchers have discovered that the potential savings are substantial when process planning and scheduling are integrated. It has been reported that typical scheduling objectives, such as minimizing makespan, maximizing equipment utilization, etc, can be significantly improved as the result of integration of these two important manufacturing system functions. In this paper, we present a review of the reported research in this area, discuss the extent of applicability of various approaches, and suggest directions for future research.
TL;DR: This work proposes a new genetic algorithm (GA) that differs from traditional GAs in the following components: it performs its parent selection by using a ranking scheme that considers successively the three criteria, and it uses a multi-point crossover operator based on the hamming distance between schedules.
TL;DR: This paper investigates the use of a memetic algorithm for the thermal generator maintenance scheduling problem and concludes that the most effective method is a Memetic approach that employs a tabu-search operator.
Abstract: The incorporation of local search operators into a genetic algorithm has provided very good results in certain scheduling problems. The resulting algorithm from this hybrid approach has been termed a memetic algorithm. This paper investigates the use of a memetic algorithm for the thermal generator maintenance scheduling problem. The local search operators alone have been found (in earlier work by the authors and others) to produce good quality results. The main purpose of this paper is to discover whether a memetic approach can produce better results. We describe the approach taken and highlight the variety of local search algorithms that were employed. We compare the memetic algorithms with a variety of algorithms that include the local search operators on their own and a range of algorithms that apply the local search operator to randomly generated solutions. We see that, for the problems tested, the memetic algorithms produce better quality solutions (although they do take more time about it). Of course, in practice, for a problem like this, the time taken to produce a solution is not a major issue. What is far more important is the quality of the solution. We conclude that the most effective method (of the ones tested here) is a memetic approach that employs a tabu-search operator.
TL;DR: It is shown that the no-wait restrictions require several adaptations of the neighborhood structure used by simulated annealing, which indicates that simulatedAnnealing consistently gives better results for a number of realistic instances than simple heuristics within acceptable computation time.
TL;DR: A dual reoptimization algorithm is combined with a local search heuristic for solving a mixed integer programming problem, and this idea is applied to the above lotsizing and scheduling problem by embedding a dual network flow algorithm into threshold accepting and simulated annealing, respectively.
TL;DR: In this article, the problem of scheduling part families and jobs within each part family in a flowline manufacturing cell where the setup times for each family are sequence dependent and it is desired to minimize the makespan while processing parts (jobs) in each family together is considered.
TL;DR: A method for assigning tasks or resources, based on a model of division of labor in social insects, is introduced and applied to a dynamic flow shop scheduling problem and both systems are able to adapt well to changing conditions.
Abstract: A method for assigning tasks or resources, based on a model of division of labor in social insects, is introduced and applied to a dynamic flow shop scheduling problem. The problem consists of assigning trucks to paint booths in a truck facility to minimize total makespan and the number of paint flushes. Similarities between the ant-based approach and a market-based approach are highlighted. Both systems are able to adapt well to changing conditions.
01 Aug 2000
TL;DR: This work addresses resource management on the downlink of CDMA packet data networks, and argues that the discretization needs to be fine tuned to address this shortcoming ofrete bandwidth conditions.
Abstract: Packet data is expected to dominate third generation wireless networks, unlike current generation voice networks. This opens up new and interesting problems. Physical and link layer issues have been studied extensively, while resource allocation and scheduling issues have not been addressed satisfactorily.In this work, we address resource management on the downlink of CDMA packet data networks. Network performance (for example, capacity) has been addressed, but user centric performance has not received much attention. Recently, various non-traditional scheduling schemes based on new metrics have been proposed, and target user performance (mostly without reference to wireless). We adapt these metrics to the CDMA context, and establish some new results for the offline scheduling problem. In addition, we modify a large class of online algorithms to work in our setup and conduct a wide range of experiments. Based on detailed simulations, we infer that: Algorithms which exploit “request sizes” seem to outperform those that do not. Among these, algorithms that also exploit channel conditions provide significantly higher network throughput.Depending on continuous or discretized bandwidth conditions, either pure time multiplexing or a combination of time and code multiplexing strikes an excellent balance between user satisfaction and network performance.Discrete bandwidth conditions can lead to degraded user level performance without much impact on network performance. We argue that the discretization needs to be fine tuned to address this shortcoming.
TL;DR: In this paper, the authors apply data mining methodologies to explore the patterns in data generated by a genetic algorithm performing a scheduling operation and to develop a rule set scheduler which approximates the genetic algorithm's scheduler.
Journal Article•
TL;DR: This work analyzes several competitive algorithms for OLDARP and presents a somewhat less natural strategy SMARTSTART, which in contrast to the other two strategies may leave the server idle from time to time although unserved requests are known.
Abstract: We consider the following online dial-a-ride problem (OLDARP): Objects are to be transported between points in a metric space. Transportation requests arrive online, specifying the objects to be transported and the corresponding source and destination. These requests are to be handled by a server which starts its work at a designated origin and which picks up and drops objects at their sources and destinations. The server can move at constant unit speed. After the end of its service the server returns to its start in the origin. The goal of OLDARP is to come up with a transportation schedule for the server which finishes as early as possible, i.e., which minimizes the makespan. We analyze several competitive algorithms for OLDARP and establish tight competitiveness results. The first two algorithms, REPLAN and IGNORE are very simple and natural: REPLAN completely discards its (preliminary) schedule and recomputes a new one when a new request arrives. IGNORE always runs a (locally optimal) schedule for a set of known requests and ignores all new requests until this schedule is completed. We show that both strategies, REPLAN and IGNORE, are 5/2-competitive. We then present a somewhat less natural strategy SMARTSTART, which in contrast to the other two strategies may leave the server idle from time to time although unserved requests are known. The SMARTSTART-algorithm has an improved competitive ratio of 2, which matches our lower bound.
TL;DR: A set partitioning approach consisting of two phases is proposed for the combined ship scheduling and allocation problem and optimal solutions are obtained on several cases of a real ship planning problem.
Abstract: We present a bulk ship scheduling problem that is a combined multi-ship pickup and delivery problem with time windows (m-PDPTW) and multi-allocation problem. In contrast to other ship scheduling problems found in the literature, each ship in the fleet is equipped with a flexible cargo hold that can be partitioned into several smaller holds in a given number of ways. Therefore, multiple products can be carried simultaneously by the same ship. The scheduling of the ships constitutes the m-PDPTW, while the partition of the ships' flexible cargo holds and the allocation of cargoes to the smaller holds make the multi-allocation problem. A set partitioning approach consisting of two phases is proposed for the combined ship scheduling and allocation problem. In the first phase, a number of candidate schedules (including allocation of cargoes to the ships' cargo holds) is generated for each ship. In the second phase, we minimise transportation costs by solving a set partitioning problem where the columns are the candidate schedules generated in phase one. The computational results show that the proposed approach works, and optimal solutions are obtained on several cases of a real ship planning problem.
TL;DR: This work has applied multiobjective versions of simulated annealing and taboo search to the resource constrained project scheduling problem (RCPSP), in order to minimise the makespan, the “weighted” lateness of activities and the violation of resource constraints.
TL;DR: The computational study shows that, using the SA methodology, significant improvements to the local search heuristic solutions can be achieved for problems of this type.
Abstract: Scheduling problems with earliness and tardiness penalties are commonly encountered in today's manufacturing environment due to the current emphasis on the just-in-time (JIT) production philosophy. The problem studied in this work is the parallel machine earliness-tardiness non-common due date sequence-dependent set-up time scheduling problem (PETNDDSP) for jobs with varying processing times, where the objective is to minimize the sum of the absolute deviations of job completion times from their corresponding due dates. The research presented provides a first step towards obtaining near optimal solutions for this problem using local search heuristics in the framework of a meta-heuristic technique known as simulated annealing (SA). The computational study shows that, using the SA methodology, significant improvements to the local search heuristic solutions can be achieved for problems of this type.
TL;DR: This paper considers the nonpreemptive scheduling of a given set of jobs on several identical, parallel machines, under a variety of assumptions about setup and processing times, and provides a mapping of the computational complexity of these problems.
TL;DR: In this paper, a branch-and-bound algorithm for the hybrid flow shop scheduling problem is proposed to minimize makespan, which can also cope with problems with release dates and tails.
01 Feb 2000
TL;DR: In this paper, the authors consider the online dial-a-ride problem (OLDARP), where objects are to be transported between points in a metric space, and the goal is to come up with a transportation schedule for the server which finishes as early as possible, i.e., which minimizes the makespan.
Abstract: We consider the following online dial-a-ride problem (OLDARP): Objects are to be transported between points in a metric space. Transportation requests arrive online, specifying the objects to be transported and the corresponding source and destination. These requests are to be handled by a server which starts its work at a designated origin and which picks up and drops objects at their sources and destinations. The server can move at constant unit speed. After the end of its service the server returns to its start in the origin. The goal of OLDARP is to come up with a transportation schedule for the server which finishes as early as possible, i.e., which minimizes the makespan.
We analyze several competitive algorithms for OLDARP and establish tight competitiveness results. The first two algorithms, REPLAN and IGNORE are very simple and natural: REPLAN completely discards its (preliminary) schedule and recomputes a new one when a new request arrives. IGNORE always runs a (locally optimal) schedule for a set of known requests and ignores all new requests until this schedule is completed. We show that both strategies, REPLAN and IGNORE, are 5/2-competitive. We then present a somewhat less natural strategy SMARTSTART, which in contrast to the other two strategies may leave the server idle from time to time although unserved requests are known. The SMARTSTART-algorithm has an improved competitive ratio of 2, which matches our lower bound.