Network flow approaches to pre-emptive open-shop scheduling problems with time-windows

Sedeño‐Noda, Antonio; Alcaide, David; González-Martín, Carlos

doi:10.1016/j.ejor.2005.01.062

Cited by 22 publications

(8 citation statements)

References 14 publications

(13 reference statements)

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“…They showed that openshop scheduling problem with preventive maintenance schedule for each machine can be solved in polynomial time. Sedeno-Noda et al [47] considered preemptive open-shop scheduling problem with time windows. That is, for each job, there is specified time window defined by its release time and due date.…”

Section: Heuristicsmentioning

confidence: 99%

Literature Review of Open Shop Scheduling Problems

Anand¹,

Panneerselvam²

2015

IIM

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This paper discusses review of literature of open shop scheduling problems. First, the problem is classified as per different measures of performance, viz., minimization of makespan, minimization of sum of completion times of jobs, minimization of sum of weighted completion times of all jobs, minimization of total tardiness of all jobs, minimization of sum of weighted tardiness of all jobs, minimization of weighted sum of tardy jobs, and miscellaneous measures of the open shop scheduling problem. In each category, the literature is further classified based on approaches used and then the contributions of researchers in the respective categories are presented. Directions for future research are discussed in the end.

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Section: Heuristicsmentioning

confidence: 99%

Literature Review of Open Shop Scheduling Problems

Anand¹,

Panneerselvam²

2015

IIM

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“…Before presenting our solution, note that the problem that we study here can be viewed as some sort of scheduling problem [26] where each data is a task which can be performed only by the peers holding it, with the network constraints acting as scheduling constraints. A common technique for solving scheduling problems is by reduction to a network flow problem [26].…”

Section: Optimal Union Plansmentioning

confidence: 99%

“…A common technique for solving scheduling problems is by reduction to a network flow problem [26]. Indeed, the first ingredient of our solution, presented below, uses network flow.…”

Section: Optimal Union Plansmentioning

confidence: 99%

Optimized union of non-disjoint distributed data sets

Dar

Milo

Verbin

2009

Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology

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In a variety of applications, ranging from data integration to distributed query evaluation, there is a need to obtain sets of data items from several sources (peers) and compute their union. As these sets often contain common data items, avoiding the transmission of redundant information is essential for effective union computation. In this paper we define the notion of optimal union plans for nondisjoint data sets residing on distinct peers, and present efficient algorithms for computing and executing such optimal plans.Our algorithms avoid redundant data transmission and optimally exploit the network bandwidth capabilities. A challenge in the design of optimal plans is the lack of a complete map of the distribution of the data items among peers. We analyze the information required for optimal planning and propose novel techniques to obtain compact, cheap to communicate, description of the data sources. We then exploit it for efficient union computation with reasonable accuracy. We demonstrate experimentally the superiority of our approach over the common naive union computation, showing it improves the performance by an order of magnitude.

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“…e OSSP is known as an NP-hard problem [13][14][15] and it is not possible to solve these problems in polynomial time except in small dimensions. erefore, approximate solution achievements including heuristic and metaheuristic methods can be more efficient than exact methods.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Parallel Algorithms for Solving Biobjective Open Shop Scheduling Problem

2022

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Open Shop Scheduling Problem (OSSP) is one of the most important scheduling problems in the field of engineering and industry. This kind of problem includes m machines and n jobs, each job contains a certain number of operations, and each operation has a predetermined processing time on its corresponding machine. The order of processing of these operations affects the completion times of all jobs. Therefore, the purpose of OSSP is to achieve a proper order of processing of jobs using specified machines, so that the completion time of all jobs is minimized. In this paper, the strengths and limitations of three methods are evaluated by comparing the results of solving the OSSP in large-scale and small-scale benchmarks. In this case, the minimized completion time and total tardiness are considered the objective functions of the adapted methods. To solve small-scale problems, we adapt a mathematical model called Multiobjective Mixed Linear Programming (MOMILP). To solve large-scale problems, two metaheuristic algorithms including Multiobjective Parallel Genetic Algorithm (MOPGA) and Multiobjective Parallel Simulated Annealing (MOPSA) are adapted. In experimental results, we randomly generated small-scale problems to compare MOMILP with the Genetic Algorithm (GA) and Simulate Annealing (SA). To compare MOPSA and MOPGA with the state of the art and show their strengths and limitations, we use a standard large-scale benchmark. The simulation results of the proposed algorithms show that although the MOPSA algorithm is faster, the MOPGA algorithm is more efficient in achieving optimal solutions for large-scale problems compared with other methods.

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Network flow approaches to pre-emptive open-shop scheduling problems with time-windows

Cited by 22 publications

References 14 publications

Literature Review of Open Shop Scheduling Problems

Literature Review of Open Shop Scheduling Problems

Optimized union of non-disjoint distributed data sets

Multiobjective Parallel Algorithms for Solving Biobjective Open Shop Scheduling Problem

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