A simulated annealing approach based simulation-optimisation to the dynamic job-shop scheduling problem

Sel, Çağrı; Hamzadayı, Alper

doi:10.5505/pajes.2017.47108

Cited by 9 publications

(4 citation statements)

References 23 publications

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“…They showed that their proposed GRASP-based approach yields viable solutions. Sel and Hamzadayı (2018) [56] proposed a simulation optimization study based on the SA carried out in the DJSP. They considered average flow time and average tardiness as the criteria.…”

Section: Dynamic Problemmentioning

confidence: 99%

A Multi-Agent Reinforcement Learning Approach to the Dynamic Job Shop Scheduling Problem

et al. 2023

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In a production environment, scheduling decides job and machine allocations and the operation sequence. In a job shop production system, the wide variety of jobs, complex routes, and real-life events becomes challenging for scheduling activities. New, unexpected events disrupt the production schedule and require dynamic scheduling updates to the production schedule on an event-based basis. To solve the dynamic scheduling problem, we propose a multi-agent system with reinforcement learning aimed at the minimization of tardiness and flow time to improve the dynamic scheduling techniques. The performance of the proposed multi-agent system is compared with the first-in–first-out, shortest processing time, and earliest due date dispatching rules in terms of the minimization of tardy jobs, mean tardiness, maximum tardiness, mean earliness, maximum earliness, mean flow time, maximum flow time, work in process, and makespan. Five scenarios are generated with different arrival intervals of the jobs to the job shop production system. The results of the experiments, performed for the 3 × 3, 5 × 5, and 10 × 10 problem sizes, show that our multi-agent system overperforms compared to the dispatching rules as the workload of the job shop increases. Under a heavy workload, the proposed multi-agent system gives the best results for five performance criteria, which are the proportion of tardy jobs, mean tardiness, maximum tardiness, mean flow time, and maximum flow time.

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Section: Dynamic Problemmentioning

confidence: 99%

A Multi-Agent Reinforcement Learning Approach to the Dynamic Job Shop Scheduling Problem

et al. 2023

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“…Over the years, many authors have proposed both general and problem-specific improvements and variants of SA [36]. Different variants of scheduling problems have been tackled using SA [37], such as the job-shop scheduling problem [38][39][40][41], university course timetabling problems [42], or sports scheduling problems [43].…”

Section: Simulated Annealing Adaptationmentioning

confidence: 99%

A Variation of the ATC Work Shift Scheduling Problem to Deal with Incidents at Airport Control Centers

2020

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This paper deals with a variation of the air traffic controller (ATC) work shift scheduling problem focusing on the tactical phase, in which the plan for the day of operations can be modified according to real-time traffic demand or other possible incidents (one or more ATCs become sick and/or there is an increase in unplanned air traffic), which may lead to a new sectorization and a lower number of available ATCs. To deal with these issues, we must reassign the available ATCs to the new sectorization established at the time the incident happens, but also taking into account the work done by the ATCs up to that point. We propose a new methodology consisting of two phases. The goal of the first phase is to build an initial possibly infeasible solution, taking into account the sectors that have been closed or opened in the new sectorization, together with the ATCs available after the incident. In the second phase, we use simulated annealing (SA) and variable neighborhood search (VNS) metaheuristics to derive a feasible solution in which the available ATCs are used and all the ATC labor conditions are met. A weighted additive objective function is used in this phase to account for the feasibility of the solution but also for the number of changes in the control center at the time the incident happens and the similarity of the derived solution with templates usually used by the network manager operations center, a center managing the air traffic flows of an entire network of control centers. The methodology is illustrated by means of seven real instances provided by the Air Traffic Management Research, Development and Innovation Reference Center (CRIDA) experts representing possible incidents that may arise. The solutions derived by SA outperform those reached by VNS in terms of both the number of violated constraints in all seven instances, and solution compactability in six out the seven instances, and both are very similar with regard to the number of control center changes at the time of the incident. Although computation times for VNS are clearly better than for SA, CRIDA experts were satisfied with SA computation times. The solutions reached by SA were preferred.

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“…They proposed branch population GA for optimization. Sel and Hamzadayi [16] used simulated annealing (SA) for JSSP and used Arena for simulations. Fu et al [17] established a multiobjective optimization model for a JSSP and used NSGA to minimize some performance measurements such as the total cost and the total completion time.…”

Section: Introductionmentioning

confidence: 99%

Optimization of welding job-shop scheduling problem under variable workstation constraint: an industrial application with Arena simulation based genetic algorithm

Karaoğlan¹

2022

Pamukkale J Eng Sci

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A simulated annealing approach based simulation-optimisation to the dynamic job-shop scheduling problem

Cited by 9 publications

References 23 publications

A Multi-Agent Reinforcement Learning Approach to the Dynamic Job Shop Scheduling Problem

A Multi-Agent Reinforcement Learning Approach to the Dynamic Job Shop Scheduling Problem

A Variation of the ATC Work Shift Scheduling Problem to Deal with Incidents at Airport Control Centers

Optimization of welding job-shop scheduling problem under variable workstation constraint: an industrial application with Arena simulation based genetic algorithm

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