Job shop scheduling with a combination of four buffering constraints

Liu, Shi Qiang; Kozan, Erhan; Masoud, Mahmoud; Zhang, Yunlong; Chan, Felix T. S.

doi:10.1080/00207543.2017.1401240

Cited by 34 publications

(14 citation statements)

References 78 publications

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“…Regarding future work, the low-carbon FJSP should be further studied by considering some practical constraints, e.g., adjustable speeds of machines, machine breakdown, arrival of new jobs, blocking and no-wait constraints [55][56][57]. For the DWOA, some effective neighborhood structures for the low-carbon FJSP should be designed to further improve the quality of the optimal solution.…”

Section: Discussionmentioning

confidence: 99%

Optimizing the Low-Carbon Flexible Job Shop Scheduling Problem with Discrete Whale Optimization Algorithm

Luan

Cai

et al. 2019

Mathematics

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The flexible job shop scheduling problem (FJSP) is a difficult discrete combinatorial optimization problem, which has been widely studied due to its theoretical and practical significance. However, previous researchers mostly emphasized on the production efficiency criteria such as completion time, workload, flow time, etc. Recently, with considerations of sustainable development, low-carbon scheduling problems have received more and more attention. In this paper, a low-carbon FJSP model is proposed to minimize the sum of completion time cost and energy consumption cost in the workshop. A new bio-inspired metaheuristic algorithm called discrete whale optimization algorithm (DWOA) is developed to solve the problem efficiently. In the proposed DWOA, an innovative encoding mechanism is employed to represent two sub-problems: Machine assignment and job sequencing. Then, a hybrid variable neighborhood search method is adapted to generate a high quality and diverse population. According to the discrete characteristics of the problem, the modified updating approaches based on the crossover operator are applied to replace the original updating method in the exploration and exploitation phase. Simultaneously, in order to balance the ability of exploration and exploitation in the process of evolution, six adjustment curves of a are used to adjust the transition between exploration and exploitation of the algorithm. Finally, some well-known benchmark instances are tested to verify the effectiveness of the proposed algorithms for the low-carbon FJSP.

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Section: Discussionmentioning

confidence: 99%

Optimizing the Low-Carbon Flexible Job Shop Scheduling Problem with Discrete Whale Optimization Algorithm

Luan

Cai

et al. 2019

Mathematics

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“…The stochastic optimisation mixed integer programming (SOMIP) approach is applied to formulate the ED optimisation problem as an ED-SOMIP model [33], [34]. In this model, the objective function is constructed to solve the patient total waiting time under limited resources [20].…”

Section: Ed Mathematical Modelmentioning

confidence: 99%

Solving the Emergency Care Patient Pathway by a New Integrated Simulation-Optimisation Approach

et al. 2021

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One of the most critical objectives in the healthcare system is maximising patient flows in the emergency care patient pathway. Patient emergency flow analysis indicates that the timetabling of a patient's movement from one activity to another through the Emergency Department (ED) is critical for treating patients. The ED deals with the patient's arrival, triage, physician assessment, imaging and laboratory studies, treatment planning, nursing procedures, and decisions to admit or discharge the patient. Any delayed activities in patient flow reduce the service level of healthcare. To address these challenges, this paper develops a stochastic ED Simulation-Optimisation approach by considering stochastic variables, such as patient interarrival times and treatment times, using statistical distributions. This type of distribution depends on two main elements: day shifts and patient categories. A hybrid evolutionary algorithm is integrated with the simulation to find a satisfactory solution for this stochastic optimisation problem in real time. Computational experiments show that the proposed approach can serve more patients in specific time windows or provide the same quality of the service with the use of fewer medical resources.

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“…The constraint on place for putting WIPs can be rephrased into buffer capacity constraint as a more general term, which has been also discussed by various research groups based on mathematical programming and graph theory [24][25][26][27] and heuristics [28][29][30].…”

Section: Considered Problemmentioning

confidence: 99%

A Neighborhood Limitation Method for Job-Shop Scheduling Based on Simulated Annealing

Teramoto

Morinaga

Wakamatsu

et al. 2020

Transactions of ISCIE

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Because of non-deterministic polynomial time hardness of job-shop scheduling problem (JSP), approximate optimization based on meta-heuristics has been actively discussed. Considering position of planners in production sites, it is desirable to develop a method in which their know-how is respected. An approach for meeting this requirement is to set the schedule generated by a planner as the initial solution and then gradually improve the solution by repeating a search in its neighborhood so that he/she can follow and thoroughly examine the improved solution. For this reason, this research is focused on scheduling using simulated annealing (SA). Because SA has a disadvantage that good solutions cannot be obtained efficiently if the initial solution has not been given appropriately, methods for solving this problem have been proposed for JSPs aiming at minimizing makespan. In high-mix low-volume manufacturing, it is also important to minimize production lead time to reduce work-in-process inventory. This research takes up production lead time defined as the time between the starting and the finishing times of a job considering strong constraint on places for putting worksin-process in production of large equipment, and deals with development of an efficient method using SA for JSPs aiming at minimizing the average value of the production lead times. Two methods of neighborhood limitation in SA for reducing the evaluation value were developed by focusing on waiting time of operations. It was proven that using one of the proposed methods in SA with appropriate probabilities is effective to JSPs of a certain size by numerical examples.

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Job shop scheduling with a combination of four buffering constraints

Cited by 34 publications

References 78 publications

Optimizing the Low-Carbon Flexible Job Shop Scheduling Problem with Discrete Whale Optimization Algorithm

Optimizing the Low-Carbon Flexible Job Shop Scheduling Problem with Discrete Whale Optimization Algorithm

Solving the Emergency Care Patient Pathway by a New Integrated Simulation-Optimisation Approach

A Neighborhood Limitation Method for Job-Shop Scheduling Based on Simulated Annealing

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