scholarly journals Applying Heuristic Algorithms to Solve Inter-hospital Hierarchical Allocation and Scheduling Problems of Medical Staff

2020 ◽  
Author(s):  
Gary Yu-Hsin Chen ◽  
Ping-Shun Chen ◽  
Wen-Tso Huang ◽  
Tsung-Huan Chiang
Keyword(s):  
Medical Staff ◽  
Heuristic Algorithms ◽  
Scheduling Problems ◽  
Hierarchical Allocation

Meta-heuristic algorithms for wafer sorting scheduling problems

2011 ◽  
Vol 62 (1) ◽  
pp. 165-174 ◽  
Author(s):  
Shih-Wei Lin ◽  
Zne-Jung Lee ◽  
Kuo-Ching Ying ◽  
Rong-Ho Lin
Keyword(s):  
Heuristic Algorithms ◽  
Scheduling Problems

scholarly journals Simultaneous Scheduling Through Heuristic Algorithms

2018 ◽  
Vol 7 (2.32) ◽  
pp. 125
Author(s):  
M Nageswara Rao ◽  
K Lokesh ◽  
V Harish ◽  
Ch Sai Bharath ◽  
Y Venkatesh ◽  
...  
Keyword(s):  
Flexible Manufacturing ◽  
Manufacturing System ◽  
Flexible Manufacturing System ◽  
Heuristic Algorithms ◽  
Information Gathering ◽  
Work Environments ◽  
Scheduling Problems ◽  
Game Plan ◽  
The Cost ◽  
Simultaneous Scheduling

Flexible Manufacturing System (FMS) is a compli-cated system because of work environments, recu-peration frameworks, mechanized putting away, and material dealing with gadgets like robots and AGVs. In this paper, an endeavor is made by con-sidering both the machine and vehicle planning angles in FMS for minimization of the make trav-erse. Game plan is ensnared with the arrangement of incomplete assets to assignments in finished time. It is like Information-gathering process. It is related with the cost, operations, time and several objectives of the industry. In this work, RAPID ACCESS (RA) heuristic algorithm is adopted to solve the scheduling problems in FMS. Eighty, two problems and their existing solutions with different approaches are examined. The RA heuristic algo-rithm provides better solutions with less computa-tional time.  

Exact and heuristic algorithms for scheduling problems

4OR ◽  
2019 ◽  
Vol 18 (1) ◽  
pp. 123-124
Author(s):  
Arthur Kramer
Keyword(s):  
Heuristic Algorithms ◽  
Scheduling Problems

Applying Constraint Programming and Integer Programming to Solve the Crew Scheduling Problem for Railroad Systems: Model Formulation and a Case Study

2021 ◽  
pp. 036119812110363
Author(s):  
Guei-Hao Chen ◽  
Jyh-Cherng Jong ◽  
Anthony Fu-Wha Han
Keyword(s):  
Integer Programming ◽  
Constraint Programming ◽  
Heuristic Algorithms ◽  
Programming Model ◽  
Hybrid Approach ◽  
Crew Scheduling ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Systems Model ◽  
Crew Scheduling Problem

Crew scheduling is one of the crucial processes in railroad operation planning. Based on current regulations and working and break time requirements, as well as the operational rules, this process aims to find a duty arrangement with minimal cost that covers all trips. Most past studies considered this subject for railroad systems as an optimization problem and solved it with mathematical programming-based methods or heuristic algorithms, despite numerous logical constraints embedded in this problem. Few studies have applied constraint programming (CP) approaches to tackle the railroad crew scheduling problem. This paper proposes a hybrid approach to solve the problem with a CP model for duty generation, and an integer programming model for duty optimization. These models have been applied to the Kaohsiung depot of Taiwan Railways Administration, the largest railroad operator in Taiwan. The encouraging results show that the proposed approach is more efficient than the manual process and can achieve 30% savings of driver cost. Moreover, the approach is robust and provides flexibility to easily accommodate related operational concerns such as minimizing the number of overnight duties. Thus, this hybrid two-phase approach seems to have the potential for applications to the railroad crew scheduling problems outside Taiwan.

A Genetic Algorithm for Hybrid Job-Shop Scheduling Problems with Minimizing the Makespan or Mean Flow Time

2018 ◽  
Vol 17 (04) ◽  
pp. 461-486
Author(s):  
Omid Gholami ◽  
Yuri N. Sotskov ◽  
Frank Werner
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Heuristic Algorithms ◽  
Flow Time ◽  
Scheduling Problems ◽  
Mean Flow ◽  
Average Percentage ◽  
Type Formula ◽  
Mean Flow Time ◽  
Specific Subset

We address a generalization of the classical job-shop problem which is called a hybrid job-shop problem. The criteria under consideration are the minimization of the makespan and mean flow time. In the hybrid job-shop, machines of type [Formula: see text] are available for processing the specific subset [Formula: see text] of the given operations. Each set [Formula: see text] may be partitioned into subsets for their processing on the machines of type [Formula: see text]. Solving the hybrid job-shop problem implies the solution of two subproblems: an assignment of all operations from the set [Formula: see text] to the machines of type [Formula: see text] and finding optimal sequences of the operations for their processing on each machine. In this paper, a genetic algorithm is developed to solve these two subproblems simultaneously. For solving the subproblems, a special chromosome is used in the genetic algorithm based on a mixed graph model. We compare our genetic algorithms with a branch-and-bound algorithm and three other recent heuristic algorithms from the literature. Computational results for benchmark instances with 10 jobs and up to 50 machines show that the proposed genetic algorithm is rather efficient for both criteria. Compared with the other heuristics, the new algorithm gives most often an optimal solution and the average percentage deviation from the optimal function value is about 4%.

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