Stochastic Bi-level Programming Model for Home Healthcare Scheduling Problems Considering the Degree of Satisfaction with Visit Time

Purpose This study aims to address the hybrid open shop problem (HOSP) with respect to the minimization of the overall finishing time or makespan. In the HOSP, we have to process n jobs in stages without preemption. Each job must be processed once in every stage, there is a set of mk identical machines in stage k and the production flow is immaterial. Design/methodology/approach Computational experiments carried out on a set of randomly generated instances showed that the minimal idleness heuristic (MIH) priority rule outperforms the longest processing time (LPT) rule proposed in the literature and the other proposed constructive methods on most instances. Findings The proposed mathematical model outperformed the existing model in the literature with respect to computing time, for small-sized instances, and solution quality within a time limit, for medium- and large-sized instances. The authors’ hybrid iterated local search (ILS) improved the solutions of the MIH rule, drastically outperforming the models on large-sized instances with respect to solution quality. Originality/value The authors formalize the HOSP, as well as argue its NP-hardness, and propose a mixed integer linear programming model to solve it. The authors propose several priority rules – constructive heuristics based on priority measures – for finding feasible solutions for the problem, consisting of adaptations of classical priority rules for scheduling problems. The authors also propose a hybrid ILS for improving the priority rules solutions.

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Mathematical models for a batch scheduling problem to minimize earliness and tardiness

Journal of Industrial Engineering and Management ◽

10.3926/jiem.2541 ◽

2018 ◽

Vol 11 (3) ◽

pp. 390 ◽

Cited By ~ 3

Author(s):

Basar Ogun ◽

Çigdem Alabas-Uslu

Keyword(s):

Mathematical Models ◽

Real World ◽

Lean Manufacturing ◽

Programming Model ◽

Computational Study ◽

Batch Scheduling ◽

Scheduling Problem ◽

Scheduling Problems ◽

Earliness And Tardiness ◽

Customer Orders

Purpose: Today’s manufacturing facilities are challenged by highly customized products and just in time manufacturing and delivery of these products. In this study, a batch scheduling problem is addressed to provide on-time completion of customer orders in the environment of lean manufacturing. The problem is to optimize partitioning of product components into batches and scheduling of the resulting batches where each customer order is received as a set of products made of various components.Design/methodology/approach: Three different mathematical models for minimization of total earliness and tardiness of customer orders are developed to provide on-time completion of customer orders and also, to avoid from inventory of final products. The first model is a non-linear integer programming model while the second is a linearized version of the first. Finally, to solve larger sized instances of the problem, an alternative linear integer model is presented.Findings: Computational study using a suit set of test instances showed that the alternative linear integer model is able to solve all test instances in varying sizes within quite shorter computer times comparing to the other two models. It was also showed that the alternative model can solve moderate sized real-world problems.Originality/value: The problem under study differentiates from existing batch scheduling problems in the literature since it includes new circumstances which may arise in real-world applications. This research, also, contributes the literature of batch scheduling problem by presenting new optimization models.

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

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211036368 ◽

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.

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Fitness Distance Correlation Strategy for Solving the RGV Dynamic Scheduling Problem

International Journal of Cognitive Informatics and Natural Intelligence ◽

10.4018/ijcini.2020070102 ◽

2020 ◽

Vol 14 (3) ◽

pp. 20-40

Author(s):

Wei Li ◽

Furong Tian ◽

Ke Li

Keyword(s):

Dynamic Scheduling ◽

Programming Model ◽

Operating Efficiency ◽

Scheduling Problem ◽

Scheduling Problems ◽

Distance Correlation ◽

Fast Running ◽

Stable Performance ◽

Fitness Distance Correlation ◽

Working Efficiency

Rail guide vehicle (RGV) problems have the characteristics of fast running, stable performance, and high automation. RGV dynamic scheduling has a great impact on the working efficiency of an entire automated warehouse. However, the relative intelligent optimization research of different workshop components for RGV dynamic scheduling problems are insufficient scheduling in the previous works. They appear idle when waiting, resulting in reduced operating efficiency during operation. This article proposes a new distance landscape strategy for the RGV dynamic scheduling problems. In order to solve the RGV dynamic scheduling problem more effectively, experiments are conducted based on the type of computer numerical controller (CNC) with two different procedures programming model in solving the RGV dynamic scheduling problems. The experiment results reveal that this new distance landscape strategy can provide promising results and solves the considered RGV dynamic scheduling problem effectively.

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Aircraft sequencing and scheduling in TMAs under wind direction uncertainties

The Aeronautical Journal ◽

10.1017/aer.2020.68 ◽

2020 ◽

Vol 124 (1282) ◽

pp. 1896-1912

Author(s):

R.K. Cecen ◽

C. Cetek ◽

O. Kaya

Keyword(s):

Stochastic Model ◽

Wind Direction ◽

Simulated Annealing Algorithm ◽

Programming Model ◽

Air Traffic ◽

Mixed Integer ◽

Scheduling Problems ◽

Traffic Demand ◽

Proposed Model ◽

Sequencing And Scheduling

ABSTRACTAircraft sequencing and scheduling within terminal airspaces has become more complicated due to increased air traffic demand and airspace complexity. A stochastic mixed-integer linear programming model is proposed to handle aircraft sequencing and scheduling problems using the simulated annealing algorithm. The proposed model allows for proper aircraft sequencing considering wind direction uncertainties, which are critical in the decision-making process. The proposed model aims to minimise total aircraft delay for a runway airport serving mixed operations. To test the stochastic model, an appropriate number of scenarios were generated for different air traffic demand rates. The results indicate that the stochastic model reduces the total aircraft delay considerably when compared with the deterministic approach.

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Incorporating batching decisions and operational constraints into the scheduling problem of multisite manufacturing environments

International Journal of Industrial Engineering Computations ◽

10.5267/j.ijiec.2021.1.002 ◽

2021 ◽

Vol 12 (3) ◽

pp. 345-364

Author(s):

Sergio Ackermann ◽

Yanina Fumero ◽

Jorge M. Montagna

Keyword(s):

Customer Service ◽

Programming Model ◽

Mixed Integer ◽

Scheduling Problems ◽

Efficient Operation ◽

Batch Plants ◽

Entire Complex ◽

Operational Constraints ◽

Manufacturing Environments ◽

The Impact

In multisite production environments, the appropriate management of production resources is an activity of fundamental relevance to optimally respond to market demands. In particular, each production facility can operate with different policies according to its objectives, prioritizing the quality and standardization of the product, customer service, or the overall efficiency of the system; goals which must be taken into account when planning the production of the entire complex. At the operational level, in order to achieve an efficient operation of the production system, the integrated problem of batching and scheduling must be solved over all facilities, instead of doing it for each plant separately, as has been usual so far. Then, this paper proposes a mixed-integer linear programming model for the multisite batching and scheduling problems, where different operational policies are considered for multiple batch plants. Through two examples, the impact of policies on the decision-making process is shown.

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Capacitated Multi-objective Disassembly Scheduling with Fuzzy Processing Time viaAFruit Fly Optimization Algorithm

10.21203/rs.3.rs-1075510/v1 ◽

2021 ◽

Author(s):

Gang Yuan ◽

Yinsheng Yang ◽

Guangdong Tian ◽

Amir Mohammad Fathollahi-Fard

Keyword(s):

Optimization Algorithm ◽

Cycle Time ◽

Processing Time ◽

Programming Model ◽

Environmental Cost ◽

Mixed Integer ◽

Fruit Fly Optimization Algorithm ◽

Scheduling Problems ◽

Fruit Fly Optimization ◽

Multi Objective

Abstract This work proposes a capacitated fuzzy disassembly scheduling model with cycle time and environmental cost, which has broad applications in remanufacturing and many other production systems. Disassembly scheduling is not always given accurately as a time quota in a production system, particularly in the obsolete products remanufacturing process. It is meaningful to study a novel model and algorithm based on uncertainty processing time to solve uncertainty disassembly scheduling problems. Therefore, a mixed-integer mathematical programming model is proposed to minimize the cycle time and environmental cost, whilst a metaheuristic approach based on a fruit fly optimization algorithm is developed to find a fuzzy disassembly scheduling scheme. To estimate the effectiveness of the proposed method, the proposed algorithm is tested with different size cases of products disassembly scheduling. Furthermore, experiments are conducted to compare with other multi-objective optimization algorithms. The computational results demonstrate the proposed algorithm outperforms other algorithms on computational efficiency and applicability performance. Finally, a case study is described to illustrate the proposed method. The main finding of this current work is to provide a new idea to solve the problem of disassembly scheduling in an uncertain environmental practically and efficiently.

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Directed Bee Colony Optimization Algorithm to Solve the Nurse Rostering Problem

Computational Intelligence and Neuroscience ◽

10.1155/2017/6563498 ◽

2017 ◽

Vol 2017 ◽

pp. 1-26 ◽

Cited By ~ 9

Author(s):

M. Rajeswari ◽

J. Amudhavel ◽

Sujatha Pothula ◽

P. Dhavachelvan

Keyword(s):

Optimization Algorithm ◽

Programming Model ◽

Particle System ◽

Nurse Rostering ◽

Scheduling Problems ◽

Bee Colony ◽

Nurse Rostering Problem ◽

Bee Colony Optimization ◽

Hard And Soft Constraints ◽

Multiobjective Mathematical Programming

The Nurse Rostering Problem is an NP-hard combinatorial optimization, scheduling problem for assigning a set of nurses to shifts per day by considering both hard and soft constraints. A novel metaheuristic technique is required for solving Nurse Rostering Problem (NRP). This work proposes a metaheuristic technique called Directed Bee Colony Optimization Algorithm using the Modified Nelder-Mead Method for solving the NRP. To solve the NRP, the authors used a multiobjective mathematical programming model and proposed a methodology for the adaptation of a Multiobjective Directed Bee Colony Optimization (MODBCO). MODBCO is used successfully for solving the multiobjective problem of optimizing the scheduling problems. This MODBCO is an integration of deterministic local search, multiagent particle system environment, and honey bee decision-making process. The performance of the algorithm is assessed using the standard dataset INRC2010, and it reflects many real-world cases which vary in size and complexity. The experimental analysis uses statistical tools to show the uniqueness of the algorithm on assessment criteria.

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