scholarly journals Outpatient Appointment Optimization: A Case Study of a Chemotherapy Service

2022 ◽  
Vol 12 (2) ◽  
pp. 659
Author(s):  
Quoc Nhat Han Tran ◽  
Nhan Quy Nguyen  ◽  
Hicham Chehade ◽  
Lionel Amodeo ◽  
Farouk Yalaoui
Keyword(s):  
Historical Data ◽  
Flow Shop ◽  
Mixed Integer ◽  
Hybrid Flow Shop ◽  
Planning Problem ◽  
Multiple Time ◽  
Time Lags ◽  
Outpatient Appointment ◽  
Flow Shop Problem

In this paper, we study a complex outpatient planning problem in the chemotherapy department. The planning concerns sequences of patients’ treatment sessions subject to exact in-between resting periods (i.e., exact time-lags). The planning is constrained by the hospital infrastructure and the availability of medical staff (i.e., multiple time-varying resources’ availability). In order to maximize the patients’ service quality, the objective of the function considered is to minimize the total wait times, which is equivalent to the criteria for minimizing the total completion time. Our main contribution is a thorough analysis of this problem, using the Hybrid Flow Shop problem as a theoretical framework to study the problem. A novel Mixed Integer Linear Programming (MILP) is introduced. Concerning the resolution methods, priority-based heuristics and an adapted genetic algorithm (GA) are presented. Numerical experiments are conducted on historical data to compare the performances of the approximate resolution methods against the MILP solved by CPLEX. Numerical results confirm the performances of the proposed methods.

Energy-efficient multi-objective scheduling algorithm for hybrid flow shop with fuzzy processing time

2019 ◽  
Vol 233 (10) ◽  
pp. 1282-1297 ◽  
Author(s):  
Binghai Zhou ◽  
Wenlong Liu
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Processing Time ◽  
Flow Shop ◽  
Scheduling Algorithm ◽  
Mixed Integer ◽  
Computational Time ◽  
Hybrid Flow Shop ◽  
Multi Objective ◽  
Fuzzy Processing Time

Increasing costs of energy and environmental pollution is prompting scholars to pay close attention to energy-efficient scheduling. This study constructs a multi-objective model for the hybrid flow shop scheduling problem with fuzzy processing time to minimize total weighted delivery penalty and total energy consumption simultaneously. Setup times are considered as sequence-dependent, and in-stage parallel machines are unrelated in this model, meticulously reflecting the actual energy consumption of the system. First, an energy-efficient bi-objective differential evolution algorithm is developed to solve this mixed integer programming model effectively. Then, we utilize an Nawaz-Enscore-Ham-based hybrid method to generate high-quality initial solutions. Neighborhoods are thoroughly exploited with a leader solution challenge mechanism, and global exploration is highly improved with opposition-based learning and a chaotic search strategy. Finally, problems in various scales evaluate the performance of this green scheduling algorithm. Computational experiments illustrate the effectiveness of the algorithm for the proposed model within acceptable computational time.

Solving hybrid flow shop problem using energetic reasoning and global operations

Omega ◽  
2001 ◽  
Vol 29 (6) ◽  
pp. 501-511 ◽  
Author(s):  
Emmanuel Néron ◽  
Philippe Baptiste ◽  
Jatinder N.D Gupta
Keyword(s):  
Flow Shop ◽  
Hybrid Flow Shop ◽  
Flow Shop Problem ◽  
Energetic Reasoning ◽  
Global Operations

Parallel tabu search algorithm for the hybrid flow shop problem

2013 ◽  
Vol 65 (3) ◽  
pp. 466-474 ◽  
Author(s):  
Wojciech Bożejko ◽  
Jarosław Pempera ◽  
Czesław Smutnicki
Keyword(s):  
Tabu Search ◽  
Flow Shop ◽  
Search Algorithm ◽  
Hybrid Flow Shop ◽  
Tabu Search Algorithm ◽  
Flow Shop Problem ◽  
Parallel Tabu Search

scholarly journals Comparing Mixed-Integer Programming and Constraint Programming Models for the Hybrid Flow Shop Scheduling Problem with Dedicated Machines

2021 ◽  
Vol 54 (4) ◽  
pp. 591-597
Author(s):  
Asma Ouled Bedhief
Keyword(s):  
Integer Programming ◽  
Mixed Integer Programming ◽  
Constraint Programming ◽  
Flow Shop ◽  
Programming Model ◽  
Flow Shop Scheduling ◽  
Mixed Integer ◽  
Hybrid Flow Shop ◽  
Dedicated Machines ◽  
Hybrid Flow Shop Scheduling

The paper considers a two-stage hybrid flow shop scheduling problem with dedicated machines and release dates. Each job must be first processed on the single machine of stage 1, and then, the job is processed on one of the two dedicated machines of stage 2, depending on its type. Moreover, the jobs are available for processing at their respective release dates. Our goal is to obtain a schedule that minimizes the makespan. This problem is strongly NP-hard. In this paper, two mathematical models are developed for the problem: a mixed-integer programming model and a constraint programming model. The performance of these two models is compared on different problem configurations. And the results show that the constraint programming outperforms the mixed-integer programming in finding optimal solutions for large problem sizes (450 jobs) with very reasonable computing times.

scholarly journals Urban Bike Lane Planning with Bike Trajectories: Models, Algorithms, and a Real-World Case Study

2021 ◽  
Author(s):  
Sheng Liu ◽  
Zuo-Jun Max Shen ◽  
Xiang Ji
Keyword(s):  
Real World ◽  
Smart City ◽  
Utility Functions ◽  
Mixed Integer ◽  
Planning Problem ◽  
Planning Model ◽  
Trajectory Data ◽  
Bike Sharing ◽  
Route Choices

Problem definition: We study an urban bike lane planning problem based on the fine-grained bike trajectory data, which are made available by smart city infrastructure, such as bike-sharing systems. The key decision is where to build bike lanes in the existing road network. Academic/practical relevance: As bike-sharing systems become widespread in the metropolitan areas over the world, bike lanes are being planned and constructed by many municipal governments to promote cycling and protect cyclists. Traditional bike lane planning approaches often rely on surveys and heuristics. We develop a general and novel optimization framework to guide the bike lane planning from bike trajectories. Methodology: We formalize the bike lane planning problem in view of the cyclists’ utility functions and derive an integer optimization model to maximize the utility. To capture cyclists’ route choices, we develop a bilevel program based on the Multinomial Logit model. Results: We derive structural properties about the base model and prove that the Lagrangian dual of the bike lane planning model is polynomial-time solvable. Furthermore, we reformulate the route-choice-based planning model as a mixed-integer linear program using a linear approximation scheme. We develop tractable formulations and efficient algorithms to solve the large-scale optimization problem. Managerial implications: Via a real-world case study with a city government, we demonstrate the efficiency of the proposed algorithms and quantify the trade-off between the coverage of bike trips and continuity of bike lanes. We show how the network topology evolves according to the utility functions and highlight the importance of understanding cyclists’ route choices. The proposed framework drives the data-driven urban-planning scheme in smart city operations management.

scholarly journals A two-stage hybrid flow shop problem with dedicated machine and release date

2018 ◽  
Vol 126 ◽  
pp. 214-223
Author(s):  
Zouhour Nabli ◽  
Soulef Khalfallah ◽  
Ouajdi Korbaa
Keyword(s):  
Flow Shop ◽  
Hybrid Flow Shop ◽  
Release Date ◽  
Two Stage ◽  
Flow Shop Problem
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