The Nurse Scheduling Problem in Real-Life

2014 ◽  
Vol 39 (1) ◽  
Author(s):  
Antoine Legrain ◽  
Hocine Bouarab ◽  
Nadia Lahrichi
Keyword(s):  
Real Life ◽  
Scheduling Problem ◽  
Nurse Scheduling

scholarly journals Solving the nuclear dismantling project scheduling problem by combining mixed-integer and constraint programming techniques and metaheuristics

2021 ◽  
Author(s):  
Felix Hübner ◽  
Patrick Gerhards ◽  
Christian Stürck ◽  
Rebekka Volk
Keyword(s):  
Constraint Programming ◽  
Project Scheduling ◽  
Computational Study ◽  
Real Life ◽  
Mixed Integer ◽  
Extensive Literature ◽  
Scheduling Problem ◽  
Multiple Modes ◽  
Project Scheduling Problem ◽  
Optimisation Techniques

AbstractScheduling of megaprojects is very challenging because of typical characteristics, such as expected long project durations, many activities with multiple modes, scarce resources, and investment decisions. Furthermore, each megaproject has additional specific characteristics to be considered. Since the number of nuclear dismantling projects is expected to increase considerably worldwide in the coming decades, we use this type of megaproject as an application case in this paper. Therefore, we consider the specific characteristics of constrained renewable and non-renewable resources, multiple modes, precedence relations with and without no-wait condition, and a cost minimisation objective. To reliably plan at minimum costs considering all relevant characteristics, scheduling methods can be applied. But the extensive literature review conducted did not reveal a scheduling method considering the special characteristics of nuclear dismantling projects. Consequently, we introduce a novel scheduling problem referred to as the nuclear dismantling project scheduling problem. Furthermore, we developed and implemented an effective metaheuristic to obtain feasible schedules for projects with about 300 activities. We tested our approach with real-life data of three different nuclear dismantling projects in Germany. On average, it took less than a second to find an initial feasible solution for our samples. This solution could be further improved using metaheuristic procedures and exact optimisation techniques such as mixed-integer programming and constraint programming. The computational study shows that utilising exact optimisation techniques is beneficial compared to standard metaheuristics. The main result is the development of an initial solution finding procedure and an adaptive large neighbourhood search with iterative destroy and recreate operations that is competitive with state-of-the-art methods of related problems. The described problem and findings can be transferred to other megaprojects.

scholarly journals Metaheuristics for Order Scheduling Problem with Unequal Ready Times

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Jan-Yee Kung ◽  
Jiahui Duan ◽  
Jianyou Xu ◽  
I-Hong Chung ◽  
Shuenn-Ren Cheng ◽  
...  
Keyword(s):  
Real Life ◽  
Experimental Tests ◽  
Service Systems ◽  
Scheduling Problem ◽  
Due Dates ◽  
Order Scheduling ◽  
Customer Order ◽  
Scheduling Model ◽  
Unequal Ready Times ◽  
Customer Order Scheduling

In recent years, various customer order scheduling (OS) models can be found in numerous manufacturing and service systems in which several designers, who have developed modules independently for several different products, convene as a product development team, and that team completes a product design only after all the modules have been designed. In real-life situations, a customer order can have some requirements including due dates, weights of jobs, and unequal ready times. Once encountering different ready times, waiting for future order or job arrivals to raise the completeness of a batch is an efficient policy. Meanwhile, the literature releases that few studies have taken unequal ready times into consideration for order scheduling problem. Motivated by this limitation, this study addresses an OS scheduling model with unequal order ready times. The objective function is to find a schedule to optimize the total completion time criterion. To solve this problem for exact solutions, two lower bounds and some properties are first derived to raise the searching power of a branch-and-bound method. For approximate solution, four simulated annealing approaches and four heuristic genetic algorithms are then proposed. At last, several experimental tests and their corresponding statistical outcomes are also reported to examine the performance of all the proposed methods.

A Hybrid Approach to Detecting the Best Solution in Nurse Scheduling Problem

2017 ◽  
pp. 710-721 ◽  
Author(s):  
Svetlana Simić ◽  
Dragan Simić ◽  
Dragana Milutinović ◽  
Jovanka Đorđević ◽  
Svetislav Simić
Keyword(s):  
Hybrid Approach ◽  
Scheduling Problem ◽  
Nurse Scheduling

A metaheuristic technique for energy-efficiency in job-shop scheduling

2016 ◽  
Vol 31 (5) ◽  
pp. 475-485 ◽  
Author(s):  
Joan Escamilla ◽  
Miguel A. Salido ◽  
Adriana Giret ◽  
Federico Barber
Keyword(s):  
Genetic Algorithm ◽  
Energy Efficiency ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Real Life ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Extended Version ◽  
Reasonable Time ◽  
Shop Scheduling

AbstractMany real life problems can be modeled as a scheduling problem. The main objective of these problems is to obtain optimal solutions in terms of processing time, cost and quality. Nowadays, energy-efficiency is also taken into consideration. However, these problems are NP-hard, so many search techniques are not able to obtain a solution in a reasonable time. In this paper, a genetic algorithm is developed to solve an extended version of the classical job-shop scheduling problem. In the extended version, each operation has to be executed by one machine and this machine can work at different speed rates. The machines consume different amounts of energy to process tasks at different rates. The evaluation section shows that a powerful commercial tools for solving scheduling problems was not able to solve large instances in a reasonable time, meanwhile our genetic algorithm was able to solve all instances with a good solution quality.

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