ABSTRACT
The permutation flow shop scheduling problem (PFSSP) is well-applied in the industry, which is confirmed to be an NP-Hard optimization problem, and the objective is to find the minimum completion time (makespan). A modified coronavirus herd immunity optimizer (CHIO) with a modified solution update is suggested in this work. Meanwhile, the simulated annealing strategy is used on the updating herd immunity population to prevent trapping on local optima, and an adjusted state mechanism is involved to prevent fast state change/ convergence. Nine instances of different problem scales on the FPSSP dataset of Taillard were tested. The experimental results show that the proposed method can find the optimal solutions for the tested instances, with ARPDs no more than 0.1, indicating that the proposed method can effectively and stably solve the PFSSP. © 2022 IEEE.
ABSTRACT
The growth in air traffic (before the Covid-19 pandemic) made airport time slots an increasingly scarce resource (and it is believed that this growth will continue after recovery). It is widely acknowledged that the grandfathering schemes used nowadays lead to inefficient allocations and that auctions would be a means to allocate valuable airport time slots efficiently. It has, however, also been pointed out that the design of such slot auctions is challenging due to the various constraints that need to be considered. The present paper proposes a market design for the sales of airport time slots at EU airports that complies with the Worldwide Scheduling Guidelines of the International Air Transport Association (IATA), most notably the reference value systems at level 3 airports. These guidelines need to be considered but lead to significant additional complexity in the market design. Capacity constraints are defined for overlapping time windows, which render the maximum welfare flight scheduling problem NP-hard. Auction formats with good incentive properties such as the Vickrey-Clarke-Groves mechanism or core-selecting auctions require an exact solution to the allocation problem. Given its hardness, it is far from obvious that the allocation problem can be solved to optimality sufficiently fast for practically relevant sizes of real-world problems. We introduce a mathematical model formulation for the maximum welfare flight scheduling problem that complies with all specified IATA constraints and evaluate it on near real-world data sets of flight requests for a full season of a major international airport. We show that the allocation can be computed within minutes and that all the payment computations for the winners can be done in less than two hours on average for realistic problem sizes. The consideration of values of airlines within the proposed auction mechanism leads to significant welfare gains of more than 35% as compared with benchmarks resulting from different standard objectives. These include the maximization of the number of movements, the minimization of the number of movements for which deviations from requested times occur, and the minimization of the total deviation of scheduled from requested times. Whereas the results indicate that auctions can be solved quickly for realistic problem sizes and promise significant welfare gains under the standard independent private values assumptions, the implementation of auctions in the field leads to additional serious challenges. For example, the regulator might have to impose allocation constraints to mitigate the market power of incumbent airlines. In addition, the valuation of slots and the interdependencies of the slot assignment with those at other coordinated airports need careful attention. Copyright: © 2022 INFORMS.
ABSTRACT
Due to the global outbreak of COVID-19, the perishable product supply chains have been impacted in different ways, and consequently, the risks of food insecurity have been increased in many affected countries. The uncertainty in supply and demand of perishable products, are among the most influential factors impacting the supply chain networks. Accordingly, the provision and distribution of food and other perishable commodities have become much more important than in the past. In this study, a bi-objective optimization model is proposed for a three-echelon perishable food supply chain (PFSC) network with multiple products to formulate an integrated supplier selection, production scheduling, and vehicle routing problem. The proposed model aims to mitigate the risks of demand and supply uncertainties and reinforce the distribution-related decisions by simultaneously optimizing the total network costs and suppliers' reliability. Using the distributionally robust modeling paradigm, the probability distribution of uncertain demand is assumed to belong to an ambiguity set with given moment information. Accordingly, distributionally robust chance-constrained approach is applied to ensure that the demands of retailers and capacity of vehicles are satisfied with high probability. Leveraging duality and linearization techniques, the proposed model is reformulated as a mixed-integer linear program. Then, the weighted goal programming approach is adopted to address the multi-objectiveness of the proposed optimization model. To certify the performance and applicability of the model, a real-world case study in the poultry industry is investigated. Finally, the sensitivity analysis is conducted to evaluate the impacts of influential parameters on the objective functions and optimal decisions, and then some managerial insights are provided based on the obtained results. © 2022 Elsevier Ltd
ABSTRACT
Purpose>One of the main advantages of the proposed model is that it is flexible to generate n-day pairings simultaneously. It means that, despite previous researches, one-day to n-day pairings can be generated in a single model. The flexibility in generating parings causes that the proposed model leads to better solutions compared to existing models. Another advantage of the model is minimizing the risk of COVID-19 by limitation of daily flights as well as elapsed time minimization. As airports are among high risk places in COVID-19 pandemic, minimization of infection risk is considered in this model for the first time. Genetic algorithm is used as the solution approach, and its efficiency is compared to GAMS in small and medium-size problems.Design/methodology/approach>One of the most complex issues in airlines is crew scheduling problem which is divided into two subproblems: crew pairing problem (CPP) and crew rostering problem (CRP). Generating crew pairings is a tremendous and exhausting task as millions of pairings may be generated for an airline. Moreover, crew cost has the largest share in total cost of airlines after fuel cost. As a result, crew scheduling with the aim of cost minimization is one of the most important issues in airlines. In this paper, a new bi-objective mixed integer programming model is proposed to generate pairings in such a way that deadhead cost, crew cost and the risk of COVID-19 are minimized.Findings>The proposed model is applied for domestic flights of Iran Air airline. The results of the study indicate that genetic algorithm solutions have only 0.414 and 0.380 gap on average to optimum values of the first and the second objective functions, respectively. Due to the flexibility of the proposed model, it improves solutions resulted from existing models with fixed-duty pairings. Crew cost is decreased by 12.82, 24.72, 4.05 and 14.86% compared to one-duty to four-duty models. In detail, crew salary is improved by 12.85, 24.64, 4.07 and 14.91% and deadhead cost is decreased by 11.87, 26.98, 3.27, and 13.35% compared to one-duty to four-duty models, respectively.Originality/value>The authors confirm that it is an original paper, has not been published elsewhere and is not currently under consideration of any other journal.
ABSTRACT
Optimisation can be described as the process of finding optimal values for the variables of a given problem in order to minimise or maximise one or more objective function(s). Brain storm optimisation (BSO) algorithm is relatively new swarm intelligence algorithm that mimics the brainstorming process in which a group of people solves a problem together. The aim of this paper is to present hybrid BSO algorithm solutions in general, and particularly: (i) a hybrid BSO for improving the performances of the original BSO algorithm;(ii) a hybrid BSO for the flexible job-shop scheduling problem;and (iii) a feature selection by a hybrid BSO algorithm for the COVID-19 classification. The hybrid BSO algorithm overcomes the lack of exploitation in the original BSO algorithm, and simultaneously, the obtained better results prove their efficiency and robustness. © 2022, Springer Nature Switzerland AG.
ABSTRACT
This study presents an optimisation model for scheduling homebound vaccination in a more efficient way to address the existing workforce management challenge. We consider a home healthcare routeing challenge for people to be vaccinated at home based on limited resources. There are different types of patients that are categorised based on the services they require and should be served by appropriate workforce teams or a single medical staff, where teams are transported by rental vehicles. In this context, our goal is to minimise the total cost of transportation while considering patient requirements and workforce qualifications, as well as resource constraints and the time limit within which the vaccine must be administered. To pursue this goal, a mathematical formulation, based on the vehicle routeing dynamics is proposed, along with an algorithm to address the challenge. A case study with a Physician who administers vaccinations at home in southeastern Italy is analysed. Driving and working times are subject to uncertainty and are defined by empirical data. Our approach allows the physician to identify the most promising solutions and thus the best one in terms of reducing work time and risk. The resulting schedule maximises the vaccine delivery rate.
ABSTRACT
Energy efficiency has become a major concern for manufacturing companies not only due to environmental concerns and stringent regulations, but also due to large and incremental energy costs. Energy-efficient scheduling can be effective at improving energy efficiency and thus reducing energy consumption and associated costs, as well as pollutant emissions. This work reviews recent literature on energy-efficient scheduling in job shop manufacturing systems, with a particular focus on metaheuristics. We review 172 papers published between 2013 and 2022, by analyzing the shop floor type, the energy efficiency strategy, the objective function(s), the newly added problem feature(s), and the solution approach(es). We also report on the existing data sets and make them available to the research community. The paper is concluded by pointing out potential directions for future research, namely developing integrated scheduling approaches for interconnected problems, fast metaheuristic methods to respond to dynamic scheduling problems, and hybrid metaheuristic and big data methods for cyber-physical production systems.
ABSTRACT
Drones are receiving popularity with time due to their advanced mobility. Although they were initially deployed for military purposes, they now have a wide array of applications in various public and private sectors. Further deployment of drones can promote the global economic recovery from the COVID-19 pandemic. Even though drones offer a number of advantages, they have limited flying time and weight carrying capacity. Effective drone schedules may assist with overcoming such limitations. Drone scheduling is associated with optimization of drone flight paths and may include other features, such as determination of arrival time at each node, utilization of drones, battery capacity considerations, and battery recharging considerations. A number of studies on drone scheduling have been published over the past years. However, there is a lack of a systematic literature survey that provides a holistic overview of the drone scheduling problem, existing tendencies, main research limitations, and future research needs. Therefore, this study conducts an extensive survey of the scientific literature that assessed drone scheduling. The collected studies are grouped into different categories, including general drone scheduling, drone scheduling for delivery of goods, drone scheduling for monitoring, and drone scheduling with recharge considerations. A detailed review of the collected studies is presented for each of the categories. Representative mathematical models are provided for each category of studies, accompanied by a summary of findings, existing gaps in the state-of-the-art, and future research needs. The outcomes of this research are expected to assist the relevant stakeholders with an effective drone schedule design. IEEE
ABSTRACT
Nowadays, Home health care (HHC) procurement has become a hot topic of research in recent years due to the importance of HHC services for the care of the elderly. With the growth of the percentage of elderly people in different cases, we are witnessing concerns about providing health services to these people in the community. With getting older, the demand for Home Health Care increases. HHC includes a wide range of medical, paramedical and social services that can be provided at home and can be an alternative to receiving these services in a location other than the hospital. Also, due to the possibility of conflict in different countries in the future, with the spread of diseases such as Covid-19 and turning all the facilities and medical and health potential of countries to these epidemics, the need for medical services and home care for the elderly and sick people increases. In this research, a green routing problem is designed for the Home Health care network for the elderly. The network is structured in such a way that the medical service provider with services teams provides services to a group of patients located in a geographical area. The problem is presented as a multi-period mixed integer mathematical model. The purpose of the model is to maximize profits under carbon dioxide emission limits. In this model, an attempt has been made to address the environmental aspects as well. Finally, the mathematical model is solved in GAMS software with numerical examples and its results and performance are presented. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
One of the critical actions that emerged during the onset of the New Normalcy after COVID-19 lockdowns, is the safe return to schools and workplaces. Therefore, dedicated transportation services need to adapt to meet new requirements such as arrival reliability for multiple bell times, the consequent staggering of arrivals and departures, and the decrease in bus capacity due to the physical distancing required by regulators. In this work, we address these issues plus additional labor conditions concerning drivers for a university context; with the goal of optimizing social interests such as covering demand and travel time under limited resources. We propose a bi-level approach, where firstly a bus routing generation sub-problem is solved before a bus scheduling sub-problem. This (strategic) solution is then considered as the baseline for subsequent dynamic (operational) routing. The latter is based on real-time demand provided by the students via a mobile app and considers stop-skipping to further minimize travel time. This integrated transport solution was tested in a university case, showing that with the same resources, it can meet these new requirements. In addition, numerical experimentation was also carried out with benchmark instances to identify, among available and literature-recommended solution algorithms and an effective tailored Tabu Search implementation, those that perform best for this type of problems.
ABSTRACT
This paper is aimed at the problem of scheduling surgeries in operating rooms. To solve this problem, we suggest using some variation of the bin packing problem. The model is based on the actual operation of 10 operating rooms, each of which belongs to a specific department of the hospital. Departments are unevenly loaded, so operations can be moved to operating rooms in other departments. The main goal is to increase patient throughput. It is also necessary to measure how many operations take place in other departments with the proposed solution. The preferred solution is a solution with fewer such operations, all other things being equal. Due to the fact that the mixed-integer linear programming model turned out to be computationally complex, two approximation algorithms were also proposed. They are based on decomposition. The complexity of the proposed algorithms is estimated, and arguments are made regarding their accuracy from a theoretical point of view. To assess the practical accuracy of the algorithms, the Gurobi solver is used. Experiments were conducted on real historical data on surgeries obtained from the Burdenko Neurosurgical Center. Two decomposition algorithms were constructed and a comparative analysis was performed for 10 operating rooms based on real data.
ABSTRACT
In this paper, we discuss the medical staff scheduling problem in the Mobile Cabin Hospital (MCH) during the pandemic outbreaks. We investigate the working contents and patterns of the medical staff in the MCH of Wuhan during the outbreak of Covid-19. Two types of medical staff are considered in the paper, i.e., physicians and nurses. Besides, two different types of physicians are considered, i.e., the expert physician and general physician, and the duties vary among different types of physicians. The objective of the studied problem is to get the minimized number of medical staff required to accomplish all the duties in the MCH during the planning horizon. To solve the studied problem, a general Variable Neighborhood Search (general VNS) is proposed, involving the initialization, the correction strategy, the neighborhood structure, the shaking procedure, the local search procedure, and the move or not procedure. The mutation operation is adopted in the shaking procedure to make sure the diversity of the solution and three neighborhood structure operations are applied in the local search procedure to improve the quality of the solution. © 2021, Springer Nature Switzerland AG.
ABSTRACT
The rehabilitation scheduling process consists of planning rehabilitation physiotherapy sessions for patients, by assigning proper operators to them in a certain time slot of a given day, taking into account several requirements and optimizations, e.g., patient’s preferences and operator’s work balancing. Being able to efficiently solve such problem is of upmost importance, in particular after the COVID-19 pandemic that significantly increased rehabilitation’s needs. In this paper, we present a solution to rehabilitation scheduling based on Answer Set Programming (ASP), which proved to be an effective tool for solving practical scheduling problems. Results of experiments performed on both synthetic and real benchmarks, the latter provided by ICS Maugeri, show the effectiveness of our solution. © 2021, Springer Nature Switzerland AG.