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Home healthcare staff dimensioning problem for temporary caregivers: A matheuristic solution approach

COMPUTERS & OPERATIONS RESEARCH 2023年第1期152卷

作者： Nikzad, Erfaneh Bashiri, Mahdi Abbasi, Babak Shahed Univ Fac Engn Ind Engn Dept Tehran Iran Coventry Univ Ctr Business Soc Coventry England RMIT Univ Sch Accounting Informat Syst & Supply Chain Melbourne Australia

Staff dimensioning, defined as determining the required numbers of caregivers with different types of skills, is a key decision for home healthcare systems. Home healthcare providers often use a combination of permanent and temporary (casual) caregivers. Determining the required number of temporary caregivers with different skill sets considering uncertainty and routing cost is the main objective of this study. To this end, we propose a two-stage stochastic programming model for the staff dimensioning problem for temporary caregivers, taking into account uncertainties in the required class of service, the required number of visits, and the required service time for each patient. Staff dimensioning decisions are defined in the first stage, and assignment with routing are positioned in the second stage of the model. To solve the problem, a two-phase matheuristic algorithm is developed where an initial solution is generated in the first phase by using an intermediate mathematical model and solving a series of Traveling Salesman Problems (TSPs), then a fix-and-optimize strategy is developed in the second phase to improve the obtained solution. The efficiency of the proposed matheuristic algorithm is examined by various test problems. The results highlight that the proposed model and solution method can be used by HHC providers to effectively utilize the option of recruitment of temporary caregivers in their resource planning considering inevitable uncertain parameters.

关键词： Home health care Staff dimensioning matheuristic algorithm Two-stage stochastic programming

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A hub-and-spoke architecture for a parcel delivery system using the cross-docking distribution strategy

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ENGINEERING OPTIMIZATION 2021年第9期53卷 1593-1612页

作者： Arbabi, Hamidreza Nasiri, Mohammad Mahdi Bozorgi-Amiri, Ali Univ Tehran Sch Ind Engn Coll Engn Tehran Iran

This article develops a hub-and-spoke architecture for a parcel delivery system using a network that includes a distribution centre and several cross-dock facilities. Several real-world assumptions, including an electric truck fleet, mobile charging station, third-party logistics, capacity constraints, last-mile deliveries and customer dissatisfaction, are incorporated in this problem. Besides, because of the high complexity of the problem, a novel matheuristic algorithm based on the combination of the fix-and-relax algorithm (FARA) and genetic algorithm (GA), namely Mb-FARGA, is developed to solve large-sized instances. A comprehensive computational analysis is accomplished to validate the proposed mathematical models and evaluate the performance of the Mb-FARGA method. According to the results, the Mb-FARGA method demonstrates a very favourable performance. Moreover, the results show that considering a distribution centre with appropriate capacity in the cross-docking system is an excellent strategy to enhance customer satisfaction, improve the system's performance and reduce capacity shortages.

关键词： Multiple cross-docking multi-period cross-docking electric trucks hub-and-spoke architecture matheuristic algorithm

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A bi-objective mathematical model for an unrelated parallel machine scheduling problem with job-splitting

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

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JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2022年第4期37卷 2293-2307页

作者： Sarac, Tugba Tutumlu, Busra Eskisehir Osmangazi Univ Fac Engn & Architecture Ind Enginering Dept TR-26480 Eskisehir Turkey Kutahya Dumlupinar Univ Fac Engn Ind Enginering Deparment TR-43100 Kutahya Turkey

Studies dealing with parallel machine scheduling problems generally assume that jobs are not split. However, splitting of jobs can offer opportunities such as delivering the jobs on time or evenly distributing the jobs to the machines. In this study, two different multi-objective mathematical models are proposed for the unrelated parallel machine scheduling problem without and with job-splitting. The first model is for the unrelated parallel machine scheduling problem without job-splitting and it was developed based on the model proposed by Sarac and Tutumlu [1]. In the second model, in which the jobs can split, besides the machines to which the jobs will be assigned and their order, it is also determined how the jobs will split and in which ratios the jobs will be processed on which machines. The objectives of both models are to minimize the completion time of the last job and the number of machines to be used. Multi-objective models were transformed into a single-objective structure with the epsilon constraint method. Randomly generated test problems were solved with the proposed models using the GAMS/Cplex solver, and the results were compared. Considering the problems for which the optimum solution is obtained, the first model found a solution in an average of 85% less time than the model in the literature [1], and the second model shortens the completion time of the last job in an average of 14%. In addition, a mathematical heuristic algorithm is proposed for solving large-size problems.

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Stochastic production routing problem for perishable products: Modeling and a solution algorithm

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COMPUTERS & OPERATIONS RESEARCH 2022年 142卷 105725-105725页

作者： Mousavi, Razieh Bashiri, Mahdi Nikzad, Erfaneh Shahed Univ Fac Engn Ind Engn Dept Tehran Iran Coventry Univ Fac Business & Law Sch Strategy & Leadership Coventry W Midlands England Coventry Univ Ctr Business Soc Coventry W Midlands England

The freshness of perishable products, in addition to other economic aspects such as production, inventory, and transportation management, is one of the main challenges of food supply chains. Therefore, integration of production, inventory, and routing decisions is essential. In this study, a new production routing model for perishable products with uncertain demand is presented. The aim is to minimize the costs of production, inventory, routing, wasted products, and penalties for non-fresh products. The model is more applicable for perishable products with limited, discrete shelf life with a high freshness value. A five-phase matheuristic algorithm is proposed to solve the stochastic mathematical model. Computational experiments show that the proposed mathematical model can result in a significant reduction of wasted products, particularly when consumer buying patterns change due to various occurrences, such as a pandemic. Also, numerical analysis for small, medium, and large instances confirms the validity and efficiency of the proposed matheuristic algorithm when compared with an exact solver.

关键词： Production routing problem Pandemic Perishable product Freshness matheuristic algorithm

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A goal programming model for balancing agent loads in the multi-resource generalized assignment problem

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

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JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2022年第1期37卷 193-205页

作者： Ozcelik, Feristah Sarac, Tugba Eskisehir Osmangazi Univ Dept Ind Engn TR-26480 Eskisehir Turkey

The multi-resource generalized assignment problem (MRGAP) is the assignment of jobs to agents under capacity constraints to minimize the total assignment. In this problem, each agent has multiple resources, each job is assigned to only one agent, and multiple jobs can be assigned to one agent. In the MRGAP problem, it is essential to distribute the loads in balance to the agents. In the literature, for balancing agent loads, bottleneck MRGAP with the aim of minimizing the maximum agent load, and balanced assignment MRGAP models with the aim of minimizing the difference between the maximum agent load and the minimum agent load have been proposed. In this study, the sum of load squares model has been adapted for MRGAP, as well as a new goal programming MRGAP model and a matheuristic algorithm for the solution of this model are developed. The performances of the proposed model and the algorithm were compared with the models taken from the literature by using randomly derived test problems. The quality of the obtained solutions is determined by considering the maximum load, coefficient of variation and total load criteria. The test results are evaluated in terms of these three criteria. The results obtained revealed the success of the proposed goal programming model and the matheuristic algorithm.

关键词： Multi-Resource generalized assignment problem load balancing goal programming matheuristic algorithm

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A matheuristic approach to the orienteering problem with service time dependent profits

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第2期273卷 488-503页

作者： Yu, Qinxiao Fang, Kan Zhu, Ning Ma, Shoufeng Tianjin Univ Coll Management & Econ Tianjin 300072 Peoples R China

This paper addresses the orienteering problem with service time dependent profits (OPSTP), in which the profit collected at each vertex is characterized by a nonlinear function of service time, and the objective is to maximize the total profits by determining a subset of the vertices to be visited and assigning appropriate service time to each of them within a given time budget. To solve this problem, a mixed integer nonlinear programming model is formulated, and a two-phase matheuristic algorithm that consists of a tabu search method and a nonlinear programming is implemented. Extensive computational experiments are conducted on both randomly generated instances and instances that are adapted from the TSPLIB. The results show that our proposed matheuristic algorithm could be quite effective in finding good-quality solutions. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Routing Orienteering problem Service time dependent profits matheuristic algorithm Nonlinear programming

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The adoption of self-driving delivery robots in last mile logistics

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2021年 146卷 102214-102214页

作者： Chen, Cheng Demir, Emrah Huang, Yuan Qiu, Rongzu Fujian Agr & Forestry Univ Sch Transportat & Civil Engn Fuzhou Peoples R China Cardiff Univ Panalpina Ctr Mfg & Logist Res Cardiff Business Sch Cardiff Wales Cardiff Univ Cardiff Business Sch Logist & Operat Management Cardiff Wales

Covid-19, the global pandemic, has taught us the importance of contactless delivery service and robotic automation. Using self-driving delivery robots can provide flexibility for on-time deliveries and help better protect both driver and customers by minimizing contact. To this end, this paper introduces a new vehicle routing problem with time windows and delivery robots (VRPTWDR). With the help of delivery robots, considerable operational time savings can be achieved by dispatching robots to serve nearby customers while a driver is also serving a customer. We provide a mathematical model for the VRPTWDR and investigate the challenges and benefits of using delivery robots as assistants for city logistics. A two-stage matheurisitic algorithm is developed to solve medium scale VRPTWDR instances. Finally, results of computational experiments demonstrate the value of self-driving delivery robots in urban areas by highlighting operational limitations on route planning.

关键词： City logistics Vehicle routing problem Self-driving delivery robot Contactless delivery matheuristic algorithm

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Two-stage stochastic programming approach for the medical drug inventory routing problem under uncertainty

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 128卷 358-370页

作者： Nikzad, Erfaneh Bashiri, Mahdi Oliveira, Fabricio Shahed Univ Dept Ind Engn Tehran Iran Aalto Univ Sch Sci Dept Math & Syst Anal Syst Anal Lab FI-00076 Aalto Finland

Medical drug shortages are an important issue in health care, since they can significantly affect patients' health. Thus, selecting the appropriate distribution and inventory policies plays an important role in decreasing drug shortages. In this context, inventory routing models can be used to determine optimal policies in the context of medical drug distribution. However, in real-world conditions, some parameters in these models are subject to uncertainty. This paper examines the effects of uncertainty in the demand by relying on a two-stage stochastic programming approach to incorporate it into the optimization model. A two-stage model is then proposed and two different approaches based on chance constraints are used to assess the validity of the proposed model. In the first model, a scenario-based two-stage stochastic programming model without probabilistic constraint is proposed, while in the other two models, proposed for validation of the first model, probabilistic constraints are considered. A mathematical-programming based algorithm (a matheuristic) is proposed for solving the models. Moreover, the Latin hypercube sampling method is employed to generate scenarios for the scenario-based models. Numerical examples show the necessity of considering the stochastic nature of the problem and the accuracy of the proposed models and solution method.

关键词： Two-stage stochastic programming Stochastic inventory routing problem Medical drug distribution Latin hypercube sampling method Chance constraints matheuristic algorithm

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Sustainable cold supply chain management under demand uncertainty and carbon tax regulation

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TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT 2020年 80卷 102245-102245页

作者： Babagolzadeh, Mahla Shrestha, Anup Abbasi, Babak Zhang, Yahua Woodhead, Alice Zhang, Anming Univ Southern Queensland Sch Commerce Toowoomba Qld Australia Univ Southern Queensland Sch Management & Enterprise Toowoomba Qld Australia RMIT Univ Sch Business IT & Logist Melbourne Vic Australia Univ Southern Queensland Rural Econ Ctr Excellence RECoE Toowoomba Qld Australia Univ British Columbia Sauder Sch Business Vancouver BC Canada

Increasing awareness of sustainability in supply chain management has prompted organizations and individuals to consider environmental impacts when managing supply chains. The issues concerning environmental impacts are significant in cold supply chains due to substantial carbon emissions from storage and distribution of temperature-sensitive product. This paper investigates the impact of carbon emissions arising from storage and transportation in the cold supply chain in the presence of carbon tax regulation, and under uncertain demand. A two-stage stochastic programming model is developed to determine optimal replenishment policies and transportation schedules to minimize both operational and emissions costs. A matheuristic algorithm based on the Iterated Local Search (ILS) algorithm and a mixed integer programming is developed to solve the problem in realistic sizes. The performance and robustness of the matheuristic algorithm are analyzed using test instances in various sizes. A real-world case study in Queensland, Australia is used to demonstrate the application of the model. The results highlight that higher emissions price does not always contribute to the efficiency of the cold supply chain system. Furthermore, the analyses indicate that using heterogeneous fleet including light duty and medium duty vehicles can lead to further cost saving and emissions reduction.

关键词： Sustainable cold supply chain Two-stage stochastic programming Carbon tax regulations Demand uncertainty matheuristic algorithm

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matheuristics for the capacitated p-median problem

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2015年第1期22卷 149-167页

作者： Stefanello, Fernando de Araujo, Olinto C. B. Mueller, Felipe M. Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil Univ Fed Santa Maria Colegio Tecn Ind Santa Maria BR-97119900 Santa Maria RS Brazil Univ Fed Santa Maria Programa Posgrad Informat BR-97119900 Santa Maria RS Brazil

The improvement in the performance of computers and mathematical programming techniques has led to the development of a new class of algorithms called matheuristics. Associated with an improvement of Mixed Integer Programming (MIP) solvers, these methods have successfully solved plenty of combinatorial optimization problems. This paper presents a matheuristic approach that hybridizes local search based metaheuristics and mathematical programming techniques to solve the capacitated p-median problem. The proposal considers reduced mathematical models obtained by a heuristic elimination of variables that are unlikely to belong to a good or optimal solution. In addition, a partial optimization algorithm based on the reduction is proposed. All mathematical models are solved by an MIP solver. Computational experiments on five sets of instances confirm the good performance of our approach.

关键词： capacitated p-median problem matheuristic algorithm size reduction

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