New Multi-Objective Approach for the Home Care Service Problem Based on Scheduling Algorithms and Variable Neighborhood Descent

Issaoui, Brahim; Zidi, Issam; Marcon, Eric; Ghédira, Khaled

doi:10.1016/j.endm.2014.11.024

Cited by 33 publications

(13 citation statements)

References 6 publications

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“…TW Simu Pre f Q Prec Reg [13] × × × [10] × × [14] × × [15] × × × × [16] × [17] × × [18] × × [19] × [20] × [21] × × × [22] × × × [23] × × × [24] × × × [25] × × [26] × × × [27] × × × [28] × × × [29] × × [30] × × × [31] × × × [32] × × × [33] × × × × [34] × × × × [35] × × × [36] × × × [37] × × × × [38] × × × [39] × × [1] × × × × [40] × × [41] × × [42] × × [43] × × × [44] × Table 4. Cont.…”

Section: Time Windows Simumentioning

confidence: 99%

“…Common solution approaches. [13] × [10] × [14] × [15] × [16] × [17] × [18] × [19] × × [20] × [21] × [22] × × [23] × [24] × [25] × [26] × [27] × [28] × [29] × [30] × × [31] × [32] × [33] × [34] × [35] × [36] × Table 5. Cont.…”

Section: Time Windows Simumentioning

confidence: 99%

“…Cont. [21] × × × [22] × × × [23] × × × [24] × × × × [25] × × × [26] × [27] × [28] × [29] × [30] × [31] × [32] × [33] × × [34] × × [35] × × [36] × × [37] × × × [38] × × [39] × × × [1] × × [40] × × [41] × × [42] × [43] × [44] × [45] × [46] × [47] × × × [48] × [49] × [50] × × × [51] × × × [52] × [53] × [54] × × × [55] × × [56] × [57] × As shown in the last table, the objective of minimizing traveling costs, waiting costs, or patient preferences is undoubtedly the most widespread in the literature. However, when multiple objective functions are handled, they are often considered within a single criterion as a weighted sum.…”

Section: Introductionmentioning

confidence: 99%

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Bicriteria Vehicle Routing Problem with Preferences and Timing Constraints in Home Health Care Services

Haddadene¹,

Labadie

Prodhon

2019

Algorithms

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Home Healthcare (HHC) is an emerging and fast-expanding service sector that gives rise to challenging vehicle routing and scheduling problems. Each day, HHC structures must schedule the visits of caregivers to patients requiring specific medical and paramedical services at home. These operations have the potential to be unsuitable if the visits are not planned correctly, leading hence to high logistics costs and/or deteriorated service level. In this article, this issue is modeled as a vehicle routing problem where a set of routes has to be built to visit patients asking for one or more specific service within a given time window and during a fixed service time. Each patient has a preference value associated with each available caregiver. The problem addressed in this paper considers two objectives to optimize simultaneously: minimize the caregivers' travel costs and maximize the patients' preferences. In this paper, different methods based on the bi-objective non-dominated sorting algorithm are proposed to solve the vehicle routing problem with time windows, preferences, and timing constraints. Numerical results are presented for instances with up to 73 clients. Metrics such as the distance measure, hyper-volume, and the number of non-dominated solutions in the Pareto front are used to assess the quality of the proposed approaches.Algorithms 2019, 12, 152 2 of 25 work (Ait Haddadene et al. [1]), the minimization of the traveling cost and the sum of non-preferences related to customers were considered as a single-objective function. The obtained results showed the conflicting nature of these criteria, which is why, we are particularly interested in solving the bi-objective version of VRPTW-SP.The problem at hand is an extension of the Vehicle Routing Problem (VRP), which is one of the most-studied combinatorial optimization problems. First introduced by Dantzig and Ramser [2], the aim of this NP-hard problem is to determine a set of minimal cost routes in which a set of customers is to be served by a fleet of vehicles based at a depot node. A detailed survey can be found in Toth and Vigo [3]. Currently, one of the most effective metaheuristics for the VRP is the hybrid genetic algorithm introduced by Vidal [4], which was latter generalized to solve more than 26 VRP variants.Among the different classes of VRP, the VRP with Time Windows (VRPTW) stands as the basis variant of our studied problem. Heuristic and metaheuristic approaches have been widely used to solve the VRPTW such as those proposed by Kindervater and Savelsbergh [5], Bräysy and Gendreau [6], Nagata [7], and Bräysy et al. [8]. In the field of healthcare, most problems are modeled as a VRPTW. In fact, the origin of vehicle routing problems in HHC systems may be linked to the door-to door-transportation of elderly or disabled persons (Dial A Ride Problems (DARP)) proposed by Bodin and Sexton [9]. A few years later, this variant was extended to the Home Healthcare (HHC) problem by Cheng and Rich [10], where caregivers are assigned to patient homes to pro...

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Section: Time Windows Simumentioning

confidence: 99%

Section: Time Windows Simumentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bicriteria Vehicle Routing Problem with Preferences and Timing Constraints in Home Health Care Services

Haddadene¹,

Labadie

Prodhon

2019

Algorithms

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“…However, Issaoui et al [25] developed a multi-objective mixed integer programming mathematical model which deals are: the increase of patient satisfaction and the number of health interventions and the decrease of the care interventions costs. To solve their problem, the authors used a three phases approach.…”

Section: Literature Reviewmentioning

confidence: 99%

A new optimization approach for a home health care problem

Aiane

Mesghouni

2015

2015 International Conference on Industrial Engineering and Systems Management (IESM)

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This paper deals with the home health care services. The home health care services are defined as a set of medical, paramedical and social services delivered to patients in their domicile rather than in hospital. In this paper, a new Mixed Integer Linear Programming (MILP) model is proposed to make a planning for a home health care problem. The model is optimizing routes and rosters for the health care staffs, while problem specific constraints are satisfied. This model integrates an original concept related to the human behavior (e.g. patient behavior). The MILP model is solved using the commercial optimization software Ilog-Cplex of IBM. Computational results on several benchmarks, generated from a real living-lab (GIS MADONAH) in Bourges (France), proved that the proposed model can solve real large-sized problems within acceptable computational time.

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“…They propose a heuristic approach to be implemented on spreadsheets and requiring almost no investment. Recently, Issaoui et al (2015) develop a three-phase meta-heuristic based on variable neighborhood search algorithm. In the first stage, they resolve the assignment problem using a scheduling algorithm which is the Longest Processing Time algorithm.…”

Section: Introductionmentioning

confidence: 99%

Maximizing the nurses’ preferences in nurse scheduling problem: mathematical modeling and a meta-heuristic algorithm

Jafari

Salmasi

2015

J Ind Eng Int

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The nurse scheduling problem (NSP) has received a great amount of attention in recent years. In the NSP, the goal is to assign shifts to the nurses in order to satisfy the hospital's demand during the planning horizon by considering different objective functions. In this research, we focus on maximizing the nurses' preferences for working shifts and weekends off by considering several important factors such as hospital's policies, labor laws, governmental regulations, and the status of nurses at the end of the previous planning horizon in one of the largest hospitals in Iran i.e., Milad Hospital. Due to the shortage of available nurses, at first, the minimum total number of required nurses is determined. Then, a mathematical programming model is proposed to solve the problem optimally. Since the proposed research problem is NP-hard, a meta-heuristic algorithm based on simulated annealing (SA) is applied to heuristically solve the problem in a reasonable time. An initial feasible solution generator and several novel neighborhood structures are applied to enhance performance of the SA algorithm. Inspired from our observations in Milad hospital, random test problems are generated to evaluate the performance of the SA algorithm. The results of computational experiments indicate that the applied SA algorithm provides solutions with average percentage gap of 5.49 % compared to the upper bounds obtained from the mathematical model. Moreover, the applied SA algorithm provides significantly better solutions in a reasonable time than the schedules provided by the head nurses.

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New Multi-Objective Approach for the Home Care Service Problem Based on Scheduling Algorithms and Variable Neighborhood Descent

Cited by 33 publications

References 6 publications

Bicriteria Vehicle Routing Problem with Preferences and Timing Constraints in Home Health Care Services

Bicriteria Vehicle Routing Problem with Preferences and Timing Constraints in Home Health Care Services

A new optimization approach for a home health care problem

Maximizing the nurses’ preferences in nurse scheduling problem: mathematical modeling and a meta-heuristic algorithm

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