Design of a bi-objective reliable healthcare network with finite capacity queue under service covering uncertainty

Mohammadi, Mehrdad; Dehbari, Saleh; Vahdani, Behnam

doi:10.1016/j.tre.2014.10.001

Cited by 50 publications

(19 citation statements)

References 46 publications

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“…So as to solve the proposed model within a reasonable time, since it is one of the NP-hard problems , two meta-heuristics are used for solving it, the first one is a genetic algorithm (GA) and the second one is an imperialist competitive algorithm (ICA). Both of these meta-heuristics are population-based and belong to the evolutionary algorithms sets (Mohammadi et al, 2014).…”

Section: Solution Methodsmentioning

confidence: 99%

A truck scheduling problem at a cross-docking facility with mixed service mode dock doors

Vahdani

Shahramfard

2019

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Purpose The purpose of this study is truck scheduling and assignment of trucks to the doors simultaneously since these issues were considered mainly separately in the previous research. Also, the door service time and its impact on truck scheduling were not taken into account, so this research endeavors to cover this gap. Design/methodology/approach In this research, a novel model has been presented for simultaneous truck scheduling and assignment problem with time window constraints for the arrival and departure of trucks, mixed service mode dock doors and truck queuing. To resolve the developed model, two meta-heuristic algorithms, namely, genetic and imperialist competitive algorithms, are presented. Findings The computational results indicate that the proposed framework leads to increased total costs, although it has a more accurate planning; moreover, these indicate that the proposed algorithms have different performances based on the criteria considered for the comparison. Research limitations/implications There are some limitations in this research, which can be considered by other researchers to expand the current study, among them the specifications of uncertainty about arrival times of inbound and outbound trucks, number of merchandises which has been loaded on inbound trucks are the main factors. If so, by considering this situation, a realistic scheme about planning of cross docking system would be acquired. Moreover, the capacity of temporary storage has been considered unlimited, so relaxing this limitation can prepare a real and suitable situation for further study. Examining the capacity in the front of each type of doors of cross-dock and executive servers are the other aspects, which could be expanded in the future. Originality/value In this study, a mathematical programing model proposed for truck scheduling to minimize total costs including holding, truck tardiness and waiting time for queue of trucks caused by the interference of each carrier’s movement. At the operational levels, this research considered a multi-door cross-docking problem with mixed service mode dock doors and time window constraints for arrival and departure time of trucks. Moreover, M/G/C queue system was developed for truck arrival and servicing of carriers to trucks.

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Section: Solution Methodsmentioning

confidence: 99%

A truck scheduling problem at a cross-docking facility with mixed service mode dock doors

Vahdani

Shahramfard

2019

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“…Araz, Fowler, and Ramirez‐Nafarrate (2014) and Ramirez‐Nafarrate, Lyon, Fowler, and Araz (2015) presented genetic algorithms for a p ‐median facility location and staffing problem for public health emergencies. Mohammadi, Dehbari, and Vahdani (2014) considered multi‐objective healthcare service network design with limited capacity under uncertainty and presented meta‐heuristic solutions for the problem. These last three papers used queuing approximations for staffing optimization, which is also critical in designing these service networks.…”

Section: Literature Reviewmentioning

confidence: 99%

Decision Assessment Algorithms for Location and Capacity Optimization under Resource Shortages

2019

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Emergency response preparedness requires strategic and operational planning. As natural disasters, disease outbreaks, and bioterrorism continue to threaten public health and the global economic system, policy makers and businesses develop plans for improving their readiness. These emergencies present challenging problems that require evaluation of several trade‐offs due to insufficient resources and capacity with limited response time pressure. Finding an optimal strategy to these problems can be computationally difficult, since they are formulated as large‐scale service network design models with location‐allocation decisions. The facility location problems in disaster management literature are typically known as NP‐hard problems. Therefore, heuristic algorithms can help find practically feasible solutions, but decision makers may have to sacrifice one of their objectives while trying to satisfy multiple of them. This paper presents a flexible algorithm to evaluate trade‐offs in resource scarce situations with a given response time. We formulate the location‐allocation problem with capacity and time constraints, and the objective of minimizing the service time for individuals in an affected area. Due to the complexity to solve the problem for large‐scale scenarios, the presented algorithm relaxes capacity and time constraints, simultaneously, and presents flexibility to assess trade‐offs. A modified NSGA‐II algorithm is used with a penalty function formulated to leverage resources. We analyze how values of the penalty parameters interact with a number of open sites in defining an efficient resource allocation strategy. Therefore, decision makers can expand their choices to design an emergency response network with their preferences taken into consideration.

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“…The operations in healthcare systems include designing, operations and control systems that define the efficiency of the hospital (Purbey et al, 2007). This further include the type of healthcare service design, planning for that service, procurement of support facilities and materials, design of work flow, inventory (Mohammadi et al, 2014; Oztekin et al, 2010). Design of the healthcare systems should have space of expansion and continuous improvement (Yang & Hsiao, 2009).…”

Section: Literature Reviewmentioning

confidence: 99%

Decoding the Relationship of Service Operations Enablers in Healthcare Industry

Sharma

Patel

2019

Journal of Operations and Strategic Planning

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The present study aims to decode the relationship among service operations enablers in healthcare industry. We identified the enablers with the help of literature and expert opinion through Delphi approach. Then, we conducted the semi-structured interview to finalize the scores of enablers in pairwise comparison matrix. We deployed the decision-making trail and evaluation laboratory (DEMATEL) in four steps to decode the degree of relationship and degree of effect and found that patient satisfaction does not directly depend on the professionalism of the staff. Location of the healthcare facility has the highest degree of relationship with other enablers, whereas the personal care abilities have a high impact on the service operations effectiveness. It is also noted that competency of pharmacist and doctor profile do not have a significant impact on service operations and thus on patient satisfaction and safety. The degree of relationship also has been presented in the form of a causal diagram toward the end of result. At the end of the study, the limitations and scope for future research are presented.

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Design of a bi-objective reliable healthcare network with finite capacity queue under service covering uncertainty

Cited by 50 publications

References 46 publications

A truck scheduling problem at a cross-docking facility with mixed service mode dock doors

A truck scheduling problem at a cross-docking facility with mixed service mode dock doors

Decision Assessment Algorithms for Location and Capacity Optimization under Resource Shortages

Decoding the Relationship of Service Operations Enablers in Healthcare Industry

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