Data have been utilised in production research in meaningful ways for decades. Recent years have offered data in larger volumes and improved quality collected from diverse sources. The ...state-of-the-art data research in production and the emerging methodologies are discussed. The review of the literature suggests that production research enabled by data has shifted from that based on analytical models to data-driven. Manufacturing and data envelopment analysis have been the most popular application areas of data-driven methodologies. The research published to date indicates that data mining is becoming a dominant methodology in production research. Future trends and opportunities for data-driven production research are presented.
•Six key socio-demographic attributes are used to estimate EV demand in Beijing.•Potential sites for public EV charging stations are identified.•Three classic facility location models are used to ...choose optimal locations.•The p-median model is more effective than the other two models.•Our location plan can help encourage car users to adopt EV.
In this paper, we present a case study on planning the locations of public electric vehicle (EV) charging stations in Beijing, China. Our objectives are to incorporate the local constraints of supply and demand on public EV charging stations into facility location models and to compare the optimal locations from three different location models. On the supply side, we analyse the institutional and spatial constraints in public charging infrastructure construction to select the potential sites. On the demand side, interviews with stakeholders are conducted and the ranking-type Delphi method is used when estimating the EV demand with aggregate data from municipal statistical yearbooks and the national census. With the estimated EV demand, we compare three classic facility location models – the set covering model, the maximal covering location model, and the p-median model – and we aim to provide policy-makers with a comprehensive analysis to better understand the effectiveness of these traditional models for locating EV charging facilities. Our results show that the p-median solutions are more effective than the other two models in the sense that the charging stations are closer to the communities with higher EV demand, and, therefore, the majority of EV users have more convenient access to the charging facilities. From the experiments of comparing only the p-median and the maximal covering location models, our results suggest that (1) the p-median model outperforms the maximal covering location model in terms of satisfying the other’s objective, and (2) when the number of charging stations to be built is large, or when minor change is required, the solutions to both models are more stable as p increases.
Due to the ageing population and the prevalence of chronic diseases, Home Health Care (HHC) practices are significantly increasing in developed countries to provide coordinated health related ...services to patients at their homes. Accordingly, the scope of HHC services is also expanding from typical nursing and postoperative care at home to cover all types of needs of elderly patients (e.g., personal care, drug delivery and meal services). This paper aims to address the pressing demand for HHC services and develop a novel and effective mathematical model and solution methodology for supporting health care service delivery decisions. Our decision support framework captures the real needs of HHC services, including the challenges of creating simultaneous schedules and route plans for a set of HHC staff and Home Delivery Vehicles (HDVs) under the requirements of synchronization between HHC staff and HDVs visits, multiple visits to patients, multiple routes of HDVs and pickup/delivery visits related precedence for HDVs. A Mixed Integer Linear Programming (MILP) model is developed to characterize the optimization problem. Considering the computational complexity of the problem, a Hybrid Genetic Algorithm (HGA) is proposed to suggest HHC planning decisions. The model formulation and proposed HGA are examined on real-life instances for demonstrating its practicality and randomly generated test instances for assessing the scalability of the proposed approach. The results show the effectiveness and efficiency of our solution methodology. Experimental results indicate that the proposed algorithm provided a good performance even with an increasing number of required synchronized services, whereas the heuristic tactics facilitate the HGA to produce better-quality solutions in a significantly shorter time. Our framework is expected to contribute to an important aspect of shared healthcare mobility.
•Home health care logistics planning for the benefits to the ageing society.•A novel model developed for the synchronization problem of multiple nurses and vehicles routing.•A computationally efficient hybrid genetic algorithm to provide high-quality solutions.•Artificial and realistic instances to demonstrate the scalability and practicality of the method.•High performance of the algorithm shown by computational experiments.
We study a home health care (HHC) problem that is characterized by prioritized patients and uncertain demands. In practice, HHC supply chain networks often struggle to meet high demand due to a ...shortage of service vehicles. Additionally, disruptions caused by natural calamities and pandemics (e.g., COVID-19) further compound these challenges, necessitating the consideration of real-life characteristics such as patient priorities, infrastructure locations, and transportation of medical supplies with uncertain demands. To formulate the problem, we propose a multi-depot and multi-period chance-constrained optimization model with precedence constraints, assuming that the demand quantities for medical supplies are random variables. Since patients’ medical conditions vary in severity, the priority of each patient is translated into a time-dependent potential healthcare cost that changes dynamically over the planning horizon. The solution to the proposed model determines the optimal locations for the base Mobile Health Facilities (MHFs) and the fleet size of HHC vehicles, and generates scheduling and routing plans to visit patients within specified time windows. We propose a unique three-phase solution approach, integrated with stochastic simulation, to address the problem. We then assess the robustness of the proposed model based on a realistic case of HHC service provision in Hong Kong and explore the optimal values for two model parameters, namely the Vehicle Threshold Index and the MHF Threshold Index. The performance evaluation tests show that the proposed solution method is efficient and effective for solving real-world problems.
•A home health care problem characterized by prioritized patients and uncertain demands.•A multi-depot and multi-period chance-constrained optimization model with precedence constraints.•A three-phase solution approach, integrated with stochastic simulation, to solve the problem.•The proposed solution method shown to be efficient and effective for solving real-world problems.
Abstract
Self‐service technologies (SSTs) have been widely adopted in industries that require the delivery of physical products by services, in which consumers evaluate a product for both the product ...value and the service value. A typical service delivery system usually involves sales agents and/or self‐service technologies, for example, online services and kiosks, to serve consumers by a coproduction process. In other words, both the sales agent (or self‐service machine) and the consumer should exert effort, with corresponding service costs. By modeling the coproduction output with a Cobb–Douglas production function, we establish a principal–agent model to study the value of self‐service technologies in designing a service delivery system wherein the sales agent's service cost is private information. We first characterize the main trade‐off between the sales agent and the self‐service machine when the firm provides only one service channel. Then, we analyze the value of the self‐service machine when the firm can provide both service channels. We find that, interestingly, the firm may possibly provide both service channels, that is, services offered by the sales agent and the self‐service machine, when the level of information uncertainty is high, and the self‐service machine's service cost is intermediate. Moreover, when both service channels are offered, only the efficient sales agent will be contracted, and the inefficient sales agents are screened out of the market by the self‐service machine; that is, the self‐service machine can help the firm eliminate the information rent. We also investigate how the firm's service weight in the coproduction process and information uncertainty influence the consumer surplus, firm's choices, contract parameters, and resulting profits. Our results are shown to be robust when our model is extended to consider a single‐contract strategy, contracting on effort, a continuous sales agent type, and a general coproduction function.
We develop a distributionally robust optimisation (DRO) model based on a risk measure for the parallel machine scheduling problem (PMSP) with random job processing times. We propose an ...underperformance risk index (URI) to control the extent of the total weighted completion time (TWCT) that exceeds target level T. With partially characterised uncertainty set information, we transform the model with URI to its equivalent mixed-integer linear programming (MILP) counterparts. Due to the NP-hardness of PMSP with different job weights, we design a hybrid algorithm with a heuristic assignment and exact subproblem for large-scale problems. The proposed hybrid algorithm reduces the computation time significantly at the expense of solution quality. We also introduce a reformulation approach under the setting of equally weighted and identical machines. Numerical results show that our model performs better than the distributionally β-robust optimisation models. Our proposed URI accounts for both the frequency and magnitude of violation from the target. The uncertainty set we used preserves a linear structure under partially characterised distributional information. Our computational results and sensitivity analysis show the effectiveness and efficiency of our proposed DRO model under various settings, including different problem sizes, different processing time variations, and information misalignment.
•We study a dynamic scheduling problem in the context of emergency departments.•The objective is to minimize the total weighted tardiness of the patients.•The deterministic version is tackled by ...exact and heuristic methods.•The dynamic version is handled by heuristics and a scenario-based planning approach.•Instances based on real data from two major emergency departments are solved.
Emergency department overcrowding is a global issue that poses a great threat to patient health and safety. The timeliness of medical services provided to patients is crucial to emergency departments as it directly impacts the mortality and morbidity of urgent patients. However, critical resources (e.g., doctors and nurses) are typically constrained due to the limited financial budget. Thus, hospital administrators may need to investigate solutions to improve the efficiency of the emergency department. In this work, we study the dynamic problem of scheduling patients to doctors, aiming at minimizing the total weighted tardiness. We propose a simple reoptimization heuristic based on multiple queues of patients in accordance with their urgency levels, and then combine it with an effective variable neighborhood search. We also propose a scenario-based planning approach that uses sampled scenarios to anticipate future events and the variable neighborhood search to schedule patients. The methods are adapted to handle a problem variant where information on arrival time and urgency level of some patients can be received in advance by the emergency department. With a comprehensive computational study on two sets of realistic instances from Hong Kong SAR of China and Italy, we validate the performance of the proposed methods, evaluating the benefits of having more doctors and receiving early information.
•We propose an innovative methodology for optimal deployment of public EV chargers.•A generalised ordered probit model is used to estimate EV purchase intention.•We derive parameters of the LA model ...based on survey, public data and interview.•Existing charging network should be substantially expanded, especially in suburbs.•Adding chargers at existing stations is more economical than building new stations.
The optimal deployment of public charging infrastructure is critical to the popularisation of electric vehicles (EVs) in high-density cities. Existing studies on public EV charging facilities have rarely integrated government policy and spatial constraints into their optimisation algorithms. To address this research gap, we proposed a contextualised EV charger optimisation model that incorporates carefully derived supply-and-demand constraints and tested it in the case of Hong Kong. From the supply side, we studied the latest planning guidelines and conducted a spatial analysis of potential charging sites. From the demand side, we conducted a questionnaire survey with local residents, estimated their EV purchase intention using a generalised ordered probit model, and then projected the usage demand for public chargers. These supply-and-demand constraints were subsequently incorporated into a location-allocation model to minimise both charging demand shortfall and travel time to charging facilities. We also conducted sensitivity analyses with varying budget, charging demand and facility service radius. Based on our results, we made several key recommendations regarding the spatial planning of public EV charging facilities in our high-density context: (1) the existing charging network should be substantially expanded to meet the projected demand; (2) the charging network should be expanded beyond the central business district and the urban core into other urban neighbourhoods and suburbs; and (3) installing more chargers at existing charging stations is more economical than building new stations. Our research provides an important reference for the spatial planning and deployment strategies for public EV charging infrastructure in high-density cities.
•Explore dual-channel e-commerce retailer's optimal pricing decisions and values of RFI.•The retailer who purchases RFI for consumers may not charge a higher price.•Salvage value, return freight cost ...and RFI premium are key factors determining the optimal policies.•When the product's salvage value is polarized or the return freight cost is low, using RFI can help increase consumer surplus.•If the channel is selected by consumers, providing RFI by retailers harms social welfare.
Today, e-commerce retailers commonly operate in a dual-channel mode. Return freight insurance (RFI) is an emerging measure to resolve online shopping disputes with product returns. If a consumer returns an insured product, the insurance company will compensate the consumer for the return-freight fee. In practice, we observe that some dual-channel e-commerce retailers offer RFI to consumers, while others do not. We build consumer-utility-based analytical models to study the retailer's optimal pricing decisions and values of RFI. In the basic models, the proportions of store-type consumers and online-type consumers are exogenously given; we examine three cases, namely Case N (RFIs are not provided), Case R (retailer purchases RFI for consumers), and Case C (consumers pay for RFI). Comparing these three cases, we uncover that the retailer who purchases RFI for consumers does not necessarily charge a higher price. We show that if the RFI premium is sufficiently (moderately) low, it is more beneficial for consumers (the retailer) to pay for the RFI. We analytically prove that (i) when the product's salvage value is polarized or the return freight cost is low, using RFI can help increase consumer surplus (CS), (ii) when the salvage value is sufficiently high, the social welfare (SW) with RFI is higher than the case without RFI. In the extended models, we explore the situation in which consumers can decide whether to purchase RFI as well as the channel to buy the products. In this case, we find that (i) the retailer should provide RFI only when the product's cost, salvage value, and return freight cost are all high, and (ii) offering RFI can increase CS but hurt SW. We also consider various extended models to prove the robustness of the research findings.
•This paper surveys the research developments on the Dial-A-Ride Problem (DARP) since 2007.•We provide a classification of the problem variants and the solution methodologies, and references to ...benchmark instances.•We also present some application areas for the DARP.•We discuss some future trends and challenges, and indicate some possible directions for future research.
There has been a resurgence of interest in demand-responsive shared-ride systems, motivated by concerns for the environment and also new developments in technologies which enable new modes of operations. This paper surveys the research developments on the Dial-A-Ride Problem (DARP) since 2007. We provide a classification of the problem variants and the solution methodologies, and references to benchmark instances. We also present some application areas for the DARP, discuss some future trends and challenges, and indicate some possible directions for future research.
