Beste Kucukyazici

Emergency Logistics Planning in Natural Disasters

Logistics planning in emergency situations involves dispatching commodities (e.g., medical materials and personnel, specialised rescue equipment and rescue teams, food, etc.) to distribution centres in affected areas as soon as possible so that relief operations are accelerated. In this study, a planning model that is to be integrated into a natural disaster logistics Decision Support System is developed. The model addresses the dynamic time-dependent transportation problem that needs to be solved repetitively at given time intervals during ongoing aid delivery. The model regenerates plans incorporating new requests for aid materials, new supplies and transportation means that become available during the current planning time horizon. The plan indicates the optimal mixed pick up and delivery schedules for vehicles within the considered planning time horizon as well as the optimal quantities and types of loads picked up and delivered on these routes.In emergency logistics context, supply is available in limited quantities at the current time period and on specified future dates. Commodity demand is known with certainty at the current date, but can be forecasted for future dates. Unlike commercial environments, vehicles do not have to return to depots, because the next time the plan is re-generated, a node receiving commodities may become a depot or a former depot may have no supplies at all. As a result, there are no closed loop tours, and vehicles wait at their last stop until they receive the next order from the logistics coordination centre. Hence, dispatch orders for vehicles consist of sets of “broken” routes that are generated in response to time-dependent supply/demand.The mathematical model describes a setting that is considerably different than the conventional vehicle routing problem. In fact, the problem is a hybrid that integrates the multi-commodity network flow problem and the vehicle routing problem. In this setting, vehicles are also treated as commodities. The model is readily decomposed into two multi-commodity network flow problems, the first one being linear (for conventional commodities) and the second integer (for vehicle flows). In the solution approach, these sub-models are coupled with relaxed arc capacity constraints using Lagrangean relaxation. The convergence of the proposed algorithm is tested on small test instances as well as on an earthquake scenario of realistic size.

Supply chain design for unlocking the value of remanufacturing under uncertainty

Owing to the technological innovations and the changing consumer perceptions, remanufacturing has gained vast economic potential in the past decade. Nevertheless, major OEMs, in a variety of sectors, remain reluctant about establishing their own remanufacturing capability and use recycling as a means to satisfy the extended producer responsibility. Their main concerns seem to be the potential for the cannibalization of their primary market by remanufactured products and the uncertainty in the return stream in terms of its volume and quality. This paper aims at assisting OEMs in the development of their remanufacturing strategy, with an outlook of pursuing the opportunities presented by the inherent uncertainties. We present a two-stage stochastic closed-loop supply chain design model that incorporates the uncertainties in the market size, the return volume as well as the quality of the returns. The proposed framework also explicitly represents the difference in customer valuations of the new and the remanufactured products. The arising stochastic mixed-integer quadratic program is not amenable to solution via commercial software. Therefore, we develop a solution procedure by integrating sample average approximation with the integer L-shaped method. In order to gather solid managerial insights, we present a case study based on BSH, a leading producer of home appliances headquartered in Germany. Our analysis reveals that, while the reverse network configuration is rather robust, the extent of the firm’s involvement in remanufacturing is quite sensitive to the costs associated with each product recovery option as well as the relative valuation of the remanufactured products by the customers. In the context of the BSH case, we find that among the sources of uncertainty, the market size has the most profound effect on the overall profitability, and it is desirable to build sufficient expansion flexibility in the forward network configuration.

Developing concurrent investment plans for power generation and transmission

Abstract Decisions on electric power generation and transmission investments may have crucial effects on the development of industrial and residential areas. Decisions made on the infrastructure should have economically beneficial consequences for producers and consumers. The aim of this paper is to propose a model that considers transmission and generation investments simultaneously. The proposed model fills in the gap between models for developing long-term power generation policies and instantaneous power flow models. Unlike other investment models, it explicitly takes the high voltage transmission network into account and the selection of new generation plants located on the interconnected network is made in a more realistic manner considering transmission bottlenecks. The problem subsumes the capacitated network location problem and the network design problem, the former being related to decisions on generation expansion and the latter to decisions on transmission network expansion. The integrated model becomes NP in both feasibility and optimality, because of the sub-problems it contains. Here, a practical procedure is proposed to achieve overall feasibility and also to improve investment decisions when the solution is feasible. The model is tested on the dense interconnected network of an industrialized region in Turkey. The implementation shows how future infeasibilities in the transmission network are highlighted by the model and how generation investment decisions are affected by network expansion alternatives.

Improving post-stroke health outcomes: Can facilitated care help?

OBJECTIVES The objectives of this study were (1) identifying the patterns of post-stroke care, (2) determining the care-provider and patient characteristics associated with optimal management of post-stroke care and (3) estimating the potential influence of various facilitated care policies on outcomes. METHODOLOGY The 3946 subjects included in the study were admitted to one of Quebecs acute-care hospitals with confirmed diagnosis of stroke and subsequently discharged to their home. The records related to fee-for-service billings of this sample were obtained for the 3 months following discharge and used to define the care-provider path for each stroke survivor. These paths were analyzed and the potential impact of various facilitated care interventions was estimated via a Markov model. RESULTS The rate of mortality for this sample was 3.2% during the first 3 months after discharge. For the patients who were re-hospitalized, however, the mortality rates were up to 10.3% depending on the care-provider visited prior to re-hospitalization. Our analyses indicate that by avoiding such critical sub-paths via facilitated care, it is possible to achieve improvements in health outcomes as well as cost. DISCUSSION There is a window of opportunity for improving community-based post-stroke care. Facilitated care policies concerning planned visits upon discharge from hospital or following ER visits can improve the outcomes.

Managing Community-based Care for Chronic Diseases: The Quantitative Approach

Community-based care (C-bC) constitutes an important element of the chronic disease management programs. The design and management of C-bC systems requires the development of new resources and services, the assessment and reorganization of the existing services/facilities as well as the design of interventions. Quantitative decision models can play a major role for helping care providers and policy makers in this context. We present a systematic view of C-bC and provide selective examples of quantitative decision models developed for various chronic diseases. We outline the building blocks of C-bC systems as well as the distinguishing features of these systems that need to be incorporated in quantitative decision models. Then, we present three representative and diverse examples of prevailing quantitative approaches for managing C-bC. Finally, we discuss some avenues for future research.

Managing Patient Admissions in a Neurology Ward

We study patient admission policies in a neurology ward where there are multiple types of patients with different medical characteristics. Patients receive specialized care inside the neurology ward and delays in admission to the ward will have negative impact on their health status. The level of this impact varies among patient types and depends on the severity of patients. Patients are also different in terms of arrival rate and length of stay at the ward. The patients normally wait in the emergency department until a ward bed is assigned to them. We formulate this problem as an infinite-horizon average cost dynamic program and propose an efficient approximation scheme to solve large-scale problem instances. The computational results from applying our model to a neurology ward show that dynamic policies generated by our approach can reduce the overall deterioration in patients’ health status compared to several alternative policies. The online appendix is available at https://doi.org/10.1287/opre.2016.1574.