Supply Allocation Research Articles

Urban preferences, amenities and age: Exploring the spatial distribution of age in Stockholm from 1991 to 2011

AbstractCities exhibit a rich and complex heterogeneity in people and activities. This poses a sizable challenge for planners when planning new neighbourhoods or the reconstruction of old ones as well as when considering the allocation of supply of and demand for amenities, e.g. kindergartens or health facilities. However, individual preferences may also exhibit common denominators that may provide structure to this heterogeneity. One such denominator is age. In this paper we introduce the concept of neighbourhood age, defined as the mean age of people living in exogenously defined squares of 1km2 in a city. We use highly disaggregated geocoded data to map how the spatial distribution of neighbourhood age changes over a 20‐year period from 1991 to 2011 in the city of Stockholm, Sweden. We then test the correlation between neighbourhood age and two categories of urban amenities: supply of local consumption amenities and distance to the city's central business district (CBD). The paper presents three main findings: First, neighbourhood age changes and polarizes significantly over the observed period, suggesting that different age groups are concentrating in different parts of the city. Second, there is a rejuvenation in the central parts of the city but also in more distant clusters of amenities. Third, over a long‐term perspective, the results suggest that local clusters of consumption amenities outside the inner city may become increasingly attractive to younger people. Our conclusion is that neighbourhood age and age‐related patterns over time provides a tool for planners to better understand the spatial distribution of age‐related demand.

Integrated model for renewable energy planning in Turkey

ABSTRACTAll economic sectors are associated with energy use; therefore, government organizations aim to supply sustainable energy for human needs and economic growth. In particular, increased environmental concerns of the public in Turkey have impacted policymaking for renewable energy (RE) management in Turkey. The primary objective for RE sources of the Turkish Ministry of Energy is to ensure that 30% of the share of electricity production is from RE resources in 2023. In this paper, the integrated multi-objective, multi-period linear programming model is presented to determine effective allocation of RE supply for seven different geographical regions in Turkey for the period of 2017 to 2024. The integrated model consists of two different stages. The first stage involves qualitative evaluations of RE sources for seven geographical regions. Analytical Hierarchy Process (AHP) is applied to determine criteria priorities and overall ratings of geographical regions across determined criteria for RE sources are computed. The second stage of the integrated model consists of a multi-objective, multi-period linear programming model. The proposed multi-objective linear programming model is coded in MPL (Mathematical Programming Language) and solved using the GUROBI 5.1.0 solver. The output of the integrated model presents the total supply amount of RE sources for geographical regions in planning period. The ε-constraints method is applied to compute the total supply amount of RE from geographical regions for the period of 2017 to 2024. In this study, a systematic decision-making model is generated to allocate renewable energy sources to the geographical regions. The presented model integrates qualitative evaluations and quantitative parameters of different geographical regions to determine the optimal supply amount of RE. The obtained results are consistent with the potential quantities of RE alternatives in geographical regions, regional specifications, and social requirements.

A robust stochastic decision-making model for inventory allocation of surgical supplies to reduce logistics costs in hospitals: A case study

In a hospital, surgical supplies can be stored in multiple locations, each of which has limited space and different associated costs. The locations include central storage, where items are retrieved to build a cart of supplies for each procedure; sterile storage adjacent to the operating rooms; and within the operating rooms themselves. In practice, the decision on allocating items to these locations is often based on the staff’s experience, rather than through optimization methods. In this research, we have identified the costs associated with each location to determine where each item should be stored and in what quantities. These costs include the cost of building the case cart, the cost of returning unused items to storage, the cost of picking items during a procedure, the cost of restocking and the cost of reviewing items to determine what needs to be replenished. Since the number of supplies required to perform a procedure is uncertain, we have developed a robust stochastic mixed-integer programming model to make the inventory allocation decision. The model also enables a hospital to assess the potential cost saving from optimization of the preference cards, which are used by surgeons to specify the requested supplies available on the case carts. The performance of the proposed model is evaluated through a case study. Three alternatives to the current configuration of the system are presented and reduction of inventory expenditure within each alternative is discussed. Finally, sensitivity analyses are performed to determine which cost parameters contribute to the model more significantly and how the model behaves against different levels of risk coefficient.

A Non-Dominated Sorting Genetic Algorithm Approach for Optimization of Multi-Objective Airport Gate Assignment Problem

Gate assignment problems (GAP) are one of the most substantial issues in airport operation. The ever-increasing demand producing high occupancy rates of gates, the potential financial loss from imbalances between supply and demand in congested airports, and the limited scope for expanding facilities present challenges that require an advanced methodology for optimal supply allocation. In principle, tackling GAP involves seeking to maintain an airport’s maximum capacity through the best possible allocation of resources (gates). There are a wide range of dependent and independent resources and limitations involved in the problem, adding to the complexity of GAP from both theoretical and practical perspectives. In this study, GAP is extended and mathematically formulated as a three-objective problem, taking into account all resources and restrictions, which can be directly linked to airport authorities’ multiple criteria decision-making processes. The preliminary goal of multi-objective formulation is to consider a wider scope, in which a higher number of objectives are simultaneously optimized, and thus to increase the practical efficiency of the final solution. The problem is solved by applying the second version of Non-dominated Sorting Genetic Algorithm (NSGA-II) as a parallel evolutionary optimization algorithm. Results illustrate that the proposed mathematical model could address most of the major criteria in the decision-making process in airport management in terms of passenger walking distances, robustness, and traditional costs. Moreover, the proposed solution approach shows promise in finding acceptable and plausible solutions compared with other multi-objective algorithms (BAT, PSO, ACO, and ABC).

Evaluating economic impacts of water deprivation in humanitarian relief distribution using stated choice experiments

This paper proposes a methodology to quantify the economic impacts of relief distribution after disasters based on discrete choice models. The research uses stated choice data about preferences for water purchases under different scenarios of deprivation. The economic impacts are estimated as the change in consumer surplus caused by the deprivation. The estimated models show that the social benefits for timely delivery of critical supplies are considerably larger than the market price under normal conditions. The findings suggest that disregarding the effects of deprivation, or incorrectly estimating the economic value of critical supplies such as water, is bound to produce a significant miss-allocation of resources. The results produced in this paper could be integrated into economically consistent humanitarian logistics mathematical models that could lead to optimal allocation and distribution of critical supplies.

A Linear Programming Approach to Water Allocation during a Drought

The economic impacts of a drought depend critically on how water is allocated to different users. Choices as to water allocation can often reflect wider economic policy, environmental, and social goals and constraints. This research applies a multi-objective linear programming input-output method to determine a suite of water supply allocations for different economic sectors in a drought. Using the UK as a case study, we develop estimates of the minimum potential economic impact associated with different water allocations under a range of climate and policy scenarios. Estimates of total impact range from −0.16% to −1.48% of total output depending on the drought scenarios tested. The approach offers the flexibility to set different policy objectives in terms of water allocations/restrictions, employment or a range of other objectives, including constraints to rebalance the economic system. In allowing for the inclusion of other economic, social, and environmental constraints, it provides a framework for policymakers to assess how water allocation decisions interact with other policy goals to determine the economic impacts of a drought. Challenging decisions about how to allocate water during a drought are likely to remain important in the future.

Implementation of emergency logistics distribution decision support system based on GIS

In order to reduce the damage of earthquake, a decision support system based on geographic information system (GIS) for the distribution and transportation of emergency materials after the earthquake is designed and implemented. The allocation and transportation of emergency supplies and transport routes were chosen as the study subjects. The occurrence of an earthquake disaster cannot be prevented. It often results in a large number of casualties, collapsed houses and damaged infrastructure. Therefore, timely assistance is an important measure to avoid further casualties. The results show that the system provides decision makers with a visual aid to the emergency materials distribution plan. Therefore, Matlab software programming can realize the model and its algorithm. The program is compiled into a platform callable file. The two submodule systems of emergency material allocation and transportation and the optimal path of vehicle transportation are combined. The optimization of material allocation model and the distribution system of emergency supplies after the earthquake are of theoretical and practical significance.

Modeling and optimization of nonlinear support stiffness of hydrostatic ram under the impact of cutting force

PurposeThis paper aims to study the nonlinear supporting performance of hydrostatic ram under the impact of cutting force and search for an optimal solution to improve its stiffness.Design/methodology/approachThe Reynolds equation was applied to resolve the carrying capability of a single oil pad numerically, and an iteration method was used to analyze the nonlinear supporting force and stiffness of a pair of oil pads placed face-to-face. The total offset of ram could be obtained after the displacement of aspectant oil pads was solved by the bisection method. From the comparison of the offset values of ram evaluated under different support conditions, the optimal solution was determined.FindingsIn this study, an optimized oil supply allocation, concluded as 1.16:0.84, is proposed to improve the performance of hydrostatic ram supporting structure.Originality/valueThe supporting performance of hydrostatic ram could be improved by appropriate allocation of oil supply without extra energy consumption.

An optimized multi agent-based modeling of smart rescue operation

Abstract. After the incidence of a disaster, a high demand for first-aid and a huge number of injured will emerge at the affected areas. In this paper, the optimum allocation of the medical assistance to the injured according to a multi-criteria decision making is performed by Multiplicatively Weighted Network Voronoi Diagram (MWNVD). For consideration of the allocation of the injured in the affected area to the appropriate hospitals using the MWNVD and decreasing the gap between the estimated and expected population in the MWNVDs, Particle Swarm Optimization (PSO) is applied to the MWNVDs. This paper proposes a multi agent-based modeling for incorporating the allocation of the medical supplies to the injured according to the generated Voronoi Diagrams of the PSO-MWNVD, wayfinding of emergency vehicles based on the minimum travel distance and time as well as using smart city facilities to expedite the rescue operation. In the proposed model, considering the priority of the injured for receiving the medical assistance, information transfer about the condition of the injured to the hospitals prior to ambulance arrival for providing appropriate treatment, updating of emergency vehicles route based on the blocked streets and etc. are optimized. The partial difference between the estimated and expected population for receiving the medical assistance in MWNVDs is computed as 37 %, while the PSO-MWNVD decreased the mentioned difference to 6 %. The relief operation time in the proposed model compared to another multi-agent rescue operation model, which uses MWNVD and does not have some facilities of the proposed model, is improved.

A model of labor supply, fixed costs and work schedules

To analyze labor supply allocations, we propose a three-dimensional labor supply framework that distinguishes between hours worked per day, days worked per week and workweeks. Individuals make labor supply choices given heterogeneous schedule-dependent fixed costs of work. The three margins are not perfect substitutes. Leisure on days not worked in a workweek has the largest weight in preferences, leisure on weeks off has the smallest weight. We use the model to analyze heterogeneous response to changes in fixed costs, schedule flexibility, and restrictions on weekly hours. Fixed costs of work affect response to each policy and determine associated losses.

Dayahead Electricity Pricing for a Heterogeneous Microgrid Under Arbitrary Utility and Cost Structures

In this paper, we consider the dayahead load and supply allocation problem in an electrical microgrid, which consists of a system manager, consumers, and suppliers. We do not impose any mathematical, operational, and economic assumptions on the consumers and suppliers other than restricting them to have a finite number of load and supply schedules. For example, a consumer can have arbitrary forms of flexibilities in its demand, such as deferrability or intermittence, and the user can choose from a set of predetermined load allocations that represent these flexibilities. Thus, our system model can accommodate heterogeneous user requirements and constraints in the same problem formulation. Under this system model, we formulate a welfare maximization problem, and derive a distributed primal–dual pricing algorithm. In our algorithm, the system manager generates prices for the load and supply allocations of the users, and given these prices, the users independently choose the schedules to their best operational and economic interests. Furthermore, the pricing algorithm incentivizes the users so that their decisions benefit the system performance, and it achieves the optimal load and supply allocation. Finally, we study the algorithm’s performance via simulations and demonstrate how the demand-side flexibilities are utilized for the system’s benefit.

Supply allocation: bi-level programming and differential evolution algorithm for Natural Disaster Relief

Delivering relief supplies to victims of natural disasters is a very complicated process faced with many challenges: damaged road network, high demand for various materials, scarcity of resources, multi-supply and multi-demand sites, limited transport capacity, etc. Traditionally, outreach maximization is the focus of emergency relief. In this research, we propose a bi-level programming model which takes the aforementioned challenges into full account. We consider two objectives: minimizing the distribution time, and maximizing the allocation fairness. Because the problem is NP-hard, we design an improved differential evolution (IDE) algorithm and numerically compare it with several conventional differential evolution algorithms, including CoDE, SaDE, JADE and JDE. We found that the proposed IDE outperforms the existing algorithms. The feasibility and validity of the proposed model and the IDE algorithm are verified by applying them to the 2008 Wenchuan earthquake in western China.

How Kabarole Hospital in western Uganda saved mothers’ lives through team-based problem-solving

Globally, the maternal mortality ratio (MMR) has declined in recent years, with 99% of maternal deaths occurring in developing countries. Most (75%) maternal deaths are due to severe bleeding, infections, pre-eclampsia and eclampsia, complications of obstructed and prolonged labour, and unsafe abortions.1 In 2013 in Uganda, the MMR was 360 per 100 000 live births compared with 210 per 100 000 live births globally.2 3 Kabarole Hospital, a private, not-for-profit general hospital in Kabarole District, was among the high-volume facilities prioritised by the United States Agency for International Development Applying Science to Strengthen and Improve Systems Project for a maternal, neonatal and child health (MNCH) quality improvement (QI) initiative in four districts of Western Uganda. The aim of the initiative was to contribute to a reduction of maternal and newborn mortality and morbidity by 50% in four districts of western Uganda including Kabarole Hospital. In May 2013, maternity staff at Kabarole Hospital were trained in QI methods applied to maternal and newborn care. In June 2013, a QI team of 10 members was formed, composed of the head of nursing (responsible for resource allocation), the medical officer assigned to the MNCH Department (provided technical oversight), the head of the MNCH Department (leader of the MNCH QI team), five midwives (provided MNCH services), the records officer (conducted data analysis and reporting), the laboratory technician (conducted laboratory investigations) and the storekeeper responsible for ordering allocation of drugs and supplies. This team started analysing their processes of care using data summarised from the maternity and antenatal registers and client charts onto a weekly summary sheet developed by the improvement team. The summary sheet contained variables such as number of clients seen per week, number of clients with obstetric complications, management of these complications and the outcome of each obstetric complication. The team recognised …

Optimizing oil spill cleanup efforts: A tactical approach and evaluation framework

Although anthropogenic oil spills vary in size, duration and severity, their broad impacts on complex social, economic and ecological systems can be significant. Questions pertaining to the operational challenges associated with the tactical allocation of human resources, cleanup equipment and supplies to areas impacted by a large spill are particularly salient when developing mitigation strategies for extreme oiling events. The purpose of this paper is to illustrate the application of advanced oil spill modeling techniques in combination with a developed mathematical model to spatially optimize the allocation of response crews and equipment for cleaning up an offshore oil spill. The results suggest that the detailed simulations and optimization model are a good first step in allowing both communities and emergency responders to proactively plan for extreme oiling events and develop response strategies that minimize the impacts of spills.

Fuzzy Goal Programming for Linear Facility Location-Allocation in a Supply Chain; The Case of Steel Industry

This paper presents a mathematical model for a facility location-allocation problem in order to design an integrated supply chain. We consider a supply chain including multiple suppliers, multiple products, multiple plants, multiple transportation alternatives and multiple customer zones. The problem is to determine a number and capacity level of plants, allocation of customers demand, and selection and order allocation of suppliers. A multi-objective mixed-integer linear programming (MOMILP) is presented with two conflicting objectives simultaneously. The first objective is to minimize the total costs of a supply chain including raw material costs, transportation costs and establishment costs of plants. The second objective function aims to minimize the total deterioration rate occurred by transportation alternatives. Finally, by applying the fuzzy goal programming, the model is solved as a single objective mixed-integer programming model. An experiment study shows that the proposed procedure can provide a promising result to design an efficient supply chain.

Adaptive multi-agent relief assessment and emergency response

The prediction of relief demand during disasters is an important operational task that should be performed before emergency response is initiated because such prediction enables the appropriate allocation and distribution of humanitarian supplies to affected zones. Current prediction methods appear to over or underestimate the calculation of relief demand—a problem that can be resolved by dispatching assessment vehicles to the affected zones of a region at the onset of a disaster. This study is aimed at facilitating the coordination of relief assessment and emergency response through the development of an adaptive multi-agent demand evaluation and demand-responsive model. Its main objective is to provide a model from which emergency response teams (ERTs) can obtain accurate information that will be used as basis by relief assessment teams (RATs) in effectively distributing humanitarian aid and conducting search and rescue operations. We propose a Markov decision process as a multi-agent assessment and response system, with reinforcement learning designed to ensure the integration of ERT and RAT operations. We use a coordination cluster system to coordinate ERT actors and use the proposed model to solve the problems occurring in a real-size network. Results show that the use of the model can improve emergency response operations and decrease death tolls.

Brain metabolism in health, aging, andneurodegeneration.

Brain cells normally respond adaptively to bioenergetic challenges resulting from ongoing activity in neuronal circuits, and from environmental energetic stressors such as food deprivation and physical exertion. At the cellular level, such adaptive responses include the "strengthening" of existing synapses, the formation of new synapses, and the production of new neurons from stem cells. At the molecular level, bioenergetic challenges result in the activation of transcription factors that induce the expression of proteins that bolster the resistance of neurons to the kinds of metabolic, oxidative, excitotoxic, and proteotoxic stresses involved in the pathogenesis of brain disorders including stroke, and Alzheimer's and Parkinson's diseases. Emerging findings suggest that lifestyles that include intermittent bioenergetic challenges, most notably exercise and dietary energy restriction, can increase the likelihood that the brain will function optimally and in the absence of disease throughout life. Here, we provide an overview of cellular and molecular mechanisms that regulate brain energy metabolism, how such mechanisms are altered during aging and in neurodegenerative disorders, and the potential applications to brain health and disease of interventions that engage pathways involved in neuronal adaptations to metabolic stress.

The Emergency Vehicle Routing Problem with Uncertain Demand under Sustainability Environments

The reasonable utilization of limited resources is critical to realize the sustainable developments. In the initial 72-h crucial rescue period after the disaster, emergency supplies have always been insufficient and the demands in the affected area have always been uncertain. In order to improve timeliness, utilization and sustainability of emergency service, the allocation of the emergency supplies and the emergency vehicle routes should be determined simultaneously. Assuming the uncertain demands follow normal distribution, an optimization model for the emergency vehicle routing, by considering the insufficient supplies and the uncertain demands, is developed. The objective function is applied to minimize the total costs, including the penalty costs induced by more or less supplies than the actual demands at all demand points, as well as the constraints of the time windows and vehicle load capacity taken into account. In more details, a solution method for the model, based on the genetic algorithm, is proposed, which solves the problem in two stages. A numerical example is presented to demonstrate the efficiency and validity of the proposed model and algorithm.

Optimization on long term supply allocation of Indonesian coal to domestic market

Indonesian coal production and consumption are expected to increase within the foreseeable period. The coal production is estimated to reach its peak in 2026 and subsequently will be decreased. Meanwhile the consumption will continually increase. The coal production that is greater than the domestic coal consumption raises a problem of resource allocation in Indonesian energy sector. In this study Indonesian coal resource allocation within the foreseeable period of 2014–2030 is solved using linear programming method. Linear programming optimizes the coal resource allocation by minimizing logistic cost which consist of coal purchasing cost and transportation cost from producer’s transshipment to consumers. The optimization results of resource allocation optimization indicate a change of Indonesian coal suppliers in the domestic market with respect to time to reach the minimum logistic cost. Sensitivity analysis indicates that the pattern of resource allocation will be more affected by coal purchasing cost than transportation cost. Also, the existence of the stock will not change the pattern of resource allocation but will affect the total logistic cost although not significant. Related to domestic market obligation (DMO), stipulation of coal basin priority as supplier of a consumer area with respect to time will ensure the fulfilment of DMO quota for the mining companies.

Winning the competition for supplier resources

PurposeThis paper examines the competition between buying firms for the supplier’s competitive resources. The purpose of this paper is to examine how indirect capabilities – the ability to access external resources – can help in obtaining preferential resource allocation from suppliers.Design/methodology/approachPartial least squares structural equation modeling is used to analyze data of 163 buying firms that assess preferential resource allocation from suppliers.FindingsTwo indirect capabilities (a buying firm’s selection capability and relational capability) positively influence the firm’s competitive advantage. These relations are significantly mediated by preferential resource allocation of suppliers. The impact of preferential resource allocation appeared stronger for manufacturing firms than for service firms.Research limitations/implicationsThis study’s data set represents the buyer’s assessment of suppliers’ resource allocation. Future research should aim for dyadic data for further validation. In addition, due to sample size limitations, this study’s data does not allow sector segmentation. A larger study that provides insights into segmentation is suggested for future research.Practical implicationsThe results inform managers about the relevance of the competition for supplier resources with rival firms that share suppliers, and the influence of this competition on firm competitiveness. Managers should not only focus on the supplier itself, but also on the capabilities of the supply chain management (SCM) function to recognize and integrate the supplier resources.Originality/valueThis study adds to the extended resource-based view literature by integrating the notion of supplier resource competition. In addition, the study shows the importance of indirect capabilities for obtaining preferential resource allocation from suppliers. Finally, the authors show the importance of separating between service and manufacturing when examining SCM practices.