Distribution network design is both strategic and tactical level problem that deals with the alternative locations selection, assignment of the customers to suppliers, and determining the product flow quantities among...
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Distribution network design is both strategic and tactical level problem that deals with the alternative locations selection, assignment of the customers to suppliers, and determining the product flow quantities among the echelons. There are various factors that affect decisions on this problem such as transportation mode availability, lead times, facility capacities, penalties, demand, unit costs, storing costs etc. In real life distribution networks, all the activities are realized in a dynamic environment and most of the above-mentioned factors include uncertainties. Obtaining an applicable solution for real life distribution network design requires a stochastic approach consideration. In this study, a two stage stochastic programming method is applied for modeling and solving the problem. In the first stage, the model tries to decide the location decision of the facilities of the network. In the second stage, the model aims to make a decision about the transported, stored and unmet demand quantities considering the demand and related handling cost. These demand and handling costs are obtained by combining the various scenarios. The proposed model is solved for a distribution network active in FMCG sector. The results are analyzed and compared with the deterministic solutions.
The main resources in software projects are human resources equipped with various skills, which makes software development a typical intelligence-intensive process. Therefore, effective human resource scheduling is in...
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The main resources in software projects are human resources equipped with various skills, which makes software development a typical intelligence-intensive process. Therefore, effective human resource scheduling is indispensable for the success of software projects. The aim of software project scheduling is to assign the right employee to the right activity at the right time. Uncertainty is inevitable in software development, which further complicates the scheduling of projects. We investigate the software project scheduling problem with uncertain activity durations (SPSP-UAD) and aim at obtaining effective scheduling policies for the problem. We present and transform a scenario-based non-linear chance-constrained stochastic programming model into an equivalent linear programming model. To solve the NP-hard SPSP-UAD efficiently, we develop a hybrid meta-heuristic TLBO-GA that combines the teaching-learning-based optimization algorithm (TLBO) and the genetic algorithm (GA). Our TLBO-GA is also equipped with some problem-specific operators, such as population initialization, rows exchange and local search. We use simulation to evaluate the scheduling policies obtained by our algorithms. Extensive computational experiments are conducted to evaluate the performance of our TLBO-GA in comparison to the exact solver CPLEX and four existing meta-heuristic algorithms. The comparative results reveal the effectiveness and efficiency of our TLBO-GA. Our TLBO-GA provides an extensible and adaptive automated scheduling decision support tool for the software project manager in the complex and uncertain software development environment.
This paper proposes a novel two-stage stochastic program to facilitate two-way energy trading (including up-regulation service) for an electric vehicle aggregator (EVA) in the electricity markets. Different from the e...
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This paper proposes a novel two-stage stochastic program to facilitate two-way energy trading (including up-regulation service) for an electric vehicle aggregator (EVA) in the electricity markets. Different from the existing works, the proposed model considers the randomness in the charging and discharging preferences of the EV owners on the operating day. Such uncertainty arises because the EV owners may randomly decide to participate on the operating day. Further, the EVA faces other uncertainties related to both the market-level data (i.e., electricity prices, real-time dispatch signal) and the fleet-level data (e.g., energy consumption of EVs, arrival and departure times) prior to participating in the electricity markets. The computation complexity of the proposed problem is tackled by constructing a small set of scenarios and applying the Benders decomposition algorithm. The performance of the proposed model is analyzed by comparing with the traditional model which ignores the randomness in the participation of the EV owners. The results show that the proposed model significantly outperforms the traditional model in terms of the total expected net cost incurred by the EVA. Further, we analyze the market-wise and fleet-wise performance under both the models through extensive simulations.
Waiting for elective procedures has become a major health concern in both rich and poor countries. The inadequate balance between the demand for and the supply of health services negatively affects the quality of life...
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Waiting for elective procedures has become a major health concern in both rich and poor countries. The inadequate balance between the demand for and the supply of health services negatively affects the quality of life, mortality, and government appraisal. This study presents the first mathematical framework shedding light on how much, when, and where to invest in health capacity to end waiting lists for elective surgeries. We model the healthcare system as a two-stage stochastic capacity expansion problem where government investment decisions are represented as a non-symmetric Nash bargaining solution. In particular, the model assesses the capacity requirements, optimal allocation, and corresponding financial investment per hospital, region, specialty, and year. We use the proposed approach to target Chile's elective surgical waiting lists (2021- 2031), considering patients' priorities, 10 regional health services, 24 hospitals, and 10 surgical specialties. We generate uncertain future demand scenarios using historical data (2012-2021) and 100 autoregressive integrated moving average prediction models. The results indicate that USD 3,331.677 million is necessary to end the waiting lists by 2031 and that the Nash approach provides a fair resource distribution with a 6% efficiency loss. Additionally, a smaller budget (USD 2,000 million) was identified as necessary to end the waiting lists in a longer planning horizon. Further analysis revealed the impact of investment in patient transfer and a decline in investment yield.
This study proposes a stochastic programming model for the transportation of emergency resource during the emergency response. Since it is difficult to predict the timing and magnitude of any disaster and its impact o...
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In this contribution we tackle the issue of portfolio management combining benchmarking and risk control. We propose a dynamic tracking error problem and we consider the problem of monitoring at discrete points the sh...
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Environmental issues and rapid load growth have led to increasing renewable resources infiltration toward achieving zero energy infrastructures;however, the uncertainty of such components increases the interactions be...
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Environmental issues and rapid load growth have led to increasing renewable resources infiltration toward achieving zero energy infrastructures;however, the uncertainty of such components increases the interactions between the local grid and energy markets for sustainable operation. In this study, day-ahead planning of a zero energy hub composed of wind turbine, photovoltaic, electric heat pump, boiler, chiller and storage units is optimized under the uncertainty of wind and solar units and energy market trading. In the designed structure, the required fuel for the local network is supplied through a power-to-hydrogen system to manage carbon emissions. In order to model the uncertainties and analyze the risk of decisions, a hybrid method consisting of the stochastic approach and information gap decision theory is applied. Furthermore, the impact of demand side elasticity for electrical, heating and cooling loads is evaluated. The results show that 10% of load participation significantly improves energy management actions and decreases operation costs by about 15.6%. The simulations also approve that the hybrid method handles the uncertainties under different conditions, where the risk-based cost functions change according to the operator attitude by about 522.44 $ for the ancillary parameter and scenario deviation equal to 10% and 20%, respectively.
We study a new variant of the classical lot sizing problem with uncertain demand where neither the planning horizon nor demands are known exactly. This situation arises in practice when customer demands arriving over ...
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We study a new variant of the classical lot sizing problem with uncertain demand where neither the planning horizon nor demands are known exactly. This situation arises in practice when customer demands arriving over time are confirmed rather lately during the transportation process. In terms of planning, this setting necessitates a rolling horizon procedure where the overall multistage problem is dissolved into a series of coupled snapshot problems under uncertainty. Depending on the available data and risk disposition, different approaches from online optimization, stochastic programming, and robust optimization are viable to model and solve the snapshot problems. We evaluate the impact of the selected methodology on the overall solution quality using a methodology-agnostic framework for multistage decision-making under uncertainty. We provide computational results on lot sizing within a rolling horizon regarding different types of uncertainty, solution approaches, and the value of available information about upcoming demands.
Soybean is one of the most important agricultural products in international markets due to its high global consumption. However, there are limited studies considering sustainability issues and global economic aspects ...
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Soybean is one of the most important agricultural products in international markets due to its high global consumption. However, there are limited studies considering sustainability issues and global economic aspects of the soybean supply chain design and optimization. Moreover, it is challenging to integrate many sustainability criteria into a single-stage optimization model for a soybean supply chain configuration. This research proposes a novel two-stage approach, integrating a multi-criteria decision-making technique and a new multi-objective optimization model to design and optimize a soybean supply chain network in Canada, considering sustainability and global factors. In Stage 1, several qualitative and quantitative sustainability criteria are considered to calculate the sustainability scores for the potential suppliers, using the Interval Type 2 Trapezoidal Best Worst Method. Then, the scores are used as input in Stage 2, where a new optimization model with four objective functions is formulated to design and optimize a soybean supply chain. The objective functions include maximization of total profit, created job opportunities, and suppliers' sustainability, and minimization of CO2 emissions. Then, the Pareto frontier is generated, using the augmented & epsilon;-constraint method which helps policymakers to make appropriate decisions. Furthermore, four cases are proposed and analyzed to assess the impacts of the objective functions on strategic and tactical decisions. The results show the importance of the presented method as an integrated approach, considering sustainability pillars in agri-food supply chains. In addition, the results indicate that the proposed stochastic multi-objective model can handle fluctuations in uncertain parameters.
To achieve the benefits as much as possible, it is required to identify the available PEV capacity and prepare scheduling plans based on that. The analysis revealed that the risk-based scheduling of the microgrid coul...
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