Design of hydrogen supply chains under demand uncertainty – a case study of passenger transport in Germany

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Anton Ochoa Bique ◽  
Leonardo K. K. Maia ◽  
Ignacio E. Grossmann ◽  
Edwin Zondervan
Keyword(s):  
Stochastic Optimization ◽  
Demand Uncertainty ◽  
Programming Model ◽  
Cost Minimization ◽  
Water Electrolysis ◽  
Scenario Tree ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Strong Impact ◽  
Hydrogen Supply

Abstract A strategy for the design of a hydrogen supply chain (HSC) network in Germany incorporating the uncertainty in the hydrogen demand is proposed. Based on univariate sensitivity analysis, uncertainty in hydrogen demand has a very strong impact on the overall system costs. Therefore we consider a scenario tree for a stochastic mixed integer linear programming model that incorporates the uncertainty in the hydrogen demand. The model consists of two configurations, which are analyzed and compared to each other according to production types: water electrolysis versus steam methane reforming. Each configuration has a cost minimization target. The concept of value of stochastic solution (VSS) is used to evaluate the stochastic optimization results and compare them to their deterministic counterpart. The VSS of each configuration shows significant benefits of a stochastic optimization approach for the model presented in this study, corresponding up to 26% of infrastructure investments savings.

scholarly journals An Optimization Approach to the Intermodal Transportation Network in Fruit Cold Chain, Considering Cost, Quality Degradation and Carbon Dioxide Footprint

2018 ◽  
Vol 25 (1) ◽  
pp. 61-69 ◽  
Author(s):  
Qianli Ma ◽  
Wenyuan Wang ◽  
Yun Peng ◽  
Xiangqun Song
Keyword(s):  
Carbon Dioxide ◽  
Programming Model ◽  
Cost Minimization ◽  
Network Planning ◽  
Cold Chain ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Intermodal Transportation ◽  
Quality Degradation ◽  
Trade Offs

AbstractThis model optimizes port hinterland intermodal refrigerated container flows, considering both cost and quality degradation, which is distinctive from the previous literature content in a way that it quantifies the influence of carbon dioxide (CO2) emission in different setting temperature on intermodal network planning. The primary contribution of this paper is that the model is beneficial not only to shippers and customers for the novel service design, but also offer, for policy-makers of the government, insights to develop inland transport infrastructures in consideration of intermodal transportation. The majority of models of multimodal system have been established with an objective of cost minimization for normal commodities. As the food quality is possible to be influenced by varying duration time required for the storage and transportation, and transportation accompanied with refrigeration producing more CO2emission, this paper aims to address cost minimization and quality degradation minimization within the constraint of CO2footprint. To achieve this aim, we put the quality degradation model in a mixed-integer linear programming model used for intermodal network planning for cold chain. The example of Dalian Port and Yingkou Port offer insight into trade-offs between transportation temperature and transport mode considering CO2footprint. Furthermore, the model can offer a useful reference for other regions with the demand for different imported food, which requires an uninterrupted cold chain during the transportation and storage.

scholarly journals Sustainable Scheduling of the Production in the Aluminum Furnace Hot Rolling Section with Uncertain Demand

2021 ◽  
Vol 13 (14) ◽  
pp. 7708
Author(s):  
Yiping Huang ◽  
Qin Yang ◽  
Jinfeng Liu ◽  
Xiao Li ◽  
Jie Zhang
Keyword(s):  
Hot Rolling ◽  
Production Scheduling ◽  
Demand Uncertainty ◽  
Flow Shop ◽  
Operation Time ◽  
Decision Time ◽  
Scenario Tree ◽  
Mixed Integer ◽  
Rolling Horizon ◽  
The Sustainable Development

In order to reduce the energy consumption of furnaces and save costs in the product delivery time, the focus of this paper is to discuss the uncertainty of demand in the rolling horizon and to globally optimize the sustainability of the production in the aluminum furnace hot rolling section in environmental and economic dimensions. First, the triples α/β/γ are used to describe the production scheduling in the aluminum furnace hot rolling section as the scheduling of flexible flow shop, satisfied to constraints of demand uncertainty, operation logic, operation time, capacity and demand, objectives of minimizing the residence time of the ingot in the furnace and minimizing the makespan. Second, on the basis of describing the uncertainty of demand in rolling horizon with the scenario tree, a multi-objective mixed integer linear programming (MILP) optimization model for sustainable production in the aluminum furnace hot rolling section is formulated. Finally, an aluminum alloy manufacturer is taken as an example to illustrate the proposed model. The computational results show that when the objective weight combination takes the value of α=0.7, β=0.3, the sustainability indicators of the environmental and economic dimensions can be optimized to the maximum extent possible at the same time. Increasingly, managerial suggestions associated with the trade-off between environmental and economic dimensions are presented. Scheduling in the rolling horizon can optimize the production process of the aluminum furnace hot rolling section globally, indicating that it is more conducive to the sustainable development of the environment and economic dimensions than scheduling in a single decision time period.

scholarly journals A two-stage stochastic optimization model for the retail multiskilled personnel scheduling problem: a k-chaining policy with $k \ge 2$

2021 ◽  
Vol 19 (1) ◽  
pp. 892-917
Author(s):  
Yessica Andrea Mercado ◽  
◽  
César Augusto Henao ◽  
Virginia I. González
Keyword(s):  
Stochastic Optimization ◽  
Service Sector ◽  
Deterministic Model ◽  
Programming Model ◽  
Simulated Data ◽  
Working Hours ◽  
Mixed Integer ◽  
Two Stage ◽  
Demand Variability

<abstract> <p>Considering an uncertain demand, this study evaluates the potential benefits of using a multiskilled workforce through a k-chaining policy with $k \ge 2$. For the service sector and, particularly for the retail industry, we initially propose a deterministic mixed-integer linear programming model that determines how many employees should be multiskilled, in which and how many departments they should be trained, and how their weekly working hours will be assigned. Then, the deterministic model is reformulated using a two-stage stochastic optimization (TSSO) model to explicitly incorporate the uncertain personnel demand. The methodology is tested for a case study using real and simulated data derived from a Chilean retail store. We also compare the TSSO approach solutions with the myopic approaches' solutions (i.e., zero and total multiskilling). The case study is oriented to answer two key questions: how much multiskilling to add and how to add it. Results show that TSSO approach solutions always report maximum reliability for all levels of demand variability considered. It was also observed that, for high levels of demand variability, a k-chaining policy with $k \ge 2$ is more cost-effective than a 2-chaining policy. Finally, to evaluate the conservatism level in the solutions reported by the TSSO approach, two truncation types in the probability density function (pdf) associated with the personnel demand were considered. Results show that, if the pdf is only truncated at zero (more conservative truncation) the levels of required multiskilling are higher than when the pdf is truncated at 5th and 95th percentiles (less conservative truncation).</p> </abstract>

scholarly journals Charging Network Planning for Electric Bus Cities: A Case Study of Shenzhen, China

2019 ◽  
Vol 11 (17) ◽  
pp. 4713 ◽  
Author(s):  
Yuping Lin ◽  
Kai Zhang ◽  
Zuo-Jun Max Shen ◽  
Lixin Miao
Keyword(s):  
Large Scale ◽  
Programming Model ◽  
Cost Minimization ◽  
Transportation Network ◽  
Power Grid ◽  
Mixed Integer ◽  
Charging Infrastructure ◽  
Second Order Cone ◽  
Charging Stations

In 2017, Shenzhen replaced all its buses with battery e-buses (electric buses) and has become the first all-e-bus city in the world. Systematic planning of the supporting charging infrastructure for the electrified bus transportation system is required. Considering the number of city e-buses and the land scarcity, large-scale bus charging stations were preferred and adopted by the city. Compared with other EVs (electric vehicles), e-buses have operational tasks and different charging behavior. Since large-scale electricity-consuming stations will result in an intense burden on the power grid, it is necessary to consider both the transportation network and the power grid when planning the charging infrastructure. A cost-minimization model to jointly determine the deployment of bus charging stations and a grid connection scheme was put forward, which is essentially a three-fold assignment model. The problem was formulated as a mixed-integer second-order cone programming model, and a “No R” algorithm was proposed to improve the computational speed further. Computational studies, including a case study of Shenzhen, were implemented and the impacts of EV technology advancements on the cost and the infrastructure layout were also investigated.

scholarly journals Application of Two-Phase Fuzzy Optimization Approach to Multiproduct Multistage Integrated Production Planning with Linguistic Preference under Uncertainty

2015 ◽  
Vol 2015 ◽  
pp. 1-20 ◽  
Author(s):  
Shan Lu ◽  
Hongye Su ◽  
Lian Xiao ◽  
Li Zhu
Keyword(s):  
Production Planning ◽  
Programming Model ◽  
Fuzzy Optimization ◽  
Service Level ◽  
Mixed Integer ◽  
Planning Problem ◽  
Optimization Approach ◽  
Two Phase ◽  
Steel Rolling ◽  
Solution Approach

This paper tackles the challenges for a production planning problem with linguistic preference on the objectives in an uncertain multiproduct multistage manufacturing environment. The uncertain sources are modelled by fuzzy sets and involve those induced by both the epistemic factors of process and external factors from customers and suppliers. A fuzzy multiobjective mixed integer programming model with different objective priorities is proposed to address the problem which attempts to simultaneously minimize the relevant operations cost and maximize the average safety stock holding level and the average service level. The epistemic and external uncertainty is simultaneously considered and formulated as flexible constraints. By defining the priority levels, a two-phase fuzzy optimization approach is used to manage the preference extent and convert the original model into an auxiliary crisp one. Then a novel interactive solution approach is proposed to solve this problem. An industrial case originating from a steel rolling plant is applied to implement the proposed approach. The numerical results demonstrate the efficiency and feasibility to handle the linguistic preference and provide a compromised solution in an uncertain environment.

A flexible mathematical model for crew pairing optimization to generate n-day pairings considering the risk of COVID-19: a real case study

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bahareh Shafipour-Omrani ◽  
Alireza Rashidi Komijan ◽  
Seyed Jafar Sadjadi ◽  
Kaveh Khalili-Damghani ◽  
Vahidreza Ghezavati
Keyword(s):  
Genetic Algorithm ◽  
Programming Model ◽  
Cost Minimization ◽  
Crew Scheduling ◽  
Mixed Integer ◽  
Medium Size ◽  
Content Type ◽  
Solution Approach ◽  
Proposed Model ◽  
Crew Pairing

PurposeOne of the main advantages of the proposed model is that it is flexible to generate n-day pairings simultaneously. It means that, despite previous researches, one-day to n-day pairings can be generated in a single model. The flexibility in generating parings causes that the proposed model leads to better solutions compared to existing models. Another advantage of the model is minimizing the risk of COVID-19 by limitation of daily flights as well as elapsed time minimization. As airports are among high risk places in COVID-19 pandemic, minimization of infection risk is considered in this model for the first time. Genetic algorithm is used as the solution approach, and its efficiency is compared to GAMS in small and medium-size problems.Design/methodology/approachOne of the most complex issues in airlines is crew scheduling problem which is divided into two subproblems: crew pairing problem (CPP) and crew rostering problem (CRP). Generating crew pairings is a tremendous and exhausting task as millions of pairings may be generated for an airline. Moreover, crew cost has the largest share in total cost of airlines after fuel cost. As a result, crew scheduling with the aim of cost minimization is one of the most important issues in airlines. In this paper, a new bi-objective mixed integer programming model is proposed to generate pairings in such a way that deadhead cost, crew cost and the risk of COVID-19 are minimized.FindingsThe proposed model is applied for domestic flights of Iran Air airline. The results of the study indicate that genetic algorithm solutions have only 0.414 and 0.380 gap on average to optimum values of the first and the second objective functions, respectively. Due to the flexibility of the proposed model, it improves solutions resulted from existing models with fixed-duty pairings. Crew cost is decreased by 12.82, 24.72, 4.05 and 14.86% compared to one-duty to four-duty models. In detail, crew salary is improved by 12.85, 24.64, 4.07 and 14.91% and deadhead cost is decreased by 11.87, 26.98, 3.27, and 13.35% compared to one-duty to four-duty models, respectively.Originality/valueThe authors confirm that it is an original paper, has not been published elsewhere and is not currently under consideration of any other journal.

Semi-liner Shipping Service Design

2020 ◽  
Vol 54 (5) ◽  
pp. 1288-1306 ◽  
Author(s):  
Yadong Wang ◽  
Qiang Meng
Keyword(s):  
Decomposition Method ◽  
Demand Uncertainty ◽  
Programming Model ◽  
Service Design ◽  
Decomposition Methods ◽  
Liner Shipping ◽  
Branch And Cut ◽  
Mixed Integer ◽  
Acceleration Techniques ◽  
Stochastic Mixed Integer Programming

Semi-liner shipping transports various types of cargo, such as containers, break-bulk cargo, and heavy-lift project cargo, between different ports. Similar to liner shipping, semi-liner shipping publishes shipping routes for customers’ reference. However, it does not strictly follow the published route and usually makes some adjustments for each ship voyage by adding some port calls to transport more cargo considering the excess ship capacity. This study first proposes the semi-liner shipping service design (SLSSD) problem that aims to maximize the shipping profit by determining a shipping route subject to the potential adjustments. The proposed SLSSD problem is subsequently formulated as a two-stage stochastic mixed integer programming model with integer recourse variables. The first stage determines the visit sequence of a set of compulsory ports under shipping demand uncertainty. The second stage decides whether to add or remove some ports in the route in view of the realized shipping demand for each ship voyage. To effectively solve the model, two decomposition methods are developed, namely, the stage decomposition method and the scenario decomposition method, that decompose the problem by stage and demand scenario, respectively. In addition, two novel acceleration techniques are also provided to expedite the scenario decomposition method. Numerical experiments reveal satisfactory efficiency of these two methods to solve the semi-liner shipping service design problem, especially the scenario decomposition method, which is generally better than the stage decomposition method and can be thousands of times faster than the classic branch-and-cut algorithm.

scholarly journals Planning for urban water supply–demand portfolio using a hybrid robust stochastic optimization approach

2020 ◽  
Vol 20 (8) ◽  
pp. 3433-3448
Author(s):  
Maryam Shabani ◽  
Naser Shams Gharneh ◽  
Seyed Taghi Akhavan Niaki
Keyword(s):  
Water Management ◽  
Water Supply ◽  
Stochastic Optimization ◽  
Cost Minimization ◽  
Supply And Demand ◽  
Demand Management ◽  
Water Shortage ◽  
Optimization Approach ◽  
Urban Water ◽  
Management Options

Abstract Water management and preventing water shortage require accurate planning with attention to the importance of urban water. The problems ahead include the increase in demand and reduction in water supply resources due to factors that cause uncertainties and the high cost of water supply infrastructures. Most studies in urban water management consider only a single criterion. However, in this research, two objective functions, namely cost minimization and per capita water consumption maximization, were used simultaneously. A portfolio approach based on the balance of water supply and demand was developed taking uncertainty into account. Then, the problem was solved using a hybrid robust–stochastic optimization approach. The results showed the selected supply augmentation and demand management options in each stage under dry, normal, and wet year scenarios.

scholarly journals A Stochastic Optimization Approach to the Design of Shale Gas/Oil Wastewater Treatment Systems with Multiple Energy Sources under Uncertainty

2019 ◽  
Vol 11 (18) ◽  
pp. 4865 ◽  
Author(s):  
Al-Aboosi ◽  
El-Halwagi
Keyword(s):  
Water Management ◽  
Wastewater Treatment ◽  
Solar Energy ◽  
Shale Gas ◽  
Programming Model ◽  
Gas Production ◽  
Mixed Integer ◽  
Management Systems ◽  
Optimization Approach ◽  
Operational Conditions

The production of shale gas and oil is associated with the generation of substantial amounts of wastewater. With the growing emphasis on sustainable development, the energy sector has been intensifying efforts to manage water resources while diversifying the energy portfolio used in treating wastewater to include fossil and renewable energy. The nexus of water and energy introduces complexity in the optimization of the water management systems. Furthermore, the uncertainty in the data for energy (e.g., solar intensity) and cost (e.g., price fluctuation) introduce additional complexities. The objective of this work is to develop a novel framework for the optimizing wastewater treatment and water-management systems in shale gas production while incorporating fossil and solar energy and accounting for uncertainties. Solar energy is utilized via collection, recovery, storage, and dispatch of heat. Heat integration with an adjacent industrial facility is considered. Additionally, electric power production is intended to supply a reverse osmosis (RO) plant and the local electric grid. The optimization problem is formulated as a multi-scenario mixed integer non-linear programming (MINLP) problem that is a deterministic equivalent of a two-stage stochastic programming model for handling uncertainty in operational conditions through a finite set of scenarios. The results show the capability of the system to address water-energy nexus problems in shale gas production based on the system’s economic and environmental merits. A case study for Eagle Ford Basin in Texas is solved by enabling effective water treatment and energy management strategies to attain the maximum annual profit of the entire system while achieving minimum environmental impact.

scholarly journals A low complexity heuristic to solve a learning objects recommendation problem

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Samuel Henrique Falci ◽  
Fabiano Azevedo Dorça ◽  
Alessandro Vivas Andrade ◽  
Daniel Henrique Mourão Falci
Keyword(s):  
Programming Model ◽  
Learning Experience ◽  
Linear Optimization ◽  
Computational Cost ◽  
Low Complexity ◽  
Learning Objects ◽  
Individual Characteristics ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Discussion Forums

Abstract The recommendation of learning objects in virtual learning environments has become the focus of research to improve online learning experience. Several approaches have been presented in an attempt to model the individual characteristics of the students and offer learning objects that best suit their particularities. Most of them, though, are impractical in real-world scenarios due to the high computational cost as a huge number of repositories offering learning objects such as Youtube, Wikipedia, Stackoverflow, Github, discussion forums, social networks and many others are available and each has a large amount of learning objects that can be retrieved. In this work, we propose a low complexity heuristic to solve this problem, comparing it to a classical mixed-integer linear programming model and classical genetic algorithm in varying dataset sizes that contain from 2000 to 1360000 learning objects. Performance and optimality were analyzed. The results showed that the proposed technique was only slightly suboptimal, while its computational cost was considerably smaller than the one presented by the linear optimization approach.

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