Multi-Period Optimization Model for Siting Capacitated Intermodal Facilities

2020 ◽  
Vol 2674 (7) ◽  
pp. 135-147
Author(s):  
Vishal Badyal ◽  
William G. Ferrell ◽  
Nathan Huynh ◽  
Bhavya Padmanabhan
Keyword(s):  
South Carolina ◽  
Network Performance ◽  
Programming Model ◽  
Sensitivity Analyses ◽  
Mixed Integer ◽  
Network Cost ◽  
Volume Mode ◽  
Increasing Demand ◽  
The Impact ◽  
Total Network

The objective of this study is to design an intermodal transport network considering multiple planning periods and accounting for product volume, mode, budget, and inventory at intermodal terminals (IMTs). A mixed integer linear programming model is developed. An experimental study is conducted for the State of South Carolina using the Freight Analysis Framework Version 4.5 (FAF4) dataset. Sensitivity analyses are performed to study the impact of budget, the maximum number of IMTs allowed, and increasing demand on the intermodal network design. The experimental results indicate that Columbia as an IMT location has a significantly effects on the total network cost and intermodal shipping share. Increasing the budget and number of IMTs allowed improved the network performance non-linearly.

Operator- and user-based rebalancing strategy for bike-sharing systems

2021 ◽  
pp. 1-12
Author(s):  
Peng-Sheng You ◽  
Yi-Chih Hsieh
Keyword(s):  
Dynamic Pricing ◽  
Programming Model ◽  
Supply And Demand ◽  
Planning Horizon ◽  
Sensitivity Analyses ◽  
Mixed Integer ◽  
Time Problem ◽  
Bike Sharing ◽  
Total Profit ◽  
The Impact

Leveraging their networks, bike rental companies usually provide customers with services for renting and returning bikes at different bike stations. Over time, however, rental networks may encounter problems with unbalanced bike stocks. The potential imbalance between supply and demand at bike stations may result in lost sales for stations with relatively high demand and underutilization for stations with relatively low demand. This paper proposed a constrained mixed-integer programming model that uses operator-based redistribution and user-based price approach to rebalance bikes across bike stations. This paper aims to maximize total profit over a planning horizon by determining operator-based bike transfers and dynamic pricing. The proposed model is a non-deterministic polynomial-time problem, and thus, a heuristic was developed based on linear programming and evolutionary computation to perform model solving. Numerical experiments reveal that the proposed method performed better than Lingo, a well-known commercial software. Sensitivity analyses were also performed to investigate the impact of changes in system parameters on computational results.

scholarly journals Efficient Operative Cost Reduction in Distribution Grids Considering the Optimal Placement and Sizing of D-STATCOMs Using a Discrete-Continuous VSA

2021 ◽  
Vol 11 (5) ◽  
pp. 2175
Author(s):  
Oscar Danilo Montoya ◽  
Walter Gil-González ◽  
Jesus C. Hernández
Keyword(s):  
Objective Function ◽  
Reactive Power ◽  
Programming Model ◽  
Search Algorithm ◽  
Solution Space ◽  
Distribution Networks ◽  
Optimal Placement ◽  
Mixed Integer ◽  
Solution Vector ◽  
The Impact

The problem of reactive power compensation in electric distribution networks is addressed in this research paper from the point of view of the combinatorial optimization using a new discrete-continuous version of the vortex search algorithm (DCVSA). To explore and exploit the solution space, a discrete-continuous codification of the solution vector is proposed, where the discrete part determines the nodes where the distribution static compensator (D-STATCOM) will be installed, and the continuous part of the codification determines the optimal sizes of the D-STATCOMs. The main advantage of such codification is that the mixed-integer nonlinear programming model (MINLP) that represents the problem of optimal placement and sizing of the D-STATCOMs in distribution networks only requires a classical power flow method to evaluate the objective function, which implies that it can be implemented in any programming language. The objective function is the total costs of the grid power losses and the annualized investment costs in D-STATCOMs. In addition, to include the impact of the daily load variations, the active and reactive power demand curves are included in the optimization model. Numerical results in two radial test feeders with 33 and 69 buses demonstrate that the proposed DCVSA can solve the MINLP model with best results when compared with the MINLP solvers available in the GAMS software. All the simulations are implemented in MATLAB software using its programming environment.

scholarly journals A Mixed Integer Linear Programming Method for Simultaneous Multi-Periodic Train Timetabling and Routing on a High-Speed Rail Network

2020 ◽  
Vol 12 (3) ◽  
pp. 1131
Author(s):  
Wenliang Zhou ◽  
Xiaorong You ◽  
Wenzhuang Fan
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
High Speed ◽  
Programming Model ◽  
Mixed Integer ◽  
High Speed Rail ◽  
Train Timetabling ◽  
Train Timetable ◽  
The Impact

To avoid conflicts among trains at stations and provide passengers with a periodic train timetable to improve service level, this paper mainly focuses on the problem of multi-periodic train timetabling and routing by optimizing the routes of trains at stations and their entering time and leaving time on each chosen arrival–departure track at each visited station. Based on the constructed directed graph, including unidirectional and bidirectional tracks at stations and in sections, a mixed integer linear programming model with the goal of minimizing the total travel time of trains is formulated. Then, a strategy is introduced to reduce the number of constraints for improving the solved efficiency of the model. Finally, the performance, stability and practicability of the proposed method, as well as the impact of some main factors on the model are analyzed by numerous instances on both a constructed railway network and Guang-Zhu inter-city railway; they are solved using the commercial solver WebSphere ILOG CPLEX (International Business Machines Corporation, New York, NY, USA). Experimental results show that integrating multi-periodic train timetabling and routing can be conducive to improving the quality of a train timetable. Hence, good economic and social benefits for high-speed rail can be achieved, thus, further contributing to the sustained development of both high-speed railway systems and society.

scholarly journals Soft Computing Methodology to Optimize the Integrated Dynamic Models of Cellular Manufacturing Systems in a Robust Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Amir-Mohammad Golmohammadi ◽  
Hasan Rasay ◽  
Zaynab Akhoundpour Amiri ◽  
Maryam Solgi ◽  
Negar Balajeh
Keyword(s):  
Machine Learning ◽  
Programming Model ◽  
Facility Layout ◽  
Cell Formation ◽  
Layout Design ◽  
Metaheuristic Algorithms ◽  
Sensitivity Analyses ◽  
Mixed Integer ◽  
Facility Layout Design ◽  
Proposed Model

Machine learning, neural networks, and metaheuristic algorithms are relatively new subjects, closely related to each other: learning is somehow an intrinsic part of all of them. On the other hand, cell formation (CF) and facility layout design are the two fundamental steps in the CMS implementation. To get a successful CMS design, addressing the interrelated decisions simultaneously is important. In this article, a new nonlinear mixed-integer programming model is presented which comprehensively considers solving the integrated dynamic cell formation and inter/intracell layouts in continuous space. In the proposed model, cells are configured in flexible shapes during the planning horizon considering cell capacity in each period. This study considers the exact information about facility layout design and material handling cost. The proposed model is an NP-hard mixed-integer nonlinear programming model. To optimize the proposed problem, first, three metaheuristic algorithms, that is, Genetic Algorithm (GA), Keshtel Algorithm (KA), and Red Deer Algorithm (RDA), are employed. Then, to further improve the quality of the solutions, using machine learning approaches and combining the results of the aforementioned algorithms, a new metaheuristic algorithm is proposed. Numerical examples, sensitivity analyses, and comparisons of the performances of the algorithms are conducted.

scholarly journals Impact of demand response on BIPV and district multi-energy systems design in Singapore and Switzerland

2021 ◽  
Vol 2042 (1) ◽  
pp. 012096
Author(s):  
Christoph Waibel ◽  
Shanshan Hsieh ◽  
Arno Schlüter
Keyword(s):  
Demand Response ◽  
Programming Model ◽  
Systems Design ◽  
Energy Systems ◽  
Mixed Integer ◽  
Peak Shaving ◽  
Climate Conditions ◽  
Building Integrated Photovoltaics ◽  
Energy Hub ◽  
The Impact

Abstract This paper demonstrates the impact of demand response (DR) on optimal multi-energy systems (MES) design with building integrated photovoltaics (BIPV) on roofs and façades. Building loads and solar potentials are assessed using bottom-up models; the MES design is determined using a Mixed-Integer Linear Programming model (energy hub). A mixed-use district of 170,000 m2 floor area including office, residential, retail, education, etc. is studied under current and future climate conditions in Switzerland and Singapore. Our findings are consistent with previous studies, which indicate that DR generally leads to smaller system capacities due to peak shaving. We further show that in both the Swiss and Singapore context, cost and emissions of the MES can be reduced significantly with DR. Applying DR, the optimal area for BIPV placement increases only marginally for Singapore (~1%), whereas for Switzerland, the area is even reduced by 2-8%, depending on the carbon target. In conclusion, depending on the context, DR can have a noticeable impact on optimal MES and BIPV capacities and should thus be considered in the design of future, energy efficient districts.

e-Supply Network

2011 ◽  
pp. 250-268
Author(s):  
Shima Mohebbi ◽  
Rasoul Shafaei ◽  
Namjae Cho
Keyword(s):  
Capacity Utilization ◽  
Coordination Mechanism ◽  
Global Network ◽  
Supply Network ◽  
Agent Based ◽  
Network Cost ◽  
Network Coordination ◽  
Supply Capacity ◽  
The Impact ◽  
Total Network

The automation of negotiation among buyer-supplier-supplier triad is an important policy in e-supply network coordination (e-SNC). In addition to the buyer-supplier coordination advantages, a further coordination among suppliers is also highly important in order to maximize the network supply capacity utilization especially when the suppliers are geographically decentralized. This chapter focuses on the impact of suppliers’ coordination in a network where the coordination among buyers and suppliers is in place. The proposed agent-based model is composed of two negotiator agents, one monitor agent, and one coordinator agent. The model begins with buyer-supplier negotiation and coordination mechanism adopted from the approach developed by Mohebbi and Shafaei (2010). Then, the suppliers’ coordination mechanism is developed to investigate the interaction among suppliers and evaluate the subsequent benefit obtained in the global network. The efficiency of the proposed approach is evaluated using a simulation model. The results demonstrate that in a network where the coordination among buyers and suppliers is in place, a further coordination among suppliers leads to the reduction of total network cost.

A mixed procurement model for humanitarian relief chains

2019 ◽  
Vol 10 (1) ◽  
pp. 45-74 ◽  
Author(s):  
Mojtaba Aghajani ◽  
S. Ali Torabi
Keyword(s):  
Programming Model ◽  
Dynamic Environment ◽  
Sensitivity Analyses ◽  
Mixed Integer ◽  
Critical Parameters ◽  
Content Type ◽  
Procurement Process ◽  
Non Linear Programming ◽  
Supply Policy ◽  
Post Disaster

Purpose The purpose of this paper is to improve the relief procurement process as one of the most important elements of humanitarian logistics. For doing so, a novel two-round decision model is developed to capture the dynamic nature of the relief procurement process by allowing demand updating. The model accounts for the supply priority of items at response phase as well. Design/methodology/approach A mixed procurement/supply policy is developed through a mathematical model, which includes spot market procurement and a novel procurement auction mechanism combining the concepts of multi-attribute and combinatorial reverse auctions. The model is of bi-objective mixed-integer non-linear programming type, which is solved through the weighted augmented e-constraint method. A case study is also provided to illustrate the applicability of the model. Findings This study demonstrates the ability of proposed approach to model post-disaster procurement which considers the dynamic environment of the relief logistics. The sensitivity analyses provide useful managerial insights for decision makers by studying the impacts of critical parameters on the solutions. Originality/value This paper proposes a novel reverse auction framework for relief procurement in the form of a multi-attribute combinatorial auction. Also, to deal with dynamic environment in the post-disaster procurement, a novel two-period programming model with demand updating is proposed. Finally, by considering the priority of relief items and model’s applicability in the setting of relief logistics, post-disaster horizon is divided into three periods and a mixed procurement strategy is developed to determine an appropriate supply policy for each period.

scholarly journals Centralized Ambulance Diversion Policy Using Rolling-Horizon Optimization Framework to Minimize Patient Tardiness

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 266
Author(s):  
Sohye Baek ◽  
Young Hoon Lee ◽  
Seong Hyeon Park
Keyword(s):  
Programming Model ◽  
Mixed Integer ◽  
Time Interval ◽  
Rolling Horizon ◽  
Decentralized Optimization ◽  
Hospital System ◽  
Ambulance Diversion ◽  
Optimization Framework ◽  
The Impact ◽  
Rolling Horizon Optimization

Ambulance diversion (AD) is a common method for reducing crowdedness of emergency departments by diverting ambulance-transported patients to a neighboring hospital. In a multi-hospital system, the AD of one hospital increases the neighboring hospital’s congestion. This should be carefully considered for minimizing patients’ tardiness in the entire multi-hospital system. Therefore, this paper proposes a centralized AD policy based on a rolling-horizon optimization framework. It is an iterative methodology for coping with uncertainty, which first solves the centralized optimization model formulated as a mixed-integer linear programming model at each discretized time, and then moves forward for the time interval reflecting the realized uncertainty. Furthermore, the decentralized optimization, decentralized priority, and No-AD models are presented for practical application, which can also show the impact of using the following three factors: centralization, mathematical model, and AD strategy. The numerical experiments conducted based on the historical data of Seoul, South Korea, for 2017, show that the centralized AD policy outperforms the other three policies by 30%, 37%, and 44%, respectively, and that all three factors contribute to reducing patients’ tardiness. The proposed policy yields an efficient centralized AD management strategy, which can improve the local healthcare system with active coordination between hospitals.

scholarly journals A Multimodal Passenger-and-Package Sharing Network for Urban Logistics

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Yuxiong Ji ◽  
Yujing Zheng ◽  
Jizhou Zhao ◽  
Yu Shen ◽  
Yuchuan Du
Keyword(s):  
Network Performance ◽  
Programming Model ◽  
Mixed Integer ◽  
Covering Problem ◽  
Hub Location ◽  
Real World Data ◽  
Hub And Spoke ◽  
Taxi Drivers ◽  
Optimal Value ◽  
Peripheral Areas

This paper envisions a multimodal passenger-and-package sharing (PPS) network for urban logistics integrating metro, taxi, and truck. A hub-and-spoke structure is designed including hubs located at metro stations and service stores connected to the hubs. Packages are transported by metro on backbone links between the hubs and are carried by taxis or trucks between service stores and hubs, depending on the unit costs of these two modes and capacity constraint of the taxi. A mixed integer linear programming model for hub location problems—fusing the multiassignment p-hub median problem without capacity constraints and the capacitated multiassignment p-hub covering problem—is formulated to optimize the multimodal PPS network. The model is implemented based on the real-world data in Shanghai (China) under a series of scenarios to evaluate the network performance from two perspectives: the number of hubs and the proportion of taxi drivers who are willing to carry packages. The scenarios show that with increased number of hubs, the spatial distribution of hubs disperses from the city center to peripheral areas and more areas can be serviced by taxis. There is, however, a trade-off between the operation cost saved by taxis and the establishment cost of an extra hub. The analysis also presents that if the proportion of taxis willing to carry packages associates with the incentive payments to taxi drivers, an optimal value of incentives exists, by balancing the operation costs of taxis and trucks.

Sign in / Sign up

Export Citation Format

Share Document