scholarly journals Optimization of Collection and Consolidation Operations in Cross-Border Multi-modal Distribution Networks

2021 ◽  
Author(s):  
Gulcin Ermis ◽  
Francesco Alesiani ◽  
Konstantinos Gkiotsalitis
Keyword(s):  
Real Time ◽  
Bin Packing ◽  
Solution Method ◽  
Distribution Networks ◽  
Mixed Integer ◽  
Due Dates ◽  
Matching Problem ◽  
Bipartite Matching ◽  
Mip Model ◽  
Dynamic Objects

This study introduces a model to solve a dynamic network optimization model on a heterogeneous graph. We use this model to optimize the collection and consolidation operations on a cross-country multi-modal distribution network. The model's dynamic objects are trucks, trailers, orders, unvisited collection and customs check points. Information about dynamic objects is extracted from a real-time database. The model's static objects include objects that are known in advance, such as warehouses. The constraints of the problem include due dates, vehicle capacity, availability of vehicles, and precedence constraints of visiting locations. We propose a mixed-integer programming model and provide a solution using matheuristics. We decompose the master MIP model into subproblems that can be solved to optimality with LP solvers. We also reduce the graph complexity by variable fixing due to optimized subproblems or by bounding the maximum number of paths to be selected due to the solutions of priority-based bin packing algorithms. Finally, we convert the resulting problem into a bipartite matching problem by expanding the graph nodes which can then be solved in polynomial time. We implement our solution method on real-time data retrieved from the tracking system of a third-party logistics company. Experiments show that our solution method significantly outperforms other heuristics in terms of solution quality which is measured with respect to lateness, empty kilometers traveled, travel times, number of required/used vehicles, load factors, and ratio of served orders.

scholarly journals Optimization of Collection and Consolidation Operations in Cross-Border Multi-modal Distribution Networks

2021 ◽  
Author(s):  
Gulcin Ermis ◽  
Francesco Alesiani ◽  
Konstantinos Gkiotsalitis
Keyword(s):  
Real Time ◽  
Bin Packing ◽  
Solution Method ◽  
Distribution Networks ◽  
Mixed Integer ◽  
Due Dates ◽  
Matching Problem ◽  
Bipartite Matching ◽  
Mip Model ◽  
Dynamic Objects

This study introduces a model to solve a dynamic network optimization model on a heterogeneous graph. We use this model to optimize the collection and consolidation operations on a cross-country multi-modal distribution network. The model's dynamic objects are trucks, trailers, orders, unvisited collection and customs check points. Information about dynamic objects is extracted from a real-time database. The model's static objects include objects that are known in advance, such as warehouses. The constraints of the problem include due dates, vehicle capacity, availability of vehicles, and precedence constraints of visiting locations. We propose a mixed-integer programming model and provide a solution using matheuristics. We decompose the master MIP model into subproblems that can be solved to optimality with LP solvers. We also reduce the graph complexity by variable fixing due to optimized subproblems or by bounding the maximum number of paths to be selected due to the solutions of priority-based bin packing algorithms. Finally, we convert the resulting problem into a bipartite matching problem by expanding the graph nodes which can then be solved in polynomial time. We implement our solution method on real-time data retrieved from the tracking system of a third-party logistics company. Experiments show that our solution method significantly outperforms other heuristics in terms of solution quality which is measured with respect to lateness, empty kilometers traveled, travel times, number of required/used vehicles, load factors, and ratio of served orders.

scholarly journals Improved Approaches to Minimize the Makespan on Single-Machine Scheduling with Periodic Preventive Maintenance Activities

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Hongming Zhou ◽  
Ya-Chih Tsai ◽  
Ful-Chiang Wu ◽  
Shenquan Huang ◽  
Fuh-Der Chou
Keyword(s):  
Single Machine ◽  
Preventive Maintenance ◽  
Bin Packing ◽  
Heuristic Algorithms ◽  
Machine Scheduling ◽  
Single Machine Scheduling ◽  
Mixed Integer ◽  
Mip Model ◽  
Periodic Preventive Maintenance ◽  
Maintenance Activities

This paper addresses a single-machine scheduling problem with periodic preventive maintenance activities that are predeterministic so that the machine is not available all the time, and jobs have to be processed between two consecutive maintenance periods. We propose a mixed integer programming (MIP) model and two heuristics to minimize the makespan. With more constraints in our model, the model is more efficient than the recent model of Perez-Gonzalez and Framinan , and our model could solve problems with up to fifty jobs. Two heuristic algorithms, namely, H (MW) and H (LB∗), are also proposed, in which two bin-packing policies of the minimum waste and minimum lower bound are used, respectively. Furthermore, we also proposed an improvement procedure. The results showed that the heuristic H (MW) outperformed other heuristics of the paper, indicating that the bin-packing policy of the minimum waste is more effective than well-known ones such as full batch and best fit. Additionally, all the heuristic algorithms addressed in this paper combined with the improvement procedure could achieve a similar and high quality of solutions with a very tiny increase in computational expense.

Minimizing Total Weighted Tardiness on Parallel Batch-Processing Machine Scheduling Problems with Varying Machine Capacities

2011 ◽  
Vol 110-116 ◽  
pp. 3906-3913 ◽  
Author(s):  
Fuh Der Chou ◽  
Hui Mei Wang
Keyword(s):  
Heuristic Algorithms ◽  
Batch Processing ◽  
Total Weighted Tardiness ◽  
Mixed Integer ◽  
Due Dates ◽  
Weighted Tardiness ◽  
Processing Machine ◽  
Release Times ◽  
Mip Model ◽  
Batch Processing Machine

This paper extends the study of Mathirajan et al. (Minimizing total weighted tardiness on a batch-processing machine with non-agreeable release times and due dates. Int. J. Adv. Manuf. Technol., 2010, doi: 10.1007/s00170-009-2342-y) to parallel batch-processing machine problems because these have not been examined to date. For the problem concerning compatible product families, job release times, non-identical job sizes, and varying machine capacities, we propose a mixed integer programming (MIP) model, and a number of simple dispatch-based heuristic and simulated annealing (SA) algorithms. Computational results revealed that the proposed SA is capable of obtaining similar solutions acquired by MIP within a short time. The SA algorithms outperform other heuristic algorithms with respect to solution quality.

Pricing Carpool Rides Based on Schedule Displacement

2020 ◽  
Vol 54 (4) ◽  
pp. 1134-1152
Author(s):  
Ruijie Li ◽  
Yu (Marco) Nie ◽  
Xiaobo Liu
Keyword(s):  
A Priori ◽  
Operating Cost ◽  
Integer Program ◽  
Mixed Integer ◽  
Absolute Difference ◽  
Mixed Integer Program ◽  
Matching Problem ◽  
Pricing Policy ◽  
Bipartite Matching ◽  
Departure Time

This paper considers a carpool matching (CaMa) problem in which participants price shared rides based on both operating cost and schedule displacement (i.e., the absolute difference between the desired and actual arrival times). By reporting their valuation of this displacement, each participant in effect bids for every possible shared ride that generates a unique value to her. The CaMa problem can be formulated as a mixed integer program (MIP) that maximizes the social welfare by choosing matching pairs and a departure time for each pair. We show the optimal departure time can be determined for each pair a priori, independent of the matching problem. This result reduces the CaMa problem to a standard bipartite matching problem. We prove that the classical Vickrey-Clarke-Groves (VCG) pricing policy ensures no participant is worse off or has the incentive to misreport their valuation of schedule displacement. To control the large deficit created by the VCG policy, we develop a single-side reward (SSR) pricing policy, which only compensates participants who are forced by the system to endure a schedule displacement. Under the assumption of overpricing tendency (i.e., no participant would want to underreport their value), we show the SSR policy not only generates substantial profits, but also retains the other desired properties of the VCG policy, notably truthful reporting. Even though it cannot rule out underreporting, our simulation experiments confirm that the SSR policy is a robust and deficit-free alternative to the VCG policy. Specifically, we find that (1) underreporting is not a practical concern for a carpool platform as it never reduces the number of matched pairs and its impact on profits is largely negligible; and (2) participants have very little to gain by underreporting their value.

scholarly journals An Agent-Based Model for Dispatching Real-Time Demand-Responsive Feeder Bus

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Xin Li ◽  
Ming Wei ◽  
Jia Hu ◽  
Yun Yuan ◽  
Huifu Jiang
Keyword(s):  
Real Time ◽  
Optimal Solution ◽  
Computation Time ◽  
Walking Distance ◽  
Mixed Integer ◽  
Access Time ◽  
Metro Station ◽  
Mip Model ◽  
Feeder Bus ◽  
Operation Cost

This research proposed a feeder bus dispatching tool that reduces rides’ effort to reach a feeder bus. The dispatching tool takes in real-time user specific request information and optimizes total cost accordingly (passenger access time cost and transit operation cost) by choosing the best pick-up locations and feeder buses’ routes. The pick-up locations are then transmitted back to passengers along with GPS guidance. The tool fits well with the Advanced Traveler Information Services (ATIS) which is one of the six high-priority dynamic mobility application bundles currently being promoted by the United State Department of Transportation. The problem is formulated into a Mixed Integer Programming (MIP) model. For small networks, out-of-the-shelf commercial solvers could be used for finding the optimal solution. For large networks, this research developed a GA-based metaheuristic solver which generates reasonably good solutions in a much shorter time. The proposed tool is evaluated on a real-world network in the vicinity of Jiandingpo metro station in Chongqing, China. The results demonstrated that the proposed ATIS tool reduces both buses operation cost and passenger walking distance. It is also able to significantly bring down computation time from more than 1 hour to about 1 min without sacrificing too much on solution optimality.

scholarly journals Optimal Placement and Sizing of D-STATCOM in Radial and Meshed Distribution Networks Using a Discrete-Continuous Version of the Genetic Algorithm

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1452
Author(s):  
Cristian Mateo Castiblanco-Pérez ◽  
David Esteban Toro-Rodríguez ◽  
Oscar Danilo Montoya ◽  
Diego Armando Giral-Ramírez
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Programming Model ◽  
Distribution Networks ◽  
Optimization Method ◽  
Optimal Placement ◽  
Mixed Integer ◽  
Power Balance ◽  
Continuous Version ◽  
Discrete Part

In this paper, we propose a new discrete-continuous codification of the Chu–Beasley genetic algorithm to address the optimal placement and sizing problem of the distribution static compensators (D-STATCOM) in electrical distribution grids. The discrete part of the codification determines the nodes where D-STATCOM will be installed. The continuous part of the codification regulates their sizes. The objective function considered in this study is the minimization of the annual operative costs regarding energy losses and installation investments in D-STATCOM. This objective function is subject to the classical power balance constraints and devices’ capabilities. The proposed discrete-continuous version of the genetic algorithm solves the mixed-integer non-linear programming model that the classical power balance generates. Numerical validations in the 33 test feeder with radial and meshed configurations show that the proposed approach effectively minimizes the annual operating costs of the grid. In addition, the GAMS software compares the results of the proposed optimization method, which allows demonstrating its efficiency and robustness.

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