scholarly journals Shortest-Path Optimization of Ship Diesel Engine Disassembly and Assembly Based on AND/OR Network

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Deng-Zhi Chen ◽  
Chen Wei ◽  
Guo-Ling Jia ◽  
Zhi-Hua Hu
Keyword(s):  
Diesel Engine ◽  
Shortest Path ◽  
Large Scale ◽  
Random Search ◽  
Computing Time ◽  
Graph Model ◽  
Mixed Integer ◽  
Mixed Integer Linear Program ◽  
Optimal Sequence ◽  
Solution Quality

Ship diesel engine disassembly and assembly (SDEDA) is essential for ship inspection and maintenance and navigation safety. The SDEDA consists of various machinery parts and operations. It is crucial to develop a system of SDEDA operations to improve the efficiency of disassembly and assembly (D&A). Considering the “AND” and “OR” relations (modeled as links) among the D&A operations (modeled as nodes), an “AND/OR” network is developed to extend a specialized graph model for the D&A sequencing problem in the context of education and training. Then, we devised a mixed-integer linear program (MILP) to optimize the SDEDA sequence based on the AND/OR network. Considering the flow balance in the AND/OR network, we developed exact algorithms and random search algorithms using breadth-first, branch cut and depth-first strategies to minimize the cost of the shortest path that represents an optimal sequence of D&A operations. To the best of our knowledge, it is the first try to formulate the D&A operations by an extended network model. Numerical experiments show that the proposed algorithms are practical for solving large-scale instances with more than 2000 D&A operations. The breadth-first shortest-path algorithm outperforms the MILP solver from the perspective of solution quality and computing time, and all developed algorithms are competitive in terms of computing time.

A Fast and Scalable Heuristic for the Solution of Large-Scale Capacitated Vehicle Routing Problems

2021 ◽  
Author(s):  
Luca Accorsi ◽  
Daniele Vigo
Keyword(s):  
Vehicle Routing ◽  
Large Scale ◽  
Computing Time ◽  
Search Space ◽  
Iterated Local Search ◽  
Solution Quality ◽  
Routing Problem ◽  
Acceptance Criterion ◽  
Acceleration Techniques ◽  
Capacitated Vehicle

In this paper, we propose a fast and scalable, yet effective, metaheuristic called FILO to solve large-scale instances of the Capacitated Vehicle Routing Problem. Our approach consists of a main iterative part, based on the Iterated Local Search paradigm, which employs a carefully designed combination of existing acceleration techniques, as well as novel strategies to keep the optimization localized, controlled, and tailored to the current instance and solution. A Simulated Annealing-based neighbor acceptance criterion is used to obtain a continuous diversification, to ensure the exploration of different regions of the search space. Results on extensively studied benchmark instances from the literature, supported by a thorough analysis of the algorithm’s main components, show the effectiveness of the proposed design choices, making FILO highly competitive with existing state-of-the-art algorithms, both in terms of computing time and solution quality. Finally, guidelines for possible efficient implementations, algorithm source code, and a library of reusable components are open-sourced to allow reproduction of our results and promote further investigations.

Modeling and scheduling hybrid open shops for makespan minimization

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kennedy Anderson Guimarães de Araújo ◽  
Tiberius Oliveira e Bonates ◽  
Bruno de Athayde Prata
Keyword(s):  
Programming Model ◽  
Computing Time ◽  
Iterated Local Search ◽  
Mixed Integer ◽  
Scheduling Problems ◽  
Priority Rules ◽  
Production Flow ◽  
Solution Quality ◽  
Content Type ◽  
Constructive Heuristics

Purpose This study aims to address the hybrid open shop problem (HOSP) with respect to the minimization of the overall finishing time or makespan. In the HOSP, we have to process n jobs in stages without preemption. Each job must be processed once in every stage, there is a set of mk identical machines in stage k and the production flow is immaterial. Design/methodology/approach Computational experiments carried out on a set of randomly generated instances showed that the minimal idleness heuristic (MIH) priority rule outperforms the longest processing time (LPT) rule proposed in the literature and the other proposed constructive methods on most instances. Findings The proposed mathematical model outperformed the existing model in the literature with respect to computing time, for small-sized instances, and solution quality within a time limit, for medium- and large-sized instances. The authors’ hybrid iterated local search (ILS) improved the solutions of the MIH rule, drastically outperforming the models on large-sized instances with respect to solution quality. Originality/value The authors formalize the HOSP, as well as argue its NP-hardness, and propose a mixed integer linear programming model to solve it. The authors propose several priority rules – constructive heuristics based on priority measures – for finding feasible solutions for the problem, consisting of adaptations of classical priority rules for scheduling problems. The authors also propose a hybrid ILS for improving the priority rules solutions.

scholarly journals On mixed-integer optimal control with constrained total variation of the integer control

2020 ◽  
Author(s):  
Sebastian Sager ◽  
Clemens Zeile
Keyword(s):  
Optimal Control ◽  
Total Variation ◽  
Optimal Control Problems ◽  
Feasible Solution ◽  
Mixed Integer ◽  
Continuous Control ◽  
Mixed Integer Linear Program ◽  
Solution Quality ◽  
Frequent Switching ◽  
Control Trajectory

AbstractThe combinatorial integral approximation (CIA) decomposition suggests solving mixed-integer optimal control problems by solving one continuous nonlinear control problem and one mixed-integer linear program (MILP). Unrealistic frequent switching can be avoided by adding a constraint on the total variation to the MILP. Within this work, we present a fast heuristic way to solve this CIA problem and investigate in which situations optimality of the constructed feasible solution is guaranteed. In the second part of this article, we show tight bounds on the integrality gap between a relaxed continuous control trajectory and an integer feasible one in the case of two controls. Finally, we present numerical experiments to highlight the proposed algorithm’s advantages in terms of run time and solution quality.

Study on the Scheduling Problem of Powder Painting Production - A Case Study

2010 ◽  
Vol 97-101 ◽  
pp. 2459-2464
Author(s):  
Zhang Yong Hu ◽  
Qiang Su ◽  
Jun Liu ◽  
Hai Xia Yang
Keyword(s):  
Large Scale ◽  
Production Efficiency ◽  
Production Performance ◽  
Mixed Integer ◽  
Scheduling Problem ◽  
Adaptive Search ◽  
Optimal Sequence ◽  
Integer Nonlinear Programming ◽  
Minlp Model

A large-scale powder-painting scheduling problem is explored. The purpose is to find out the optimal sequence of a number of batches that dynamically arrive from upstream processes within a given scheduling horizon. The objective is to enhance the production efficiency and decrease the production cost as well. To solve this problem, a mixed integer nonlinear programming (MINLP) model is constructed and an algorithm called greedy randomized adaptive search procedure (GRASP) is designed. Case studies demonstrate that the proposed approach can improve the production performance significantly.

scholarly journals Distribution Endpoint Estimation Assessment for the use in Metaheuristic Optimization Procedure

MENDEL ◽  
2018 ◽  
Vol 24 (1) ◽  
pp. 93-100
Author(s):  
Jan Holesovky
Keyword(s):  
Extreme Value Theory ◽  
Large Scale ◽  
Optimization Problems ◽  
Random Search ◽  
Optimization Procedure ◽  
Short Review ◽  
Value Theory ◽  
Extreme Value ◽  
Mixed Integer ◽  
Endpoint Estimation

Metaheuristic algorithms are often applied to numerous optimization problems, involving large-scale and mixed-integer instances, specifically. In this contribution we discuss some refinements from the extreme value theory to the lately proposed modification of partition-based random search. The partition-based approach performs iterative random sampling at given feasible subspaces in order to exclude the less favourable regions. The quality of particular regions is evaluated according to the promising index of a region. From statistical perspective, determining the promising index is equivalent to the endpoint estimation of a probability distribution induced by the objective function at the sampling subspace. In the following paper, we give a short review of the recent endpoint estimators derived on the basis of extreme value theory, and compare them by simulations. We discuss also the difficulties in their application and suitability of the estimators for various optimization instances.

Efficient airplane arrival scheduling using a set partitioning-based branch-and-price method

2017 ◽  
Vol 232 (16) ◽  
pp. 2939-2951
Author(s):  
Jae-Hoon Song ◽  
Han-Lim Choi
Keyword(s):  
Large Scale ◽  
Time Window ◽  
Heuristic Method ◽  
Solution Space ◽  
Exact Algorithm ◽  
Set Partitioning ◽  
Mixed Integer ◽  
Branch And Price ◽  
Mixed Integer Linear Program ◽  
Public Data

This article presents an exact algorithm that is combined with a heuristic method to find the optimal solution for an airplane landing problem. For a given set of airplanes and runways, the objective is to minimize the accumulated deviations from the target landing time of the airplanes. A cost associated with landing either earlier or later than the target landing time is incurred for each airplane within its predetermined time window. In order to manage this type of large-scale optimization problem, a set partitioning formulation that results in a mixed integer linear program is proposed. One key contribution of this article is the development of a branch-and-price methodology, in which the column generation method is integrated with the branch-and-bound method in order to find the optimal integer solution. In addition to the exact algorithm, a simple heuristic method is also presented to tighten the solution space. Numerical experiments are undertaken for the proposed algorithm in order to confirm its effectiveness using public data from the OR-Library. As an application in the real-world situation of airplane landing, air traffic data from Incheon International Airport is employed to assure the efficiency of the proposed algorithm.

Toll Pricing and Heterogeneous Users

2005 ◽  
Vol 1923 (1) ◽  
pp. 28-36 ◽  
Author(s):  
Hani S. Mahmassani ◽  
Xuesong Zhou ◽  
Chung-Cheng Lu
Keyword(s):  
Shortest Path ◽  
Large Scale ◽  
Approximation Error ◽  
Computation Time ◽  
Time Dependent ◽  
Approximation Schemes ◽  
Value Of Time ◽  
Solution Quality ◽  
Shortest Path Problems ◽  
Toll Pricing

This paper presents both exact and approximation algorithms for finding extreme efficient time-dependent shortest paths for use with dynamic traffic assignment applications to networks with variable toll pricing and heterogeneous users (with different value of time preferences). A parametric least-generalized cost path algorithm is presented to determine a complete set of extreme efficient time-dependent paths that simultaneously consider travel time and cost criteria. However, exact procedures may not be practical for large networks. For this reason, approximation schemes are devised and tested. Based on the concept of ε-efficiency in multiobjective shortest path problems, a binary search framework is developed to find a set of extreme efficient paths that minimize expected approximation error, with the use of the underlying value of time distribution. Both exact and approximation schemes (along with variants) are tested on three actual traffic networks. The experimental results indicate that the computation time and the size of the solution set are jointly determined by several key parameters such as the number of time intervals and the number of nodes in the network. The results also suggest that the proposed approximation scheme is computationally efficient for large-scale bi-objective time-dependent shortest path applications while maintaining satisfactory solution quality.

scholarly journals A General Cooperative Optimization Approach for Distributing Service Points in Mobility Applications

Algorithms ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 232
Author(s):  
Thomas Jatschka ◽  
Günther R. Raidl ◽  
Tobias Rodemann
Keyword(s):  
Objective Function ◽  
Matrix Factorization ◽  
Large Scale ◽  
User Interaction ◽  
User Preferences ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Mixed Integer Linear Program ◽  
User Interactions ◽  
The Matrix

This article presents a cooperative optimization approach (COA) for distributing service points for mobility applications, which generalizes and refines a previously proposed method. COA is an iterative framework for optimizing service point locations, combining an optimization component with user interaction on a large scale and a machine learning component that learns user needs and provides the objective function for the optimization. The previously proposed COA was designed for mobility applications in which single service points are sufficient for satisfying individual user demand. This framework is generalized here for applications in which the satisfaction of demand relies on the existence of two or more suitably located service stations, such as in the case of bike/car sharing systems. A new matrix factorization model is used as surrogate objective function for the optimization, allowing us to learn and exploit similar preferences among users w.r.t. service point locations. Based on this surrogate objective function, a mixed integer linear program is solved to generate an optimized solution to the problem w.r.t. the currently known user information. User interaction, refinement of the matrix factorization, and optimization are iterated. An experimental evaluation analyzes the performance of COA with special consideration of the number of user interactions required to find near optimal solutions. The algorithm is tested on artificial instances, as well as instances derived from real-world taxi data from Manhattan. Results show that the approach can effectively solve instances with hundreds of potential service point locations and thousands of users, while keeping the user interactions reasonably low. A bound on the number of user interactions required to obtain full knowledge of user preferences is derived, and results show that with 50% of performed user interactions the solutions generated by COA feature optimality gaps of only 1.45% on average.

Demand Modeling in the Presence of Unobserved Lost Sales

2020 ◽  
Author(s):  
Shivaram Subramanian ◽  
Pavithra Harsha
Keyword(s):  
Large Scale ◽  
Partial Information ◽  
Optimal Parameter ◽  
Arrival Rate ◽  
Mixed Integer ◽  
Mixed Integer Program ◽  
Optimization Approach ◽  
Estimation Model ◽  
Solution Quality ◽  
Demand Models

We present an integrated optimization approach to parameter estimation for discrete choice demand models where data for one or more choice alternatives are censored. We employ a mixed-integer program (MIP) to jointly determine the prediction parameters associated with the customer arrival rate and their substitutive choices. This integrated approach enables us to recover proven, (near-) optimal parameter values with respect to the chosen loss-minimization (LM) objective function, thereby overcoming a limitation of prior multistart heuristic approaches that terminate without providing precise information on the solution quality. The imputations are done endogenously in the MIP by estimating optimal values for the probabilities of the unobserved choices being selected. Under mild assumptions, we prove that the approach is asymptotically consistent. For large LM instances, we derive a nonconvex-contvex alternating heuristic that can be used to obtain quick, near-optimal solutions. Partial information, user acceptance criteria, model selection, and regularization techniques can be incorporated to enhance practical efficacy. We test the LM model on simulated and real data and present results for a variety of demand-prediction scenarios: single-item, multi-item, time-varying arrival rate, large-scale instances, and a dual-layer estimation model extension that learns the unobserved market shares of competitors. This paper was accepted by Yinyu Ye, optimization.

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