scholarly journals Multivalued decision diagrams for prize-collecting job sequencing with one common and multiple secondary resources

2019 ◽  
Author(s):  
Johannes Maschler ◽  
Günther R. Raidl
Keyword(s):  
Variable Neighborhood Search ◽  
Optimization Problems ◽  
Neighborhood Search ◽  
Decision Diagrams ◽  
Top Down ◽  
Combinatorial Optimization Problems ◽  
Job Sequencing ◽  
Refinement Method ◽  
Prize Collecting ◽  
Dual Bounds

AbstractMultivalued decision diagrams (MDD) are a powerful tool for approaching combinatorial optimization problems. Relatively compact relaxed and restricted MDDs are applied to obtain dual bounds and heuristic solutions and provide opportunities for new branching schemes. We consider a prize-collecting sequencing problem in which a subset of given jobs has to be found that is schedulable and yields maximum total prize. The primary aim of this work is to study different methods for creating relaxed MDDs for this problem. To this end, we adopt and extend the two main MDD compilation approaches found in the literature: top down construction and incremental refinement. In a series of computational experiments these methods are compared. The results indicate that for our problem the incremental refinement method produces MDDs with stronger bounds. Moreover, heuristic solutions are derived by compiling restricted MDDs and by applying a general variable neighborhood search (GVNS). Here we observe that the top down construction of restricted MDDs is able to yield better solutions as the GVNS on small to medium-sized instances.

scholarly journals Quantum Inspired General Variable Neighborhood Search (qGVNS) for Dynamic Garbage Collection

2017 ◽  
Author(s):  
Christos Papalitsas ◽  
Panayiotis Karakostas ◽  
Theodore Andronikos ◽  
Spyros Sioutas ◽  
Konstantinos Giannakis
Keyword(s):  
Variable Neighborhood Search ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Real Life ◽  
Neighborhood Search ◽  
Np Hard ◽  
Combinatorial Optimization Problems ◽  
Np Hard Problem ◽  
Gps System ◽  
Tools And Techniques

General Variable Neighborhood Search (GVNS) is a well known and widely used metaheuristic for efficiently solving many NP-hard combinatorial optimization problems. Quantum General Variable Neighborhood Search (qGVNS) is a novel, quantum inspired extension of the conventional GVNS. Its quantum nature derives from the fact that it takes advantage and incorporates tools and techniques from the field of quantum computation. Travelling Salesman Problem (TSP) is a well known NP-Hard problem which has broadly been used for modelling many real life routing cases. As a consequence, TSP can be used as a basis for modelling and finding routes for Geographical Systems (GPS). In this paper, we examine the potential use of this method for the GPS system of garbage trucks. Specifically, we provide a thorough presentation of our method accompanied with extensive computational results. The experimental data accumulated on a plethora of symmetric TSP instances (symmetric in order to faithfully simulate GPS problems), which are shown in a series of figures and tables, allow us to conclude that the novel qGVNS algorithm can provide an efficient solution for this type of geographical problems.

scholarly journals Variable neighborhood search for minimum linear arrangement problem

2016 ◽  
Vol 26 (1) ◽  
pp. 3-16 ◽  
Author(s):  
Nenad Mladenovic ◽  
Dragan Urosevic ◽  
Dionisio Pérez-Brito
Keyword(s):  
Variable Neighborhood Search ◽  
Optimization Problems ◽  
State Of The Art ◽  
The State ◽  
Computational Experiments ◽  
Neighborhood Search ◽  
Np Hard ◽  
Linear Arrangement ◽  
Arrangement Problem ◽  
Linear Arrangement Problem

The minimum linear arrangement problem is widely used and studied in many practical and theoretical applications. It consists of finding an embedding of the nodes of a graph on the line such that the sum of the resulting edge lengths is minimized. This problem is one among the classical NP-hard optimization problems and therefore there has been extensive research on exact and approximative algorithms. In this paper we present an implementation of a variable neighborhood search (VNS) for solving minimum linear arrangement problem. We use Skewed general VNS scheme that appeared to be successful in solving some recent optimization problems on graphs. Based on computational experiments, we argue that our approach is comparable with the state-of-the-art heuristic.

scholarly journals Solving the multi-level location routing problem considering the environmental impact using a hybrid metaheuristic

2021 ◽  
Vol 13 ◽  
pp. 184797902110173
Author(s):  
Chalermchat Theeraviriya ◽  
Kitchanut Ruamboon ◽  
Nat Praseeratasang
Keyword(s):  
Environmental Impact ◽  
Optimization Problems ◽  
Reverse Flow ◽  
Neighborhood Search ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Hybrid Metaheuristic ◽  
Combinatorial Optimization Problems ◽  
Location Routing ◽  
Multi Level

The multi-level location routing problem (MLLRP) is an extension of the capacitated location routing problem (CLRP). MLLRPs are considered a class of combinatorial optimization problems that arise in transportation applications, such as agricultural logistic planning. Along with concerns regarding the environmental harmfulness, recent studies have considered “green” logistics. This paper addressed a low environmental impact model for MLLRP by considering characteristics, emissions, and traffic congestion that have not appeared in the recent literature. The mathematical model was formulated to deal with a reverse flow problem, which was a real case that occurs in Thailand agriculture. We developed a hybrid metaheuristic algorithm to solve the MLLRP by integrating a variable neighborhood search (VNS) with an adaptive large neighborhood search (ALNS). The experimental results shown that the hybrid algorithm had clear advantages in the time consumption and quality of the solution. The extended study indicated that the proposed algorithm obtained competitive results compared with the previously published methods. The proposed practice is useful not only for the agricultural industry but also for other industries.

scholarly journals Influence of a neighborhood shape on the efficiency of continuous variable neighborhood search

2020 ◽  
Vol 30 (1) ◽  
pp. 3-17
Author(s):  
Milan Drazic
Keyword(s):  
Global Optimization ◽  
Variable Neighborhood Search ◽  
Optimization Problems ◽  
Continuous Variable ◽  
Efficient Algorithms ◽  
Geometric Shape ◽  
Neighborhood Search ◽  
High Dimensional ◽  
Neighborhood Structures ◽  
Continuous Global Optimization

The efficiency of a Variable neighborhood search metaheuristic for continuous global optimization problems greatly depends on geometric shape of neighborhood structures used by the algorithm. Among the neighborhoods defined by balls in ?p, 1 ?p ? ? metric, we tested the ?1, ?2, and ?? ball shape neighborhoods, for which there exist efficient algorithms for obtaining uniformly distributed points. On many challenging high-dimensional problems, our exhaustive testings showed that, popular and the easiest for implementation, ?? ball shape of neighborhoods performed the worst, and much better efficiency was obtained with ?1 and ?2.

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