Integer programming formulations of discrete hub location problems

Variable neighborhood search (VNS) is a framework for building heuristics, based upon systematic changes of neighborhoods both in a descent phase, to find a local minimum, and in a perturbation phase to escape from the corresponding valley. In this paper, we present some of VNS basic schemes as well as several VNS variants deduced from these basic schemes. In addition, the paper includes parallel implementations and hybrids with other metaheuristics.

Variable neighborhood search: basics and variants

Construct, Merge, Solve & Adapt (CMSA) is a general hybrid metaheuristic for solving combinatorial optimization problems. At each iteration, CMSA (1) constructs feasible solutions to the tackled problem instance in a probabilistic way and (2) solves a reduced problem instance (if possible) to optimality. The construction of feasible solutions is hereby problem-specific, usually involving a fast greedy heuristic. The goal of this paper is to design a problem-agnostic CMSA variant whose exclusive input is an integer linear program (ILP). In order to reduce the complexity of this task, the current study is restricted to binary ILPs. In addition to a basic problem-agnostic CMSA variant, we also present an extended version that makes use of a constraint propagation engine for constructing solutions. The results show that our technique is able to match the upper bounds of the standalone application of CPLEX in the context of rather easy-to-solve instances, while it generally outperforms the standalone application of CPLEX in the context of hard instances. Moreover, the results indicate that the support of the constraint propagation engine is useful in the context of problems for which finding feasible solutions is rather difficult.

Hybrid Metaheuristics

A new initial population strategy has been developed
to improve the genetic algorithm for solving the well-known combinatorial
optimization problem, traveling salesman problem. Based on the k-means
algorithm, we propose a strategy to restructure the traveling route by reconnecting
each cluster. The clusters, which randomly disconnect a link to connect
its neighbors, have been ranked in advance according to the distance among
cluster centers, so that the initial population can be composed of the random
traveling routes. This process is -means initial population strategy. To test the performance of our strategy, a series of experiments on 14 different
TSP examples selected from TSPLIB have been carried out. The results show
that KIP can decrease best error value of random initial population strategy
and greedy initial population strategy with the ratio of approximately between
29.15% and 37.87%, average error value between 25.16% and 34.39% in the
same running time.

/pdf/an-improved-genetic-algorithm-with-initial-population-31ozagxvyl.pdf

An Improved Genetic Algorithm with Initial Population Strategy for Symmetric TSP

In this paper, we show how to extend solution methods for vehicle-routing problems with intermediate stops (using the example of an Adaptive Variable Neighborhood Search (AVNS) algorithm) to solve the recently introduced battery swap station location-routing problem with capacitated electric vehicles. The problem calls for the simultaneous determination of (i) the battery swap stations (BSSs) to be constructed out of a set of candidate locations, and (ii) the electric vehicle routes to serve a set of customers with the goal of minimizing the sum of construction and routing cost. On the benchmark instances from the literature, the extended AVNS is able to significantly improve the previously known best solutions for the large majority of instances while using only a small fraction of the run-times reported for the comparison methods of Yang and Sun (2015). Moreover, the AVNS proves robust with regard to its average solution quality and is able to strongly reduce the number of constructed BSSs in the solutions compared to the results from the literature. Therefore, we generate additional benchmark instances which prove to be more meaningful with respect to the necessity of using BSSs and that are suitable to analyze the impact of varying construction cost on the location decision.

Solving the battery swap station location-routing problem with capacitated electric vehicles using an AVNS algorithm for vehicle-routing problems with intermediate stops

Less is more: Basic variable neighborhood search for minimum differential dispersion problem

The circular economy is gaining in importance globally and locally. The COVID-19 crisis, as an exceptional event, showed the limits and the fragility of supply chains, with circular economy practices as a potential solution during and post-COVID. Reverse logistics (RL) is an important dimension of the circular economy which allows management of economic, social, and environmental challenges. Transportation is needed for RL to effectively operate, but research study on this topic has been relatively limited. New digitalization opportunities can enhance transportation and RL, and therefore further enhance the circular economy. This paper proposes to review practical research and concerns at the nexus of transportation, RL, and blockchain as a digitalizing technology. The potential benefits of blockchain technology through example use cases on various aspects of RL and transportation activities are presented. This integration and applications are evaluated using various capability facets of blockchain technology, particularly as an immutable and reliable ledger, a tracking service, a smart contract utility, as marketplace support, and as tokenization and incentivization. We also briefly introduce the physical internet concept within this context. The physical internet paradigm proposed last decade, promises to also disrupt the blockchain, transportation, and RL nexus. We include potential research directions and managerial implications across the blockchain, transportation, and RL nexus.

https://www.mdpi.com/2071-1050/13/5/2895/pdf

Digitalizing the Closing-of-the-Loop for Supply Chains: A Transportation and Blockchain Perspective

Local search heuristic that explores several neighborhood structures in a deterministic way is called variable neighborhood descent (VND). Its success is based on the simple fact that different neighborhood structures do not usually have the same local minimum. Thus, the local optima trap problem may be resolved by deterministic change of neighborhoods. VND may be seen as a local search routine and therefore could be used within other metaheuristics. In this chapter, we discuss typical problems that arise in developing VND heuristic: what neighborhood structures could be used, what would be their order, what rule of their change during the search would be used, etc. Comparative analysis of usual sequential VND variants is performed in solving traveling salesman problem.

Variable Neighborhood Descent.

: General Variable Neighborhood Search (GVNS) is shown to be a powerful and robust methodology for solving travelling salesman and vehicle routing problems. However, its efficient implementation may play a significant role in solving large size instances. In this paper we suggest new GVNS heuristic for solving Travelling salesman problem with time windows. It uses different set of neighborhoods, new feasibility checking procedure and a more efficient data structure than the recent GVNS method that can be considered as a state-of-the-art heuristic. As a result, our GVNS is much faster and more effective than the previous GVNS. It is able to improve 14 out of 25 best known solutions for large test instances from the literature. Keywords : Travelling Salesman Problem, Time windows, Variable Neighborhood Search.

/pdf/an-efficient-general-variable-neighborhood-search-for-large-3itpkzlhar.pdf

Raca Todosijević

Papers

Variable neighborhood search: basics and variants

Less is more: Basic variable neighborhood search for minimum differential dispersion problem

Digitalizing the Closing-of-the-Loop for Supply Chains: A Transportation and Blockchain Perspective

Variable Neighborhood Descent.

An efficient general variable neighborhood search for large travelling salesman problem with time windows