Adapting a Hyper-heuristic to Respond to Scalability Issues in Combinatorial Optimisation

Mapping Intimacies ◽

10.26686/wgtn.17009384 ◽

2021 ◽

Author(s):

◽

Richard J. Marshall

Keyword(s):

Combinatorial Optimisation ◽

Computational Time ◽

Consistent Difference ◽

Problem Domain ◽

Acceptance Criteria ◽

Heuristic Research ◽

Routing Problem ◽

Domain Specific ◽

Specific Variation ◽

Problem Instances

<p>The development of a heuristic to solve an optimisation problem in a new domain, or a specific variation of an existing problem domain, is often beyond the means of many smaller businesses. This is largely due to the task normally needing to be assigned to a human expert, and such experts tend to be scarce and expensive. One of the aims of hyper-heuristic research is to automate all or part of the heuristic development process and thereby bring the generation of new heuristics within the means of more organisations. A second aim of hyper-heuristic research is to ensure that the process by which a domain specific heuristic is developed is itself independent of the problem domain. This enables a hyper-heuristic to exist and operate above the combinatorial optimisation problem “domain barrier” and generalise across different problem domains. A common issue with heuristic development is that a heuristic is often designed or evolved using small size problem instances and then assumed to perform well on larger problem instances. The goal of this thesis is to extend current hyper-heuristic research towards answering the question: How can a hyper-heuristic efficiently and effectively adapt the selection, generation and manipulation of domain specific heuristics as you move from small size and/or narrow domain problems to larger size and/or wider domain problems? In other words, how can different hyperheuristics respond to scalability issues? Each hyper-heuristic has its own strengths and weaknesses. In the context of hyper-heuristic research, this thesis contributes towards understanding scalability issues by firstly developing a compact and effective heuristic that can be applied to other problem instances of differing sizes in a compatible problem domain. We construct a hyper-heuristic for the Capacitated Vehicle Routing Problem domain to establish whether a heuristic for a specific problem domain can be developed which is compact and easy to interpret. The results show that generation of a simple but effective heuristic is possible. Secondly we develop two different types of hyper-heuristic and compare their performance across different combinatorial optimisation problem domains. We construct and compare simplified versions of two existing hyper-heuristics (adaptive and grammar-based), and analyse how each handles the trade-off between computation speed and quality of the solution. The performance of the two hyper-heuristics are tested on seven different problem domains compatible with the HyFlex (Hyper-heuristic Flexible) framework. The results indicate that the adaptive hyper-heuristic is able to deliver solutions of a pre-defined quality in a shorter computational time than the grammar-based hyper-heuristic. Thirdly we investigate how the adaptive hyper-heuristic developed in the second stage of this thesis can respond to problem instances of the same size, but containing different features and complexity. We investigate how, with minimal knowledge about the problem domain and features of the instance being worked on, a hyper-heuristic can modify its processes to respond to problem instances containing different features and problem domains of different complexity. In this stage we allow the adaptive hyper-heuristic to select alternative vectors for the selection of problem domain operators, and acceptance criteria used to determine whether solutions should be retained or discarded. We identify a consistent difference between the best performing pairings of selection vector and acceptance criteria, and those pairings which perform poorly. This thesis shows that hyper-heuristics can respond to scalability issues, although not all do so with equal ease. The flexibility of an adaptive hyper-heuristic enables it to perform faster than the more rigid grammar-based hyper-heuristic, but at the expense of losing a reusable heuristic.</p>

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Adapting a Hyper-heuristic to Respond to Scalability Issues in Combinatorial Optimisation

10.26686/wgtn.17009384.v1 ◽

2021 ◽

Author(s):

◽

Richard J. Marshall

Keyword(s):

Combinatorial Optimisation ◽

Computational Time ◽

Consistent Difference ◽

Problem Domain ◽

Acceptance Criteria ◽

Heuristic Research ◽

Routing Problem ◽

Domain Specific ◽

Specific Variation ◽

Problem Instances

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The Inventory Routing Problem with Demand Moves

SN Operations Research Forum ◽

10.1007/s43069-020-00042-z ◽

2021 ◽

Vol 2 (1) ◽

Author(s):

Annelieke C. Baller ◽

Said Dabia ◽

Guy Desaulniers ◽

Wout E. H. Dullaert

Keyword(s):

Urban Areas ◽

Research Literature ◽

Inventory Routing ◽

Routing Problem ◽

Inventory Routing Problem ◽

Solution Approach ◽

Holding Cost ◽

Inventory Holding Cost ◽

Problem Instances ◽

Inventory Holding

AbstractIn the Inventory Routing Problem, customer demand is satisfied from inventory which is replenished with capacitated vehicles. The objective is to minimize total routing and inventory holding cost over a time horizon. If the customers are located relatively close to each other, one has the opportunity to satisfy the demand of a customer by inventory stored at another nearby customer. In the optimization of the customer replenishments, this option can be included to lower total costs. This is for example the case for ATMs in urban areas where an ATM-user that wants to withdraw money could be redirected to another ATM. To the best of our knowledge, the possibility of redirecting end-users is new to the operations research literature and has not been implemented, but is being considered, in the industry. We formulate the Inventory Routing Problem with Demand Moves in which demand of a customer can (partially) be satisfied by the inventory of a nearby customer at a service cost depending on the quantity and the distance. We propose a branch-price-and-cut solution approach which is evaluated on problem instances from the literature. Cost improvements over the classical IRP of up to 10% are observed with average savings around 3%.

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Efficient Heuristics for Large-Scale Vehicle Routing Problems Using Particle Swarm Optimization

International Journal of Green Computing ◽

10.4018/jgc.2012070103 ◽

2012 ◽

Vol 3 (2) ◽

pp. 34-50

Author(s):

A. Chandramouli ◽

L. Vivek Srinivasan ◽

T. T. Narendran

Keyword(s):

Particle Swarm Optimization ◽

Vehicle Routing ◽

Large Scale ◽

Particle Swarm ◽

Computational Effort ◽

Swarm Optimization ◽

Routing Problem ◽

Customer Base ◽

Benchmark Datasets ◽

Problem Instances

This paper addresses the Capacitated Vehicle Routing Problem (CVRP) with a homogenous fleet of vehicles serving a large customer base. The authors propose a multi-phase heuristic that clusters the nodes based on proximity, orients them along a route, and allots vehicles. For the final phase of determining the routes for each vehicle, they have developed a Particle Swarm Optimization (PSO) approach. Benchmark datasets as well as hypothetical datasets have been used for computational trials. The proposed heuristic is found to perform exceedingly well even for large problem instances, both in terms of quality of solutions and in terms of computational effort.

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ANALISA PERBANDINGAN METODE SIMULATED ANNEALING DAN LARGE NEIGHBORHOOD SEARCH UNTUK MEMECAHKAN MASALAH LOKASI DAN RUTE KENDARAAN DUA ESELON

JURNAL MANAJEMEN INDUSTRI DAN LOGISTIK ◽

10.30988/jmil.v4i1.311 ◽

2020 ◽

Vol 4 (1) ◽

pp. 35-46

Author(s):

Winarno (Universitas Singaperbangsa Karawang) ◽

A. A. N. Perwira Redi (Universitas Pertamina)

Keyword(s):

Simulated Annealing ◽

Computational Time ◽

Neighborhood Search ◽

Large Neighborhood Search ◽

Location Routing Problem ◽

Routing Problem ◽

Large Neighborhood ◽

Location Routing ◽

Node Removal ◽

Better Than

AbstractTwo-echelon location routing problem (2E-LRP) is a problem that considers distribution problem in a two-level / echelon transport system. The first echelon considers trips from a main depot to a set of selected satellite. The second echelon considers routes to serve customers from the selected satellite. This study proposes two metaheuristics algorithms to solve 2E-LRP: Simulated Annealing (SA) and Large Neighborhood Search (LNS) heuristics. The neighborhood / operator moves of both algorithms are modified specifically to solve 2E-LRP. The proposed SA uses swap, insert, and reverse operators. Meanwhile the proposed LNS uses four destructive operator (random route removal, worst removal, route removal, related node removal, not related node removal) and two constructive operator (greedy insertion and modived greedy insertion). Previously known dataset is used to test the performance of the both algorithms. Numerical experiment results show that SA performs better than LNS. The objective function value for SA and LNS are 176.125 and 181.478, respectively. Besides, the average computational time of SA and LNS are 119.02s and 352.17s, respectively.AbstrakPermasalahan penentuan lokasi fasilitas sekaligus rute kendaraan dengan mempertimbangkan sistem transportasi dua eselon juga dikenal dengan two-echelon location routing problem (2E-LRP) atau masalah lokasi dan rute kendaraan dua eselon (MLRKDE). Pada eselon pertama keputusan yang perlu diambil adalah penentuan lokasi fasilitas (diistilahkan satelit) dan rute kendaraan dari depo ke lokasi satelit terpilih. Pada eselon kedua dilakukan penentuan rute kendaraan dari satelit ke masing-masing pelanggan mempertimbangan jumlah permintaan dan kapasitas kendaraan. Dalam penelitian ini dikembangkan dua algoritma metaheuristik yaitu Simulated Annealing (SA) dan Large Neighborhood Search (LNS). Operator yang digunakan kedua algoritma tersebut didesain khusus untuk permasalahan MLRKDE. Algoritma SA menggunakan operator swap, insert, dan reverse. Algoritma LNS menggunakan operator perusakan (random route removal, worst removal, route removal, related node removal, dan not related node removal) dan perbaikan (greedy insertion dan modified greedy insertion). Benchmark data dari penelitian sebelumnya digunakan untuk menguji performa kedua algoritma tersebut. Hasil eksperimen menunjukkan bahwa performa algoritma SA lebih baik daripada LNS. Rata-rata nilai fungsi objektif dari SA dan LNS adalah 176.125 dan 181.478. Waktu rata-rata komputasi algoritma SA and LNS pada permasalahan ini adalah 119.02 dan 352.17 detik.

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Production, Replenishment and Inventory Policies for Perishable Products in a Two-Echelon Distribution Network

Sustainability ◽

10.3390/su12114735 ◽

2020 ◽

Vol 12 (11) ◽

pp. 4735

Author(s):

Mingyuan Wei ◽

Hao Guan ◽

Yunhan Liu ◽

Benhe Gao ◽

Canrong Zhang

Keyword(s):

Inventory Management ◽

Valid Inequalities ◽

Distribution Network ◽

Planning Horizon ◽

Branch And Cut ◽

Computational Time ◽

Perishable Products ◽

Inventory Cost ◽

Routing Problem ◽

Selection Of

The research on production, delivery and inventory strategies for perishable products in a two-echelon distribution network integrates the production routing problem (PRP) and two-echelon vehicle routing problem (2E-VRP), which mainly considers the inventory and delivery sustainability of perishable products. The problem investigated in this study is an extension of the basic problems, and it simultaneously optimizes production, replenishment, inventory, and routing decisions for perishable products that will deteriorate over the planning horizon. Additionally, the lead time has been considered in the replenishment echelon, and the unit inventory cost varying with the inventory time is considered in the inventory management. Based on a newly designed model, different inventory strategies are discussed in this study: old first (OF) and fresh first (FF) strategies both for the first echelon and second echelon, for which four propositions to model them are proposed. Then, four valid inequalities, including logical inequalities, a ( ℓ , S , W W ) inequality, and a replenishment-related inequality, are proposed to construct a branch-and-cut algorithm. The computational experiments are conducted to test the efficiency of valid inequalities, branch-and-cut, and policies. Experimental results show that the valid inequalities can effectively increase the relaxed lower bound by 4.80% on average and the branch-and-cut algorithm can significantly reduce the computational time by 58.18% on average when compared to CPLEX in small and medium-sized cases. For the selection of strategy combinations, OF–FF is suggested to be used in priority.

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Algorithms for Large Directed Capacitated Arc Routing Problem Instances

Recent Advances in Evolutionary Computation for Combinatorial Optimization - Studies in Computational Intelligence ◽

10.1007/978-3-540-70807-0_16 ◽

2008 ◽

pp. 259-274 ◽

Cited By ~ 4

Author(s):

Vittorio Maniezzo ◽

Matteo Roffilli

Keyword(s):

Arc Routing ◽

Routing Problem ◽

Capacitated Arc Routing Problem ◽

Problem Instances ◽

Capacitated Arc Routing

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Two metaheuristic approaches for solving the multi-compartment vehicle routing problem

Operational Research ◽

10.1007/s12351-018-0403-4 ◽

2018 ◽

Vol 20 (4) ◽

pp. 2085-2108 ◽

Cited By ~ 4

Author(s):

Hiba Yahyaoui ◽

Islem Kaabachi ◽

Saoussen Krichen ◽

Abdulkader Dekdouk

Keyword(s):

Vehicle Routing ◽

Vehicle Routing Problem ◽

Variable Neighborhood Search ◽

Neighborhood Search ◽

Large Problem ◽

Solution Quality ◽

Routing Problem ◽

Petrol Station ◽

Capacity Limitations ◽

Problem Instances

Abstract We address in this paper a multi-compartment vehicle routing problem (MCVRP) that aims to plan the delivery of different products to a set of geographically dispatched customers. The MCVRP is encountered in many industries, our research has been motivated by petrol station replenishment problem. The main objective of the delivery process is to minimize the total driving distance by the used trucks. The problem configuration is described through a prefixed set of trucks with several compartments and a set of customers with demands and prefixed delivery. Given such inputs, the minimization of the total traveled distance is subject to assignment and routing constraints that express the capacity limitations of each truck’s compartment in terms of the pathways’ restrictions. For the NP-hardness of the problem, we propose in this paper two algorithms mainly for large problem instances: an adaptive variable neighborhood search (AVNS) and a Partially Matched Crossover PMX-based Genetic Algorithm to solve this problem with the goal of ensuring a better solution quality. We compare the ability of the proposed AVNS with the exact solution using CPLEX and a set of benchmark problem instances is used to analyze the performance of the both proposed meta-heuristics.

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Simple Constructive, Insertion, and Improvement Heuristics Based on the Girding Polygon for the Euclidean Traveling Salesman Problem

Algorithms ◽

10.3390/a13010005 ◽

2019 ◽

Vol 13 (1) ◽

pp. 5 ◽

Cited By ~ 3

Author(s):

Víctor Pacheco-Valencia ◽

José Alberto Hernández ◽

José María Sigarreta ◽

Nodari Vakhania

Keyword(s):

Traveling Salesman Problem ◽

Real Life ◽

Traveling Salesman ◽

Computational Time ◽

Problem Instance ◽

Dimensional Euclidean Space ◽

Benchmark Problem ◽

Small Constant ◽

Euclidean Traveling Salesman Problem ◽

Problem Instances

The Traveling Salesman Problem (TSP) aims at finding the shortest trip for a salesman, who has to visit each of the locations from a given set exactly once, starting and ending at the same location. Here, we consider the Euclidean version of the problem, in which the locations are points in the two-dimensional Euclidean space and the distances are correspondingly Euclidean distances. We propose simple, fast, and easily implementable heuristics that work well, in practice, for large real-life problem instances. The algorithm works on three phases, the constructive, the insertion, and the improvement phases. The first two phases run in time O ( n 2 ) and the number of repetitions in the improvement phase, in practice, is bounded by a small constant. We have tested the practical behavior of our heuristics on the available benchmark problem instances. The approximation provided by our algorithm for the tested benchmark problem instances did not beat best known results. At the same time, comparing the CPU time used by our algorithm with that of the earlier known ones, in about 92% of the cases our algorithm has required less computational time. Our algorithm is also memory efficient: for the largest tested problem instance with 744,710 cities, it has used about 50 MiB, whereas the average memory usage for the remained 217 instances was 1.6 MiB.

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Berth scheduling at marine container terminals

Maritime Business Review ◽

10.1108/mabr-08-2019-0032 ◽

2019 ◽

Vol 5 (1) ◽

pp. 30-66 ◽

Cited By ~ 4

Author(s):

Masoud Kavoosi ◽

Maxim A. Dulebenets ◽

Olumide Abioye ◽

Junayed Pasha ◽

Oluwatosin Theophilus ◽

...

Keyword(s):

Population Based ◽

Container Terminals ◽

Metaheuristic Algorithms ◽

Computational Time ◽

Scheduling Problem ◽

Metaheuristic Algorithm ◽

Content Type ◽

Marine Container Terminals ◽

Problem Instances ◽

Berth Scheduling

Purpose Marine transportation has been faced with an increasing demand for containerized cargo during the past decade. Marine container terminals (MCTs), as the facilities for connecting seaborne and inland transportation, are expected to handle the increasing amount of containers, delivered by vessels. Berth scheduling plays an important role for the total throughput of MCTs as well as the overall effectiveness of the MCT operations. This study aims to propose a novel island-based metaheuristic algorithm to solve the berth scheduling problem and minimize the total cost of serving the arriving vessels at the MCT. Design/methodology/approach A universal island-based metaheuristic algorithm (UIMA) was proposed in this study, aiming to solve the spatially constrained berth scheduling problem. The UIMA population was divided into four sub-populations (i.e. islands). Unlike the canonical island-based algorithms that execute the same metaheuristic on each island, four different population-based metaheuristics are adopted within the developed algorithm to search the islands, including the following: evolutionary algorithm (EA), particle swarm optimization (PSO), estimation of distribution algorithm (EDA) and differential evolution (DE). The adopted population-based metaheuristic algorithms rely on different operators, which facilitate the search process for superior solutions on the UIMA islands. Findings The conducted numerical experiments demonstrated that the developed UIMA algorithm returned near-optimal solutions for the small-size problem instances. As for the large-size problem instances, UIMA was found to be superior to the EA, PSO, EDA and DE algorithms, which were executed in isolation, in terms of the obtained objective function values at termination. Furthermore, the developed UIMA algorithm outperformed various single-solution-based metaheuristic algorithms (including variable neighborhood search, tabu search and simulated annealing) in terms of the solution quality. The maximum UIMA computational time did not exceed 306 s. Research limitations/implications Some of the previous berth scheduling studies modeled uncertain vessel arrival times and/or handling times, while this study assumed the vessel arrival and handling times to be deterministic. Practical implications The developed UIMA algorithm can be used by the MCT operators as an efficient decision support tool and assist with a cost-effective design of berth schedules within an acceptable computational time. Originality/value A novel island-based metaheuristic algorithm is designed to solve the spatially constrained berth scheduling problem. The proposed island-based algorithm adopts several types of metaheuristic algorithms to cover different areas of the search space. The considered metaheuristic algorithms rely on different operators. Such feature is expected to facilitate the search process for superior solutions.

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