A simple metaheuristic for the fleetsize and mix problem with TimeWindows

This paper presents a powerful new single-parameter metaheuristic to solve the Fleet Size and Mix Vehicle Routing Problem with Time Windows. The key idea of the new metaheuristic is to perform a random number of random-sized jumps in random order through four well-known local search operators. Computational testing on the 600 large-scale benchmarks of Bräysy et al. (Expert Syst Appl 36(4):8460–8475, 2009) show that the new metaheuristic outperforms previous best approaches, finding 533 new best-known solutions. Despite the significant number of random components, it is demonstrated that the variance of the results is rather low. Moreover, the suggested metaheuristic is shown to scale almost linearly up to 1000 customers

Alternative algorithms for bit-parallel string matching

Abstract. We consider bit-parallel algorithms of Boyer-Moore type for exact string matching. We introduce a two-way modification of the BNDM algorithm. If the text character aligned with the end of the pattern is a mismatch, we continue by examining text characters after the alignment. Besides this two-way variation, we present a simplified version of BNDM without prefix search and an algorithm scheme for long patterns. We also study a different bit-parallel algorithm, which keeps the history of examined characters in a bit-vector and where shifting is based on this bit-vector. We report experiments where we compared the new algorithms with existing ones. The simplified BNDM is the most promising of the new algorithms in practice.

Processing of Huffman Compressed Texts with a Super-Alphabet

Abstract. We present an efficient algorithm for scanning Huffman compressed texts. The algorithm parses the compressed text in O(n log 2 σ b time, where n is the size of the compressed text in bytes, σ is the size of the alphabet, and b is a user specified parameter. The method uses b a variable size super-alphabet, with an average size of O ( ) sym-H log2 σ bols, where H is the entropy of the text. Each super-symbol is processed in O(1) time. The algorithm uses O(2 b) space and O(b2 b) preprocessing time. The method can be easily augmented by auxiliary functions, which can e.g. decompress the text or perform pattern matching in the compressed text. We give three example functions: decoding the text in average time O(n log2 σ), where w is the number of bits in a Hw machine word; an Aho-Corasick dictionary matching algorithm, which works in time O(n log2 σ + t), where t is the number of occurrences b reported; and a shift-or string matching algorithm that works in time O(n log2 σ ⌈(m + s − 1)/w ⌉ + t), where m is the length of the pattern and b s depends on the encoding. The Aho-Corasick algorithm uses an automaton with variable length moves, i.e. it processes variable number of states at each step. The shift-or algorithm makes variable length shifts, effectively also processing variable number of states at each step. The b H log2 σ number of states processed in O(1) time is O (). The method can be applied to several other algorithms as well. We conclude with some experimental results.

Applying artificial bee colony algorithm to the multidepot vehicle routing problem

With advanced information technologies and industrial intelligence, Industry 4.0 has been witnessing a large scale digital transformation. Intelligent transportation plays an important role in the new era and the classic vehicle routing problem (VRP), which is a typical problem in providing intelligent transportation, has been drawing more attention in recent years. In this article, we study multidepot VRP (MDVRP) that considers the management of the vehicles and the optimization of the routes among multiple depots, making the VRP variant more meaningful. In addressing the time efficiency and depot cooperation challenges, we apply the artificial bee colony (ABC) algorithm to the MDVRP. To begin with, we degrade MDVRP to single-depot VRP by introducing depot clustering. Then we modify the ABC algorithm for single-depot VRP to generate solutions for each depot. Finally, we propose a coevolution strategy in depot combination to generate a complete solution of the MDVRP. We conduct extensive experiments with different parameters and compare our algorithm with a greedy algorithm and a genetic algorithm (GA). The results show that the ABC algorithm has a good performance and achieve up to 70% advantage over the greedy algorithm and 3% advantage over the GA