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Formal Epistemology in a Tropical Savanna
Published in Evelyn Brister, Robert Frodeman, A Guide to Field Philosophy, 2020
Second, though we had realized that the maximum representation problem was computationally more complicated than the minimum area problem, we had implicitly thought of them as “conversely” related to each other—in the mathematical terminology of computer science, as “dual” problems. We now came to realize that this was not the case, because of the ambiguity in the phrase “as much of the features.” For instance, should we maximize the number of features that met their target? Or the extent to which all of them met their targets? There was no good reason to treat any one of the formulations as necessarily being the correct one for all contexts. Our software began to offer multiple options. The ResNet family of programs was not sophisticated enough for addressing all these options. A new approach to software was needed, and Pappas was the first to propose that we turn to a new family of metaheuristic algorithms, most notably, tabu search. (Tabu search is a metaheuristic algorithm for optimization.)
Multi-Objective Optimization Concepts
Published in Yezid Donoso, Ramon Fabregat, Multi-Objective Optimization in Computer Networks Using Metaheuristics, 2016
Tabu search (TS) is a metaheuristic procedure used for a local search heuristic algorithm to explore the space of solutions beyond the simple local optimum. It has been applied to a wide range of practical optimization applications, producing an accelerated growth of Tabu search algorithms in recent years. Using TS or TS hybrids with other heuristics or algorithmic procedures, one can establish new records to find better solutions in programming, sequencing, resource assignment, investment planning, telecommunications problems, etc.
Heuristic and metaheuristic algorithms
Published in Dušan Teodorović, Miloš Nikolić, Quantitative Methods in Transportation, 2020
Dušan Teodorović, Miloš Nikolić
The tabu search is characterized by an exploration of the solution space in the neighborhood of the current solution. Every step in the tabu search represents a move from the current solution to a new, potentially better solution in the neighborhood of the current solution.
An optimization-based planning tool for on-demand mobility service operations
Published in International Journal of Sustainable Transportation, 2022
H. M. Abdul Aziz, Venu Garikapati, Tony K. Rodriguez, Lei Zhu, Bingrong Sun, Stanley E. Young, Yuche Chen
The route refinement procedure follows a request interchange technique enabled by the Tabu search framework. Tabu search, proposed by Glover (1989), is an iterative meta-heuristic in which neighbors of the current solution are examined. Among the vetted neighbors, the one with the best objective value is chosen as the current solution for the next iteration. Tabu search is a local search mechanism with short-term memory, while the request interchange defines what local means. Tabu search allows escaping of locally optimal solutions that may not be globally optimal. A Tabu list of recently visited solutions is maintained, and existing solutions in the Tabu list are not examined as potential iterates. A request interchange involves selecting two pickup/delivery requests that appear in different routes and interchanging them. For each interchange, the algorithm evaluates the route feasibility and the cost as a result of making the interchange.
Integrated routing optimization for post-disaster rapid-detailed need assessment
Published in International Journal of General Systems, 2020
Xiang Li, Xuexin Liu, Hongguang Ma, Songtao Hu
The routing problem in the rapid need assessment stage is similar to the Team Orienteering Problem (TOP), since both of them focus on selecting the most beneficial nodes and constructing efficient routes for each team (Chao, Golden, and Wasil 1996). The routing problem in the detailed need assessment stage is a Multiple Travelling Salesman Problem (MTSP), which determines multiple routes starting and ending at a depot with the minimum cost. Since both the TOP and the MTSP are NP-hard, the integrated routing problem of the rapid need assessment stage and the detailed need assessment stage is certainly a NP-hard problem. As stressed by Balcik (2017) and Pamukcu and Balcik (2020), the tabu search algorithm is competitive in solving the vehicle routing problem and its variants. Therefore in the following, we will concentrate on developing a tabu search algorithm to solve the proposed integrated routing optimization model of the post-disaster rapid-detailed need assessment.
Two-stage no-wait hybrid flow-shop scheduling with sequence-dependent setup times
Published in International Journal of Systems Science: Operations & Logistics, 2020
Shijin Wang, Xiaodong Wang, Li Yu
The contributions of this paper can be summarised as follows: A two-stage no-wait hybrid flow-shop scheduling problem with sequence-dependent setup times at the first stage is derived from the real-world operational environment. A lower bound and three upper bounds are proposed.A problem-tailored branch and bound algorithm integrated with proposed bounds are developed to obtain exact solutions for small-scale problems. For large-scale problems, a tabu search algorithm is designed and tuned to obtain high-quality solutions in reasonable computation time.Performance of the proposed methods are tested and validated through extensive computational experiments on problem instances randomly generated.The remainder of this paper is organised as follows. In Section 2, we review the literature related to the problem. In Section 3, the problem is described and formulated. Section 4 provides a B&B algorithm. Section 5 presents a tabu search method. The parameters of the tabu search method are tuned with the Taguchi method. Two different categories of numerical experiments with various combinations are conducted in Section 6. Finally, some conclusions and suggestions for further research are drawn in Section 7.