A Cooperative Local Search Method for Solving the Traveling Tournament Problem

Khelifa, Meriem; Boughaci, Dalila

doi:10.4149/cai_2018_6_1386

Cited by 3 publications

(1 citation statement)

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“…Several metaheuristic algorithms have been popular and widely considered in sports scheduling. For example, many researchers used Simulated Annealing (SA) [9,[44][45][46][47] and the Tabu Search (TS) [4,11,48] to solve travelling tournament problems. Chen et al [10] and Uthus et al [49] used the Ant Colony Optimization (ACO) hybridizing with a forward checking and conflicting-directed backjumping algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Variable Neighborhood Search for Major League Baseball Scheduling Problem

et al. 2021

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Modern society pays more and more attention to leisure activities, and watching sports is one of the most popular activities for people. In professional leagues, sports scheduling plays a very critical role. To efficiently arrange a schedule while complying with the relevant rules in a sports league has become a challenge for schedule planners. This research uses Major League Baseball (MLB) of the year 2016 as a case study. The study proposed the Variable Neighborhood Search (VNS) algorithm with different coding structures to optimize the objective function—minimize the total travelling distance of all teams in the league. We have compared the algorithmic schedules with the 2016 and 2019 MLB regular-season schedules in the real-world case for its performance evaluation. The results have confirmed success in reducing the total travelling distances by 2.48% for 2016 and 6.02% in 2019 while lowering the standard deviation of total travelling distances by 7.06% for 2016.

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Section: Literature Reviewmentioning

confidence: 99%

Variable Neighborhood Search for Major League Baseball Scheduling Problem

et al. 2021

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A new approach based on graph matching and evolutionary approach for sport scheduling problem

Khelifa¹,

Boughaci²,

Aïmeur

2021

IDT

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The Traveling Tournament Problem (TTP) is concerned with finding a double round-robin tournament schedule that minimizes the total distances traveled by the teams. It has attracted significant interest recently since a favorable TTP schedule can result in significant savings for the league. This paper proposes an original evolutionary algorithm for TTP. We first propose a quick and effective constructive algorithm to construct a Double Round Robin Tournament (DRRT) schedule with low travel cost. We then describe an enhanced genetic algorithm with a new crossover operator to improve the travel cost of the generated schedules. A new heuristic for ordering efficiently the scheduled rounds is also proposed. The latter leads to significant enhancement in the quality of the schedules. The overall method is evaluated on publicly available standard benchmarks and compared with other techniques for TTP and UTTP (Unconstrained Traveling Tournament Problem). The computational experiment shows that the proposed approach could build very good solutions comparable to other state-of-the-art approaches or better than the current best solutions on UTTP. Further, our method provides new valuable solutions to some unsolved UTTP instances and outperforms prior methods for all US National League (NL) instances.

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Novel Solutions to the Multidimensional Knapsack Problem Using CPLEX: New Results on ORX Benchmarks

Meriem,

Saliha,

abdelaziz

et al. 2024

JUCCT

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The Multidimensional Knapsack Problem (MKP) is a challenging combinatorial optimization problem that extends the classical knapsack problem by introducing multiple capacity constraints across various dimensions. This problem has significant practical applications, including resource allocation in supply chain management, portfolio optimization in finance, and cargo loading in logistics, where the goal is to maximize the total profit of selected items while adhering to these constraints. In this research, the CPLEX solver was applied to address the MKP using a set of complex instances from the OR-Library, specifically the ORX Benchmarks. The study focuses on 270 MKP instances characterized by varying numbers of variables (n = 100, 250, 500), constraints (m = 5), and tightness ratios (α = 0.25). Through advanced CPLEX techniques, new results were successfully obtained by employing advanced CPLEX methods, contributing to the existing literature, and setting new benchmarks for these instances.

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A Cooperative Local Search Method for Solving the Traveling Tournament Problem

Cited by 3 publications

References 0 publications

Variable Neighborhood Search for Major League Baseball Scheduling Problem

Variable Neighborhood Search for Major League Baseball Scheduling Problem

A new approach based on graph matching and evolutionary approach for sport scheduling problem

Novel Solutions to the Multidimensional Knapsack Problem Using CPLEX: New Results on ORX Benchmarks

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