Combining Tabu Search and Genetic Algorithm Heuristic Techniques to Solve Spatial Harvest Scheduling Problems

A composite heuristic for the single machine early/tardy job scheduling problem

An important goal in scheduling products through a manufacturing facility is to assure that the work is completed as close as possible to its due date. Work that is late creates downstream delays, while early completion can be detrimental if storage space is limited. This paper reports initial results in developing a scheduling procedure for an automated steel plate fabrication facility. The approach uses Tabu search combined with simulation to schedule product through a set of machines. Performance of the procedure is evaluated by comparison to the optimal solution for small problem instances, and to a good heuristic for larger problems. Results show that the Tabu search method works well for this problem. Combining Tabu search with simulation allows the incorporation of more realistic constraints on system operation.

An indirect search heuristic is described for solving harvest-scheduling problems under adjacency constraints. This method works in combination with a greedy algorithm by diversifying the search through random changes in prioritized harvest queues. The indirect search is tested on a series of tactical problems and compared with published results for tabu search, simulated annealing, integer programming and linear programming. Results for large strategic problems are compared to a simulated annealing search algorithm. Objective function values are comparable to tabu search and simulated annealing, and solution times range from 38 seconds to 40 minutes, depending on the problem size and the number of iterations. Benefits of the indirect search method are: (1) objective function values can be higher than those computed through other heuristic algorithms, and (2) the algorithm produces good results without time-consuming experimentation with parameters of the search algorithm. The method also has potential for solving more complicated, multiple objective problems. FOR. SCI. 49(1):1–11.

Tabu search techniques have been applied to a wide class of optimization problems. Tabu search is a meta procedure that requires a subroutine to find local optima. In most examples of tabu search the subroutine used is problem specific. However, in order to apply tabu search successfully to general 0-1 integer programs, the subroutine must be general. The pivot and complement heuristic is a procedure that frequently finds feasible solutions for general 0-1 integer programs. In this paper we implement several elements of strategies proposed by tabu search method that result in general heuristics for 0-1 integer programs that could be included in an integer programming code. These heuristics employ the ZOOM/XMP implementation of the pivot and complement heuristic as a subroutine. Computational results indicate that these heuristics provide good bounds for many problems. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.

Presently, most tabu search designers devise their applications without considering the potential of design and code reuse, which consequently prolong the development of subsequent applications. In this paper, we propose a software solution known as Tabu Search Framework (TSF), which is a generic C++ software framework for tabu search implementation. The framework excels in code recycling through the use of a well- designed set of generic abstract classes that clearly define their collaborative roles in the algorithm. Additionally, the framework incorporates a centralized process and control mechanism that enhances the search with intelligence. This results in a generic framework that is capable of solving a wide range of combinatorial optimization problems using various tabu search techniques and adaptive strategies. The applications of TSF are demonstrated on the implementation of two NP-hard problems, the Vehicle Routing Problem with Time Windows (VRPTW) and Quadratic Assignment Problem (QAP). We show that TSF is able to obtain quality solutions within reasonable implementation as well as computation time.Key wordsTabu Searchsoftware frameworkreusabilitycombinatorial optimization

Mobile Crowd Sensing (MCS) is a widely adopted approach for data collection across diverse applications. However, as the number of tasks and participants in MCS continues to grow, task allocation and dynamic pricing challenges have become increasingly complex. Existing research primarily focuses on single-task allocation problems, often overlooking the diversity and complexity inherent in multi-task, multi-worker scenarios. To address these challenges, this paper proposes the Enhanced Heuristic Search with Tabu and Local Search (EH-STLS) algorithm, alongside a Kolmogorov-Arnold Deep Q Network (KDQN) model, both grounded in a Stackelberg game framework. The EH-STLS algorithm employs a multi-objective optimization framework that combines tabu search and local search strategies to improve the efficiency of worker-task matching while ensuring high task quality. The KDQN model views task publishers as leaders and treats crowdsourcing platforms, encryption agencies, and workers as followers to achieve optimal dynamic pricing and utility allocation. Extensive experiments on synthetic datasets generated from real-world data reveal that the proposed methods substantially outperforms the baseline algorithms regarding task allocation quality and pricing efficiency, achieving up to a 21.7% increase in task quality and a 16.4% improvement in pricing effectiveness.

In this paper, we present some new results obtained for the traveling salesman problem (TSP) by using the iterated tabu search (ITS) meta-heuristic. ITS is a promising extension to the ordinary tabu search scheme. It seeks near-optimal solutions by combining intensification (standard tabu search) and diversification (perturbation of solutions) in a proper way. For the TSP, the main effect is achieved due to decomposition of the solution neighbourhood structure and considerably speeding-up the tabu search process, which is used, namely, in the role of intensification. This fast-iterated tabu search (FITS) technique resulted in quite encouraging solutions for the TSP instances from the TSP instance library TSPLIB. FITS obviously outperformed the other heuristic algorithms used in the experimentation, especially, on the smaller TSP instances.

Loop layout is a common layout in the Flexible Manufacturing Systems (FMS), machines arranged around the loop and materials are transported in unidirectional only. The intention of the loop layout problem (LLP) is the determination of regulating of machines around a loop, to minimize the maximum congestion among the family of parts. Artificial immune system (AIS) algorithm, Tabu Search (TS) and Improved Tabu Search (ITS) algorithms are employed to solve these loop layout problems. The algorithm is tested and validated through large and small-sized randomly generated hypothetical problems with minimum required machine sequence. Further, the efficiency of algorithms is compared with existing algorithm for benchmark problems. Computational results reveal that ITS algorithm outperforms the AIS, TS and existing algorithm for large-sized hypothetical problems.

Tabu search for dedicated resource-constrained multiproject scheduling to minimise the maximal cash flow gap under uncertainty

Designing an efficient method for tandem AGV network design problem using tabu search

Maastricht University is offering a MBA program for people that have a bachelor degree and at least 5 years of working experience. Within the MBA program, students work in groups of 5 during a two year cycle. This thesis is about the formation of the student groups. The MBA program contains 60 students. Every year, two intake moments take place that usually allow 15 new students to enter. All 60 students follow the same course at the same time, implying that the order in which a student follows the courses depends only on the moment at which he/she starts the program. Every two periods, The university creates new student groups according to a set of hard and soft constraints, such that well-diversified groups are formed. Therefore, the student-with-student history, gender, nationality, and level of expertise of each student is taken into account. Hence a mapping from a set of students to groups is created that takes into account the corresponding constraints. The university chooses a group leader for each group. Two general solution methods are applied to the MBA sectioning problem. The first method uses the simplex algorithm to solve the problem. Therefore an integer linear program formulation of the problem was needed, and used as an input for an efficient ILP solver. The second approach starts with an initial feasible solution and improves upon this feasible solution using different improvement algorithms. The quality of each feasible solution depends on the calculated objective function value that measures the level of satisfaction of the different constraints. Different initial solution and improvement algorithms are discussed that help to obtain a feasible solution with an objective function value that is as low as possible. The implemented improvement algorithms are the Descent Improvement algorithm, Tabu Search, Simulated Annealing, and the Bipartite Weighted Matching Improvement algorithm. The first three algorithms make individual students swap between existing group formations. The Bipartite Weighted Matching Improvement algorithm iteratively selects a student from each group, and finds local optimal solutions for a bipartite matching problem in order to improve the overall objective value of the whole problem. In order to test the algorithms, one has to make sure that the instance on which the algorithms are tested mimics a real life example. Therefore, a simulation program is established that mimics the two year cycle and produces such an instance. Empirical results show that the best improvement algorithm considered is the Bipartite Weighted Matching Improvement algorithm. This algorithm, combined with an initial solution algorithm, is now being implemented into the current computer system of Maastricht University.

Wireless Sensor Networks (WSNs) can be defined as a cluster of sensors with a restricted power supply deployed in a specific area to gather environmental data. One of the most challenging areas of research is to design energy-efficient data gathering algorithms in large-scale WSNs, as each sensor node, in general, has limited energy resources. Literature review shows that with regards to energy saving, clustering-based techniques for data gathering are quite effective. Moreover, cluster head (CH) optimization is a non-deterministic polynomial (NP) hard problem. Both the lifespan of the network and its energy efficiency are improved by choosing the optimal path in routing. The technique put forth in this paper is based on multi swarm optimization (MSO) (i.e., multi-PSO) together with Tabu search (TS) techniques. Efficient CHs are chosen by the proposed system, which increases the optimization of routing and life of the network. The obtained results show that the MSO-Tabu approach has a 14%, 5%, 11%, and 4% higher number of clusters and a 20%, 6%, 14%, and 6% lesser average packet loss rate as compared to a genetic algorithm (GA), differential evolution (DE), Tabu, and MSO based clustering, respectively. Moreover, the MSO-Tabu approach has 136%, 36%, 136%, and 38% higher lifetime computation, and 22%, 16%, 51%, and 12% higher average dissipated energy. Thus, the study’s outcome shows that the proposed MSO-Tabu is efficient, as it enhances the number of clusters formed, average energy dissipated, lifetime computation, and there is a decrease in mean packet loss and end-to-end delay.

In this work, a systematic and simple procedure is proposed to develop an integrated fuzzy-based guidance law which consists of three fuzzy logic controllers. Each one of them is activated in a region of the interception. To allow smooth transition between these controllers, additional fuzzy-based switching system is introduced. The parameters of all the fuzzy controllers which include the distribution of the membership functions and the rules are initially obtained from the engineering experience by observing the function of each controller. Furthermore, these parameters are optimally tuned by tabu search technique through solving an optimization problem to minimize the interception time, the missile acceleration commands, and the miss distance. The simulation results show that the proposed technique is able to generate a guidance law that has better performance compared with the proportional navigation guidance law and the fuzzy guidance laws developed by engineering experience.

A tabu search method guided by shifting bottleneck for the job shop scheduling problem

Searching secret key of classical ciphers in the keyspace is a challenging NP-complete problem that can be successfully solved using metaheuristic techniques. This article proposes two metaheuristic techniques: improved genetic algorithm (IGA) and a new discrete cuckoo search (CS) algorithm for solving a classical substitution cipher. The efficiency and effectiveness of the proposed techniques are compared to the existing tabu search (TS) and genetic algorithm (GA) techniques using three criteria: (a) average number of key elements correctly detected, (b) average number of keys examined before determining the required key, and (c) the mean performance time. As per the results obtained, the improved GA is comparatively better than the existing GA for criteria (a) and (c), while the proposed CS strategy is significantly better than rest of the algorithms (i.e., GA, IGA, and TS) for all three criteria. The obtained results indicate that the proposed CS technique can be an efficient and effective option for solving other similar NP-complete combinatorial problems also.