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The performance of a welding process determines not only the cost, but also the quality of the product. How to control the welding process in order to ensure good welding performance with less cost and higher Productivity has become critical. The objective of this study is twofold: (1) developing artificial neural networks to predict welding performance using different learning algorithms: back propagation, simulated annealing and tabu search; (2) comparing and discussing the performance of neural networks trained using those algorithms. Statistical analysis shows that back propagation is able to make more accurate prediction than the other algorithms for this particular application. However, all three algorithms demonstrate impressive flexibility and robustness.

This paper aims to introduce the efficiency improvement in the operating room (OR) of a local hospital using the integration of simulation and optimization.

Based on the simulation model, a Tabu search (TS) algorithm is developed as an optimizer for the meta-heuristic optimization method to find the optimum configuration of resources for the OR operation.

The computational efficiency is improved for the optimum search. Results show that 21 percent more patients can be processed compared to the existing operation. The average time stay of patients in the OR is reduced by 17 percent.

Limited resources considered in the model may limit the capacity of the proposed method, more resources including nurses, beds in post-operative units, and beds in inpatient wards will be included in the decision variables.

Long waiting lists in the OR lead to the low performance of healthcare systems. It is crucial to identify inefficiency and to improve the OR operation efficiently.

The TS-based heuristic optimizing method developed in this research shows the promise in time saving of the optimal solution search for the OR efficiency improvement.

This paper aims to optimize the halal product distribution by minimizing the transportation cost while ensuring halal integrity of the product. The problem is considered as a capacitated vehicle routing problem (CVRP), based on the assumption that two different types of vehicles are used for distribution: vehicles dedicated for halal product distribution and vehicles dedicated for nonhalal products distribution. The problem is modeled as an integer linear program (ILP), termed CVRP-halal and nonhalal products distribution (CVRP-HNPD). It is solved using tabu-search (TS)-based algorithm and is suitable for application to real-life sized halal product distribution.

Two approaches are used in solving the problem: exact approach (integer-linear program) and approximate approach (TS). First, the problem is modeled as ILP and solved using CPLEX Solver. To solve life-sized problems, a TS-based algorithm is developed and run using MATLAB.

The experiments on numerical data and life-sized instances validate the proposed model and algorithm and show that cost-minimizing routes for HNPD are developed while ensuring the halal integrity of the products.

The proposed model and algorithm are suitable as decision support tools for managers responsible for distribution of halal products as they facilitate the development of minimum cost distribution routes for halal and nonhalal products while maintaining the integrity of halal products. The model and algorithm provide a low transportation cost strategy at the operational level of halal products distribution while fulfilling the halal logistics requirement.

To the best of the author’s knowledge, this is the first study that specifically deals with the CVRP of halal products distribution by proposing CVRP-HNPD model and TS-CVRP-HNPD algorithm. The proposed model and algorithm ensure the integrity of halal products along the distribution chain, from the warehouse (distribution center) to the retailer, while achieving lowest transportation cost.

This paper deals with the application of a Tabu Search (TS) metaheuristic to the design of digital filters, with coefficient values expressed as the sum of power of two. The performances of the algorithm are heavily affected by the choice of its parameters, which in turn are related to different implemented strategies. The tuning of these parameters has been performed after many tests. The results of the proposed technique are compared to those obtained by simply rounding the optimal values of coefficients obtained by means of Remetz algorithm, and to those obtained using a simulated annealing algorithm.

Notes that, until now, to route robotics travel, most investigations have utilized the fixed coordinate of placement points and magazine of the traveling salesman problem (TSP) method to sequence the placement points after the magazine has been arbitrarily assigned. Points out that, in fact, robotics travel routing should be based on a relative coordinate because the robotics, board and magazine simultaneously move at different speeds during assembly. Consequently, the coordinates of placement point and magazine are constantly changing. In this study, a novel tabu search (TS) based approach is presented. The proposed approach can arrange the placement sequence and assign the magazine slots to yield a performance better than the conventional one. Results presented herein also demonstrate that the larger the number of placement points and/or part numbers, the better the performance.

The purpose of this paper is to present a real world application of an innovative hybrid system reliability optimization algorithm combining Tabu search with an evolutionary algorithm (TSEA). This algorithm combines Tabu search and Genetic algorithm to provide a more efficient search method.

The new algorithm is applied to an aircraft structure to optimize its reliability and maintain its structural integrity. For retrofitting the horizontal stabilizer under severe stall buffet conditions, a decision support system (DSS) is developed using the TSEA algorithm. This system solves a reliability optimization problem under cost and configuration constraints. The DSS contains three components: a graphical user interface, a database and several modules to provide the optimized retrofitting solutions.

The authors found that the proposed algorithm performs much better than state-of-the-art methods such as Strength Pareto Evolutionary Algorithms on bench mark problems. In addition, the proposed TSEA method can be easily applied to complex real world optimization problem with superior performance. When the full combination of all input variables increases exponentially, the DSS become very efficient.

This paper presents an application of the TSEA algorithm for solving nonlinear multi-objective reliability optimization problems embedded in a DSS. The solutions include where to install doublers and stiffeners. Compromise programming is used to rank all non-dominant solutions.

The proposed hybrid algorithm (TSEA) assigns fitness based upon global dominance which ensures its convergence to the non-dominant front. The high efficiency of this algorithm came from using Tabu list to guidance the search to the Pareto-optimal solutions.

This research explores mitigating carbon emissions and integrating sustainability in e-commerce logistics by optimizing the multi-depot pollution routing problem with time windows (MDPRPTW). A proposed model contrasts non-collaborative and collaborative decision-making for order assignment among logistics service providers (LSPs), incorporating low-carbon considerations.

The model is substantiated using improved adaptive large neighborhood search (IALNS), tabu search (TS) and oriented ant colony algorithm (OACA) within the context of e-commerce logistics. For model validation, a normal distribution is employed to generate random demand and inputs, derived from the location and requirements files of LSPs.

This research validates the efficacy of e-commerce logistics optimization and IALNS, TS and OACA algorithms, especially when demand follows a normal distribution. It establishes that cooperation among LSPs can substantially reduce carbon emissions and costs, emphasizing the importance of integrating sustainability in e-commerce logistics optimization.

This paper proposes a meta-heuristic algorithm to solve the NP-hard problem. Methodologies such as reinforcement learning can be investigated in future work.

This research can help logistics managers understand the status of sustainable and cost-effective logistics operations and provide a basis for optimal decision-making.

This paper describes the complexity of the MDPRPTW model, which addresses both carbon emissions and cost reduction. Detailed information about the algorithm, methodology and computational studies is investigated. The research problem encompasses various practical aspects related to routing optimization in e-commerce logistics, aiming for sustainable development.

The purpose of this study is to formulate an algorithm designed to discern the optimal routes for efficient municipal solid waste (MSW) collection.

The research method is simulation. The proposed algorithm combines heuristics derived from the constructive genetic algorithm (CGA) and tabu search (TS). The algorithm is applied in a municipality located at Southern Brazil, with 40,000 inhabitants, circa.

The implementation achieved a remarkable 25.44% reduction in daily mileage of the vehicles, resulting in savings of 150.80 km/month and 1,809.60 km/year. Additionally, it reduced greenhouse gas emissions (including fossil CO₂, CH₄, N₂O, total CO₂e and biogenic CO₂) by an average of 26.15%. Moreover, it saved 39 min of daily working time.

Further research should thoroughly analyze the feasibility of decision-making regarding planning, scheduling and scaling municipal services using digital technology.

The municipality now has a tool to improve public management, mainly related with municipal solid waste. The municipality reduced the cost of public management of municipal solid waste, redirecting funds to other priorities, such as public health and education.

The study integrates MSW collection service with an online platform based on Google MapsTM. The advantages of employing geographical information systems are agility, low cost, adaptation to changes and accuracy.

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.

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.

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.

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.

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.

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.