Tabu Search Approach Research Articles

Bi-Objective, Dynamic, Multiprocessor Open-Shop Scheduling: A Hybrid Scatter Search–Tabu Search Approach

Bi-Objective, Dynamic, Multiprocessor Open-Shop Scheduling: A Hybrid Scatter Search–Tabu Search Approach

Balancing parallel assembly lines with human-robot collaboration: problem definition, mathematical model and tabu search approach

Human-robot collaboration (HRC), as an emerging production mode, has garnered significant attention in the context of the growing smart manufacturing. This study aims to investigate the application of human-robot collaboration in parallel assembly lines and explore its potential for improving productivity, resource utilisation and flexibility. To the best of our knowledge, this is one of the first attempts to consider the combination of parallel assembly line balancing problem (PALBP) and human-robot collaboration. In this study, the parallel assembly line balancing problem with human-robot collaboration (PALBP-HRC) is introduced and characterised. The collaboration mode allows human workers and robots to execute tasks both in parallel and in collaboration. A mixed-integer programming model (MIP) that minimises the cycle time is formulated and a lower bound is proposed. Due to the NP-hardness, we develop an adapted tabu search (TS) algorithm specifically tailored for problem characterisation. Experimental results indicate that the proposed TS algorithm achieves competing performance compared to the MIP and other algorithms, including genetic algorithm (GA) and simulated annealing (SA) algorithm. The comparison between PALBP and PALBP-HRC demonstrates that the integration of human-robot collaboration to parallel assembly line can achieve significant improvements in efficiency and flexibility.

A hybrid matheuristic approach for the integrated location routing problem of the pineapple supply chain

This paper proposes a matheuristic approach for the location-routing of industrial platforms of the pineapple supply chain problem. We have proposed a three-phase methodology to solve the considered problem. The first phase consists of obtaining the potential supply in terms of suitability and productivity, the potential location of platforms, and the times of the value chain echelons. In the second phase, a mathematical optimization model for the location problem of platforms considering the coverage in terms of timing is proposed. Finally, the final phase proposes a cluster-routing and a granular reactive tabu search approach for the routing phase. The proposed methodology uses official information on production times, speed, and capacity and georeferenced aptitude, spatial, economic, and land yield information for the first time. The proposed approach has been validated through scenarios, particularly pineapple exports for the Colombian country. The obtained results show the efficiency of the proposed approach.

A bi-objective model for aircraft and crew recovery considering individual flight legs and maintenance requirements

This paper proposes a new bi-objective aircraft and crew recovery model. Unlike previous researches, the proposed model considers all individual flight legs in the recovery process. The objective functions minimize the recovery cost regarding crew swapping, flight cancellation, flight delay, and crew deadhead costs. Besides, the crew sit time is reduced considering the maximum legal working hours and the minimum essential rest time. Moreover, maintenance operations can be done during the ground time, not necessarily at the end of duty day. The model is never infeasible because flight cancelation is an alternative solution for recovering a schedule. A modified Tabu Search (TS) approach has been developed to solve the model. The performance of the model and solution approach is examined by several test problems in small, medium, and large scales. The results indicate that TS reaches optimal and close-optimal in a short computational time, especially in large scale problems.

SAP: Subchain-Aware NFV Service Placement in Mobile Edge Cloud

Existing Network Function Virtualization (NFV) service placements that reuse already deployed network functions either reuse an entire Service Function Chain (SFC) or only individual network functions while ignoring the chain configuration cost for configuring the SFC traffic steering and ignoring the reliability of the network functions. Also, the Mobile Edge Cloud (MEC) frameworks that are required to implement an NFV service placement should ideally seamlessly cooperate with the various existing NFV Management and Orchestration (MANO) frameworks. However, the existing MEC frameworks lack multi-MANO support. We formulate the novel Subchain-Aware NFV service Placement (SAP) optimization model that accounts for the configuration cost for stitching together reused network functions to an SFC and strives to reuse existing subchains of consecutive network functions (with already deployed SFC traffic steering), while accounting for the recovery cost of network functions with limited reliability. We develop Tabu-SAP, a Tabu search approach to solve the SAP optimization problem. Furthermore, we introduce the novel Automated Provisioning framework for MEC (APMEC) with open-source OpenStack implementation to enable the deployment of Tabu-SAP in real networks; APMEC supports multiple MANOs through a loose coupling MANO-MEC design. Our Tabu-SAP evaluations indicate an around eightfold increase of the number of supported SFCs compared to the state-of-the-art reuse of individual network functions, while substantially reducing the total cost, which includes the chain configuration cost. Also, for long SFCs of seven or more network functions, the Tabu-SAP total cost is less than 10% higher than the optimal solution (which requires over ten times longer execution time).

Optimization of a rural bus service integrated with e-commerce deliveries guided by a new sustainable policy in China

Electronic business has expanded, resulting in flourishing rural e-commerce globally, and particularly in China. This paper introduces a novel rural public transit system that integrates passenger and freight services—a rural electric bus service that provides services for passengers and e-commerce packages. This blended service reduces logistical costs and generates additional income for the transit operator. The objective is to minimise cost for the transit user and operator by using a mixed-integer linear programming model that solves bus routing and bus trip scheduling for multiple rural towns; in addition, the model is tested by the Gurobi solver at small-scale instances. The developed solution is a hybrid heuristic algorithm based on adaptive large neighborhood search and tabu search approaches. Finally, a case study in China is conducted to examine the proposed cost-effective methodology and ensure that the integrated scheme will significantly save operational system costs without causing any passenger inconvenience. The research also shows how to optimally design the size of rural electric buses to reduce purchasing and maintenance costs. The optimal results of the case study, comprising 5 towns and 78 villages, show an annual cost saving of approximately ¥847,000 (approximately $121,000) and an annual mileage saving of approximately 66,000 km for the proposed scheme. In addition, the case study explicates key parameters selected for transfer package requirements and the effective deployment of the proposed scheme.

Software-defined networking enabled big data tasks scheduling: A tabu search approach

The growth of information technology along with the revolution of the industry and business has led to the generation of an enormous amount of data. This big data needs a platform beyond the traditional data possessing context that relies on some computational servers communicating through a network in its lower layer. One of the most important challenges in data processing is how to transfer the big batches of data between the servers to achieve fast responsiveness. Consequently, the underlying network plays a critical role in the performance of a big data analysis platform. Ideally, this network must use the shortest path that has the lowest amount of load, to transfer the large-scale data. To address this issue, we propose a software-defined networking (SDN) enabled scheduling method that uses the tabu search algorithm to schedule big data tasks. The proposed algorithm not only considers data locality but also uses the network traffic status for efficient scheduling. Our extensive simulative study in the Mininet emulator shows that the proposed scheme gives high performance and minimizes job completion time.

Application of the Gradient-Based Metaheuristic Optimizerto Solve the Optimal Conductor Selection Problemin Three-Phase Asymmetric Distribution Networks

This study addresses the problem of selecting the conductor sizes for medium-voltage distribution networks with radial configurations. The optimization model that represents this problem is part of the mixed-integer non-linear programming (MINLP) models, in which a power flow must be solved for each possible combination of conductor sizes. The main objective of this optimization problem is to find the best set of conductor sizes that minimize an economic objective function composed of the total costs of conducting materials added with the expected annual costs of the energy losses by proposing a new hybrid optimization methodology from the family of combinatorial optimization methods. To solve the MINLP model, a master–slave optimization method based on the modified version of the gradient-based metaheuristic optimizer (MGbMO) combined with the successive approximation power flow method for unbalanced distribution networks is presented. The MGbMO defines the set of conductor sizes assignable for each distribution line using an integer codification. The slave stage (three-phase power flow) quantifies the total power losses and their expected annual operating costs. Numerical results in the IEEE 8-, 27-, and 85-bus grids demonstrate the effectiveness of the proposed master–slave optimizer when compared with multiple combinatorial optimization methods (vortex search algorithm, the Newton-metaheuristic optimizer, the traditional and Chu and Beasley genetic algorithms, and the tabu search approaches). Two scenarios regarding the demand behavior were analyzed for the IEEE 8- and 27-bus grids: a peak load operation was considered, and, for the IEEE 85-bus grid, the daily demand behavior, including the presence of renewable generators, was considered. The 85-bus grid allowed showing that the most realistic operative scenario for selecting conductors is the case where a demand curve is implemented since reductions over 40% in the annual investment and operating costs were found when compared to the peak load operating condition. All numerical validations were performed in MATLAB software.

Tabu-Based Adaptive Large Neighborhood Search for Multi-Depot Petrol Station Replenishment With Open Inter-Depot Routes

The petrol station replenishment problem (PSRP) refers to the process of transporting petroleum products from oil depots to petrol stations via tank trucks. It mainly consists of two parts: allocating petroleum products to tank trucks and planning the travel route of each truck. In this study, we examine a new variant of PSRP by considering a multi-depot vehicle routing problem with open inter-depot routes (MDVRPOI). Each depot can act as an intermediate replenishment facility, and each truck can be reloaded at any depot any number of times within the working period. Moreover, trucks can end their routes at any depot instead of making a long empty drive to the start depot. The trucks are heterogeneous with multiple load-specific compartments. We formulate the problem as a mixed-integer linear programming (MILP) model. Given the problem’s complexity, a tabu-based adaptive large neighborhood search (T-ALNS) algorithm is proposed, which integrates the tabu search approach into ALNS to solve the problem effectively. The T-ALNS executes multiple problem-tailored destroy/repair operators on the station, trip, and route levels. A local search procedure with problem-specific operators and an adaptive strategy is further embedded into T-ALNS. We use the real data of an oil company in China to evaluate our algorithm. Computational results show that our T-ALNS significantly outperforms the CPLEX solver and other algorithms in terms of solution quality and computation time. Further, it realizes an average reduction in transportation cost of about 45% compared to the company’s actual strategy.

Fuzzy modeling system based on GA fuzzy rule extraction and hybrid of differential evolution and Tabu search approaches: Application in synthesis gas conversion to valuable hydrocarbons process

In the present study, a fuzzy approach is employed in order to develop a model for the conversion of synthesis gas to valuable hydrocarbon process. The fuzzy rules are extracted by clustering technique in this study. The numbers of fuzzy sets for input and output variables are optimized by genetic algorithm (GA) so that the number of data which follows the inconsistent rules, is minimized. Firstly, the center values of clusters are estimated by a proposed logarithmic distance method and the clusters and their centers are then calculated by k-means method. The prediction of CO conversion and olefin and paraffin selectivities is performed based on five input variables: H2/CO content, Time on Stream (TOS), temperature, Weight Hourly Space Velocity (WHSV) and phosphorous content by the proposed fuzzy model so that the parameters of which are optimized by combination of Differential Elevation and Tabu Search optimization approaches (DETS). The comparison of the performance of the designed approaches with two other models (i.e. fuzzy and GA fuzzy models) is executed and the results clearly demonstrate that the developed DETS- GA clustering fuzzy model can be defined as a powerful tool in modeling of the conversion of synthesis gas to hydrocarbons process with regression analysis (R2) of over 96%.

Low complexity hybrid layered tabu‐likelihood ascent search for large MIMO detection with perfect and estimated channel state information

AbstractIn this work, we proposed a low‐complexity hybrid layered tabu‐likelihood ascent search (LTLAS) algorithm for large multiple‐input multiple‐output (MIMO) system. The conventional layered tabu search (LTS) approach involves many partial reactive tabu searches (RTSs), and each RTS requires an initialization and searching phase. In the proposed algorithm, we restricted the upper limit of the number of RTS operations. Once RTS operations exceed the limit, RTS will be replaced by low‐complexity likelihood ascent search (LAS) operations. The block‐based detection approach is considered to maintain a higher signal‐to‐noise ratio (SNR) detection performance. An efficient precomputation technique is derived, which can suppress redundant computations. The simulation results show that the bit error rate (BER) performance of the proposed detection method is close to the conventional LTS method. The complexity analysis shows that the proposed method has significantly lower computational complexity than conventional methods. Also, the proposed method can reduce almost 50% of real operations to achieve a BER of 10−3.

Nurse-patient relationship for multi-period home health care routing and scheduling problem.

This article proposes a novel dynamic objective function in a multi-period home health care (HHC) problem, known as the nurse-patient relationship (NPR). The nurse-patient relationship score indicating the trust a patient has for his or her care worker increases when the same people meet regularly and decreases when they are apart. Managing human resources in HHC is a combination of routing and scheduling problems. Due to computational complexity of the HHC problem, a 28-day home health care problem is decomposed into daily subproblems, and solved sequentially with the tabu search. The solutions are then combined to give a solution to the original problem. For problems with less complex constraints, the NPR model can also be solved using exact methods such as CPLEX. For larger scale instances, however, the numerical results show that the NPR model can only be solved in reasonable times using our proposed tabu search approach. The solutions obtained from the NPR models are compared against those from existing models in the literature such as preference and continuity of care. Essentially, the analysis revealed that the proposed NPR models encouraged the search algorithm to assign the same care worker to visit the same patient. In addition, it had a tendency to assign a care worker on consecutive days to each patient, which is one of the key factors in promoting trust between patients and care workers. This leads to the efficacy of monitoring patient’s disease progression and treatment.

Fuzzy Modeling System Based on Ga Fuzzy Rule Extraction and Hybrid of Differential Evolution and Tabu Search Approaches: Application in Synthesis Gas Conversion to Valuable Hydrocarbons Process

Fuzzy Modeling System Based on Ga Fuzzy Rule Extraction and Hybrid of Differential Evolution and Tabu Search Approaches: Application in Synthesis Gas Conversion to Valuable Hydrocarbons Process

Hybrid Tabu Search and Genetic Algorithm Imputation Approach for Incomplete Data

The common problem for data collection is happening missing value during the data collection and processing process that the quality of the data testing is decreased. A computational based technique for dealing with missing values, namely Genetic Algorithm Imputation (GAI). The usage was used to estimate the dataset's missing values. GAI generates the optimal set of missing values with the acquisition of information as a function of fitness to measure individual solutions' performance. GAI conducts continuous searching until the missing criteria value is found according to best fitness. So, it is trapped in optimal conditions temporarily. The improvement of GAI with tabu search is known as TS-GAI, that strength is two metaheuristic techniques modified at the mutase stage to distract the local optima's search. In applying missing values, this technique works better when many possible values are used instead of the mixed attribute having missing values. Because the new generation chromosome values generate many opportunities to make up for the missing values. The experimental results show that the TS-GAI shows better performance on 30% MV with a fitness value of 0.212. It converges at 159 iterations. Generally, TS-GAI is a faster iteration than simple GAI and it has a lower RMSE level than other imputation techniques.

A hybrid dynamic programming - Tabu Search approach for the long-term hydropower scheduling problem

A hybrid dynamic programming - Tabu Search approach for the long-term hydropower scheduling problem

Multi-operator communication based differential evolution with sequential Tabu Search approach for job shop scheduling problems

This paper develops a multi-operator based differential evolution with a communication strategy (MCDE) being integrated with a sequential Tabu Search (MCDE/TS) to solve the job shop scheduling problem (JSSP) with the objective of minimizing makespan. The three variants of DE which are implemented in the proposed algorithm evolve as independent sub-populations, which relate to a communication strategy that maintains the diversity and quality of each sub-population by employing a proposed mixed selection strategy to avoid premature convergence. The best solution order obtained from MCDE is then passed to Tabu Search (TS) and the evolution process is continued, creating neighbour solutions with N7 neighbourhood structure. This algorithm ensures the population diversity with curving the premature convergence but experiences faster convergence. The design of experiment for parameter tuning is employed for the best combination of the proposed algorithm’s parameter. The performance of the proposed MCDE/TS algorithm is evaluated against a number of state-of-the-art algorithms to show its competence in solving 122 standard benchmark instances.

Location Dynamic Tabu Routing Protocol for MANETs

Mobile Ad-Hoc Networks are currently experiencing a second youth in terms of research interest as well as providing benefits to our society. Clearly, this has been fostered by the wide range of applications that have become actually feasible thanks to the pervasive and increasing presence of smartphones, drones, sensors and other small devices with communication and sensing capability. Aiming to deploy solutions able to self-organize without the need for any infrastructure support, packet routing remains an open and critical research problem. Several routing solutions have been conceived to guarantee delivery and low overhead in this context. A promising approach, trying to limit the information needed and stored in the network, is represented by stateless solutions, a class of solutions not relying on topology state information. However, since they exploit local knowledge, the achieved performance in terms of packet delivery and latency is not always up to expectations. To this aim, we propose a dynamic routing protocol based on a tabu search approach, relying on local network knowledge and in-packet short-term memory to alleviate the local minima problem. A through experimental assessment is conducted, measuring protocol performance under different configurations and profiles, evidencing its benefits.

A hybrid variable neighborhood search approach for the multi-depot green vehicle routing problem

The Multi-Depot Green Vehicle Routing Problem (MDGVRP) is an extension of the well-known Green Vehicle Routing Problem (GVRP) where a fleet of alternative fuel-powered vehicles (AFVs) are used to serve the customers. GVRP consists of determining AFV tours such that the total distance travelled is minimum. The AFVs depart from the depot, serve a set of customers, and complete their tours at the depot without exceeding their driving range and the maximum tour duration. AFVs may refuel en-route at public refueling stations. In MDGVRP, the AFVs are dispatched from different depot locations and may refuel during the day at any depot or refueling station. We formulate MDGVRP as a mixed integer linear programming model and develop a hybrid General Variable Neighborhood Search and Tabu Search approach by proposing new problem-specific neighborhood structures to solve the problem effectively. We assess the performance of our method using the GVRP dataset from the literature. Our results show that the proposed method can provide high quality solutions in short computation times. Then, we extend these instances to the multi-depot case and compare our solutions for small-size instances with the optimal solutions. We also report our results for large-size problems and investigate the trade-offs associated with operating multiple depots and adopting different refueling policies to provide further insights for both academicians and practitioners.

Solving the buffer allocation problem using simulation-based optimisation

In production lines, buffers function as a means to decouple stations, which reduce the effect that station failures and varying process times have on the complete line’s throughput. However, adding larger buffers can be costly, for example, in the automotive industry where it results in increased working capital. This manuscript addresses the buffer allocation problem (BAP), seeking the smallest total buffer size while meeting a prescribed throughput by employing a simulation-based optimisation approach. A Tabu Search algorithm searches the solution space for the optimal buffer configuration while a discrete event simulation model evaluates each configuration, accounting for the machine (un)reliability. Since the multiple simulations add a sizeable computational burden, our approach introduces a novel neighbourhood search mechanism, which borrows from the Theory of Constrains. Solving test sets available in the literature suggest that this approach is 18 times faster than prior Adaptive Tabu Search approaches for small problems, and more than five times faster for medium-sized problems.

Minimizing the cycle time in the distributed flow shop scheduling problem

Abstract We deal with the problem of cyclic scheduling tasks in the distributed manufacturing system with factories having the flow shop layout. The optimization aim is to minimize the cycle time. We have proposed the proprietary model and introduced some special properties of the problem, which allow us to find the cycle time for each given particular solution quickly. Since the formulated problem is NP-hard, we propose a metaheuristic algorithm based on the tabu search approach. The quality of the algorithm has been verified experimentally on common benchmarks and compared to other methods known in the literature, namely the scatter search and list scheduling. The problem also has a relation with the problem of balancing workloads of identical parallel production lines.