Proposed Tabu Search Algorithm Research Articles

Low-complexity IRS beamforming based on sphere decoding and tabu search

The real-time reconfiguration of intelligent reflecting surface (IRS) is one of the crucial, yet challenging tasks required to exploit the benefits of IRS. In this paper, we study an IRS-aided multiple-input single-output (MISO) wireless network, where a coarsely quantized (few resolution bits) IRS is reconfigured to help the point-to-point communication between a multi-antenna access point (AP) and a single-antenna user. Specifically, the transmit precoders at the AP and the reflecting discrete phase shifters at the IRS are jointly optimized to maximize the signal- to-noise ratio (SNR) at the user. The optimization is studied in two practical wireless propagation environments. First, we consider an environment where the direct AP-user link is in deep fade, which enables the relaxation of the optimization problem such that it can be optimally solved by the proposed sphere-decoder (SD) algorithm. Second, a normal propagation environment for all links is considered, wherein we propose the tabu search (TS) algorithm to solve the joint active and passive beamforming problem with low complexity. The proposed TS algorithm is shown to work well, even in deep-fade AP-user link scenarios. The presented numerical results confirm the validity of our analysis and demonstrate the effectiveness of the proposed schemes over the benchmark schemes on different system setups and propagation environments.

Integer Factorization Problem Solving Using Tabu Search

Tabu Search (TS) technique is one way of search for goal algorithm in the Artificial Intelligent (AI). TS is used to solve several optimization problems. In this paper a proposed TS algorithm will be presented to solve the integer factorization problem that plays very big role in the cryptanalysis of public-key cryptography. The experimental result of this work shows that the proposed algorithm can factorize the numbers in good time compared with genetic algorithm technique.

Customer order scheduling with job-based processing on a single-machine to minimize the total completion time

This study considers a customer order scheduling (COS) problem in which each customer requests a variety of products (jobs) processed on a single flexible machine, such as the computer numerical control (CNC) machine. A sequence-independent setup for the machine is needed before processing each product. All products in a customer order are delivered to the customer when they are processed. The product ordered by a customer and completed as the last product in the order defines the customer order’s completion time. We aim to find the optimal schedule of the customer orders and the products to minimize the customer orders’ total completion time. We have studied this customer order scheduling problem with a job-based processing approach in which the same products from different customer orders form a product lot and are processed successively without being intermingled with other products. We have developed two mixed-integer linear programming models capable of solving the small and medium-sized problem instances optimally and a heuristic algorithm for large-sized problem instances. Our empirical study results show that our proposed tabu search algorithm provides optimal or near-optimal solutions in a very short time. We have also compared the job-based and order-based processing approaches for both setup and no-setup cases and observed that the job-based processing approach yields better results when jobs have setup times.

FAST: Flexible and Low-Latency State Transfer in Mobile Edge Computing

Mobile Edge Computing (MEC) brings the benefits of cloud computing, such as computation, networking, and storage resources, close to end users, thus reducing end-to-end latency and enabling various novel use cases, such as vehicle platooning, autonomous driving, and the tactile internet. However, frequent user mobility makes it challenging for the MEC to guarantee the close proximity to the users. To tackle this challenge, the underlying network has to be capable of seamlessly migrating applications between multiple MEC sites. This application migration requires the quick and flexible migration of the application states without service interruption, while minimizing the state transfer cost. In this article, we first study the state transfer optimization problem in the MEC. To solve this problem, we propose a metaheuristic algorithm based on Tabu search. We then propose Flexible and Low-Latency State Transfer in Mobile Edge Computing (FAST), the first programmable state forwarding framework. FAST flexibly and directly forwards states between source instance and destination instance based on Software-Defined Networking (SDN). Both simulation results and practical testbed results demonstrate the favorable performance of the proposed Tabu search algorithm and the FAST framework compared to the state-of-the-art schemes.

A tabu search for the design of capacitated rooted survivable planar networks

Consider a rooted directed graph G with a subset of vertices called terminals, where each arc has a positive integer capacity and a non-negative cost. For a given positive integer k, we say that G is k-survivable if every of its subgraphs obtained by removing at most k arcs admits a feasible flow that routes one unit of flow from the root to every terminal. We aim at determining a k-survivable subgraph of G of minimum total cost. We focus on the case where the input graph G is planar and propose a tabu search algorithm whose main procedure takes advantage of planar graph duality properties. In particular, we prove that it is possible to test the k-survivability of a planar graph by solving a series of shortest path problems. Experiments indicate that the proposed tabu search algorithm produces optimal solutions in a very short computing time, when these are known.

Optimal process mining of timed event logs

The problem of determining the optimal process model of an event log of traces of events with temporal information is presented. A formal description of the event log and relevant complexity measures are detailed. Then the process model and its replayability score that measures model fitness with respect to the event log are defined. Two process models are formulated, taking into account temporal information. The first, called grid process model, is reminiscent of Petri net unfolding and is a graph with multiple layers of labeled nodes and arcs connecting lower to upper layer nodes. Our second model is an extension of the first. Denoted the time grid process model, it associates a time interval to each arc. Subsequently, a Tabu search algorithm is constructed to determine the optimal process model that maximizes the replayability score subject to the constraints of the maximal number of nodes and arcs. Numerical experiments are conducted to assess the performance of the proposed Tabu search algorithm. Lastly, a healthcare case study was conducted to demonstrate the applicability of our approach for clinical pathway modeling. Special attention was paid on readability, so that final users could interpret the process mining results.

A multi-cover routing problem for planning rapid needs assessment under different information-sharing settings

In this paper, we introduce a multi-cover routing problem (MCRP), which is motivated by post-disaster rapid needs assessment operations performed to evaluate the impact of the disaster on different affected community groups. Given a set of sites, each carrying at least one community group of interest, the problem involves selecting the sites to be visited and constructing the routes. In practice, each community group is observed multiple times at different sites to make reliable evaluations; therefore, the MCRP ensures that pre-specified coverage targets are met for all community groups within the shortest time. Moreover, we assume that the completion time of the assessment operations depends on the information-sharing setting in the field, which depends on the availability of information and communication technologies (ICT). Specifically, if remote communication is possible, each assessment team can share its findings with the central coordinator immediately after completing the site visits; otherwise, all teams must return to the origin point to share information and finalize the assessments. To address these different information-sharing settings, we define two MCRP variants with different objectives and present alternative formulations for these variants. We propose two constructive heuristics and a tabu search algorithm to solve the MCRP, and conduct an extensive computational study to evaluate the performance of our heuristics with respect to different benchmark solutions. Our results show that the proposed tabu search algorithm can achieve high-quality solutions for both MCRP variants quickly. The results also highlight the importance of considering the availability of ICT in the field while devising assessment plans.

An iterated local search and tabu search for two-parallel machine scheduling problem to minimize the maximum total completion time

The parallel machine scheduling problems exist in many real-life industrial examples. In order to effectively utilize the machines and decline the total waiting time of jobs assigned to each machine, the maximum total completion time per machine on a parallel machine is thus addressed. Due to the NP-hardness of the problem under consideration, in this paper the authors explore an optimal solution by a branch-and-bound method, incorporating several dominances and a lower bound. In addition, the authors also adopted an iterated local search (ILS) and a Tabu search to find its near-optimal solution for the problem. The computational results on randomly generated instances illustrate that the proposed Tabu search algorithm performs better than the two existing algorithms, and the ILS method for the small jobs, but the ILS method performs better than the two existing algorithms and Tabu search algorithm for the bigger jobs.

Multiple-Choice Hardware/Software Partitioning for Tree Task-Graph on MPSoC

Abstract Hardware/software (HW/SW) partitioning, that decides which components of an application are implemented in hardware and which ones in software, is a crucial step in embedded system design. On modern heterogeneous embedded system platform, each component of application can typically have multiple feasible configurations/implementations, trading off quality aspects (e.g. energy consumption, completion time) with usage for various types of resources. This provides new opportunities for further improving the overall system performance, but few works explore the potential opportunity by incorporating the multiple choices of hardware implementation in the partitioning process. This paper proposes three algorithms for multiple-choice HW/SW partitioning of tree-shape task graph on multiple processors system on chip (MPSoC) with the objective of minimizing execution time, while meeting area constraint. Firstly, an efficient heuristic algorithm is proposed to rapidly generate an approximate solution. The obtained solution produced by the first algorithm is then further refined by a customized Tabu search algorithm. We also propose a dynamic programming algorithm to calculate the exact solutions for relatively smaller scale instances. Simulation results show that the proposed heuristic algorithm is able to quickly generate good approximate solutions, and the solutions become very close to the exact solutions after refined by the proposed Tabu search algorithm, in comparison to the exact solutions produced by the dynamic programming algorithm.

New Tabu Algorithm for Positioning Mining Drillholes with Blocks Uncertainty

This paper proposes a new approach to the mining exploration drillholes positioning problem (DPP) that incorporates both geostatistical and optimization techniques. A metaheuristic was developed to solve the DPP taking into account an uncertainty index that quantifies the reliability of the current interpretation of the mineral deposit. The uncertainty index was calculated from multiple deposit realizations obtained by truncated Gaussian simulations conditional to the available drillholes samplings. A linear programming model was defined to select the subset of future drillholes that maximizes coverage of the uncertainty. A Tabu Search algorithm was developed to solve large instances of this set partitioning problem. The proposed Tabu Search algorithm is shown to provide good quality solutions approaching 95% of the optimal solution in a reasonable computing time, allowing close to optimal coverage of uncertainty for a fixed investment in drilling.

Balancing stochastic parallel assembly lines

In industrial systems, efficient design of production units is important for productivity. An assembly line is a special type of industrial systems, and it has been widely used in various manufacturing industries. One possible way to achieve high efficiency in an assembly line design is to use two or more assembly lines, referred to as parallel assembly lines. Additionally, the occurrence of random events in industrial systems is inevitable. Among these random events that disturb the production process are operator unavailability, technical failures in the production line, loss of motivation, lack of training, non-qualified operators, complex tasks, etc. So, it is more realistic to consider industrial systems as stochastic context rather than deterministic one. For this purpose, in this paper, the problem of balancing parallel assembly lines with stochastic task times (SPALBP) is introduced and characterised for the first time. A chance-constrained, piecewise-linear, mixed integer programming (CPMIP) formulation and a tabu search (TS) algorithm are proposed to model and to solve the SPALBP. The objective of CPMIP and TS algorithm is minimizing the number of stations for a given cycle time. A simple lower bound calculation on the number of stations for SPALBP is proposed, and an example problem is solved using the proposed CPMIP. A set of test problems taken from the literature is solved in order to test the proposed TS algorithm, and some computational properties of the proposed solution methodologies are given. The computational results show that the proposed algorithm is very effective and successful for the SPALBP.

A capacitated vehicle routing problem with order available time in e-commerce industry

ABSTRACTIn this article, a variant of the well-known capacitated vehicle routing problem (CVRP) called the capacitated vehicle routing problem with order available time (CVRPOAT) is considered, which is observed in the operations of the current e-commerce industry. In this problem, the orders are not available for delivery at the beginning of the planning period. CVRPOAT takes all the assumptions of CVRP, except the order available time, which is determined by the precedent order picking and packing stage in the warehouse of the online grocer. The objective is to minimize the sum of vehicle completion times. An efficient tabu search algorithm is presented to tackle the problem. Moreover, a Lagrangian relaxation algorithm is developed to obtain the lower bounds of reasonably sized problems. Based on the test instances derived from benchmark data, the proposed tabu search algorithm is compared with a published related genetic algorithm, as well as the derived lower bounds. Also, the tabu search algorithm is compared with the current operation strategy of the online grocer. Computational results indicate that the gap between the lower bounds and the results of the tabu search algorithm is small and the tabu search algorithm is superior to the genetic algorithm. Moreover, the CVRPOAT formulation together with the tabu search algorithm performs much better than the current operation strategy of the online grocer.

Disassembly Sequencing Using Tabu Search

End-of-life disassembly has developed into a major research area within the sustainability paradigm, resulting in the emergence of several algorithms and structures proposing heuristics techniques such as Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Neural Networks (NN). The performance of the proposed methodologies heavily depends on the accuracy and the flexibility of the algorithms to accommodate several factors such as preserving the precedence relationships during disassembly while obtaining near- optimal and optimal solutions. This paper improves a previously proposed Genetic Algorithm model for disassembly sequencing by utilizing a faster metaheuristic algorithm, Tabu search, to obtain the optimal solution. The objectives of the proposed algorithm are to minimize (1) the traveled distance by the robotic arm, (2) the number of disassembly method changes, and (3) the number of robotic arm travels by combining the identical-material components together and hence eliminating unnecessary disassembly operations. In addition to improving the quality of optimum sequence generation, a comprehensive statistical analysis comparing the previous Genetic Algorithm and the proposed Tabu Search Algorithm is also included

Modeling and solution for the ship stowage planning problem of coils in the steel industry

AbstractWe consider a ship stowage planning problem where steel coils with known destination ports are to be loaded onto a ship. The coils are to be stowed on the ship in rows. Due to their heavy weight and cylindrical shape, coils can be stowed in at most two levels. Different from stowage problems in previous studies, in this problem there are no fixed positions on the ship for the coils due to their different sizes. At a destination port, if a coil to be unloaded is not at a top position, those blocking it need to be shuffled. In addition, the stability of ship has to be maintained after unloading at each destination port. The objective for the stowage planning problem is to minimize a combination of ship instability throughout the entire voyage, the shuffles needed for unloading at the destination ports, and the dispersion of coils to be unloaded at the same destination port. We formulate the problem as a novel mixed integer linear programming model. Several valid inequalities are derived to help reducing solution time. A tabu search (TS) algorithm is developed for the problem with the initial solution generated using a construction heuristic. To evaluate the proposed TS algorithm, numerical experiments are carried out on problem instances of three different scales by comparing it with a model‐based decomposition heuristic, the classic TS algorithm, the particle swarm optimization algorithm, and the manual method used in practice. The results show that for small problems, the proposed algorithm can generate optimal solutions. For medium and large practical problems, the proposed algorithm outperforms other methods. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 564–581, 2015

Pharmacy duty scheduling problem

In this study, we define the pharmacy duty scheduling problem, which requires a subset of pharmacies to be on duty on national holidays, at weekends, and at nights, in order to be able to satisfy the emergency medicine needs. We model the pharmacy duty scheduling problem as a multiperiod p-median problem with special side constraints, and analyze the computational complexity. We propose a Tabu Search heuristic and develop lower bound (LB) algorithms. We test the performance of mathematical models, Tabu Search heuristic, and the LBs on randomly generated instances. We analyze the current system in Izmir, the third largest city in Turkey, with a population of 3.5 million, and apply solution methods. Our results show that the proposed Tabu Search algorithm suggests improvements on the current system.

A tabu search algorithm to minimize total weighted tardiness for the job shop scheduling problem

This research presents a tabu search algorithm with a restart (TSA-R) approach to minimize total weighted tardiness (TWT) for the job shop scheduling problem. Jobs have non-identical due dates. The problem belongs to the class of NP-hard problems. The TSA-R approach uses dispatching rules to obtain an initial solution and searches for new solutions in a neighborhood based on the critical paths of jobs and blocks of operations. The TSA-R applies a new diversification scheme to exploit the initial solutions and its neighborhood structures so as to overcome entrapment issues and to enhance solutions. A computational result based on standard benchmark instances from the literature is presented to show the effectiveness of the proposed tabu search algorithm.

A Cutting-Plane Neighborhood Structure for Fixed-Charge Capacitated Multicommodity Network Design Problem

In this paper, a cutting-plane neighborhood structure is proposed for the fixed-charge capacitated multicommodity network design (CMND) problem. In the proposed structure, different strategies are used to select an open arc in the incumbent solution to be closed. Then a linear programming (LP) model is generated on the basis of the modified incumbent solution by relaxing binary variables and adding new constraints. The generated LP solution is improved using different cutting-plane inequalities. Subsequently, a new sub-mixed integer programming (MIP) model is created by fixing a number of variables in the generated LP solution. Then the local branching algorithm is used to solve the sub-MIP model and its solution is considered as a neighboring solution. A tabu search algorithm is used to evaluate the proposed neighborhood structure. To tune the parameters of the tabu search algorithm, we have used the design of experiments method. Standard problems with different sizes are employed to evaluate the proposed tabu search algorithm. Results show the efficiency and effectiveness of the tabu search algorithm compared to the best methods found in the literature.

Flow shop batching and scheduling with sequence-dependent setup times

This paper addresses the flow shop batching and scheduling problem where sequence-dependent family setup times are present and the objective is to minimize makespan. We consider violating the group technology assumption by dividing product families into batches. In order to reduce setup times, inconsistent batches are formed on different machines, which lead to non-permutation schedules. To the best of our knowledge, this is the first time that the splitting of job families into inconsistent batches has been considered in a flow shop system. A tabu search algorithm is developed which contains several neighbourhood functions, double tabu lists and a multilevel diversification structure. Compared to the state-of-the-art meta-heuristics for this problem, the proposed tabu search algorithm achieves further improvement when the group scheduling assumption is dropped. Also, various experiments conducted on the benchmark problem instances confirm the benefits of batching. Therefore, it will be prudent for the practitioners to consider adopting inconsistent batches and non-permutation schedules to improve their operational efficiency within a reasonable amount of computational effort.

Scheduling of Jobs on Identical Parallel Machines to Minimize Total Flow Time Subject to Optimal Makespan

Algorithms based on Simulated Annealing and Tabu search has been proposed and implemented on scheduling a problem of parallel machines. The identical parallel machine scheduling problem has been considered to minimize the total flow-time subject to optimal makespan. The proposed algorithms have two phases. In the first phase, an initial solution has been obtained using Longest Processing Time (LPT) dispatching rule and in the second phase, simulated annealing and tabu search have been applied to reach a near optimal solution. The performance of the both proposed algorithms have been evaluated by comparing their results for different number of jobs and processing times. The computational results indicate that the proposed Tabu Search algorithm is capable of obtaining better solutions for the given scheduling problem as compared to the Simulated Annealing algorithm. Although both of these algorithms provide the best solutions as compared to the other heuristic algorithms but in comparison of these two; Tabu Search provides the better solutions for the given problem.

Optimal Operation of Smart Grid with Fuel Cell in Isolated Islands

From the perspective of global warming suppression and depletion of energy resources, renewable energy such as wind generation (WG), photovoltaic facility (PV) and fuel cell (FC) are getting attention in distribution systems. On the other hand, the usage of an all-electrification house is increasing in the world. So the controllable loads such as electric water heater, heat pump, and electric vehicles are introduced to the power system. In addition, the controllable load could accomplish to shift the peak demand in daytime to the low demand in nighttime and it is effective method for leveling the load and upgrading the load factor. The authors propose an optimization approach to determine operational planning of WG, PV facility, diesel generator (DG), battery energy storage system (BESS), FC and hydrogen generation facility. In this optimization approach, it is assumed that forecasted data of wind speed, solar insolated, and load demands are available. The proposed method uses tabu search and genetic algorithm for optimization method. Optimizing procedure is divided into two parts. Firstly, actual load is controlled by controllable load. Secondly, the schedule of diesel generator units commitment problem is decided in the revised load. The simulation results show the reduction of operational cost.