Developing exact and Tabu search algorithms for simultaneously determining AGV loop and P/D stations in single loop systems

There are some issues which have to be addressed when designing an automated guided vehicles system (AGVS) such as flow-path layout, traffic management, the number and the location of pick up and delivery points, vehicle routing and so on. One of the AGVS guide path configurations discussed in the previous researches includes a single-loop which is the subject of this paper. In unidirectional single loop systems, determining the loop for the motion of an AGV, and the location of pick up and delivery (P/D) stations in the cells, are prominent points which, when considered simultaneously, lead to better results than determining each one independently. However, in the literature it is proved that the problem of separately determining the shortest feasible loop is a NP-complete problem. In this paper, by considering a from-to chart and a block layout as the input of problem, we try to determine: (1) a single loop, with at least one shared edge with each cell, (2) the direction of the flow and (3) the location of P/D stations on the loop, all at the same time, in a way that the total travel distance on the loop be minimised. In this regard, first a new exact algorithm is presented and then three heuristic algorithms are developed utilising a Tabu search (TS) method. Solving randomly generated test problems shows that our exact algorithm is capable of solving small size problems; also all three TS algorithms work efficiently in solving problems that could not be solved by exact algorithms.     

Tabu search optimization of externally pressurized barrels and domes

Barrelled cylinders and domes of generalized elliptical profile are optimized for their buckling resistance when loaded by static external pressure. The optimum shells are found using either a static or adaptive tabu search method, which utilizes a repeat structural analysis tool. Results show that it is possible, through correct profiling of a meridian, to achieve failure pressures 40% and 20% higher than a benchmark cylinder and hemisphere, respectively. Numerical predictions are confirmed by pressurizing a series of laboratory-scale shells to destruction. Correlation between numerical predictions and experimental results is good.     

Optimal flow path design of unidirectional AGV systems

This paper describes an alternative formulation of the AGV flow path layout (FPL) problem which was first formulated by Gaskins and Tanchoco (1987) as a zero-one integer programming problem. A computationally efficient procedure is proposed which is based on the branch-and-bound technique. An algorithm for satisfying the reachability condition for nodes in the AGV flow path network is also presented. A simple illustrative example is discussed to demonstrate the procedure, and a more complex problem is also given.     

Tabu search for the redundancy allocation problem of homogenous series–parallel multi-state systems

This paper develops an efficient tabu search (TS) heuristic to solve the redundancy allocation problem for multi-state series–parallel systems. The system has a range of performance levels from perfect functioning to complete failure. Identical redundant elements are included in order to achieve a desirable level of availability. The elements of the system are characterized by their cost, performance and availability. These elements are chosen from a list of products available in the market. System availability is defined as the ability to satisfy consumer demand, which is represented as a piecewise cumulative load curve. A universal generating function technique is applied to evaluate system availability. The proposed TS heuristic determines the minimal cost system configuration under availability constraints. An originality of our approach is that it proceeds by dividing the search space into a set of disjoint subsets, and then by applying TS to each subset. The design problem, solved in this study, has been previously analyzed using genetic algorithms (GAs). Numerical results for the test problems from previous research are reported, and larger test problems are randomly generated. Comparisons show that the proposed TS out-performs GA solutions, in terms of both the solution quality and the execution time.     

A tabu search algorithm for the pallet loading problem

This paper presents a new heuristic algorithm for the pallet loading problem, the problem of packing the maximum number of identical rectangular boxes onto a rectangular pallet. The problem arises in distribution and logistics and has many practical applications. We have developed a tabu search algorithm based on new types of moves. Instead of moving individual boxes, we propose moving blocks, sets of boxes with the same orientation. We have tested our algorithm on the whole sets Cover I and Cover II, usually taken as a reference for this problem, and we obtain excellent results in very short computing times.This complete issue was revised and published online in November 2004. The previous version contained a false date. Correspondence to: R. Alvarez-ValdesThis paper has been partially supported by the Project PBC-02-002, Consejeria de Ciencia y Tecnologia, JCCM, the Spanish Ministry of Science and Technology, DPI2002-02553 and the Valencian Science and Technology Agency, GRUPOS03/174     

Tabu search approach based on a similarity coefficient for cell formation in generalized group technology

Both a similarity coefficient method (SCM)-based algorithm and meta-heuristics have been widely applied to various cell formation problems; however, few studies have explored the combination of the two methods. This paper addresses a hybrid algorithm, in which, based on the initial solution produced by a new SCM-based hierarchical clustering method, a fast and effective tabu search approach is presented to solve cell formation in group technology (GT). The proposed algorithm is applied to several problems from literature and a group of the randomly generated instances with alternative process plans and compared with simulated annealing (SA) and other TS; the results demonstrate that the proposed algorithm is available and efficient for cell formation in generalized GT.     

Simultaneous design of a product family and its related supply chain using a Tabu Search algorithm

Product family design is currently facing a multitude of challenges, the main problem stemming from the diversity offered to consumers. To design a product family, designers have to define an efficient bill of materials which ensures product assembly within a predefined length of time in order to satisfy the synchronised delivery principle. In addition, the modules used to assemble the finished products have to be competitive in terms of logistical costs. The ability to anticipate the constraints associated with the production process and with transportation is consequently of great interest. In this paper, we focus on the process of identifying a set of modules to be used in the assembly of the finished product. The objective is to define the bill of materials for each product from the modules belonging to that set, and to assign these modules to distant facilities where they will be manufactured and then shipped to a nearby facility for final assembly within a specific time. We use a set partitioning formulations to represent the problem, and solve it by adapting a Tabu Search algorithm in which the assembly process and the supply chain design are considered at the same time.     

A bioinspired path planning approach for mobile robots based on improved sparrow search algorithm

In this paper, a bioinspired path planning approach for mobile robots is proposed. The approach is based on the sparrow search algorithm, which is an intelligent optimization algorithm inspired by the group wisdom, foraging, and anti-predation behaviors of sparrows. To obtain high-quality paths and fast convergence, an improved sparrow search algorithm is proposed with three new strategies. First, a linear path strategy is proposed, which can transform the polyline in the corner of the path into a smooth line, to enable the robot to reach the goal faster. Then, a new neighborhood search strategy is used to improve the fitness value of the global optimal individual, and a new position update function is used to speed up the convergence. Finally, a new multi-index comprehensive evaluation method is designed to evaluate these algorithms. Experimental results show that the proposed algorithm has a shorter path and faster convergence than other state-of-the-art studies.The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-021-00366-x     

基于粒子群遗传算法的泊车系统路径规划研究

针对智能停车库自动导引运输车(automated guided vehicle,AGV)存取车路径规划问题,提出了一种基于粒子群和遗传算法的动态自适应混合算法.在标准粒子群算法和遗传算法的基础上,通过引入动态自适应调整策略分别对惯性权重系数、学习因子以及交叉变异概率公式进行了优化.在进化初期,通过在惯性权重系数和学习因子之间建立动态联动关系来实现对粒子速度和位置的实时有效更新;在进化后期,通过引入自适应遗传算法的交叉、变异操作来增强混合算法的全局搜索能力,提高算法的进化速度和收敛精度.为验证混合算法的可行性和有效性,选用MATLAB软件对其进行仿真测试.仿真测试结果显示,与禁忌搜索算法、蚁群算法以及遗传算法相比,混合算法表现出较强的全局搜索能力和较好的收敛性能,表明混合算法可行和有效.     

Fast local neighborhood search algorithm for the no-wait flow shop scheduling with total flow time minimization

A fast local neighbourhood search (FLNS) algorithm is proposed in this paper to minimise the total flow time in the no-wait flow shop scheduling problem, which is known to be NP-hard for more than two machines. In this work, an unscheduled job sequence is constructed firstly according to the total processing time and standard deviation of jobs on the machines. This job sequence is undergone an initial optimisation using basic neighbourhood search algorithm. Then, an innovative local neighbourhood search scheme is designed to search for the partial neighbourhood in each iterative processing and calculate the neighbourhood solution with an objective increment method. This not only improves the solution quality significantly, but also speeds up the convergence of the solution of the algorithm. Moreover, a probabilistic acceptance criterion is adopted to help our method escape from the local optima. Based on Taillard’s benchmarks, the experimental results show that the proposed FLNS algorithm is superior to major existing algorithms (IHA, IBH_LS, GA-VNS and DHS) in terms of both quality and robustness, and can provide best upper bounds. The in-depth statistical analysis demonstrates that the promising performance of our proposed algorithm is also statistically significant.     

An efficient iterated local search algorithm for the total tardiness blocking flow shop problem

This paper deals with the blocking flow shop problem and proposes an Iterated Local Search (ILS) procedure combined with a variable neighbourhood search (VNS) for the total tardiness minimisation. The proposed ILS makes use of a NEH-based procedure to generate the initial solution, and uses a local search to intensify the exploration that combines the insertion and swap neighbourhood and uses a perturbation mechanism consisting of three neighbourhood operators to diversify the search. The computational evaluation has shown the effectiveness of combining the insertion and swap neighbourhood during the search despite the insertion neighbourhood being more effective than the swap neighbourhood for this problem. Finally, the computation of this algorithm when evaluated against two other algorithms from the literature shows good performance.     

基于Dijkstra-蚁群算法的泊车系统路径规划研究

针对智能停车库中自动导引运输车(automated guided vehicle,AGV)存取车路径规划问题,提出了一种基于Dijkstra-蚁群算法(Dijkstra-ACO)的泊车系统路径规划方法.首先利用链接可视图法建立环境模型,并在此环境模型下,采用Dijkstra算法规划出AGV的初始路径;其次,通过引入节点随机选择机制、调整信息素更新方式和限定信息素阈值策略等对基本蚁群算法进行优化改进;最后,选用改进的蚁群算法对初始路径进行优化.结果显示:Dijkstra算法和混合算法均能使AGV有效避开障碍物,然后搜索到一条从起点到终点的无碰优化路径;与Dijkstra算法相比,混合算法能有效提高路径搜索效率,缩短搜索路径长度,改善搜索路径质量,表明该算法正确、可行及有效,且具有较强的全局搜索能力和较好的收敛性能,能够满足AGV存取车路径规划的要求.     

An integrated approach to determine the block layout,AGV flow path and the location of pick-up/delivery points in single-loop systems

Layout design and material handling system design are two of the major aspects of facility planning. Although both aspects directly influence each other, the classical approach to the layout design is carried out in two separate steps: in the first step the block layout, i.e. the location of the departments in the workshop, is constructed, and in the second step, the material handling system is designed. The separate optimisation of these two aspects of the problem leads to solutions that can be far from the global optimum. In this paper, we develop an integrated algorithm to design the facilities and material handling systems. We focus on single-loop AGV systems. The proposed algorithm determines the block layout, AGV single-loop flow path and pick-up delivery stations, simultaneously. The associated from–to chart and the area of departments are the principal inputs of the algorithm. The objective is minimising total material flow distance among all departments. The results of our computational experiments show the algorithm was coded using MATLAB 7.0, and that our integrated algorithm is more efficient in terms of both the objective function value and the runtime.     

A variable neighbourhood search algorithm for the flexible job-shop scheduling problem

The flexible job-shop scheduling problem (FJSP) is a generalisation of the classical job-shop scheduling problem which allows an operation of each job to be executed by any machine out of a set of available machines. FJSP consists of two sub-problems which are assigning each operation to a machine out of a set of capable machines (routing sub-problem) and sequencing the assigned operations on the machines (sequencing sub-problem). This paper proposes a variable neighbourhood search (VNS) algorithm that solves the FJSP to minimise makespan. In the process of the presented algorithm, various neighbourhood structures related to assignment and sequencing problems are used for generating neighbouring solutions. To compare our algorithm with previous ones, an extensive computational study on 181 benchmark problems has been conducted. The results obtained from the presented algorithm are quite comparable to those obtained by the best-known algorithms for FJSP.     

A two-stage path planning approach for multiple car-like robots based on PH curves and a modified harmony search algorithm

In this article, collision-avoidance path planning for multiple car-like robots with variable motion is formulated as a two-stage objective optimization problem minimizing both the total length of all paths and the task’s completion time. Accordingly, a new approach based on Pythagorean Hodograph (PH) curves and Modified Harmony Search algorithm is proposed to solve the two-stage path-planning problem subject to kinematic constraints such as velocity, acceleration, and minimum turning radius. First, a method of path planning based on PH curves for a single robot is proposed. Second, a mathematical model of the two-stage path-planning problem for multiple car-like robots with variable motion subject to kinematic constraints is constructed that the first-stage minimizes the total length of all paths and the second-stage minimizes the task’s completion time. Finally, a modified harmony search algorithm is applied to solve the two-stage optimization problem. A set of experiments demonstrate the effectiveness of the proposed approach.     

A hybrid Pareto-based local search algorithm for multi-objective flexible job shop scheduling problems

This paper presents a hybrid Pareto-based local search (PLS) algorithm for solving the multi-objective flexible job shop scheduling problem. Three minimisation objectives are considered simultaneously, i.e. the maximum completion time (makespan), the total workload of all machines, and the workload of the critical machine. In this study, several well-designed neighbouring approaches are proposed, which consider the problem characteristics and thus can hold fast convergence ability while keep the population with a certain level of quality and diversity. Moreover, a variable neighbourhood search (VNS) based self-adaptive strategy is embedded in the hybrid algorithm to utilise the neighbouring approaches efficiently. Then, an external Pareto archive is developed to record the non-dominated solutions found so far. In addition, a speed-up method is devised to update the Pareto archive set. Experimental results on several well-known benchmarks show the efficiency of the proposed hybrid algorithm. It is concluded that the PLS algorithm is superior to the very recent algorithms, in term of both search quality and computational efficiency.     

A tabu search algorithm for solving a multicriteria flexible job shop scheduling problem

The problem that we consider in this article is a flexible job shop scheduling problem issued from the printing and boarding industry. Two criteria have to be minimised, the makespan and the maximum lateness. Two tabu search algorithms are proposed for finding a set of non-dominated solutions: the first is based on the minimisation of one criterion subject to a bound on the second criterion (ε-constraint approach) and the second is based on the minimisation of a linear combination of criteria. These algorithms are tested on benchmark instances from the literature and the results are discussed. The total tardiness is considered as a third criterion for the second tabu search and results are presented and discussed.     

Redundancy allocation of series-parallel systems using a variable neighborhood search algorithm

This paper presents a meta-heuristic algorithm, variable neighborhood search (VNS), to the redundancy allocation problem (RAP). The RAP, an NP-hard problem, has attracted the attention of much prior research, generally in a restricted form where each subsystem must consist of identical components. The newer meta-heuristic methods overcome this limitation and offer a practical way to solve large instances of the relaxed RAP where different components can be used in parallel. Authors’ previously published work has shown promise for the variable neighborhood descent (VND) method, the simplest version among VNS variations, on RAP. The variable neighborhood search method itself has not been used in reliability design, yet it is a method that fits those combinatorial problems with potential neighborhood structures, as in the case of the RAP. Therefore, authors further extended their work to develop a VNS algorithm for the RAP and tested a set of well-known benchmark problems from the literature. Results on 33 test instances ranging from less to severely constrained conditions show that the variable neighborhood search method improves the performance of VND and provides a competitive solution quality at economically computational expense in comparison with the best-known heuristics including ant colony optimization, genetic algorithm, and tabu search.     

A hybrid water flow algorithm for multi-objective flexible flow shop scheduling problems

In this article, the multi-objective flexible flow shop scheduling problem with limited intermediate buffers is addressed. The objectives considered in this problem consist of minimizing the completion time of jobs and minimizing the total tardiness time of jobs. A hybrid water flow algorithm for solving this problem is proposed. Landscape analysis is performed to determine the weights of objective functions, which guide the exploration of feasible regions and movement towards the optimal Pareto solution set. Local and global neighbourhood structures are integrated in the erosion process of the algorithm, while evaporation and precipitation processes are included to enhance the solution exploitation capability of the algorithm in unexplored neighbouring regions. An improvement process is used to reinforce the final Pareto solution set obtained. The performance of the proposed algorithm is tested with benchmark and randomly generated instances. The computational results and comparisons demonstrate the effectiveness and efficiency of the proposed algorithm.     

Optimal solution for the flow path design problem of a balanced unidirectional AGV system

An optimal flow path layout (FPL) design method is introduced as a handy tool for an automated guided vehicle (AGV) system planing stage. The problem is analysed and formulated by linear mixed-integer programming. A procedure based on the branch-and-bound depth-first search technique is proposed to solve the FPL problem. The procedure is implemented as an efficient computer program and yields an optimal solution in a small number of iterations. Using the transportation model for calculating the required and optimal flow of empty vehicles, system balance is achieved. Finally, two examples are given. A simple illustrative example is discussed to demonstrate the procedure, and a realistic FPL problem with 23 nodes, 66 arcs and nine pick-up/delivery stations is solved.