Methodologies to Optimize Automated Guided Vehicle Scheduling and Routing Problems: A Review Study

Transcription

1 J Intell Robot Syst (2015) 77: DOI /s Methodologies to Optimize Automated Guided Vehicle Scheduling and Routing Problems: A Review Study Hamed Fazlollahtabar Mohammad Saidi-Mehrabad Received: 6 June 2013 / Accepted: 13 November 2013 / Published online: 14 December 2013 Springer Science+Business Media Dordrecht 2013 Abstract Automated guided vehicles (AGVs) are used as a material handling device in flexible manufacturing systems. Traditionally, AGVs were mostly used at manufacturing systems, but currently other applications of AGVs are extensively developed in other areas, such as warehouses, container terminals and transportation systems. This paper discusses literature related to different methodologies to optimize AGV systems for the two significant problems of scheduling and routing at manufacturing, distribution, transshipment and transportation systems. We categorized the methodologies into mathematical methods (exact and heuristics), simulation studies, metaheuristic techniques and artificial intelligent based approaches. Keywords Automated guided vehicle systems Literature survey Scheduling Routing H. Fazlollahtabar ( ) M. Saidi-Mehrabad Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran hfazl@iust.ac.ir M. Saidi-Mehrabad mehrabad@iust.ac.ir 1 Introduction An automated guided vehicle (AGV) is a driverless material handling system used for horizontal movement of materials. AGVs were introduced in 1955 [62]. The use of AGVs has grown enormously since their introduction. The number of areas of application and variation in types has increased significantly. AGVs can be used in inside and outside environments, such as manufacturing, distribution, transshipment and (external) transportation areas. At manufacturing areas, AGVs are used to transport all types of materials related to the manufacturing process. According to Gotting [30] over 20,000 AGVs were used in industrial applications. The author states that the usage of AGVs will pay off for environments with repeating transportation patterns. Examples of these environments are distribution, transshipment and transportation systems. Warehouses and cross docking centers are examples of distribution areas. AGVs are used in these areas for the internal transport of, for example, pallets between the various departments, such as receiving, storage, sorting and shipment areas. At transshipment systems, such as container terminals, AGVs take care of the transport of products between the various modes of transport. Gotting [30] presented an overview of available technology for automation in container terminals. Furthermore, navigation and vehicle guidance systems applicable in various indoor/outdoor environments are described. Haefner and Bieschke [32] stated that AGV systems

2 526 J Intell Robot Syst (2015) 77: can provide benefits to both the port and its customers by executing transportation requests between vessels and inland transportation. Namely, in non-automated terminals this transportation process is one of the least efficient and most costly processes. AGVs can also be used in the outdoor transportation process. An example of such a transportation system is an underground automated transportation system with AGVs travelling in tubes between companies and an airport (see [94, 95]). In such systems, we notice a high traffic density and long tube driving times. It has even been studied if AGVs can be used as a communication system between work stations (see [59]). Clearly, the specifications of AGVs differ per environment. To transport a container, the capacity of an AGV should at least be equal to 40 tons. Less capacity is required for the transport of pallets at warehouses. Furthermore, at container terminals self-lifting automated guided vehicles (ALVs) are used. For this type of AGV no other equipment is required to transfer a load to the vehicle. Vis and Harika [98] and Yang et al. [101] discussed this new type of AGVs in more detail. In this paper, we will discuss literature concerning the usage of AGVs in manufacturing and the new areas of application, namely distribution, transshipment and transportation systems. The most important differences between traditional and new areas of application are the number of AGVs used, the number of transportation requests, the occupancy degree of AGVs, the distances to be travelled and the number of pick-up and delivery points where transportation requests become available. At manufacturing systems, a small number of AGVs with relatively low occupancy degrees are used to transport a small number of requests over short distances between a few pickup and delivery points. For continuous mass transport in these systems conveyors are used instead of large numbers of AGVs (see [30]). In contrast to manufacturing systems, large numbers of AGVs (up to 400; [94]) were used to execute a large number of repeating transportation tasks at container terminals and external transportation systems. Furthermore, operational conditions, such as weather conditions and spatial dimensions, found in outside environments (container terminals and external transportation systems) differ from the operational conditions in inside areas (manufacturing and distribution systems). In general the objective functions studied for the twp problems of scheduling and routing are listed in Table 1. In previous studies, a survey of research in the design and control of automated guided vehicle systems has been found being focused on literature related to design and control issues of AGV systems at manufacturing, distribution, transshipment and transportation systems [97]. It was concluded that most models can be applied for design problems at manufacturing centers. Some of these models and new models already proved to be successful in large AGV systems. In fact, new analytical and simulation models need to be developed for large AGV systems to overcome large computation times, NP completeness, congestion, deadlocks and delays in the system and finite planning horizons. The present work discusses literature related to different methodologies to optimize AGV systems for the two significant problems of scheduling and routing at manufacturing, distribution, transshipment and transportation systems. We categorized the methodologies into mathematical methods, simulation studies, metaheuristic techniques and artificial intelligent based approaches. The motivation for categorization of scheduling and routing is based on their significance in flexible manufacturing systems. Regardless of the objective Table 1 Objective functions for scheduling and routing problems No. Scheduling Routing 1 Makespan The global transportation cost 2 Total (Weighted) Completion Time Drivers and vehicles fixed costs 3 (Weighted) Mean Flow Time Number of vehicles and/or drivers 4 Mean Waiting Time Balancing of the routes 5 Lateness Penalties for not/partially served customers 6 Tardiness

3 J Intell Robot Syst (2015) 77: functions of decision making, the path an AGV moves and the time it arrives at a shop or workstation is substantial for economic decision making. Thus, classifying different optimization approaches having various objective functions lead to better conceptualization and implementation of such systems in research and application. In general, optimization techniques are classified in three categories: exact approaches, heuristics and meta-heuristics. Exact approaches seek global optimality and generally fail to provide good solutions on NP-hard problems (although many counter examples exist). Heuristics are problem-specific approaches which take advantage of the problem properties to derive solution strategies. Meta-heuristics are general heuristics schemes that can be applied to many optimization problems. 2 Scheduling The literature discussed so far on scheduling of AGVs hardly considers (side) constraints such as capacity constraints of the machines where transportation jobs become available, schedules of other types of equipment and limited parking space for vehicles. These side constraints become more and more important in real life situations with large AGV systems. To take these constraints into account more attention should be paid to integrated scheduling of different types of material handling equipment, which also meet space and capacity requirements. 2.1 Mathematical Optimization Exact Approaches A carefully designed and efficiently managed material handling system plays an important role in planning and operation of a flexible manufacturing system. Most of the researchers have addressed machine and vehicle scheduling as two independent problems and most of the research has been emphasized only on single objective optimization. Multiobjective problems in scheduling with conflicting objectives are more complex and combinatorial in nature and hardly have a unique solution. In [70] a scheduling problem for automated guided vehicles in container terminals was defined and formulated as a Minimum Cost Flow model. The problem was then solved by a novel algorithm, NSA+, which extended the standard Network Simplex Algorithm (NSA). Like NSA, NSA+ is a complete algorithm, which means that it guarantees optimality of the solution if it finds one within the time available. To complement NSA+, an incomplete algorithm Greedy Vehicle Search (GVS) was designed and implemented. The NSA+ and GVS were compared and contrasted to evaluate their relative strength and weakness. With polynomial time complexity, NSA+ can be used to solve very large problems. Nishi et al. [64] addressed a bilevel decomposition algorithm for solving the simultaneous scheduling and conflict-free routing problems for automated guided vehicles. The overall objective was to minimize the total weighted tardiness of the set of jobs related to these tasks. A mixed integer formulation was decomposed into two levels: the upper level master problem of task assignment and scheduling; and the lower level routing subproblem. The master problem was solved by using Lagrangian relaxation and a lower bound was obtained. Either the solution turns out to be feasible for the lower level or a feasible solution for the problem was constructed, and an upper bound was obtained. If the convergence was not satisfied, cuts were generated to exclude previous feasible solutions before solving the master problem again. Two types of cuts were proposed to reduce the duality gap. The effectiveness of the proposed method was investigated from computational experiments. Bing [4], Veeravalli et al. [96] and Zaremba et al. [104] proposed analytical models for the scheduling of AGVs. Sinriech and Palni [84] developed an optimal scheduling algorithm for a single multiple-load vehicle travelling in a closed loop during a finite planning horizon. The arrival time and processing time of each job were known. Sinriech and Kotlarski [83] extended this algorithm such that it can be used to schedule dynamically multiple-load vehicles in a single loop while minimizing transfer times of jobs and the number of loops travelled by the vehicle. Due to the dynamic nature of the algorithm, changes in the scheduling plan can be made to react to unexpected events. It has been shown that this dynamic

4 528 J Intell Robot Syst (2015) 77: algorithm outperforms existing commonly used nondynamic scheduling rules from a perspective of cycle timesandworkinprogress. Hartmann [34] introduced a general model for scheduling of material handling equipment at container terminals such that the average lateness of a job and the average set up time were minimized. The model was, in contrast to other literature, not applicable for a single type of equipment, but it can be used for various types of equipment, such as AGVs and straddle carriers. Meersmans [60] gave mathematical models for the integrated scheduling of automated handling equipment at container terminals, such as AGVs and automated stacking cranes. Exact and heuristic algorithms are presented to solve these models. To test the performance of these algorithms computational studies were performed. Meersmans and Wagelmans [61] proposed a model that deals with the integrated scheduling of all types of material handling equipments at an automated container terminal. The objective was to optimize the overall performance of the container terminal, by minimizing the makespan of the schedule. A branch and bound algorithm was proposed which produced optimal or near optimal schedules. For large problems a beam search heuristic was presented. It has been shown that with the heuristic solutions, close to optimal solutions can be obtained in reasonable computation times. Both the integrated approach of Meersmans [60] and Meersmans and Wagelmans [61] did not take into account that there is a limited space for AGVs waiting to be (un)loaded at the cranes. A summary of the exact approaches is shown in Table Heuristic Approaches Scheduling of flexible manufacturing systems is a well-known NP-hard problem which is very complex, due to additional considerations like material handling, alternative routing, and alternative machines. Improvement in the performance of a flexible manufacturing system can be expected by efficient utilization of its resources, by proper integration and synchronization of their scheduling. Reddy and Rao [71] addressed multiobjective scheduling problems in a flexible manufacturing environment using evolutionary algorithms. The authors made an attempt to consider simultaneously the machine and vehicle scheduling aspects in an FMS and addressed the combined problem for the minimization of makespan, mean flow time and mean tardiness objectives. Differential evolution is a powerful tool which proved itself as a better alternative for solving optimization problems like scheduling. Satish Kumar et al. [76] addressed simultaneous scheduling of both machines and material handling system with alternative machines for the makespan minimization objective. The authors proposed a machine selection heuristic and a vehicle assignment heuristic which were incorporated in the differential evolution approach to assign the tasks, to appropriate machine and vehicle, and to minimize cycle time. Ebben et al. [19] presented a generic approach to model integrated scheduling problems with a finite horizon such that capacity, parking space and release time constraints were met. The main decision within the model concerns when and how to transport all jobs from their origins to their destinations. The output of Table 2 Summary of exact approaches in scheduling No. Researcher Approach 1 Zaremba et al. [104] Analytical models 2 Sinriech and Palni [84] Dynamic program 3 Bing [4] Analytical models 4 Meersmans and Wagelmans [61] Branch and bound algorithm 5 Veeravalli et al. [96] Analytical models 6 Meersmans [60] Mathematical programming 7 Sinriech and Kotlarski [83] Dynamic algorithm 8 Hartmann [34] General mathematical model 9 Rashidi and Tsang [70] Minimum Cost Flow 10 Nishietal.[64] Bilevel decomposition algorithm

5 J Intell Robot Syst (2015) 77: the model includes an assignment of orders to vehicles, a route for each vehicle and an assignment of vehicles to parking areas. Clearly, in this solution routing and scheduling aspects are combined. A constructive heuristic is presented which transforms sequences resulting from an (infeasible) solution to better and feasible solutions. Both the model and heuristic are applied for an automated underground transportation system. It has been shown that computation times are small. 2.2 Meta-Heuristics AGVs are among various advanced material handling techniques that are finding increasing applications today. They can be interfaced to various other production and storage equipment and controlled through an intelligent computer control system. Both the scheduling of operations on machine centers as well as the scheduling of AGVs are essential factors contributing to the efficiency of the overall flexible manufacturing system (FMS). An increase in the performance of the FMS under consideration would be expected as a result of making the scheduling of AGVs an integral part of the overall scheduling activity. Jerald et al. [39] studied simultaneous scheduling of parts and AGVs for a particular type of FMS environment by using a non-traditional optimization technique called the adaptive genetic algorithm (AGA). The problem considered was a large variety problem (16 machines and 43 parts) and combined objective function (minimizing penalty cost and minimizing machine idle time). If the parts and AGVs were properly scheduled, then the idle time of the machining center was minimized; as such, their utilization was maximized. Minimizing the penalty cost for not meeting the delivery date was also considered in their work. Two contradictory objectives were to be achieved simultaneously by scheduling parts and AGVs using the adaptive genetic algorithm. The results were compared to those obtained by conventional genetic algorithm. 2.3 Artificial Intelligent The increased use of FMS to efficiently provide customers with diversified products has created a significant set of operational challenges. Although extensive research has been conducted on design and operational problems of automated manufacturing systems, many problems remain unsolved. In particular, the scheduling task, the control problem during the operation, is of importance owing to the dynamic nature of the FMS such as flexible parts, tools and AGV routings. The FMS scheduling problem has been tackled by various traditional optimization techniques. While these methods can give an optimal solution to smallscale problems, they are often inefficient when applied to larger-scale problems. Jerald et al. [38] designed different scheduling mechanisms to generate optimum scheduling; these included non-traditional approaches such as genetic algorithm (GA), simulated annealing (SA) algorithm, memetic algorithm (MA) and particle swarm algorithm (PSA) by considering multiple objectives, i.e., minimizing the idle time of the machine and minimizing the total penalty cost for not meeting the deadline concurrently. The MA presented was essentially a GA with an element of SA. The results of the different optimization algorithms (MA, GA, SA, and PSA) were compared and conclusions were presented. Solving multi-objective scientific and engineering problems is, generally, a very difficult goal. In these optimization problems, the objectives often conflict across a high-dimensional problem space and require extensive computational resources. Saravana Sankar et al. [75] developed a migration model of parallelization for a GA based multi-objective evolutionary algorithm (MOEA). The MOEA generated a near-optimal schedule by simultaneously achieving two contradicting objectives of an FMS. The parallel implementation of the migration model showed a speedup in computation time and needed less objective function evaluations when compared to a single-population algorithm. So, even for a single-processor computer, implementing the parallel algorithm in a serial manner (pseudo-parallel) delivers better results. Two versions of the migration model were constructed and the performance of two parallel GAs was compared for their effectiveness in bringing genetic diversity and minimizing the total number of functional evaluations. 2.4 Simulation Gaur et al. [28] studied the problem of scheduling an AGV in a flexible manufacturing system while minimizing completion times. A vehicle needs to visit each site only after its release time and before its due time.

6 530 J Intell Robot Syst (2015) 77: Sabuncuoglu [72]usedsimulationtotestvariousAGV scheduling rules. Fazlollahtabar et al. [22] concerned with applying tandem automated guided vehicle (TAGV) configurations as material handling devices and optimizing the production time considering the effective time parameters in a flexible automated manufacturing system (FAMS) using Monte Carlo simulation. Due to different configurations of TAGVs in an FAMS, the material handling activities are performed. With respect to various stochastic time parameters and the TAGV defects during material handling processes, sample data were collected and their corresponding probability distributions were fitted. Using the probability distributions, they modelled the TAGV material handling problem via Monte Carlo simulation. The effectiveness of the proposed model was illustrated in a case study. 3Routing If the dispatching decision is carried out, a route and schedule should be planned for the AGV to move the job from its origin to its destination within the AGV network. A route implies the path which should be taken by the AGV when making a pick-up or delivery. The related schedule gives arrival and departure times of the AGV at each part, pick-up and delivery point and intersection during the route to ensure collision free routing. The selection of a certain route and schedule is effective on the performance of the system. The longer it takes to transport a job, the fewer the jobs that can be handled within a certain time. Therefore, one of the objectives of the routing of AGVs is to minimize transportation times. Algorithms have to be developed to solve the routing problem. Two categories of algorithms can be distinguished, namely static and dynamic algorithms. Analogies between these problems from transportation literature and routing and scheduling problems for AGVs in automated guided vehicle systems are clear. A number of loads at various locations have to be transported by vehicles at a certain start time or at a certain moment within a time window. However, the use of the described models from transportation literature is not always possible. These models do not take into account congestion in the system. Furthermore, most models are not developed to deal with real time response to dynamically changing transportation requests. Therefore, attention is paid in the literature to developing non-conflicting routes for AGVs. With a non-conflicting route, an AGV arrives as early as possible at the destination without conflicting with other AGVs. In AGV routing with static algorithms the route from node i to node j is determined in advance and is always used if a load has to be transported from i to j. In this way, a simple assumption is to choose the route with the shortest distance from i to j. However, these static algorithms are not able to adapt to changes in the system and traffic conditions. In dynamic routing, the routing decision is made based on real-time information and, as a result, various routes between i and j can be chosen. Static routing problems in AGV systems are related to vehicle routing problems (VRP) studied in transportation literature. In the vehicle routing problem a set of n clients with known demands need to be served by a fleet of m vehicles with limited capacity. The vehicles are all housed at one depot. The route of each vehicle starts and ends at this depot. m least costs (length) routes have to be planned such that each customer is served exactly once and that the total demand of the customers served by each vehicle does not exceed the capacity of each vehicle. The objective is to minimize the total distance of all m routes under previously mentioned conditions. This is an NP-hard problem to solve. The vehicle routing problem has been studied extensively in literature. Bodin et al. [6], Laporte [56] and Fisher [24] provided an overview of literature in this area. A more recent paper observing this problem is from Kelly and Xu [44]. They proposed a set partitioning based heuristic. In a systematic way fragments of routes are combined to obtain high-quality solutions. Vehicle scheduling problems can be seen as routing problems with additional constraints concerning times at which certain activities (e.g. delivery of a load) have to be executed. Vehicle activities have to be sequenced both in time and space. An overview of methods to solve vehicle scheduling problems has been given in Bodin et al. [6]. Numerous studies on the vehicle routing problem with time windows have been executed. Branch and bound methods [52], insertion heuristics [85], extensions of vehicle routing problem heuristics [87], Lagrangian relaxation [25, 50], constrained shortest

7 J Intell Robot Syst (2015) 77: path relaxation [15, 51] and set covering formulations [9] can be used to find solutions to the problem. Desrochers et al. [16] provided an overview of solution methods. More recently, Cordeau et al. [11] presented a survey of approximation and optimal approaches to solve the vehicle routing problem with time windows. 3.1 Mathematical Optimization Exact Approaches Ilic [36] examined some of the general principles and analysis methods that were used to design an automated material handling system. Their paper focused on one type of automated handling system that seems especially suitable for automation in discrete-product manufacturing. These types of automated handling systems are called automated guided vehicle systems. These systems are most applicable for the automation of low and medium-volume handling situations, where the routing of materials is more individualized. For guided vehicles, a new quantitative method for analyzing these systems was developed. Examples were presented to demonstrate the method. Langevin et al. [55] proposed a dynamic programming based method to solve exactly instances with two vehicles. They solved the combined problem of dispatching and conflict free routing. Rajotia et al. [68] proposed a semidynamic time window constrained routing strategy. They used the notions of reserved and free time windows to manage the motion of vehicles. FMS comprise, automated machine tools, automated material handling, and automated storage and automated retrieval systems (AS/RS) as essential components. Effective sequencing and scheduling of the material handling systems (MHS) can have a major impact on the productivity of the manufacturing system. The material handling cannot be neglected while scheduling the production tasks. It is necessary to take into account the interaction between machines, material handling systems and computer. Rajotia et al. [69] added time windows to nodes to represent arrival and departure times of the vehicle which is going to occupy the node. Other vehicles are allowed to travel through the specific node at a time point not included in one of the time windows. Furthermore, the direction on an arc for an arriving vehicle is indicated with a time window. Dijkstra s algorithm is applied on this network with time windows to find the least congested and fastest routes for AGVs. The methods of Desrochers et al. [15] andfisher et al. [25] were capable of solving the problem to optimality for a problem size with 100 customers. Kohl et al. [51] even solved a problem with 150 customers by using a strong valid inequality, the 2-path cut, to produce better lower bounds and by using an effective separation algorithm. Due to the difficulty of the problem, solving larger problems in reasonable times are difficult. According to Savelsbergh and Sol [77] the dynamic routing of independent vehicles can be solved by applying a branch and price algorithm. Gans and Van Ryzin [27] represented the problem as a classical set covering model. Performance is measured in terms of congestion. The vehicle routing problem with time windows (VRPTW) is a generalization of the vehicle routing problem. For each customer a time window is defined. The time window [s,t] restricts the service time of the customer to fall into the time interval from s to t. Such time-windows arise, for example, due to traffic restrictions or fixed time schedules of customers and their products. Finding a feasible solution to the vehicle routing problem with time windows is an NP-complete problem. A generalization of the vehicle routing problem with time windows is the pick-up and delivery problem with time windows (PDPTW). Optimal routes have to be constructed such that transportation requests requiring pick-up and delivery are met. The VRPTW is a special case of the PDPTW, in which all destinations are the same depot. Qiu and Hsu [67] presented an algorithm to route large numbers of AGVs conflict-free over a bidirectional guide path while minimizing travel times. Desaulniers et al. [14] proposed an exact method that enables to solve instances with up to four vehicles. Their approach combined a greedy search heuristic (to find a feasible solution and set bound on delays), column generation and a branch and cut procedure. Their method presented however some limits since its efficiency depends highly on the performance of the starting heuristic. If no feasible solution is found by the search heuristic, then no optimal solution can be found. The search heuristic performed poorly when the level of congestion increased.

8 532 J Intell Robot Syst (2015) 77: Yoo et al. [102] presented a simple and easily adaptable deadlock avoidance algorithm for an AGV system. The algorithm used the graph-theoretic approach. Unlike Petri-net-based methods, which are complex and static, it is easy to modify the existing model as the configuration of the system changes. Therefore, it is suitable for the AGV system in an FMS and a retail or postal distribution center. Moreover, because it is very simple, it is appropriate for real-time control mechanisms. Their work consisted of two parts: the first part presented an AGV deadlock avoidance algorithm that used the graph-theoretic approach, and the second suggested appropriate routing strategies based on the proposed algorithm. The results showed that this deadlock avoidance algorithm can be modified easily whenever the configuration of an FMS changes and provide high-performance on the deadlock avoidance. Finally, experimental results that confirmed the validity of the approach were provided. Corréa et al. [12] proposed a hybrid method designed to solve a problem of dispatching and conflict free routing of AGVs in an FMS. The problem consisted in the simultaneous assignment, scheduling and conflict free routing of the vehicles. The approach consisted in a decomposition method where the master problem (scheduling) was modelled with constraint programming and the subproblem (conflict free routing) with mixed integer programming. Logic cuts were generated by the subproblems and used in the master problem to prune optimal scheduling solutions whose routing plan exhibits conflicts. The hybrid method presented herein allowed solving instances with up to six AGVs. An automated manufacturing system (AMS) is a complex network of processing, inspecting, and buffering nodes connected by system of transportation mechanisms. For an AMS, it is desirable to be capable to increase or decrease the output with the rise and fall of demand. Such specifications show the complexity of decision making in the field of AMSs and the need for concise and accurate modeling methods. Therefore, Fazlollahtabar et al. [23] proposed a flexible jobshop automated manufacturing system to optimize the material flow. The flexibility was on the multi-shops of the same type and also multiple products that can be produced. An automated guided vehicle was applied for material handling. The objective was to optimize the material flow regarding the demand fluctuations and machine specifications. When simple AGVs having no random access load transfer mechanism are used for carrying multiple loads between workstations, the loads cannot be handled independently. Lack of standardization is often a key problem area limiting the applications of any new automated microhandling technology, due to that equipment makers may have to spend an excessive amount of time and resources to customize automation solutions. Carrier-based material handling systems provide product-independent solutions. Product independency is an essential requirement for reusing material handling systems, because they allow product change without mechanical changes in the transport devices. Sanchez-Salmeron et al. [74] described a new automated inter-machine material handling system for micro-manufacturing integration, based on the standard carrier DIN The main task of the system was to transport (full/empty) carriers between different stations/machines in a micro-manufacturing plant, to integrate assembly and manufacturing. The authors designed a conveyor belt and an automated guided vehicle system to fit into a linear pick-andplace micro-manufacturing plant. Prototypes of the different components were then developed and tested. Salehipour et al. [73] presented a new solution framework to locate the workstations in the TAGV systems. So far, the research has focused on minimizing the total flow or minimizing the total AGV transitions in each zone. The authors focused on minimizing total cumulative flow among workstations. This objective allocates workstations to an AGV route such that total waiting time of workstations to be supplied by the AGV is minimized. They developed a property which simplified the available mathematical formulation of the problem. Also a development in a heuristic algorithm was proposed for the problem. Computational results showed that the heuristic could yield very high-quality solutions and in many cases optimal solutions. A summary of the exact approaches is shown in Table Heuristic Approaches A number of authors have addressed the conflict free routing problem with a static transportation requests set, i.e., with all requests known a priori. Krishnamurthy et al. [53] proposed an optimization approach. Their objective was to minimize the

9 J Intell Robot Syst (2015) 77: Table 3 Summary of exact approaches in routing No. Researcher Approach 1 Krishnamurthy et al. [53] Column generation 2 Ilic [36] Quantitative method 3 Langevin et al. [55] Dynamic programming 4 Rajotia et al. [69] Semidynamic time window model 5 Desaulniers et al. [14] Branch and cut and column generation 6 Levitina and Abezgaouz [58] LIFO model 7 Yoo et al. [102] Graph theory 8 Corréa et al. [12] Mixed integer programming 9 Sanchez-Salmeron et al. [74] Analytical model 10 Salehipour et al. [73] Mathematical formulation 11 Fazlollahtabar et al. [23] Mathematical programming makespan. They assumed that the assignment of tasks to AGVs was given and they solved the routing problem by column generation. Their method generated very good solutions in spite of the fact that it is not optimal (column generation is performed at the root node of the search tree only). Lee et al. [57] presented a two-staged traffic control scheme to solve a conflict free routing problem. Their heuristic method consisted of generating off-line k-shortest paths in the first stage before the on-line traffic controller picks a conflict free shortest path whenever a dispatch command for an AGV was issued (second stage). Jawahar et al. [37] attempted to link the operation of AGVs with the production schedule and suggested a heuristic algorithm that employed vehicle dispatching rules (VDR) for conflict resolution. The vdrs considered in their work were: shortest operation time, longest operation time, longest travel time and shortest travel time. The performance of the vdrs in the proposed heuristic was compared with makespan criteria. The results showed that the shortest travel time provided the best solutions compared to other VDRs. Oboth et al. [65] presented a heuristic method to solve the dispatching and routing problems but not simultaneously. Scheduling was performed first and a sequential path generation heuristic (SPG) was used to generate conflict free routes. The SPG was inspired from Krishnamurthy et al. [53] static version of the AGV routing problem and applied to a dynamic environment while relaxing some of the limiting assumptions like equal and constant speeds of AGVs. When conflict is encountered, no feedback is sent to the scheduling module. The AGV being routed has to be delayed if an alternate route cannot be generated. The authors used rules for positioning idle AGVs instead of letting the system manage them. Levitina and Abezgaouz [58] considered the case when loads were placed in 5 at pallets and each new picked up pallet was loaded on the top of batch of pallets already carried by the AGV. To avoid use of excessive space and time needed to reorder pallets in the batch, the loading unloading procedures should be performed in accordance with last-in-firstout (LIFO) rule. The authors formulated the condition of existence of AGV routes in which, it visited each workstation only once and meets LIFO constraint. Dumas et al. [18] presented an algorithm using column generation to solve the problem. Overviews of literature in this area have been given in Solomon and Desrosiers [86] and Savelsbergh and Sol [78]. Krishnamurthy et al. [53] observed a static routing problem in which AGVs have to be routed on a bidirectional network in a conflict-free way such that the makespan is minimized. The problem is solved by applying column generation. Kim and Tanchoco [46] also discussed the problem of finding conflict free routes in a bidirectional network. The algorithm they proposed was based on the shortest path methods of Dijkstra. According to Taghaboni-Dutta and Tanchoco [91] dynamic route planning can be done in two ways, namely complete route planning and incremental route planning. With complete route planning the entire route from origin to the final destination is determined at once. With incremental route planning, the route is planned segment for segment until the vehicle reaches its destination. The disadvantage of complete route planning is that a route may become invalid during

10 534 J Intell Robot Syst (2015) 77: operation due to unexpected events. However, with applying incremental route planning, the optimality of the complete route is disregarded. Taghaboni-Dutta and Tanchoco [91] gave a dynamic approach to incremental route planning. The traffic control is estimated by modelling the guide path as a queuing network. Oboth et al. [65] discussed the problem of dynamic conflict routing of AGVs in bidirectional networks. The solution methodologies from Krishnamurthy et al. [53] were implemented in a dynamic environment. Literature on intelligent routing of AGVs includes Dhouib and Kadi [17], Soylu et al. [88] and Wu et al. [99]. Levitina and Abezgaouz [58] studied the routing of multiple load AGVs. Han and McGinnis [33] and Bookbinder and Krik [7] presented heuristics for routing vehicles. Analytical algorithms are presented in Blair et al. [5] andchen[10]. 3.2 Meta-Heuristics A TAGV system deals with grouping workstations into some nonoverlapping areas and assigning to each area exactly one AGV. Tavakkoli-Moghaddam et al. [92] presented a new non-linear integer mathematical model to group n machines into N loops to minimize both inter-loop and intra-loop flow simultaneously based on balanced-loops strategy and inter-machine flows taken from ideas of cellular manufacturing systems. Due to computational difficulties of exact methods in solving the proposed model, an SA algorithm was proposed. A number of test problems were generated at random and solved by the proposed SA in order to show the efficiency of the algorithm. Finally, the results were reported by both the Lingo software and the proposed SA algorithm. A tandem AGV configuration connects all cells of a manufacturing facility/plant by means of nonoverlapping, single-vehicle closed loops. Each loop has at least one additional P/D station, provided as an interface between adjacent loops. The study by Zanjirani Farahani et al. [103] described the development of tabu search and genetic algorithm procedures for designing tandem AGV systems. The objective was to minimize the maximum workload of the system. Both algorithms had mechanisms to prevent solutions with intersecting loops. The new algorithms and the partitioning algorithm presented by Bozer and Srinivasan [8] were compared using randomly generated test problems. Results showed that for large-scale problems, the partitioning algorithm often leads to infeasible configurations with crossed loops in spite of its shorter running time. However the newly developed algorithm avoided infeasible configurations and often yields better objective function values. Effective design of automatic material handling devices is one of the most important decisions in flexible cellular manufacturing systems. Minimization of material handling operations could lead to optimization of overall operational costs. The tandem layout is according to dividing workstations to some non-overlapping closed zones to which a TAGV is allocated for internal transfers. Shirazi et al. [80] illustrated a non-linear multi-objective problem for minimizing the material flow intra and inter-loops and minimization of maximum amount of inter cell flow, considering the limitation of TAGV work-loading. For reducing variability of material flow and establishing balanced zone layout, some new constraints have been added to the problem based on six sigma approach. Due to the complexity of the machine grouping control problem, a modified ant colony optimization algorithm was used for solving this model. Finally, to validate the efficacy of the proposed model, numerical illustrations have been worked out. Genetic algorithms can also be used to solve vehicle scheduling problems (see, for example, [3]). Dynamic vehicle routing problems are also studied in transportation literature. Multiple demands for service employing in a real-time way have to be satisfied by vehicles. Psaraftis [66] indicated the differences between static and dynamic vehicle routing. Gendreau et al. [29] proposed a parallel tabu search method for real-time vehicle routing and dispatching. 3.3 Artificial Intelligent Owing to the lack of robust design and control algorithms, most current applications of AGV systems employ simple control methods despite the fact that the system is far from efficient. Petri nets have evolved into a powerful tool for modelling complex manufacturing systems. One of the advantages of the use of Petri nets is that analysis, simulation and on-line control can all be done on the same model once the model is built. By also using petri nets, conflict free routes can be determined (see [105]). The purpose of Hsieh and Lin [35] was to establish the research fundamentals in the field of the Petri-net modelling of

11 J Intell Robot Syst (2015) 77: an AGVS. The main contribution of the paper was to define basic traffic-control nets which can be used directly to model an AGVS without too much thinking. Some basic AGV Petri-net control elements were described and illustrated in detail. Also, difficulties in the use of bidirectional flows were discussed, and it has been determined how Petri nets can be used to solve these problems. The main problem for an AGV dispatching system is to assign vehicles to transport demands which optimize some predetermined objectives of a manufacturing shop. Singh and Tiwari [81] presented a framework for an AGV dispatching system based on an object oriented approach using the unified modeling language (UML), and the development of a dispatching algorithm to facilitate a human controller to dispatch efficiently a fleet of AGVs in response to calls from any shop floor (or machine) operator. The main reason for their work was to model an AGV dispatching system as well as to develop a dispatching algorithm which can record details of the AGV position and movement and ensure their allocation of new orders. The provision of both immediate and pre-booked orders for an AGV was also incorporated in the proposed AGVs dispatching system. The underlying AGV dispatching system and algorithm were capable of dispatching a vehicle automatically to handle a call at the required time. In order to overcome difficulties associated with tackling immediate orders, pre-booked orders, and processing of information related to AGVs, a comprehensive dispatching algorithm was developed which aims to minimize lateness, traveling time and distance of empty vehicles in a simulated jobshop scenario. Kizil et al.[49] evaluated the effects of various dispatching rules on the operation and performance of cellular manufacturing systems (CMS). When the study of a CMS considers the automated material handling, it is crucial to reduce the gridlock probability (i.e., the probability of an unsuccessful load transfer attempt occurring in the interface point between the intercell and intracell handling system). Preventing an unsuccessful load transfer is critical for the operation of the entire system as a blockage between the AGV and the overloaded cell results in a total system shutdown. The gridlock probability was influenced by the dispatching rule used to schedule the load transfers in the system. Therefore, in order to reduce this probability it was necessary to use a dispatching rule that will decrease the number of waiting loads in the transfer spurs. The main objective of the paper was to identify a dispatching rule that maintains the system operational at all times. A group of dispatching rules, including the first come first served, shortest imminent operation, longest imminent operation, most remaining operations, shortest processing time, shortest remaining process time, and a newly developed rule proposed by the authors, loads with the minimum number of processing first, were tested and evaluated with respect to whether the capacity of the transfer spurs of the cells was exceeded. The paper presented a simulation model of a cellular manufacturing system, which was used to further explore the effects of the dispatching rules on the system performance. AGVs are the most flexible means to transport materials among workstations of a flexible manufacturing system. Complex issues associated with the design of AGV control of these systems are conflictfree shortest path, minimum time motion planning and deadlock avoidance. Srivastava et al. [89] presented an intelligent agent-based framework to overcome the inefficacies associated with the aforementioned issues. Proposed approach described the operational control of AGVs by integrating different activities such as path generation, journey time enumeration, collision and deadlock identification, waiting node location and its time estimation, and decision making on the selection of the conflict-free shortest feasible path. It represented efficient algorithms and rules associated with each agent for finding the conflictfree minimum time motion planning of AGVs, which were needed to navigate unidirectional and bidirectional flow path network. A collaborative architecture of AGV agent and its different modules were also presented. Three complex experimental scenarios were simulated to test the robustness of the proposed approach. It was shown that the proposed agentbased controller was capable of generating optimal, collision- and deadlock-free path with less computational efforts. The objective of Aized [1] was to model and maximize performance of an integrated AGVS, which is embedded in a pull type multi-product, multistage and multi-line FMS. The author examined the impact of guide-path flexibility on system performance through the development of three different guide-path configurations which range from dedicated to flexible relationships between AGVs and

12 536 J Intell Robot Syst (2015) 77: machine/assembly station resources. The system was modelled using coloured Petri net method (CPN) and the simulation results lead to identify the resource redundancy which can be rectified to achieve lower overall cost of the system through the development of flexible guide-path configurations. The study was extended to seek global near-optimal conditions for each guide-path configuration using response surface method, which yields improvements in system throughput and cycle time along with a decrease in the numbers of AGVs. Material handling in manufacturing systems is becoming easier as the automated machine technology is improved. Nowadays, most of the research aims at increasing the flexibility and improving the performance of the AGV. Yahyaei et al. [100] designed and made AGV in the Industrial Control Laboratory in Royce Lab at the University of Manchester Institute of Science and Technology. For controlling the navigation of the AGV, a newly developed controller integrated fuzzy logic with programmable logic controller was used. By using integrated fuzzy logic controller with programmable logic controller (IFLPLC), the flexibility of AGV was increased and they achieved great advantages. Since that AGV used programmable logic controller and fuzzy logic controllers together, it proved usefulness for factories which implement FMS. Online maintenance and sending the commands to other machines from AGV and so on were the advantages that can be used in FMS. Fazlollahtabar and Mahdavi-Amiri [20] proposed an approach for finding an optimal path in a flexible jobshop manufacturing system considering two criteria of time and cost. A network was configured in which the nodes are considered to be the shops with arcs representing the paths among the shops. An automated guided vehicle functioned as a material handling device through the manufacturing network. The expert system for cost estimation was based on fuzzy rule backpropagation network to configure the rules for estimating the cost under uncertainty. A multiple linear regression model was applied to analyze the rules and find the effective rules for cost estimation. The objective was to find a path minimizing an aggregate weighted unscaled time and cost criteria. A fuzzy dynamic programming approach was presented for computing a shortest path in the network. Then, a comprehensive economic and reliability analysis was worked out on the obtained paths to find the optimal producer s behavior. Singh and Tiwari [82] presented an intelligent agent framework to find a conflict-free shortest-time path for an AGV travelling in a bi- or unidirectional network. Fazlollahtabar and Mahdavi-Amiri [21] proposed an approach for finding an optimal path in a flexible jobshop manufacturing system considering two criteria of time and cost. With rise in demands, advancement in technology and increase in production capacity, the need for more shops persists. Therefore, a flexible jobshop system has more than one shop with the same duty. The difference among shops with the same duty is in their machines with various specifications. A network was configured in which the nodes were considered to be the shops with arcs representing the paths among the shops. An AGV functioned as a material handling device through the manufacturing network. To account for uncertainty, the authors considered time to be a triangular fuzzy number and applied an expert system to infer cost. The objective was to find a path minimizing both the time and cost criteria, aggregately. Since time and cost have different scales, a normalization procedure was proposed to remove the scales. The model being biobjective, the analytical hierarchy process weighing method was applied to construct a single objective. Finally, a dynamic programming approach was presented for computing a shortest path in the network. The efficiency of the proposed approach was illustrated by a numerical example. 3.4 Simulation Software aids for simulation are very important to practitioners of simulation. The widespread availability of inexpensive computing power now allows computer assistance in each stage of simulation activities such as input data analysis, modelling, programming, output analysis and so on. Therefore Ashayeri and Gelders [2] described an interactive microcomputer GPSS simulation program generator for automated material handling systems. The program was written in Pascal and consisted of several modules to capture data, build the model, and generate the corresponding GPSS simulation program for automated guided vehicle systems as well as surge systems. The application of the program to a real life project was