Scheduling automated transport vehicles for material distribution systems

The rise of advanced manufacturing technology has led to enhanced, efficient material handling equipment in manufacturing and container terminal environments. In order to exploit the full potential of advanced material handling equipment in real-world industrial environments, novel scheduling approaches capable of ensuring integrated operations for multiple automated transport vehicles need to be developed. This research develops a methodology for scheduling automated transport vehicles to ensure the smooth flow of materials in production and container terminal environments. The procedure consists of a mixed-integer programming model and two meta-heuristic-based algorithms that are proposed to achieve quality schedules within a reasonable amount of time. The obtained results show a significant reduction in the earliness or lateness of material delivery tasks and an improvement in operational performance, demonstrating that the proposed approaches are capable of ensuring smooth material distribution by scheduling automated transport vehicles in an integrated manner.     

Real-time holonic scheduling of material handling operations in a dynamic manufacturing environment

In a real-world manufacturing environment, finding the right sequences and associated schedules with resource, precedence, and timing constraints is a difficult task. Moreover, a decision time period of hours or even minutes is simply too long. Good solutions are often needed in real time. One approach to overcome the limitations of classical scheduling is the use of distributed schemes such as agent or holonic-based control architectures. This paper presents a solution for scheduling material handling devices in the cellular manufacturing environment using the holonic control approach. In holonic systems, under real-time constraints, a feasible schedule for the material handling devices emerges from the combination of individual material handling holons’ schedules. Internal evaluation and allocation algorithms and specific cooperation mechanisms between the holons in the architecture are the basis for the resultant emergent schedules. These evaluation algorithms are developed using several scenarios that take into account uncertainties that usually exist in a dynamic manufacturing environment, such as new orders entering into the system. The study results obtained show that the holonic system is capable of accommodating new arriving jobs and delivers good solutions in real time.     

The PCP: A predictive model for satisfying conflicting objectives in scheduling problems

The economic viability of a manufacturing organisation depends on its ability to maximise customer services, maintain efficient, low-cost operations and to minimise total investment. These objectives conflict with one another and are thus difficult to achieve on an operational basis. Much of the work in the area of automated scheduling systems recognises this problem but does not address it effectively. The work presented in this paper is motivated by the desire to generate ‘good’ cost-effective schedules in dynamic and stochastic manufacturing environments.It is argued that, to achieve the required balance between objectives, a scheduling system must have the ability to relate the consequence of a decision to the satisfaction of overall objectives. This paper introduces the concept of a preference capacity plan (PCP) in an attempt to give automated schedulers this ability. A PCP takes cognisance of both predicted demand for capacity and the interactions that exist between scheduling objectives.Experimental analysis is used to illustrate the power, versatility and practicality of the PCP approach within an advanced scheduling architecture. In conclusion, it is argued that by reasoning about the satisfaction of global objectives during the scheduling process solutions of a consistently high quality can be produced.     

An efficient search method for job-shop scheduling problems

We present an efficient search method for job-shop scheduling problems. Our technique is based on an innovative way of relaxing and subsequently reimposing the capacity constraints on some critical operations. We integrate this technique into a fast tabu search algorithm. Our computational results on benchmark problems show that this approach is very effective. Upper bounds for 11 well-known test problems are thus improved. Through the work presented We hope to move a step closer to the ultimate vision of an automated system for generating optimal or near-optimal production schedules. The peripheral conditions for such a system are ripe with the increasingly widespread adoption of enterprise information systems and plant floor tracking systems based on bar code or wireless technologies. One of the remaining obstacles, however, is the fact that scheduling problems arising from many production environments, including job-shops, are extremely difficult to solve. Motivated by recent success of local search methods in solving the job-shop scheduling problem, we propose a new diversification technique based on relaxing and subsequently reimposing the capacity constraints on some critical operations. We integrate this technique into a fast tabu search algorithm and are able to demonstrate its effectiveness through extensive computational experiments. In future research, we will consider other diversification techniques that are not restricted to critical operations.     

Multicriteria meta-heuristics for AGV dispatching control based on computational intelligence.

In many manufacturing environments, automated guided vehicles are used to move the processed materials between various pickup and delivery points. The assignment of vehicles to unit loads is a complex problem that is often solved in real-time with simple dispatching rules. This paper proposes an automated guided vehicles dispatching approach based on computational intelligence. We adopt a fuzzy multicriteria decision strategy to simultaneously take into account multiple aspects in every dispatching decision. Since the typical short-term view of dispatching rules is one of the main limitations of such real-time assignment heuristics, we also incorporate in the multicriteria algorithm a specific heuristic rule that takes into account the empty-vehicle travel on a longer time-horizon. Moreover, we also adopt a genetic algorithm to tune the weights associated to each decision criteria in the global decision algorithm. The proposed approach is validated by means of a comparison with other dispatching rules, and with other recently proposed multicriteria dispatching strategies also based on computational Intelligence. The analysis of the results obtained by the proposed dispatching approach in both nominal and perturbed operating conditions (congestions, faults) confirms its effectiveness.     

Decentralized Traffic Control for Non-Holonomic Flexible Automated Guided Vehicles in Industrial Environments

This paper deals with the problem of coordinating flexible automated guided vehicles (AGVs) in real manufacturing systems. The problem consists of ensuring safe and successful task execution while several AGVs operate as a distributed transportation system in real industrial environments. The proposed solution combines different decentralized techniques to increase the flexibility and scalability of the multirobot system. The coordination is addressed by dividing the problem into path planning, obstacle avoidance and traffic control problems. The path planning method takes into account the location of mates for replanning the routes. The obstacle avoidance technique considers the kinematic constraints of the platform for reactive motion control. The traffic control approach makes use of a decentralized control policy that takes into account the capabilities of vehicles. By combining all these techniques and configuring the system properly, we present the successful development of a distributed transportation system composed of a team of flexible AGVs. The proposed solution has been validated using both a set of custom-modified AGVs operating in a real factory and a simulation of several AGVs operating in a virtual scenario.     

A model with a solution algorithm for the cash transportation vehicle routing and scheduling problem

Cash transportation vehicle routing and scheduling are essential for security carriers to minimize their operating costs and ensure safe cash conveyance. In real operations, to increase cash conveyance safety, there must be significant variation in daily cash transportation vehicle routes and schedules, making such vehicle routes and schedules difficult to formulate. However, for convenient planning purposes, security carriers normally plan such routes and schedules based on personal experience, without considering variations in routes and schedules from a system perspective. As a result, the obtained routes and schedules are neither safe nor efficient for transporting cash. In this study, a model is developed where the time–space network technique is utilized to formulate the potential movements of cash transportation vehicles among all demand points in the dimensions of time and space. This model incorporates a new concept of similarity of time and space for routing and scheduling, which is expected to help security carriers formulate more flexible routing and scheduling strategies. This is helpful to reduce the risk of robbery. Mathematically, the model is formulated as an integer multiple-commodity network flow problem. A solution algorithm, based on a problem decomposition/collapsing technique, coupled with the use of a mathematical programming software, is developed to efficiently solve the problem. The case study results show that our model and solution algorithm could be useful references for security carriers in actual practice.     

Hybrid metaheuristics for scheduling of machines and transport robots in job shop environment

In real manufacturing environments, the control of some elements in systems based on robotic cells, such as transport robots has some difficulties when planning operations dynamically. The Job Shop scheduling Problem with Transportation times and Many Robots (JSPT-MR) is a generalization of the classical Job Shop scheduling Problem (JSP) where a set of jobs additionally have to be transported between machines by several transport robots. Hence, the JSPT-MR is more computationally difficult than the JSP presenting two NP-hard problems simultaneously: the job shop scheduling problem and the robot routing problem. This paper proposes a hybrid metaheuristic approach based on clustered holonic multiagent model for the JSPT-MR. Firstly, a scheduler agent applies a Neighborhood-based Genetic Algorithm (NGA) for a global exploration of the search space. Secondly, a set of cluster agents uses a tabu search technique to guide the research in promising regions. Computational results are presented using two sets of benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.     

Scheduling the truckload operations in automated warehouses with alternative aisles for pallets

In this study, the scheduling of truck load operations in automated storage and retrieval systems is investigated. The problem is an extension of previous ones such that a pallet can be retrieved from a set of alternative aisles. It is modelled as a flexible job shop scheduling problem where the loads are considered as jobs, the pallets of a load are regarded as the operations, and the forklifts used to remove the retrieving items to the trucks are seen as machines. Minimization of maximum loading time is used as the objective to minimize the throughput time of orders and maximize the efficiency of the warehouse. A priority based genetic algorithm is presented to sequence the retrieving pallets. Permutation coding is used for encoding and a constructive algorithm generating active schedules for flexible job shop scheduling problem is applied for decoding. The proposed methodology is applied to a real problem arising in a warehouse installed by a leading supplier of automated materials handling and storage systems.     

Public transit planning and scheduling based on AVL data in China

The public transit operations planning process commonly includes the following activities: network route design, service planning (frequency setting and timetabling), and scheduling (vehicle scheduling, crew scheduling, and rostering). However, the network route design is generally the only one widely recognized, while service planning and scheduling are often ignored in China. This leads to the lack of elaborate timetables and schedules, hence, transit operation is often in disorder with high operating costs. To raise the service level and the utilization of resources, this paper presents an applied study for three cities in China, focusing on the enhancement of the recognition and execution of public transit planning and scheduling. A comprehensive framework of public transit planning is first proposed, which is composed of three traditional Chinese items (i.e. network route design, land use for depots, and deployment of vehicles) and the following newly added items: intelligent public transit system (IPTS) planning, service planning, and scheduling. This is pioneering work in China, in which an IPTS plan is conceived and a new vehicle scheduling approach based on AVL data is developed. Experiments during actual projects show that vehicle schedules with high on‐time probability and low cost were compiled, while the essential input parameters such as headways and trip times were set automatically. It is anticipated that the fruits of this research and the practical experience obtained would be of great benefit in improving service and management levels and resource use in public transport in China and some other developing countries.     

Scheduling logistic activities to improve hospital supply systems

This paper presents an innovative approach for improving hospital logistics by coordinating the procurement and distribution operations while respecting inventory capacities. Instead of focusing on multi-echelon inventory decisions, our approach put the emphasis on the scheduling decisions: when to buy a product, when to deliver to each care unit, when each employee should work and what task should he do, etc. This promising strategy requires the elaboration of coordinated schedules that balance the activities through the purchasing cycle. We introduce two modelling approaches that can account for the numerous scheduling decisions in such environment. We present a tabu search metaheuristic that explores four different neighborhoods and which accommodates the two modelling approaches. We tested our models and algorithms on a real case extracted from a hospital based in Montreal, Canada. The supply schedules generated by our algorithm were considered by the hospital managers as efficient and well balanced. The approach may help hospitals to improve their logistics by better coordinating purchasing and procurement.     

Sliding mode dynamics in continuous feedback control for distributed discrete-event scheduling

A continuous feedback control approach for real-time scheduling of discrete events is presented in this paper motivated by the need for control theoretic techniques to analyze and design such systems in distributed manufacturing applications. These continuous feedback control systems exhibit highly nonlinear and discontinuous dynamics. Specifically, when the production demand in the manufacturing system exceeds the available resource capacity then the control system “chatters” and exhibits sliding modes. This sliding mode behavior is advantageously used in the scheduling application by allowing the system to visit different schedules within an infinitesimal region near the sliding surface. In this paper, an analytical model is developed to characterize the sliding mode dynamics. This model is then used to design controllers in the sliding mode domain to improve the effectiveness of the control system to “search” for schedules with good performance. Computational results indicate that the continuous feedback control approach can provide near-optimal schedules and that it is computationally efficient compared to existing scheduling techniques.     

带工序跳跃的绿色混合流水车间机器与AGV联合调度

针对带工序跳跃的绿色混合流水车间机器和自动引导车(AGV)联合调度问题,提出改进memetic algorithm (MA)以同时最小化最大完工时间和总能耗.首先,设计基于工序、机器和转速的三层编码策略,最大程度保证算法在整个解空间中搜索;然后,设计混合种群初始化方法以提高初始种群解的质量,同时设计交叉和变异算子以及两种基于问题的邻域搜索策略来平衡算法的全局搜索和局部搜索能力;最后,通过大量仿真实验验证MA算法求解该问题的有效性和优越性.     

An anticipative scheduling approach with controllable processing times

In practice, machine schedules are usually subject to disruptions which have to be repaired by reactive scheduling decisions. The most popular predictive approach in project management and machine scheduling literature is to leave idle times (time buffers) in schedules in coping with disruptions, i.e. the resources will be under-utilized. Therefore, preparing initial schedules by considering possible disruption times along with rescheduling objectives is critical for the performance of rescheduling decisions. In this paper, we show that if the processing times are controllable then an anticipative approach can be used to form an initial schedule so that the limited capacity of the production resources are utilized more effectively. To illustrate the anticipative scheduling idea, we consider a non-identical parallel machining environment, where processing times can be controlled at a certain compression cost. When there is a disruption during the execution of the initial schedule, a match-up time strategy is utilized such that a repaired schedule has to catch-up initial schedule at some point in future. This requires changing machine–job assignments and processing times for the rest of the schedule which implies increased manufacturing costs. We show that making anticipative job sequencing decisions, based on failure and repair time distributions and flexibility of jobs, one can repair schedules by incurring less manufacturing cost. Our computational results show that the match-up time strategy is very sensitive to initial schedule and the proposed anticipative scheduling algorithm can be very helpful to reduce rescheduling costs.     

Agent-based optimisation of logistics and production planning

Manufacturing and logistics service provision enterprises are currently moving towards open virtual enterprise collaboration networks to meet the needs of the Global Economy. In such networks, manufacturing and logistics planning and scheduling is challenging due to the difficulties in integrating information from different partners and in exploring a large and dynamically changing number of planning and scheduling alternatives. Agent-based technology is considered suitable to support planning and scheduling in such enterprises because agents can dynamically adapt their behaviour to changing requirements and they can reduce the number of planning and scheduling alternatives via negotiation.This paper presents an agent-based approach for supporting logistics and production planning, taking into account not only production schedules but also availability and cost of logistic service providers. This is achieved through efficient negotiation mechanisms based on an extended contracting protocol. The agent infrastructure is being developed within the context of Agentcities, a successful EU-funded initiative to build a world-wide distributed and open platform which provides agent-based services.The proposed approach is illustrated in a case study concerning optimisation of production planning of a virtual manufacturing enterprise in relation to sub-contracted logistic services used to transport materials between the enterprise units.     

Intelligent heuristic for FMS scheduling using grouping

This paper presents an approach to scheduling production in a flexible manufacturing system (FMS) environment by employing intelligent grouping of parts which results in good schedules that are easily solvable. Scheduling production in a realistic setting represents a very hard managerial task defying exact solutions, except in very few instances. This article presents and tests a methodology which produces scheduling solutions for large problems with an average small deviation from the theoretical lower bounds. Since open-shop scheduling is the most frequently encountered scheduling discipline in FMS, we restrict the analysis to this setting. The methodology presented combines manufacturing concepts developed in the group technology context with insightful understanding of machine scheduling problems. With the increasing interest in FMS such an approach is both promising and timely.This work was conducted at the Industrial Engineering and Management Department, Ben-Gurion University of the Negev, Beer-Sheva, Israel.     

A novel methodology for optimal single mobile robot scheduling using whale optimization algorithm

One of the fundamental requirements for creating an intelligent manufacturing environment is to develop a reliable, efficient and optimally scheduled material transport system. Besides traditional material transport solutions based on conveyor belts, industrial trucks, or automated guided vehicles, nowadays intelligent mobile robots are becoming widely used to satisfy this requirement. In this paper, the authors analyze a single mobile robot scheduling problem in order to find an optimal way to transport raw materials, goods, and parts within an intelligent manufacturing system. The proposed methodology is based on biologically inspired Whale Optimization Algorithm (WOA) and is aimed to find the optimal solution of the nondeterministic polynomial-hard (NP-hard) scheduling problem. The authors propose a novel mathematical model for the problem and give a mathematical formulation for minimization of seven fitness functions (makespan, robot finishing time, transport time, balanced level of robot utilization, robot waiting time, job waiting time, as well as total robot and job waiting time). This newly developed methodology is extensively experimentally tested on 26 benchmark problems through three experimental studies and compared to five meta-heuristic algorithms including genetic algorithm (GA), simulated annealing (SA), generic and chaotic Particle Swarm Optimization algorithm (PSO and cPSO), and hybrid GA–SA algorithm. Furthermore, the data are analyzed by using the Friedman statistical test to prove that results are statistically significant. Finally, generated scheduling plans are tested by Khepera II mobile robot within a laboratory model of the manufacturing environment. The experimental results show that the proposed methodology provides very competitive results compared to the state-of-art optimization algorithms.     

Knowledge based system for scheduling and control of an automated manufacturing cell

Cell Manager (Cm) is a prototype knowledge based system for real-time control and short-term planning of an automated manufacturing facility. It uses background of mathematical programming, simulation, and heuristic search. The Cm engine allows tracking of time related events and facts in discrete or continuous time. The use of Cm is illustrated on a cell that processes discrete batches of parts. There are four workstations and the material handling system consists of automated guided vehicles.     

A distributed routing method for AGVs under motion delay disturbance

In real transportation environments for semi-conductor manufacturing, unexpected disturbances such as motion delays and/or sudden requests for transportation may cause collisions among AGVs increasing total transportation time. It is required to generate a collision-free routing for multiple automated guided vehicles (AGVs) within a few seconds. In this paper, we propose a distributed routing method under motion delay disturbance for multiple AGVs. The proposed method features a characteristic that each AGV subsystem derives its optimal route to minimize the sum of the transportation time and the penalties with respect to collision probability with other AGVs. The penalties reflect the collision probability distribution function for motion delay disturbances. The proposed method is applied to a routing problem for transportation in the semiconductor fabrication bay with 143 nodes and 20 AGVs. The computational results show that the total transportation time obtained by the proposed method is shorter than that of the conventional method. For dynamic transportation environments, an optimal timing for re-routing multiple AGVs under motion delay disturbance is systematically determined by the tradeoff relationship between the total computation time to derive a solution and the uncertainties for re-routings. Markov chain is used to represent uncertainty distribution for re-routings. The proposed method is implemented in an experimental transportation system with 51 nodes and 5 AGVs. The experimental results demonstrate that the proposed method is applicable to real transportation environments.     

Evolutionary tuning of a fuzzy dispatching system for automatedguided vehicles

This paper develops a novel genetic algorithm (GA) based methodology for optimal tuning of a reported fuzzy dispatching system for a fleet of automated guided vehicles in a flexible manufacturing environment. The reported dispatching rules are transformed into a continuously adaptive procedure to capitalize the on-line information available from a shop floor at all times. Simulation results obtained show that the GA is very powerful and effective to achieve optimal fuzzy dispatching rules for higher shop floor productivity and operational efficiency.