Large-Scale Vehicle Platooning:Advances and Challenges in Scheduling and Planning Techniques

Through vehicle-to-vehicle(V2V)communication,autonomizing a vehicle platoon can significantly reduce the distance between vehicles,thereby reducing air resistance and improving road traffic efficiency.The gradual maturation of platoon control technology is enabling vehicle platoons to achieve basic driving functions,thereby permitting large-scale vehicle platoon scheduling and planning,which is essential for industrialized platoon applications and generates significant economic benefits.Scheduling and planning are required in many aspects of vehicle platoon operation;here,we outline the advantages and challenges of a number of the most important applications,including platoon formation scheduling,lane-change planning,passing traffic light scheduling,and vehicle resource allocation.This paper’s primary objective is to integrate current independent platoon scheduling and planning techniques into an integrated architecture to meet the demands of large-scale platoon applications.To this end,we first summarize the general techniques of vehicle platoon scheduling and planning,then list the primary scenarios for scheduling and planning technique application,and finally discuss current challenges and future development trends in platoon scheduling and planning.We hope that this paper can encourage related platoon researchers to conduct more systematic research and integrate multiple platoon scheduling and planning technologies and applications.

Research on Shortest Path BFS Strategy in Multi-AGV Scheduling System

With the increasing maturity of automated guided vehicles(AGV)technology and the widespread application of flexible manufacturing systems,enhancing the efficiency of AGVs in complex environments has become crucial.This paper analyzes the challenges of path planning and scheduling in multi-AGV systems,introduces a map-based path search algorithm,and proposes the BFS algorithm for shortest path planning.Through optimization using the breadth-first search(BFS)algorithm,efficient scheduling of multiple AGVs in complex environments is achieved.In addition,this paper validated the effectiveness of the proposed method in a production workshop experiment.The experimental results show that the BFS algorithm can quickly search for the shortest path,reduce the running time of AGVs,and significantly improve the performance of multi-AGV scheduling systems.

A Strategy for the Integration of Production Planning and Scheduling in Refineries under Uncertainty

A strategy for the integration of production planning and scheduling in refineries is proposed. This strategy relies on rolling horizon strategy and a two-level decomposition strategy. This strategy involves an upper level multiperiod mixed integer linear programming （MILP） model and a lower level simulation system, which is extended from our previous framework for short-term scheduling problems [Luo, C.E, Rong, G, ＂Hierarchical apthis extended framework is to reduce the number of variables and the size of the optimization model and, to quickly find the optimal solution for the integrated planning/scheduling problem in refineries. Uncertainties are also considered in this article. An integrated robust optimization approach is introduced to cope with uncertain parameters with both continuous and discrete probability distribution.

Dynamic Scheduling and Path Planning of Automated Guided Vehicles in Automatic Container Terminal

The uninterrupted operation of the quay crane(QC)ensures that the large container ship can depart port within laytime,which effectively reduces the handling cost for the container terminal and ship owners.The QC waiting caused by automated guided vehicles(AGVs)delay in the uncertain environment can be alleviated by dynamic scheduling optimization.A dynamic scheduling process is introduced in this paper to solve the AGV scheduling and path planning problems,in which the scheduling scheme determines the starting and ending nodes of paths,and the choice of paths between nodes affects the scheduling of subsequent AGVs.This work proposes a two-stage mixed integer optimization model to minimize the transportation cost of AGVs under the constraint of laytime.A dynamic optimization algorithm,including the improved rule-based heuristic algorithm and the integration of the Dijkstra algorithm and the Q-Learning algorithm,is designed to solve the optimal AGV scheduling and path schemes.A new conflict avoidance strategy based on graph theory is also proposed to reduce the probability of path conflicts between AGVs.Numerical experiments are conducted to demonstrate the effectiveness of the proposed model and algorithm over existing methods.

The Planning and Scheduling of Business Process in Agile Supply Chain System

The rapidly developing global competition is leadin g to the worldwide enterprise alliance, with which the geographical dispersion of production, assembly and distribution operations comes into being. Supply chain system is such a kind of enterprise alliance, managing the material and informat ion flows both in and between enterprises, such as vendors, manufacturing and assembly plants and distribution centers. In the present research work, we can see that supply chain system can quickly respond to customer needs and adapt to the dynamic change of the market so as to improve the competence of enterprises in the chain. Thus, in supply chain system, it’s most important to enhance the speed with which the products are produced and distributed to the customers who order them and reduce the operating costs at the same time. However, because of the special characteristics, such as dynamic and distributed , etc., often in Agile Supply Chain System (ASCS), there are many dynamic tasks, and many urgent changes of the processes, which make the planning work and mana gement become very difficult and complex. Thus, in Agile Supply Chain System, we first need an efficient planning work, which can program the processes properly to get a primary scheme. And then the local scheduling work based on the primar y scheme will play a important role to deal with the dynamic and distributed pro blems in the business process in ASCS. So, this paper will be organized as below . At the first of this paper, we will discuss the situation in which Agile Suppl y Chain System is applied, and then we will elaborate the characteristics of Agi le Supply Chain System. With that, the shortcomings of the process managements t hat are, at present, used in Supply Chain Systems will be displayed clearly. Sec ond, we will introduce the planning methods in our research work. And then, the local scheduling will be discussed in detail, based on the primarily planned wor kflow. To realize the goal, first we build the mathematic model to describe the scheduling goal of system optimum, based on the categories of the cooperating-r elation among the operation nodes, which we defined in our research work, in Agi le Supply Chain System. And then the optimized algorithm to solve the model woul d be introduced, in succession. At the final of this paper, we will introduce some knowledge of the process mana gement and the realization of ASCS and summarize our work.

Integration of real options into short-term mine planning and production scheduling

Commodity prices have fallen sharply due to the global financial crisis. This has adversely affected the viability of some mining projects, including leading to the possibility of bankruptcy for some companies. These price falls reflect uncertainties and risks associated with mining projects. In recent years, much work has been published related to the application of real options pricing theory to value life-of-mine plans in response to long term financial uncertainty and risk. However, there are uncertainties and risks associated with medium/short-term mining operations. Real options theory can also be applied to tactical decisions involving uncertainties and risks. This paper will investigate the application of real options in the mining industry and present a methodology developed at University of Queensland, Australia, for integrating real options into medium/short-term mine planning and production scheduling. A case study will demonstrate the validity and usefulness of the methodology and techniques developed.

DYNAMIC ADVANCED PLANNING AND SCHEDULING WITH FROZEN INTERVAL FOR NEW ORDERS

A dynamic advanced planning and scheduling （DAPS） problem is addressed where new orders arrive on a continuous basis. A periodic policy with frozen interval is adopted to increase stability on the shop floor. A genetic algorithm is developed to find a schedule at each rescheduling point for both original orders and new orders that both production idle time and penalties on tardiness and earliness of orders are minimized. The proposed methodology is tested on a small example to illustrate the effect of the frozen interval. The results indicate that the suggested approach can improve the schedule stability while retaining efficiency.

Development and Implementation of an Advanced Planning & Scheduling (APS) System for Semiconductor Backend Assembly

In this paper, the design, customization and implem en tation of an integrated Advanced Planning and Scheduling (APS) system for a Semi conductor Backend Assembly environment is described. The company is one of the w orldwide market leaders in semiconductor packaging technology. The project was d riven by the company’s quest to achieve a competitive edge as a manufacturing po werhouse by providing the shortest possible cycle time with a high degree of fle xibility through the application of Computer Integrated Manufacturing (CIM) tech nology. Gintic was responsible for the Planning & Scheduling functions through o ur APS tool kit, which is called Gintic Scheduling System (GSS). Our APS system is to be integrated with the other two key software systems, namely, the Enterpr ise Resource Planning (ERP) and Manufacturing Execution System (MES), with the C IM framework. The project was divided into four major execution phases. Phase One activities w ere focused on the gathering and analysis of the end users requirements in order to establish the ’As-Is’ situation and the wish list & the expectation of the ’To-Be’ system. Planning and Scheduling prototypes were built using GSS to iden tify the functionality gap between the existing GSS system and the ’To-Be’ mode l, in order to determine the customization effort needed. The project team perfo rmed detailed system analysis, design and development of the ’To-Be’ system dur ing Phase Two of the project. There are a total of four planning and scheduling modules, including Capacity Planning (CP), Daily Lot Release (DLR), Daily Produc tion Scheduling (DPS) and Dynamic Operation Scheduling (DOS). The detailed desig n specifications of each of the features and functionality were confirmed and ac cepted by the end users before the commencement of the development effort. The c ompleted and tested modules were delivered in stages for testing and acceptance by the end user during the Phase Three of the project. Pilot product line was se lected for live testing of the developed planning and scheduling modules, before they are proliferated to the rest of the product lines. System fine-tuning req uests were raised during the last phase of the project; the Planning & Schedulin g modules were fine-tuned to satisfy the end user requirements. This paper will conclude by highlighting the actual benefits achieved by the suc cessful deployment of the GSS system. The company has expressed their deep s atisfaction and has requested Gintic to look into the automation of the Plan ning and Scheduling functions in the Pre-Assembly and Test operations.

Integrating Process Planning and Scheduling with an Intelligent Facilitator

This paper introduces a dynamic facilitating mechan is m for the integration of process planning and scheduling in a batch-manufacturi ng environment. This integration is essential for the optimum use of production resources and generation of realistic process plans that can be readily executed with little or no modification. In this paper, integration is modeled in two le vels, viz., process planning and scheduling, which are linked by an intelligent facilitator. The process planning module employs an optimization approach in whi ch the entire plan solution space is first generated and a search algorithm is t hen used to find the optimal plan. Based on the result of scheduling module an u nsatisfactory performance parameter is fed back to the facilitator, which then i dentifies a particular job and issues a change to its process plan solution spac e to obtain a satisfactory schedule.

Two-Stage Planning of Distributed Power Supply and Energy Storage Capacity Considering Hierarchical Partition Control of Distribution Network with Source-Load-Storage

Aiming at the consumption problems caused by the high proportion of renewable energy being connected to the distribution network,it also aims to improve the power supply reliability of the power system and reduce the operating costs of the power system.This paper proposes a two-stage planning method for distributed generation and energy storage systems that considers the hierarchical partitioning of source-storage-load.Firstly,an electrical distance structural index that comprehensively considers active power output and reactive power output is proposed to divide the distributed generation voltage regulation domain and determine the access location and number of distributed power sources.Secondly,a two-stage planning is carried out based on the zoning results.In the phase 1 distribution network-zoning optimization layer,the network loss is minimized so that the node voltage in the area does not exceed the limit,and the distributed generation configuration results are initially determined;in phase 2,the partition-node optimization layer is planned with the goal of economic optimization,and the distance-based improved ant lion algorithm is used to solve the problem to obtain the optimal distributed generation and energy storage systemconfiguration.Finally,the IEEE33 node systemwas used for simulation.The results showed that the voltage quality was significantly improved after optimization,and the overall revenue increased by about 20.6%,verifying the effectiveness of the two-stage planning.

A Graph-based Ant Colony Optimization Approach for Integrated Process Planning and Scheduling

This paper considers an ant colony optimization algorithm based on AND/OR graph for integrated process planning and scheduling(IPPS). Generally, the process planning and scheduling are studied separately. Due to the complexity of manufacturing system, IPPS combining both process planning and scheduling can depict the real situation of a manufacturing system. The IPPS is represented on AND/OR graph consisting of nodes, and undirected and directed arcs. The nodes denote operations of jobs, and undirected/directed arcs denote possible visiting path among the nodes. Ant colony goes through the necessary nodes on the graph from the starting node to the end node to obtain the optimal solution with the objective of minimizing makespan. In order to avoid local convergence and low convergence, some improved strategy is incorporated in the standard ant colony optimization algorithm. Extensive computational experiments are carried out to study the influence of various parameters on the system performance.

Robust Optimization for a Multi-Product Integrated Problem of Planning and Scheduling under Products Uncertainty

This paper presents robust optimization models for a multi-product integrated problem of planning and scheduling (based on the work of Terrazas-Moreno & Grossmann (2011) [1]) under products prices uncertainty. With the objective of maximizing the total profit in planning time horizon, the planning section determines the amount of each product, each product distributed to each market, and the inventory level in each manufacturing site during each scheduling time period;the scheduling section determines the products sequence, start and end time of each product running in each production site during each scheduling time period. The uncertainty sets used in robust optimization model are box set, ellipsoidal set, polyhedral set, combined box and ellipsoidal set, combined box and polyhedral set, combined box, ellipsoidal and polyhedral set. The genetic algorithm is utilized to solve the robust optimization models. Case studies show that the solutions obtained from robust optimization models are better than the solutions obtained from the original integrated planning and scheduling when the prices are changed.

Process Planning and Scheduling Integration in an Open Manufacturing Environment

New open manufacturing environments have been proposed aiming at realizing more flexible distributed manufacturing paradigms,which can deal with not only dynamic changes in volume and variety of products,but also changes of machining equipments,dispersals of processing locations,and also with unscheduled disruptions.This research is to develop an integrated process planning and scheduling system,which is suited to this open,dynamic,distributed manufacturing environment.Multi-agent system(MAS)approaches are used for integration of manufacturing processing planning and scheduling in an open distributed manufacturing environment,in which process planning can be adjusted dynamically and manufacturing resources can increase/decrease according to the requirements.One kind of multi-level dynamic negotiated approaches to process planning and scheduling is presented for the integration of manufacturing process planning and scheduling.

An Architecture of Computer Aided Process Planning System Integrated with Scheduling Using Decision Support System

Process planning and scheduling are two major plann in g and control activities that consume significant part of the lead-time, theref ore all attempts are being made to reduce lead-time by automating them. Compute r Aided Process Planning (CAPP) is a step in this direction. Most of the existin g CAPP systems do not consider scheduling while generating a process plan. Sched uling is done separately after the process plan has been generated and therefore , it is possible that a process plan so generated is either not optimal or feasi ble from scheduling point of view. As process plans are generated without consid eration of job shop status, many problems arise within the manufacturing environ ment. Investigations have shown that 20%～30% of all process plans generated are not valid and have to be altered or suffer production delays when production sta rts. There is thus a major need for integration of scheduling with computer aide d process planning for generating more realistic process plans. In doing so, eff iciency of the manufacturing system as a whole is expected to improve. Decision support system performs many functions such as selection of machine too ls, cutting tools, sequencing of operations, determination of optimum cutting pa rameters and checking availability of machine tool before allocating any operati on to a machine tool. The process of transforming component data, process capabi lity and decision rules into computer readable format is still a major obstacle. This paper proposes architecture of a system, which integrates computer aided p rocess-planning system with scheduling using decision support system. A decisio n support system can be defined as " an interactive system that provides the use rs with easy access to decision models in order to support semi-structured or u nstructured decision making tasks".

Project Planning and Scheduling in the Face of the Fourth Industrial Revolution(4IR)

This paper addresses the integrated processes of planning and scheduling of projects using technologies advanced tools as means for managing change in the era of the 4IR.The paper explores the traditional project management planning and scheduling tools in conjunction with what technology has to offer,to bridge the gap between the traditional project management planning and scheduling tools and what the modern-day business market demands.An analysis of literature covering a wide range of theoretic and empirical studies was performed.The theories underlying various planning and scheduling methods were analysed in relation to the design of projects.A descriptive quantitative secondary data was used as a tool to assess the impact of technology on project planning for scheduling.The analysis of the study’s data was conducted using the principles of cross-tabulation.Inferences were drawn on the significant impact of the use of advanced technological tools on project planning for scheduling in current business time.Organisations can make use of the findings of this study to correctly apply the available advanced technological tools for more efficient schedule management planning to enhance the successful delivery of their projects.Further,this research can be used to provide learning opportunities for new and inexperienced planners and schedulers,and as a basis for further research in this field of knowledge.

Energy-efficient Approach to Minimizing the Energy Consumption in An Extended Job-shop Scheduling Problem

The traditional production planning and scheduling problems consider performance indicators like time, cost and quality as optimization objectives in manufacturing processes. However, environmentally-friendly factors like energy consumption of production have not been completely taken into consideration. Against this background, this paper addresses an approach to modify a given schedule generated by a production plarming and scheduling system in a job shop floor, where machine tools can work at different cutting speeds. It can adjust the cutting speeds of the operations while keeping the original assignment and processing sequence of operations of each job fixed in order to obtain energy savings. First, the proposed approach, based on a mixed integer programming mathematical model, changes the total idle time of the given schedule to minimize energy consumption in the job shop floor while accepting the optimal solution of the scheduling objective, makespan. Then, a genetic-simulated annealing algorithm is used to explore the optimal solution due to the fact that the problem is strongly NP-hard. Finally, the effectiveness of the approach is performed small- and large-size instances, respectively. The experimental results show that the approach can save 5%-10% of the average energy consumption while accepting the optimal solution of the makespan in small-size instances. In addition, the average maximum energy saving ratio can reach to 13%. And it can save approximately 1%-4% of the average energy consumption and approximately 2.4% of the average maximum energy while accepting the near-optimal solution of the makespan in large-size instances. The proposed research provides an interesting point to explore an energy-aware schedule optimization for a traditional production planning and scheduling problem.

Mathematical Modeling and a Multiswarm Collaborative Optimization Algorithm for Fuzzy Integrated Process Planning and Scheduling Problem

Considering both process planning and shop scheduling in manufacturing can fully utilize their complementarities,resulting in improved rationality of process routes and high-quality and efficient production. Hence,the study of Integrated Process Planning and Scheduling (IPPS) has become a hot topic in the current production field. However,when performing this integrated optimization,the uncertainty of processing time is a realistic key point that cannot be neglected. Thus,this paper investigates a Fuzzy IPPS (FIPPS) problem to minimize the maximum fuzzy completion time. Compared with the conventional IPPS problem,FIPPS considers the fuzzy process time in the uncertain production environment,which is more practical and realistic. However,it is difficult to solve the FIPPS problem due to the complicated fuzzy calculating rules. To solve this problem,this paper formulates a novel fuzzy mathematical model based on the process network graph and proposes a MultiSwarm Collaborative Optimization Algorithm (MSCOA) with an integrated encoding method to improve the optimization. Different swarms evolve in various directions and collaborate in a certain number of iterations. Moreover,the critical path searching method is introduced according to the triangular fuzzy number,allowing for the calculation of rules to enhance the local searching ability of MSCOA. The numerical experiments extended from the well-known Kim benchmark are conducted to test the performance of the proposed MSCOA. Compared with other competitive algorithms,the results obtained by MSCOA show significant advantages,thus proving its effectiveness in solving the FIPPS problem.

An MILP approach for detailed scheduling of oil depots along a multi-product pipeline

Oil depots along products pipelines are important components of the pipeline transportation system and down-stream markets.The operating costs of oil depots account for a large proportion of the total system’s operating costs.Meanwhile,oil depots and pipelines form an entire system,and each operation in a single oil depot may have influence on others.It is a tough job to make a scheduling plan when considering the factors of delivering contaminated oil and batches migration.So far,studies simultaneously considering operating constraints and contaminated oil issues are rare.Aiming at making a scheduling plan with the lowest operating costs,the paper establishes a mixed-integer linear programming model,considering a sequence of operations,such as delivery, export, blending,fractionating and exchanging operations,and batch property differences of the same oil as well as influence of batch migration on contaminated volume.Moreover,the paper verifies the linear relationship between oil concentration and blending capability by mathematical deduction.Finally,the model is successfully applied to one of the product pipelines in China and proved to be practical.

Modeling for UAV resource scheduling under mission synchronization

Unmanned aerial vehicle（UAV） resource scheduling means to allocate and aggregate the available UAV resources depending on the mission requirements and the battlefield situation assessment.In previous studies,the models cannot reflect the mission synchronization;the targets are treated respectively,which results in the large scale of the problem and high computational complexity.To overcome these disadvantages,a model for UAV resource scheduling under mission synchronization is proposed,which is based on single-objective non-linear integer programming.And several cooperative teams are aggregated for the target clusters from the available resources.The evaluation indices of weapon allocation are referenced in establishing the objective function and the constraints for the issue.The scales of the target clusters are considered as the constraints for the scales of the cooperative teams to make them match in scale.The functions of the intersection between the ＂mission time-window＂ and the UAV ＂arrival time-window＂ are introduced into the objective function and the constraints in order to describe the mission synchronization effectively.The results demonstrate that the proposed expanded model can meet the requirement of mission synchronization,guide the aggregation of cooperative teams for the target clusters and control the scale of the problem effectively.