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.

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 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.

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.

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.

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.

Stochastic optimization of mine production scheduling with uncertain ore/metal/waste supply

Optimization of long-term mine production scheduling in open pit mines deals with the management of cash flows, typically in the order of hundreds of millions of dollars. Conventional mine scheduling utilizes optimization methods that are not capable of accounting for inherent technical uncertainties such as uncertainty in the expected ore/metal supply from the underground, acknowledged to be the most critical factor. To integrate ore/metal uncertainty into the optimization of mine production scheduling a stochastic integer programming(SIP) formulation is tested at a copper deposit. The stochastic solution maximizes the economic value of a project and minimizes deviations from production targets in the presence of ore/metal uncertainty. Unlike the conventional approach, the SIP model accounts and manages risk in ore supply, leading to a mine production schedule with a 29% higher net present value than the schedule obtained from the conventional, industry-standard optimization approach, thus contributing to improving the management and sustainable utilization of mineral resources.

Development and challenges of planning and scheduling for petroleum and petrochemical production

Production planning and scheduling are becoming the core of production management,which support the decision of a petrochemical company.The optimization of production planning and scheduling is attempted by every refinery because it gains additional profit and stabilizes the daily production.The optimization problem considered in industry and academic research is of different levels of realism and complexity,thus increasing the gap.Operation research with mathematical programming is a conventional approach used to address the planning and scheduling problem.Additionally,modeling the processes,objectives,and constraints and developing the optimization algorithms are significant for industry and research.This paper introduces the perspective of production planning and scheduling from the development viewpoint.

A CASE STUDY FOR ADVANCED PLANNING AND SCHEDULING (APS)

This paper presents a case study for the advanced planning and scheduling （APS） problem encountered in a light source manufacturer. The APS problem explicitly considers due dates of products, operation sequences among items, and capacity constraints of the manufacturing system. The objective of the problem is to seek the minimum cost of both production idle time and tardiness or earliness penalty of an order. An intelligent heuristic is applied to the problem, and the results demonstrate that significant production performances can be achieved while ensuring customer satisfaction as opposed to normal oractices followed in the company relying on human expertise.

Optimization of multi-objective integrated process planning and scheduling problem using a priority based optimization algorithm

For increasing the overall performance of modem manufacturing systems, effective integration of process planning and scheduling functions has been an important area of consideration among researchers. Owing to the complexity of handling process planning and scheduling simultaneously, most of the research work has been limited to solving the integrated process planning and scheduling （IPPS） problem for a single objective function. As there are many conflicting objectives when dealing with process planning and scheduling, real world problems cannot be fully captured considering only a single objective for optimization. Therefore considering multi-objective IPPS （MOIPPS） problem is inevitable. Unfortunately, only a handful of research papers are available on solving MOIPPS problem. In this paper, an optimization algorithm for solving MOIPPS problem is presented. The proposed algorithm uses a set of dispatch- ing rules coupled with priority assignment to optimize the IPPS problem for various objectives like makespan, total machine load, total tardiness, etc. A fixed sized external archive coupled with a crowding distance mechanism is used to store and maintain the non-dominated solutions. To compare the results with other algorithms, a C-matric based method has been used. Instances from four recent papers have been solved to demonstrate the effectiveness of the proposed algorithm. The experimental results show that the proposed method is an efficient approach for solving the MOIPPS problem.

Real-time and dynamic schedule optimization of construction projects on the basis of lean construction

Lean construction has been newly applied to construction industry. The best performance of a project can be achieved through the precise definition of construction product, rational work break structure, lean supply chain, decrease of resources waste, objective control and so forth. Referring to the characteristics of schedule planning of construction projects and lean construction philosophy, we proposed optimizing methodology of real-time and dynamic schedule of construction projects based on lean construction. The basis of the methodology is process reorganization and lean supply in construction enterprises. The traditional estimating method of the activity duration is fuzzy and random;however, a newly proposed lean forecasting method employs multi-components linear-regression, back-propagation artificial neural networks and learning curve. Taking account of the limited resources and the fixed duration of a project, the optimizing method of the real-time and dynamic schedule adopts the concept of resource driving. To optimize the schedule of a construction project timely and effectively, an intellectualized schedule management system was developed. It can work out the initial schedule, optimize the real-time and dynamic schedule, and display the schedule with the Gant Chart, the net-work graph and the space-time line chart. A case study was also presented to explain the proposed method.

A Virtual Erection Simulation System for a Steel Structure Based on 3-D Measurement Data

he virtual erection simulation system was explained for a steel structure including ship and ocean plant blocks. The simulation system predicted the erection state to optimize any gap or overlap of blocks based on 3-D measurement data. The blocks were modified （cut） on the basis of the simulation result on the ground before erecting them by crane. The re-cutting process was not required and the blocks were erected into a mother ship speedily. Therefore, the erection time is reduced, increasing the dock turnover.

Rescheduling of observing spacecraft using fuzzy neural network and ant colony algorithm

This paper aims at rescheduling of observing spacecraft imaging plans under uncertainties. Firstly, uncertainties in spacecraft observation scheduling are analyzed. Then, considering the uncertainties with fuzzy features, this paper proposes a fuzzy neural network and a hybrid rescheduling policy to deal with them. It then establishes a mathematical model and manages to solve the rescheduling problem by proposing an ant colony algorithm, which introduces an adaptive control mechanism and takes advantage of the information in an existing schedule. Finally, the above method is applied to solve the rescheduling problem of a certain type of earth-observing satellite. The computation of the example shows that the approach is feasible and effective in dealing with uncertainties in spacecraft observation scheduling. The approach designed here can be useful in solving the problem that the original schedule is contaminated by disturbances.

A framework for ICT-enabled real-time production planning and control

This paper aims to develop a conceptual framework for real-time production planning and control（PPC）.Firstly,we discuss the most prominently applied contemporary information and communication technologies for PPC.Enterprise resource planning（ERP） systems that integrate the value chain in an enterprise,manufacturing execution systems that manage and control the production on shopfloor,and advanced planning and scheduling（APS）systems that develop solutions for complex planning problems are the planning and control systems that have been analyzed.We emphasize the application of radio frequency identification as the most advanced and promising emerging real-time data capture technology that is currently available to manufacturers.Having analyzed the features and shortcomings of the individual systems perse,and by considering the advantages that may be realized through effective integration of these otherwise discrete systems,we propose a framework for real-time PPC.

Planning to Plan-Integrating Control Flow

In many planning situations, computation itself becomes a resource to be planned and scheduled. We model such computational resources as conventional resources which are used by control-flow actions, e.g., to direct the planning process. Control-flow actions and conventional actions are planned/scheduled in an integrated way and can interact with each other. Control-flow actions are then executed by the planning engine itself. The approach is illustrated by examples, e.g., for hierarchical planning, in which tasks that are temporally still far away impose only rough constraints on the current schedule, and control-flow tasks ensure that these tasks are refined as they approach the current time. Using the same mechanism, anytime algorithms can change appropriate search methods or parameters over time, and problems like scheduling critical time-outs for garbage collection can be made part of the planning itself.

Design and performance evaluation of a parking management system for automated, multi-story and robotic parking structure

Purpose–The purpose of this paper is to present design and performance evaluation through simulation of a parking management system(PMS)for a fully automated,multi-story,puzzle-type and robotic parking structure with the overall objective of minimizing customer wait times while maximizing the space utilization.Design/methodology/approach–The presentation entails development and integration of a complete suite of path planning,elevator scheduling and resource allocation algorithms.The PMS aims to manage multiple concurrent requests,in real time and in a dynamic context,for storage and retrieval of vehicles loaded onto robotic carts for a fully automated,multi-story and driving-free parking structure.The algorithm suite employs the incremental informed search algorithm D*Lite with domain-specific heuristics and the uninformed search algorithm Uniform Cost Search for path search and planning.An optimization methodology based on nested partitions and Genetic algorithm is adapted for scheduling of a group of elevators.The study considered a typical business day scenario in the center of a metropolis.Findings–The simulation study indicates that the proposed design for the PMS is able to serve concurrent storage-retrieval requests representing a wide range of Poisson distributed customer arrival rates in real time while requiring reasonable computing resources under realistic scenarios.The customer waiting times for both storage andretrievalrequestsare withinacceptable bounds,whichare set as nomore than 5min,evenin the presence of up to 100 concurrent storage and retrieval requests.The design is able to accommodate a variety of customer arrival rates and presence of immobilized vehicles which are assumed to be scattered across the floors of the structure to make it possible for deployment in real-time environments.Originality/value–The intelligent system design is novel as the fully automated robotic parking structures are just in the process of being matured from a technology standpoint.

Skyline-join query processing in distributed databases

The skyline-join operator, as an important variant of skylines, plays an important role in multi-criteria decision making problems. However, as the data scale increases, previous methods of skyline-join queries cannot be applied to new applications. Therefore, in this paper, it is the first attempt to propose a scalable method to process skyline-join queries in distributed databases. First, a tailored distributed framework is presented to facilitate the computation of skyline-join queries. Second, the distributed skyline-join query algorithm （DSJQ） is designed to process skyline-join queries. DSJQ contains two phases. In the first phase, two filtering strategies are used to filter out unpromising tuples from the original tables. The remaining tuples are transmitted to the corresponding data nodes according a partition function, which can guarantee that the tuples with the same join value are transferred to the same node. In the second phase, we design a scheduling plan based on rotations to calculate the final skyline-join result. The scheduling plan can ensure that calculations are equally assigned to all the data nodes, and the calculations on each data node can be processed in parallel without creating a bottleneck node. Finally, the effectiveness of DSJQ is evaluated through a series of experiments.