FLEXIBLE JOB-SHOP SCHEDULING WITH FUZZY GOAL THROUGH IOCDGA

The fuzzy goal flexible job-shop scheduling problem （FGFJSP） is the extension of FJSP. Compared with the convention JSP, it can solve the fuzzy goal problem and meet suit requirements of the key job. The multi-object problem, such as the fuzzy cost, the fuzzy due-date, and the fuzzy makespan, etc, can be solved by FGFJSP. To optimize FGFJSP, an individual optimization and colony diversity genetic algorithm （IOCDGA） is presented to accelerate the convergence speed and to avoid the earliness. In IOCDGA, the colony average distance and the colony entropy are defined after the definition of the encoding model. The colony diversity is expressed by the colony average distance and the colony entropy. The crossover probability and the mutation probability are controlled by the colony diversity. The evolution emphasizes that sigle individual or a few individuals evolve into the best in IOCDGA, but not the all in classical GA. Computational results show that the algorithm is applicable and the number of iterations is less.

INTEGRATED OPERATOR GENETIC ALGORITHM FOR SOLVING MULTI-OBJECTIVE FLEXIBLE JOB-SHOP SCHEDULING

In the flexible job-shop scheduling problem （FJSP）, each operation has to be assigned to a machine from a set of capable machines before alocating the assigned operations on all machines. To solve the multi-objective FJSP, the Grantt graph oriented string representation （GOSR） and the basic manipulation of the genetic algorithm operator are presented. An integrated operator genetic algorithm （IOGA） and its process are described. Comparison between computational results and the latest research shows that the proposed algorithm is effective in reducing the total workload of all machines, the makespan and the critical machine workload.

Job-Shop Scheduling问题的一个快速算法

Job-Shop Scheduling问题是优化组合中一个著名的难题,即使规模不大的算例在计算上也是很棘手的。文章给出了一个性能很好的算法,该算法找到了所计算的16个算例中12个算例的最优解,而且每个算例在一台个人计算机(CPU为赛扬633)上所花的计算时间不超过1分钟。

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.

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.

A bi-objective model for job-shop scheduling problem to minimize both energy consumption and makespan

The issue of reducing energy consumption for the job-shop scheduling problem in machining systems is addressed, whose dual objectives are to minimize both the energy consumption and the makespan. First, the bi- objective model for the job-shop scheduling problem is proposed. The objective function value of the model represents synthesized optimization of energy consumption and makespan. Then, a heuristic algorithm is developed to locate the optimal or near optimal solutions of the model based on the Tabu search mechanism. Finally, the experimental case is presented to demonstrate the effectiveness of the proposed model and the algorithm.

APPLYING PARTICLE SWARM OPTIMIZATION TO JOB-SHOPSCHEDULING PROBLEM

A new heuristic algorithm is proposed for the problem of finding the minimummakespan in the job-shop scheduling problem. The new algorithm is based on the principles ofparticle swarm optimization (PSO). PSO employs a collaborative population-based search, which isinspired by the social behavior of bird flocking. It combines local search (by self experience) andglobal search (by neighboring experience), possessing high search efficiency. Simulated annealing(SA) employs certain probability to avoid becoming trapped in a local optimum and the search processcan be controlled by the cooling schedule. By reasonably combining these two different searchalgorithms, a general, fast and easily implemented hybrid optimization algorithm, named HPSO, isdeveloped. The effectiveness and efficiency of the proposed PSO-based algorithm are demonstrated byapplying it to some benchmark job-shop scheduling problems and comparing results with otheralgorithms in literature. Comparing results indicate that PSO-based algorithm is a viable andeffective approach for the job-shop scheduling problem.

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.

Solving Job-Shop Scheduling Problem Based on Improved Adaptive Particle Swarm Optimization Algorithm

An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal factor(HF),composed of an adaptive local hormonal factor(H l)and an adaptive global hormonal factor(H g),is devised to strengthen the information connection between particles.Using HF,each particle of the swarm can adjust its position self-adaptively to avoid premature phenomena and reach better solution.The computational results validate the effectiveness and stability of the proposed IAPSO,which can not only find optimal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization(PSO)algorithms.

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.

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.

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.

Necessary and Sufficient Conditions for Feasible Neighbourhood Solutions in the Local Search of the Job-Shop Scheduling Problem

The meta-heuristic algorithm with local search is an excellent choice for the job-shop scheduling problem(JSP).However,due to the unique nature of the JSP,local search may generate infeasible neighbourhood solutions.In the existing literature,although some domain knowledge of the JSP can be used to avoid infeasible solutions,the constraint conditions in this domain knowledge are sufficient but not necessary.It may lose many feasible solutions and make the local search inadequate.By analysing the causes of infeasible neighbourhood solutions,this paper further explores the domain knowledge contained in the JSP and proposes the sufficient and necessary constraint conditions to find all feasible neighbourhood solutions,allowing the local search to be carried out thoroughly.With the proposed conditions,a new neighbourhood structure is designed in this paper.Then,a fast calculation method for all feasible neighbourhood solutions is provided,significantly reducing the calculation time compared with ordinary methods.A set of standard benchmark instances is used to evaluate the performance of the proposed neighbourhood structure and calculation method.The experimental results show that the calculation method is effective,and the new neighbourhood structure has more reliability and superiority than the other famous and influential neighbourhood structures,where 90%of the results are the best compared with three other well-known neighbourhood structures.Finally,the result from a tabu search algorithm with the new neighbourhood structure is compared with the current best results,demonstrating the superiority of the proposed neighbourhood structure.

A Grafted Genetic Algorithm for the Job-Shop Scheduling Problem

The standard genetic algorithm has limitations of a low convergence rate and premature convergence in solving the job-shop scheduling problem.To overcome these limitations,this paper presents a new improved hybrid genetic algorithm on the basis of the idea of graft in botany.Through the introduction of a grafted population and crossover probability matrix,this algorithm accelerates the convergence rate greatly and also increases the ability to fight premature convergence.Finally,the approach is tested on a set of standard instances taken from the literature and compared with other approaches.The computation results validate the effectiveness of the proposed algorithm.

Genetic Algorithm and the Application for Job-Shop Group Scheduling

Genetic algorithm(GA)is a heuristic and random search technique for mimicking na-ture.This paper presents the basic principle and principal character of GA,and the defini-tion and function of the genetic operators.On the basis of these,the paper proposes a newmethod of solving the Job-Shop group scheduling problem by use of GA,and discusses thecoded representation method of the feasible solution and the particular limitation to the ge-netic operators.

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.