Heuristic procedures for reactive project scheduling

This paper describes new heuristic reactive project scheduling procedures that may be used to repair resource-constrained project baseline schedules that suffer from multiple activity duration disruptions during project execution. The objective is to minimize the deviations between the baseline schedule and the schedule that is actually realized.We discuss computational results obtained with priority-rule based schedule generation schemes, a sampling approach and a weighted-earliness tardiness heuristic on a set of randomly generated project instances.     

Heuristic search for scheduling flexible manufacturing systems using lower bound reachability matrix

For scheduling flexible manufacturing systems efficiently, we propose new heuristic functions for A^* algorithm that is based on the T-timed Petri net. In minimizing makespan, the proposed heuristic functions are usually more efficient than the previous functions in the required number of states and computation time. We prove that these heuristic functions are all admissible and one of them is more informed than that using resource cost reachability matrix. We also propose improved versions of these heuristic functions that find a first near-optimal solution faster. In addition, we modify the heuristic function of Yu, Reyes, Cang, and Lloyd (2003b) and propose an admissible version in all states. The experimental results using a random problem generator show that the proposed heuristic functions perform better as we expected.     

Heuristic search for scheduling flexible manufacturing systems using lower bound reachability matrix

For scheduling flexible manufacturing systems efficiently, we propose new heuristic functions for A^* algorithm that is based on the T-timed Petri net. In minimizing makespan, the proposed heuristic functions are usually more efficient than the previous functions in the required number of states and computation time. We prove that these heuristic functions are all admissible and one of them is more informed than that using resource cost reachability matrix. We also propose improved versions of these heuristic functions that find a first near-optimal solution faster. In addition, we modify the heuristic function of Yu, Reyes, Cang, and Lloyd (2003b) and propose an admissible version in all states. The experimental results using a random problem generator show that the proposed heuristic functions perform better as we expected.     

Fuzzy-based methodology for multi-objective scheduling in a robot-centered flexible manufacturing cell

A fuzzy logic based methodology for generating the sequence of part movements in a multi-product batch processing through a computerized machine cell is presented in this paper. A number of production objectives are taken into account. Two fuzzy based strategies: fuzzy-job and fuzzy-machine are proposed and their performance is compared to two well known dispatching rules such as SPT (Shortest Processing Time) and WEED (Weighted Earliest Due Date). The sequencing algorithm was implemented on a standard personnel computer and the scheduler was interfaced to a robot controller for implementing loading and unloading strategy within the cell. The proposed fuzzy-based methodologies especially fuzzy-job shows a superior performance compared to the traditional dispatching rules considered.     

Learning-based multi-pass adaptive scheduling for a dynamic manufacturing cell environment

Because the essential attributes are uncertain in a dynamic manufacturing cell environment, to select a near-optimal subset of manufacturing attributes to enhance the generalization ability of knowledge bases remains a critical, unresolved issue for classical artificial neural network-based (ANN-based) multi-pass adaptive scheduling (MPAS). To resolve this problem, this study develops a hybrid genetic /artificial neural network (GA/ANN) approach for ANN-based MPAS systems. The hybrid GA/ANN approach is used to evolve an optimal subset of system attributes from a large set of candidate manufacturing system attributes and, simultaneously, to determine configuration and learning parameters of the ANN according to various performance measures. In the GA/ANN-based MPAS approach, for a given feature subset and the corresponding topology and learning parameters of an ANN decoded by a GA, an ANN was applied to evaluate the fitness in the GA process and to generate the MPAS knowledge base used for adaptive scheduling control mechanisms. The results demonstrate that the proposed GA/ANN-based MPAS approach has, according to various performance criteria, a better system performance over a long period of time than those obtained with classical machine learning-based MPAS approaches and the heuristic individual dispatching rules.     

An agent-based framework for global purchasing and manufacturing in a shoe industry

Global competition is forcing the present day industry to produce high quality product more fast and inexpensively. The need for fast information exchange has increased among the remote locations for the cooperation and coordination. In this study, a virtual global manufacturing system in a shoe company is constructed that distributes production schedule among remote places, acts as a bridge between the headquarters and manufacturing plants, distributes tasks and collates different solutions between demand and supply using an agent. It generates an order and a procurement plan. An order-supply agent is proposed and implemented to automate the process of ordering and supplying of goods in a shoe making company from the Internet environment.     

Application of videotex in a manufacturing company

The article describes the use of videotex throughout ICI, a major UK manufacturing company. Among the applications discussed are access to up-to-date technical information and customer sales statistics and the use of videotex as a messaging system for brief exchanges of all kinds of information.     

Evolutionary algorithms for scheduling a flowshop manufacturing cell with sequence dependent family setups

This paper considers the problem of scheduling part families and jobs within each part family in a flowshop manufacturing cell with sequence dependent family setups times where it is desired to minimize the makespan while processing parts (jobs) in each family together. Two evolutionary algorithms—a Genetic Algorithm and a Memetic Algorithm with local search—are proposed and empirically evaluated as to their effectiveness in finding optimal permutation schedules. The proposed algorithms use a compact representation for the solution and a hierarchically structured population where the number of possible neighborhoods is limited by dividing the population into clusters. In comparison to a Multi-Start procedure, solutions obtained by the proposed evolutionary algorithms were very close to the lower bounds for all problem instances. Moreover, the comparison against the previous best algorithm, a heuristic named CMD, indicated a considerable performance improvement.     

Effect of cell based team work in productivity improvement at a manufacturing company

This paper report is part of a comprehensive study aiming at establishing a productivity improvement model for an Iranian manufacturing company. We firstly describe the state of affairs encompassing the effects of the prevailing behavioural system which is in turn affected by cultural and environmental conditions, impact of history, geographical situation, state of technology and people's attitude. A team work effort is organised around a cellular manufacturing exercise to demonstrate an effective change in the performance and esisting conditions.     

Heuristic approaches for master planning in semiconductor manufacturing

In this paper, we propose heuristic approaches for solving master planning problems that arise in semiconductor manufacturing networks. The considered problem consists of determining appropriate wafer quantities for several products, facilities, and time periods by taking demand fulfillment (i.e., confirmed orders and forecasts) and capacity constraints into account. In addition, fixed costs are used to reduce production partitioning. A mixed-integer programming (MIP) formulation is presented and the problem is shown to be NP-hard. As a consequence, two heuristic procedures are proposed: a product based decomposition scheme and a genetic algorithm. The performance of both heuristics is assessed using randomly generated test instances. It turns out that the decomposition scheme is able to produce high-quality solutions, while the genetic algorithm achieves results with reasonable quality in a short amount of time.     

Heuristic algorithms for assigning and scheduling flight missions in a military aviation unit

We consider an operations planning problem in a military aviation unit that performs a number of flight missions with multiple identical aircrafts. The problem is to assign the flight missions to the aircrafts and to schedule these assigned missions on each aircraft. Sequence-dependent setup times are required between the missions, and multiple aircrafts may be needed for a mission, but the aircrafts assigned to the same mission should start the mission simultaneously. We develop heuristic algorithms for the problem with the objective of minimizing makespan, i.e., the time by which all the missions have been completed. For evaluation of the performance of the algorithms, a series of computational tests was performed on a number of problem instances, and results show that the proposed algorithms give good or near optimal solutions in a reasonable amount of time.     

Optimum loading of machines in a flexible manufacturing system using a mixed-integer linear mathematical programming model and genetic algorithm

Machine loading problem in a flexible manufacturing system (FMS) encompasses various types of flexibility aspects pertaining to part selection and operation assignments. The evolution of flexible manufacturing systems offers great potential for increasing flexibility by ensuring both cost-effectiveness and customized manufacturing at the same time. This paper proposes a linear mathematical programming model with both continuous and zero-one variables for job selection and operation allocation problems in an FMS to maximize profitability and utilization of system. The proposed model assigns operations to different machines considering capacity of machines, batch-sizes, processing time of operations, machine costs, tool requirements, and capacity of tool magazine. A genetic algorithm (GA) is then proposed to solve the formulated problem. Performance of the proposed GA is evaluated based on some benchmark problems adopted from the literature. A statistical test is conducted which implies that the proposed algorithm is robust in finding near-optimal solutions. Comparison of the results with those published in the literature indicates supremacy of the solutions obtained by the proposed algorithm for attempted model.     

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.     

Intra-cell manpower transfers and cell loading in labor-intensive manufacturing cells

Labor-intensive manufacturing cells consist of simple machines and equipment that require continuous operator attendance and involvement. Operators are often re-assigned to different machines when a new product is released to the cell. The main reason for this re-assignment is to maximize the output rate of the cell by balancing the flow of products through several machines with varying capacities. In this paper, first a product-sequencing problem with the objective of minimizing the total intra-cell manpower transfers is introduced. A three-phase hierarchical methodology is proposed to solve the problem optimally. Next, manpower transfer matrix values are modified considering the distances traveled among machines. In the second part of the paper, a machine-level-based similarity coefficient that uses the number of machines as a similarity measure is discussed. Later, these coefficients are used during the cell loading process to minimize makespan and also machine and space requirements. Manpower allocation decisions are made along with scheduling decisions that are critical in most labor-intensive manufacturing cells and both approaches are illustrated with an example problem.     

Metaheuristics for scheduling a non-permutation flowline manufacturing cell with sequence dependent family setup times

The broad applications of cellular manufacturing make flowline manufacturing cell scheduling problems with sequence dependent family setup times a core topic in the field of scheduling. Due to computational complexity, almost all published studies focus on using permutation schedules to deal with this problem. To explore the potential effectiveness of treating this argument using non-permutation schedules, three prominent types of metaheuristics—a simulated annealing, a genetic algorithm and a tabu search—are proposed and empirically evaluated. The experimental results demonstrate that in general, the improvement made by non-permutation schedules over permutation schedules for the due-date-based performance criteria were significantly better than that for the completion-time-based criteria. The results of this study will provide practitioners a guideline as to when to adopt a non-permutation schedule, which may exhibit better performance with additional computational efforts.     

离散制造业中生产批量计划问题的模型与算法

制造业是衡量一个国家和地区的综合经济实力和发展水平的重要标准,是民生之本和产业主体。制造业的重要组成成分之一是离散制造业,其中的重要问题是生产批量计划问题（Lot-sizing Problem,LP）。生产批量计划问题是企业生产过程中的重要决策问题,与运筹学、管理科学和工业工程等领域联系紧密。迄今为止,国内外学者对该问题的研究历史已超过50年。在参考国内外相关文献的基础上,对生产批量计划问题的模型和求解算法作了系统、全面的总结,指出了将来可以进一步研究的重点方向。     

Heuristic factory planning algorithm for advanced planning and scheduling

This study focuses on solving the factory planning (FP) problem for product structures with multiple final products. In situations in which the capacity of the work center is limited and multiple job stages are sequentially dependent, the algorithm proposed in this study is able to plan all the jobs, while minimizing delay time, cycle time, and advance time. Though mixed integer programming (MIP) is a popular way to solve supply chain factory planning problems, the MIP model becomes insolvable for complex FP problems, due to the time and computer resources required. For this reason, this study proposes a heuristic algorithm, called the heuristic factory planning algorithm (HFPA), to solve the supply chain factory planning problem efficiently and effectively. HFPA first identifies the bottleneck work center and sorts the work centers according to workload, placing the work center with the heaviest workload ahead of the others. HFPA then groups and sorts jobs according to various criteria, for example, dependency on the bottleneck work center, the workload at the bottleneck work center, and the due date. HFPA plans jobs individually in three iterations. First, it plans jobs without preempting, advancing, and/or delaying. Jobs that cannot be scheduled under these conditions are scheduled in the second iteration, which allows preemption. In the final iteration, which allows jobs to be preempted, advanced, and delayed, all the remaining jobs are scheduled. A prototype was constructed and tested to show HFPA's effectiveness and efficiency. This algorithm's power was demonstrated using computational and complexity analysis.     

求解柔性机器人车间调度问题的混合蚁群算法

在柔性作业车间调度问题的基础上,考虑多台搬运机器人执行不同工序在不同机床之间的搬运,形成柔性机器人作业车间调度问题,提出混合蚁群算法。用改进析取图对问题进行描述,使用混合选择策略、自适应伪随机比例规则和改进信息素更新规则优化蚁群算法,结合遗传算子完成机床选择和工序排序。使用一种多机器人排序算法完成搬运机器人分配和搬运工序排序。通过多组算例仿真测试并与其他算法进行比较,验证了算法的有效性和可靠性。     

Heuristic algorithms for a three-dimensional loading capacitated vehicle routing problem in a carrier

In this paper, we present heuristic algorithms for a three-dimensional loading capacitated vehicle routing problem arising in a real-world situation. In this problem, customers make requests of goods, which are packed in a sortment of boxes. The objective is to find minimum cost delivery routes for a set of identical vehicles that, departing from a depot, visit all customers only once and return to the depot. Apart of the usual 3D container loading constraints which ensure that the boxes are packed completely inside the vehicles and that the boxes do not overlap each other in each vehicle, the problem also takes into account constraints related to the vertical stability of the cargo and multi-drop situations. The algorithms are based on the combination of classical heuristics from both vehicle routing and container loading literatures, as well as two metaheuristic strategies, and their use in more elaborate procedures. Although these approaches cannot assure optimal solutions for the respective problems, they are relatively simple, fast enough to solve real instances, flexible enough to include other practical considerations, and normally assure relatively good solutions in acceptable computational times in practice. The approaches are also sufficiently generic to be embedded with algorithms other than those considered in this study, as well as they can be easily adapted to consider other practical constraints, such as the load bearing strength of the boxes, time windows and pickups and deliveries. Computational tests were performed with these methods considering instances based on the vehicle routing literature and actual customers’ orders, as well as instances based on a real-world situation of a Brazilian carrier. The results show that the heuristics are able to produce relatively good solutions for real instances with hundreds of customers and thousands of boxes.     

Design of a suitable production management system for a manufacturing company

This paper reports on the design of a suitable production management system (PMS) for a manufacturing company, located in Western Australia. The company was experiencing problems in scheduling and plant layout leading to further problems in materials flow, labour control, inventory and purchasing, material handling system, and production space.

Group technology (GT) was used to design a new plant layout. A G.T. algorithm was developed to minimise machine duplications. A Kanban system was designed to assist execution of scheduling based on a pull system. The design was tested by simulation using actual data collected on existing operations.