Manufacturing cell formation using similarity coefficients and pair-wise interchange: formulation and comparison

Many algorithms have been proposed to form manufacturing cells from component routeings. Most of these methods require specialized algorithms for implementation. Some others use well-known procedures such as integer programming. But these may be difficult for practising managers to comprehend. In this study we propose a simple method that can be implemented using spreadsheet software and an inexpensive layout package such as CRAFT. In addition, we also compare our procedure with many existing procedures using eight well-known problems from the literature. The results show that the proposed procedure compares well with the existing procedures using three evaluation measures. Therefore, this procedure may be useful to practitioners and researchers.     

Decomposition algorithms for the integrated process planning and scheduling problem

There are several algorithms to solve the integrated process planning and scheduling (IPPS) problem (i.e., flexible job shop scheduling with process plan flexibility) in the literature. All the existing algorithms for IPPS are heuristic-based search methods and no research has investigated the use of exact solution methods for this problem. We develop several decomposition approaches based on the logic-based Benders decomposition (LBBD) algorithm. Our LBBD algorithm allows us to partition the decision variables in the IPPS problem into two models, master-problem and sub-problem. The master-problem determines process plan and operation-machine assignment, while the sub-problem optimizes sequencing and scheduling decisions. To achieve faster convergence, we develop two relaxations for the optimal makespan objective function and incorporate them into the master-problem. We analyze the performance and further enhance the algorithm with two ideas, a Benders optimality cut based on the critical path and a faster heuristic way to solve the sub-problem. 16 standard benchmark instances available in the literature are solved to evaluate and compare the performances of our algorithms with those of the state-of-the-art methods in the literature. The proposed algorithm either results in the optimal solution or improves the best-known solutions in all the existing instances, demonstrating its superiority to the existing state-of-the-art methods in literature.     

Commercial territory design planning with realignment and disjoint assignment requirements

A territory design problem motivated by a bottled beverage distribution company is addressed. The problem consists of finding a partition of the entire set of city blocks into a given number of territories subject to several planning criteria. Each unit has three measurable activities associated to it, namely, number of customers, product demand, and workload. The plan must satisfy planning criteria such as territory compactness, territory balancing with respect to each of the block activity measures, and territory connectivity, meaning that there must exist a path between any pair of units in a territory totally contained in it. In addition, there are some disjoint assignment requirements establishing that some specified units must be assigned to different territories, and a similarity with existing plan requirement. An optimal design is one that minimizes a measure of territory dispersion and similarity with existing design. A mixed-integer linear programming model is presented. This model is unique in the commercial territory design literature as it incorporates the disjoint assignment requirements and similarity with existing plan. Previous methods developed for related commercial districting problems are not applicable. A solution procedure based on an iterative cut generation strategy within a branch-and-bound framework is proposed. The procedure aims at solving large-scale instances by incorporating several algorithmic strategies that helped reduce the problem size. These strategies are evaluated and tested on some real-world instances of 5000 and 10,000 basic units. The empirical results show the effectiveness of the proposed method and strategies in finding near optimal solutions to these very large instances at a reasonably small computational effort.     

A modified hierarchical production planning system integrated with MRP: A case study

In this paper, a hierarchical planning system is proposed which integrates aggregate capacity planning with MRP. This system is to be implemented in a metal box manufacturing company which multi-user MRP system covering manufacturing activities as well as procurement sales order processing and accounting systems. The hierarchical planning system includes a medium-range aggregate planning model adapted to the firm's requirements and strategies. The model consists of a mathematical formulation which covers labour capacity has already installed a constraints and includes certain cost estimations in the objective function. The planning horizon of the medium range planning is taken as twelve months in order to cover sales seasonality. The aggregate production quantities resulting from the optimized medium-range planning model are disaggregated according to procedures already found in the literature. Furthermore, the theoretical infeasibilities pertaining to the disaggregation procedures are also resolved in an heuristic manner. Using the latter modified disaggregation procedure, a feasible disaggregated plan is generated for the whole planning horizon. The proposed plan is compared with the current production policy of the firm and it is observed that the proposed plan leads to backorder reduction.     

基于杂合遗传算法的工艺路线可变Job Shop调度研究

提出一种将遗传算法与启发式规则、模拟退火法等搜索方法结合在一起的杂合遗传算法,用于求解工艺路线可变的JobShop调度问题。通过对某双极型集成电路封装企业的JobShop调度仿真,结果表明算法是有效和可行的。     

The use of demand information in selecting production plans and schedules

The strategic problem of selecting a production plan for a given planning horizon is usually treated as independent of the tactical problem of scheduling the production plan. This paper approaches both selecting the production plan and scheduling it as one problem. The problem is formulated as a zero-one integer program. The formulation accommodates many real-life considerations. The integer program is solved using a branch and bound procedure which provides the optimal production plan and schedule as well as the importance indices of the orders, a concept which is introduced and used in this study to rank the available orders within the planning horizon according to their importance to the firm. The integer program and the search procedure can be used as a decision supporting tool to respond to any changes in the demand information, capacity of the firm, or its operating strategy, and it guarantees the selection of feasible production plan(s) and optimal schedules.     

Hierarchical production planning for multi-product lines in the beverage industry¶

Multi-commodity production and distribution scheduling is one of the most complex and crucial problems facing many manufacturing companies. For a major European manufacturer specialising in bottling juices and drinks, we have designed and developed a hierarchical decomposition approach to the solution of the multi-commodity production planning problem. In this paper we focus our attention on the coarsest decomposition level, called multi-commodity aggregate production planning (MCAP). It concerns the choice of the best feasible production plan for a set of products (commodities) over an extended time horizon so as to meet forecast aggregate demands throughout the horizon. At this level, the problem constraints include hard constraints (such as production lines having a maximum capacity and products having short life-times), and soft constraints (budgetary concerns.) The objective is to determine the production plan that covers each period's demands as best as possible, while minimizing all relevant costs. Our method for solving MCAP produces optimal plans in negligible times in commodity PC workstations.     

Material planning with uncertain product quality†

Traditional material requirement planning (MRP) is a system procedure to determine input quantities and ordering times to meet a specified demand in a deterministic framework. A variety of modifications to MRP have been proposed to incorporate different forms of uncertainty in production systems. In this paper, wc consider the case where the uncertainty is due to quality variations in the production process, and we develop an approach which links material planning with quality variations and control.     

A Load‐based and Due‐date‐oriented Approach to Order Review/Release in Job Shops*

In this paper, a new order review/release (ORR) method is proposed for shop floor control systems. The proposed method utilizes both job due date and shop load information to improve the effectiveness of the ORR function in production systems. The performance of the new method is compared to those of a few well‐known ORR methods under four experimental conditions. The results of extensive simulation experiments indicate that the proposed method is superior with respect to the mean absolute deviation measure. In general, it is also better than existing methods for the other performance measures. Furthermore, we show that the proposed method is more robust to variations in system load and processing times than the other ORR methods examined.     

Genetic algorithm to production planning and scheduling problems for manufacturing systems

Fundamental and extended multi-objective (MO) models are designed to address earliness/tardiness production scheduling planning (ETPSP) problems with multi-process capacity balance, multi-product production and lot-size consideration. A canonical genetic algorithm (GA) approach and a prospective multi-objective GA (MOGA) approach are proposed as solutions for different practical problems. Simulation results as well as comparisons with other techniques demonstrate the effectiveness of the MOGA approach, which is a noted improvement to any of the existing techniques, and also in practice provides a new trend of integrating manufacturing resource planning (MRPII) with just-in-time (JIT) in the production planning procedure.     

A robust optimization model for stochastic aggregate production planning

The aggregate production planning (APP) problem considers the medium-term production loading plans subject to certain restrictions such as production capacity and workforce level. It is not uncommon for management to often encounter uncertainty and noisy data, in which the variables or parameters are stochastic. In this paper, a robust optimization model is developed to solve the aggregate production planning problems in an environment of uncertainty in which the production cost, labour cost, inventory cost, and hiring and layoff cost are minimized. By adjusting penalty parameters, decision-makers can determine an optimal medium-term production strategy including production loading plan and workforce level while considering different economic growth scenarios. Numerical results demonstrate the robustness and effectiveness of the proposed model. The proposed model is realistic for dealing with uncertain economic conditions. The analysis of the tradeoff between solution robustness and model robustness is also presented.     

Integration of process planning and production planning and control in cellular manufacturing

Organisations willing to succeed in global competition have to integrate their internal and external processes. This especially includes planning and production control (PPC) processes. Optimised allocation of the production resources and quick response to demand changes result in lower cost and improvement of production performance. Practitioners and researchers have been trying to achieve these goals using production planning techniques. Although the results are significant, it seems necessary to integrate production operations in order to improve the production performance. The goals, information and decisions taken in production planning and control and process planning are often very different and difficult to integrate in Cellular Manufacturing (CM) environments. Designing an efficient PPC system and integrating it with process planning in a cellular environment is of the same importance. The following paper proposes first a comprehensive framework of integrated process planning and production planning and control in CM. Then, with respect to this framework and utilising the domain knowledge in the area of CM systems, an integrated model based on Integrated Definition Modeling Language is developed. The application of the models has been considered as a case study for a production system in electronics and telecommunication sector in a plant in Iran. The validity and completeness of the proposed model is tested by a panel of experts in the areas of production planning and control in CM environments.     

基于GBOM的供应链网络及其有生产能力约束的集成生产计划模型

提出了基于通用物料单构建供应链网络的方法,并以此为基础建立了其有生产能力约束的集成生产计划模型。由于任一成员都有生产能力上限,且生产能力可相互转化,故总生产能力须进行标准化。模型的目标是供应链联盟整体收益最大化,模型解反映了优化的集成生产计划和生产合作关系。     

Genetic algorithms with path relinking for the minimum tardiness permutation flowshop problem

In this work three genetic algorithms are presented for the permutation flowshop scheduling problem with total tardiness minimisation criterion. The algorithms include advanced techniques like path relinking, local search and a procedure to control the diversity of the population. We also include a speed up procedure in order to reduce the computational effort needed for the local search technique, which results in large CPU time savings. A complete calibration of the different parameters and operators of the proposed algorithms by means of a design of experiments approach is also given. We carry out a comparative evaluation with the best methods that can be found in the literature for the total tardiness objective, and with adaptations of other state-of-the-art methods originally proposed for other objectives, mainly makespan. All the methods have been implemented with and without the speed up procedure in order to test its effect. The results show that the proposed algorithms are very effective, outperforming the remaining methods of the comparison by a considerable margin.     

Study on Compatibility between Converters and Casting Machines for Daily Steelmaking and Continuous Casting Scheduling

In this paper, daily production scheduling is studied based on the Third Steelmaking Plant of Wuhan Iron and steel corporation (WISCO). To make sure the daily production plan is feasible, method of casting gToup is established, and the compatibility between two converters and three continuous casting devices in the Third Steelmaking Plant of WISCO is analyzed. The process flow chart of daily production scheduling is given in this paper. Then, algorithms and procedures for the simulation of daily production plan is developed. Using the actual data from the Third Steelmaking Plant, the feasible daily steelmaking plan and cast plan are given. The plan contains 7 groups of cast plan, figured out 54 converters, and a- bout 13,500 tons steel.     

Experimental push/pull production planning and control system

We introduce an experimental push/ pull production planning and control software system which is designed as an alternative to a MRP-II system for mass manufacturing enterprises in China. It has the following distinguishing features: (1) putting the philosophy of JIT into the master production scheduling of MRP-II via the earliness/ tardiness production planning method; (2) controlling material input by push and processing/ assembly by pull; and (3) adjusting the parameters of the production line by the‘ suggestion for improvement of production line’ module. Simulation results have shown that the proposed system can achieve better planning and control performance than existing systems.     

Linking aggregate production planning with marketing pricing decisions

In this paper, we link production planning decisions to marketing decisions that involve the price of product groups. The focus of this paper is the development of a closed-loop procedure for aggregate production planning and pricing. We seek to satisfy uncertain demand while minimizing total costs that include material, labour, and inventory holding costs. The procedure is useful for variable demand to update short-term aggregate plans.     

Improving throughput for an electronic assembly line using a constraint analysis methodology

Electronic assembly operations are vital to industries such as telecommunications, computers and consumer electronics. This paper presents a constraint analysis methodology for planning and improving electronic assembly operations that draws on concepts from queueing theory, simulation and production planning. The proposed methodology identifies the operational bottleneck and predicts the utilization, throughput and lead time of the assembly line. It also quantifies the relationship between yields and utilization for the assembly operations. A case study is presented that applies the methodology at an Ericsson, Inc., telecommunications equipment assembly facility. The constraint analysis methodology provided valuable decision support as the managers of Ericsson evaluated the costs and benefits of additional production capacity. Although the focus of this paper is electronic assembly operations, the methodology can be applied to general flow line assembly systems with feedback loops for test and rework under dedicated high-volume production.