Optimisation of cutting parameters using a multi-objective genetic algorithm

This paper presents a new optimisation technique based on genetic algorithms (GA) for determination of cutting parameters in machining operations. The cutting parameters considered in this study are cutting speed, feed rate and cutting depth. The effect of these parameters on production time, production cost and roughness is mathematically formulated. A genetic algorithm with multiple fitness functions is proposed to solve the formulated problem. The proposed algorithm finds multiple solutions along the Pareto optimal frontier. Experimental results show that the proposed algorithm is both effective and efficient, and can be integrated into an intelligent process planning system for solving complex machining optimisation problems.     

An integrated model for inventory and production planning in a two-stage hybrid production system

A two-stage hybrid flow-shop production system is considered. The first stage is a process production system and the second stage is a job-shop production system. The two stages are separated by an intermediate warehouse to introduce flexibility (some independence) in the planning of production at both stages. The inventory level at the warehouse should be optimized to provide a trade-off between the cost of carrying the inventory of the semi-finished products, the minimum batch size requirement in the first stage, and the required service level at the second stage. An integrated model for planning the production in these hybrid flow-shop production systems types is developed. The objectives of optimizing the production and inventory costs at the two stages of the system, including the warehouse, while satisfying customer demands, are considered. An algorithm to solve the suggested model is described in detail, and a solution is provided for a real world case, which has inspired the study. A computational study to measure the performance of the approach was also carried out and the results are reported.     

Optimization of multipass turning operations with genetic algorithms

The paper proposes a new optimization technique based on genetic algorithms for the determination of the cutting parameters in multipass machining operations. The cutting process simultaneously considers multipass rough machining and finish machining. The optimum machining parameters are determined by minimizing the unit production cost subject to practical machining constraints. The cutting model formulated is a non-linear-constrained programming (NCP) problem with 20 machining parameter constraints. Experimental results show that the proposed genetic algorithm-based procedure for solving the NCP problem is both effective and efficient, and can be integrated into an intelligent manufacturing system for solving complex machining optimization problems.     

How does variability in input load relate to the probability of critically delayed delivery in a simple multipart re-entrant flow-line problem?

Aspects of the computation of tail-probabilities by simulation in the context of a generic job/flow-shop model are discussed. The job/flow-shop model consists of structural elements such as bottlenecks, re-entrance as well as a mixture of these two fundamental types of production complexity and is operated as a multi-part production set-up. Standard simulation methodology relies heavily on the Central Limit Theorem (CLT), but as powerful as this statistical concept might be, it has its pitfalls. As is shown, it can be quite deceptive and consequently harmful. Given a certain production batch size, the discussion will focus on the estimation of the probability of critically delayed delivery beyond a specified threshold and try to establish a relation to certain parameters that can be linked to the degree of regularity of the arrival stream of parts to the job/flow-shop. This last aspect relates remotely to the Lean Thinking philosophy that praises the smooth and uninterrupted production flow as being beneficial to the overall operation of productive plants in general, and the findings will also be linked to this discussion.     

Cutting optimization with variable-sized stock and inventory status data

Many production environments require economical cutting of one-dimensional items according to bills of materials from objects of several standard lengths. However, even with optimized cutting substantial trim loss may occur. This trim loss should not be regarded as waste. It is returned to store and can be reused in future optimizations. Optimization of packing linear items into standard lengths is presented for items that cannot be packed into available lengths from inventory status data. The core of the proposed optimization tackles the variablesized bin packing problem (VBPP). The article presents a hybrid genetic algorithm that packs items into both available objects from the inventory and variablesized objects from the stock. The algorithm tries to minimize waste. Large trimloss items are returned as remnants to the inventory for subsequent optimizations.     

An algorithm for generating optimal constrained one-stage homogenous strip cutting patterns

The cutting and stamping process is often used to divide a stock plate into items to make products. It includes two phases. A guillotine machine cuts the plate orthogonally into homogenous strips at the cutting phase; then a stamping press stamps out the items from the strips at the stamping phase. A homogenous strip contains items of the same type. A one-stage homogenous strip cutting pattern (OSHSCP) contains parallel homogenous strips of the same length. The OSHSCP is useful because it can be used independently or as elements to compose other types of cutting patterns. This article presents a dynamic programming algorithm for generating the optimal constrained OSHSCP, where the maximum demand for each item type is specified and the objective is to maximize the total value of items included in the pattern. The computational results indicate that the computation time is reasonable.     

Adaptive and dynamic process planning using neural networks

Although feature-based process planning plays a vital role in automating and integrating design and manufacturing for efficient production, its off-line properties prevent the shop floor controller from rapidly coping with dynamic shop floor status such as unexpected production errors and rush orders. This paper proposes a conceptual framework of the adaptive and dynamic process planning system that can rapidly and dynamically generate the needed process plans based on shop floor status. In particular, the generic schemes for constructing dynamic planning models are suggested. The dynamic planning models are constructed as neural network forms, and then embedded into each process feature in the process plan. The shop floor controller will execute them to determine machine, cutting tools, cutting parameters, tool paths and NC codes just before the associated process feature is machined. The dynamic nature of process planning enables the shop floor controller to increase flexibility and efficiency in unexpected situations.     

Sequencing CONWIP flow-shops: Analysis and heuristics

In this paper, we address the backlog sequencing problem in a flow-shop controlled by a CONWIP production control system, with the objective to minimize the makespan. We characterize the problem and analyse its similarities and differences with the unconstrained permutation flow-shop problem. A comparison of some well-known flow-shop heuristics is carried out, and a simple and fast dispatching rule is proposed. Regarding the more simple and faster heuristics, the proposed dispatching rule outperforms those commonly used for the unconstrained permutation flow-shop problem.     

The constrained two-dimensional guillotine cutting problem with defects: an ILP formulation,a Benders decomposition and a CP-based algorithm

This paper addresses a variant of two-dimensional cutting problems in which rectangular small pieces are obtained by cutting a rectangular object through guillotine cuts. The characteristics of this variant are (i) the object contains some defects, and the items cut must be defective-free; (ii) there is an upper bound on the number of times an item type may appear in the cutting pattern; (iii) the number of guillotine stages is not restricted. This problem commonly arises in industrial settings that deal with defective materials, e.g. either by intrinsic characteristics of the object as in the cutting of wooden boards with knotholes in the wood industry, or by the manufacturing process as in the production of flat glass in the glass industry. We propose a compact integer linear programming (ILP) model for this problem based on the discretisation of the defective object. As solution methods for the problem, we develop a Benders decomposition algorithm and a constraint-programming (CP) based algorithm. We evaluate these approaches through computational experiments, using benchmark instances from the literature. The results show that the methods are effective on different types of instances and can find optimal solutions even for instances with dimensions close to real-size.     

Parallelized sequential value correction procedure for the one-dimensional cutting stock problem with multiple stock lengths

A heuristic approach with parallel computation is presented for the one-dimensional cutting stock problem with multiple stock lengths. The algorithm is based on the sequential heuristic procedure that generates each pattern to produce some items and repeats until all the required items are fulfilled. A recursion is used to solve the bounded knapsack problem heuristically in the pattern generation process to reduce running time. The item values are adjusted after the generation of each pattern using a value correction formula. The computational results show that the algorithm is more effective than a recently published evolutionary heuristic in improving solution quality, and can reduce computational time because of the efficient parallel implementation.     

A practical approach to scheduling a multistage,multiprocessor flow-shop problem

This paper describes a rubber manufacturing system and the numerous technological, operating and quality constraints governing its scheduling process. The complex system resembles a flow-shop problem where all jobs share the same general processing order on a progression of machines. The manufacturing system is defined in terms of production stages and parallel processing lines. One stage can process two or more jobs simultaneously. Jobs are often split on the last stage so that two or more parallel processing lines process the same job simultaneously. The scheduling for such a multistage, multiprocessor flow shop is dominated by the quality constraints. A pragmatic approach is provided in which a schedule is constructed through extensive iterations of analysis and simulation.     

飞剪式纸护角定长剪切机系统的研制

目的根据纸护角生产线的生产要求,设计一种满足纸护角连续送料时定长剪切的飞剪式定长剪切机构。方法运用旋转式飞剪原理,采用伺服系统的闭环控制,使飞剪式定长剪切机构能实现在定长剪切过程中对纸护角跟踪的"速度-位置双同步"。结果飞剪式定长剪切机构省去了传统机构在送料过程中剪切所需的停顿时间,大幅度提高了生产效率。同时,可以对不同厚度、不同长度以及不同送料速度的物料进行定长剪切。结论飞剪式定长剪切机构结构简单、使用方便、成本低廉,可广泛用于多种产品的定长剪切。     

ESTIMATING ACCEPTANCE-SAMPLING PLANS FOR DEPENDENT PRODUCTION PROCESSES

When acceptance sampling is used to judge the quality of an ongoing production process, the quality of successive items may exhibit statistical dependence that is not accounted for in standard acceptance-sampling plans. Computing the probabilities required to design sampling plans for general dependent processes is often complex, and sometimes intractable. This paper presents an efficient method for estimating single-sampling attribute plans for any production process model that can be simulated. Numerical illustrations are given.     

Dynamic lot-sizing with rework of defective items and minimum lot-size constraints

In most production processes, defective items may result from an imperfect production system and the need of reworking them is inevitable in many production environments. Despite the great importance of rework in real-world manufacturing, the body of literature is very limited. This paper deals with the effects of defective items and rework on the Capacitated Lot-Sizing Problem (CLSP). We present a mixed-integer programming formulation of the CLSP with rework of defective items and minimum lot-size constraints on production lots. The formulation describes an imperfect production process that leads to a fraction of defective items that have to be reworked before they can be sold to customers. Detailed numerical experiments show that while the occurrence of defective items significantly increases the computational times, reasonably sized minimum lot-size constraints, besides their practical importance, can be a good strategy to accelerate the solution process.     

Consignment stock policy with unequal shipments and process unreliability for a two-level supply chain

This paper considers a vendor managed inventory model with consignment stock policy in which a single vendor delivers a single product to a single buyer in unequal-sized shipments. The vendor’s production process may produce some defective items during a production run. The buyer performs a screening process immediately after receiving each delivery from the vendor and the vendor bears the warranty cost of defective item, if any. The buyer either scraps or repairs the defective items by sending them to a repair factory. The average expected profit of the integrated system is derived using renewal reward theorem and a solution procedure is suggested to determine the optimal shipment policy of the vendor. Numerical examples are taken to determine both the equal and unequal shipment policies and compare their relative performances.     

Real-time routing in flexible flow shops: a self-adaptive swarm-based control model

This paper presents a self-adaptive, swarm-based control model for real-time part routing in a flexible flow-shop environment. The proposed control model is a multi-agent system that exhibits adaptive behaviour, which has been inspired from the natural system of the wasp colony. The production problem, which has been previously studied in the literature by several researchers, involves assigning trucks to paint booths in real-time in a flexible flow-shop environment with the objective of throughput maximization and minimization of number of paint flushes accrued by the production system, assuming no a priori knowledge of the colour sequence or colour distribution of trucks is available. The proposed control model is benchmarked with the results of previous studies reported in the literature in solving the same production problem. The proposed control model uses self-adapting threshold parameters to facilitate the production flow in real time. A simulation-based software environment is designed and developed to investigate its performance. The simulation results show that the proposed control model is more robust to environmental changes, and it outperforms the previously reported studies on the basis of throughput, number of setups, and cycle time performance measures.     

An integrated cutting speed dispatch policy model

In this paper an integrated model is given for determining the total cost involved in the (1) machining, (2) stock holding, and (3) dispatching of a customer's order for a specified number of machined items. A search procedure has been presented for determining the optimum cutting speed and the optimum number of lots in which the machined items should be dispatched. An example has been solved to illustrate the search procedure.     

A simulated annealing approach for optimization of multi-pass turning operations

In this paper, an optimization algorithm based on the simulated annealing (SA) algorithm and the Hooke-Jeeves pattern search (PS) is developed for optimization of multi-pass turning operations. The cutting process is divided into multi-pass rough machining and finish machining. Machining parameters are determined to optimize the cutting conditions in the sense of the minimum unit production cost under a set of practical machining constraints. Experimental results indicate that the proposed nonlinear constrained optimization algorithm, named SA/PS, is effective for solving complex machining optimization problems. The SA/PS algorithm can be integrated into a CAPP system for generating optimal machining parameters.     

一种热封剪切装置的设计与仿真

目的为了实现大型或多件物品热收缩包装过程中薄膜高效精准的热封、剪切,设计、分析并优化一种热封剪切装置。方法基于双膜裹包和热封、剪切工艺要求,从机械设计角度出发,结合生产实际,对机械结构进行设计,并进行运动仿真和结构优化。结果通过仿真分析,证实切刀和热封板之间实现了"先封后切"工序,一次热封剪切过程持续时间约为1 s,热封板间工作压力约为250 N,装置的整体工作性能良好。结论该装置设计合理、工作稳定、动作精准,能够实现热收缩包装中的热封剪切功能。     

Two-machine flow-shop scheduling to minimize total late work: revisited

This article revisits the scheduling problem in a two-machine flow-shop system with the total late work criterion, which penalizes parts of jobs executed after their due dates. Firstly, it is shown that a lower bound presented previously in the literature, in the context of a branch-and-bound algorithm proposed for the same problem, is invalid. Then a novel proposal of the branch-and-bound method is given equipped with a new lower-bound technique, as well as an upper-bound and dominance rules. Numerical experiments show that the newly proposed lower-bound technique works well in cutting unpromising branches.