Coordinated optimisation of platform-driven product line planning by bilevel programming

Product line planning (PLP) aims at an optimal combination of product feature offerings, suggesting itself to be a determinant decision for a company to satisfy diverse customer needs and gain competitive advantages. Fulfilment of planned product lines must make trade-offs between product variety and production costs. To balance the costs of product lines, manufacturers often adopt a product platform configuration (PPC) approach to redesign product and process platforms by adding new modules to the legacy platforms. The PPC is an effective means of providing product variety while controlling the manufacturing costs. The PLP and PPC problems have traditionally been investigated separately in the marketing research and engineering design fields. It is important to coordinate PLP and PPC decisions within a coherent optimisation framework. This paper proposes a bilevel mixed 0–1 nonlinear programming model to formulate coordinated optimisation for platform-driven product line planning. The upper level deals with the PLP problem by maximising the profit of an entire product line, whilst the lower level copes with the multiple product platforms optimisation for the optimal PPC in accordance with the upper level decisions of product line structure. To solve this bilevel programming model, a bilevel genetic algorithm is developed to find the optimal solution. A case study of coordinated optimisation between an automobile line and its product platforms is presented to demonstrate the feasibility and effectiveness of the proposed bilevel programming in comparison with a typical ‘all-in-one’ approach and a non-joint optimisation programming.     

Reverse supply chain optimisation with disassembly line balancing

Due to responding environmental issues, conforming governmental legislations and providing economic benefits, there has been a growing interest in recycling activities through the supply chains. Reverse supply chain (RSC) optimisation problem has a great potential as an efficient tactic to achieve this goal. While disassembly, one of the main activities in RSC, enables reuse and recycling of products and prevents the overuse, disassembly line balancing problem involves determination of a line design in which used products are partially/completely disassembled to obtain available components. The aim of this study is to optimise a RSC, involving customers, collection/disassembly centres and plants, that minimises the transportation costs while balancing the disassembly lines, which minimises the total fixed costs of opened workstations, simultaneously. A non-linear mixed-integer programming model, which simultaneously determines: (i) optimal distribution between the facilities with minimum cost, (ii) the number of disassembly workstations that will be opened with minimum cost, (iii) the cycle time in each disassembly centre and (iv) optimal assignment of tasks to workstations, is developed. A numerical example is given to illustrate the applicability of the proposed model. Different scenarios have been conducted to show the effects of sensitivity analyses on the performance measures of the problem.     

Ant colony optimisation with elitist ant for sequencing problem in a mixed model assembly line

Optimised sequencing in the Mixed Model Assembly Line (MMAL) is a major factor to effectively balance the rate at which raw materials are used for production. In this paper we present an Ant Colony Optimisation with Elitist Ant (ACOEA) algorithm on the basis of the basic Ant Colony Optimisation (ACO) algorithm. An ACOEA algorithm with the taboo search and elitist strategy is proposed to form an optimal sequence of multi-product models which can minimise deviation between the ideal material usage rate and the practical material usage rate. In this paper we compare applications of the ACOEA, ACO, and two other commonly applied algorithms (Genetic Algorithm and Goal Chasing Algorithm) to benchmark, stochastic problems and practical problems, and demonstrate that the use of the ACOEA algorithm minimised the deviation between the ideal material consumption rate and the practical material consumption rate under various critical parameters about multi-product models. We also demonstrate that the convergence rate for the ACOEA algorithm is significantly more than that for all the others considered.     

Ergonomics in assembly line balancing based on energy expenditure: a multi-objective model

In many assembly systems, ergonomics can have great impact on productivity and human safety. Traditional assembly systems optimisation approaches consider only time and cost variables, while few studies include also ergonomics aspects. In this study, a new multi-objective model for solving assembly line balancing problem is developed and discussed in order to include also the ergonomics aspect. First, based on main features of assembly workstations, the energy expenditure concept is used in order to estimate the ergonomics level, thanks to a new technique, called Predetermined Motion Energy System, which helps rapidly estimate the energy expenditure values. Then, a multi-objective approach, based on four different objective functions, is introduced in order to define the efficient frontiers of optimal solutions. To complete the study, a simple numerical example for a real case is presented to analyse the behaviour of Pareto frontiers varying several parameters linked to the energy and time value.     

Product quality improvement method in manufacturing process based on kernel optimisation algorithm

Quality data in manufacture process has the features of mixed type, uneven distribution, dimension curse and data coupling. To apply the massive manufacturing quality data effectively to the quality analysis of the manufacture enterprise, the data pre-processing algorithm based on equivalence relation is employed to select the characteristic of hybrid data and preprocess data. KML-SVM (Optimised kernel-based hybrid manifold learning and support vector machines algorithm) is proposed. KML is adopted to solve the problems of manufacturing process quality data dimension curse. SVM is adopted to classify and predict low-dimensional embedded data, as well as to optimise support vector machine kernel function so that the classification accuracy can be maximised. The actual manufacturing process data of AVIC Shenyang Liming Aero-Engine Group Corporation Ltd is demonstrated to simulate and verify the proposed algorithm.     

Disassembly line design with multi-manned workstations: a novel heuristic optimisation approach

As the first and the most time consuming step of product recovery, disassembly is described as the systematic separation of constituent parts from end-of-life products through a series of operations. In this context, designing and balancing disassembly lines are critical in terms of the efficiency of product recovery. Recent research on disassembly line balancing (DLB) has focused on classical stations where only one worker is allocated. However, such a line results in larger space requirement and longer disassembly lead time. In this paper, disassembly line balancing problem (DLBP) with multi-manned stations is introduced to the relevant literature as a solution to overcome these disadvantages. A mixed integer linear programming (MILP) model and two novel framework heuristic algorithms are developed to minimise the number of workers and workstations. MILP model has been applied to a dishwasher disassembly system. The application results indicate the superiority of establishing multi-manned stations over classical disassembly system design with single-worker stations with shorter disassembly lead time (80.9%) and line length (60.2%). Moreover, the proposed heuristics have been compared on newly generated test problems (instances) for DLBP. The results validate that the heuristics provide acceptable solutions in a reasonable amount of time even for large-sized problems.     

Modelling and optimisation of energy-efficient U-shaped robotic assembly line balancing problems

Within U-shaped assembly lines, the increase of labour costs and subsequent utilisation of robots has led to growing energy consumption, which is the current main expense of auto and electronics industries. However, there are limited researches concerning both energy consumption reduction and productivity improvement on U-shaped robotic assembly lines. This paper first develops a nonlinear multi-objective mixed-integer programming model, reformulates it into a linear form by linearising the multiplication of two binary variables, and then refines the weight of multiple objectives so as to achieve a better approximation of true Pareto frontiers. In addition, Pareto artificial bee colony algorithm (PABC) is extended to tackle this new complex problem. This algorithm stores all the non-dominated solutions into a permanent archive set to keep all the good genes, and selects one solution from this set to overcome the strong local minima. Comparative experiments based on a set of newly generated benchmarks verify the superiority of the proposed PABC over four multi-objective algorithms in terms of generation distance, maximum spread, hypervolume ratio and the ratio of non-dominated solution.     

A profit maximising product line optimisation model under monopolistic competition

Existing research on product line optimisation have focused mainly on designing a product line based on the trade-offs between sellers’ and buyers’ utility, without any explicit consideration of the underlying market structure. A few studies can be found that consider the monopolists’ optimal product line offering. In this study, we examine the optimal product line decision of an entrant firm under monopolistic competitive markets. Specifically, we develop a mathematical programming formulation of an entrant firm’s problem of deciding on the market segments to enter and the corresponding product designs to offer, to maximise its profit. A heuristic has been presented for solving the resulting mixed integer non-linear programming problem. The specifications of the problem increase exponentially with the size of the problem and as such, commercial solvers are not useful for solving a generalised instance. A small example has been presented and solved using both the heuristic and the ILOG CPLEX 10.2. Both result in identical solutions. We attempt an explanation on why the heuristic gives an optimal solution.     

Sustainable disassembly line balancing model based on triple bottom line

End of life (EOL) phase of a product is receiving more attention due to increase in environmental concerns, and many studies have been conducted for value creation in EOL, focusing on concepts as remanufacturing, reuse and recycling in sustainable production manner. This study especially focuses on one of global problem, e-waste. To minimise the amount of wastes and maximise recovered materials from EOL, disassembly is one of the most important concept, associated with reuse, and balancing disassembly line in an optimal way is essential for organisations. In disassembly line balancing (DLB), not only precedence of tasks, but also risk criteria related to environment and human safety should be considered for sustainability. The aim of this study is to propose a model based on triple bottom line (TBL) dimensions, i.e. human safety, environmental safety and business criteria. To achieve sustainability in DLB, and for risk assessment in sustainable DLB, it had been decided to use a multi-criteria method, i.e. TODIM, acronym in Portuguese of ‘Tomada de Decisão Iterativa Multicritério’. The proposed model included 22 disassembly criteria categorised under TBL dimensions, which are derived from the literature. Implementation of the study was conducted for computer disassembly processes, and as a result of the study approximately 12% an improvement in cycle time was succeeded. In the long run, the integration of sustainability in disassembly operations may contribute to the competitive advantage of the company in terms of differentiation and corporate image by achieving business, environment and human targets simultaneously.     

Multi-objective particle swarm optimisation based integrated production inventory routing planning for efficient perishable food logistics operations

Sustainable and efficient food supply chain has become an essential component of one’s life. The model proposed in this paper is deeply linked to people's quality of life as a result of which there is a large incentive to fulfil customer demands through it. This proposed model can enhance food quality by making the best possible food quality accessible to customers, construct a sustainable logistics system considering its environmental impact and ensure the customer demand to be fulfilled as fast as possible. In this paper, an extended model is examined that builds a unified planning problem for efficient food logistics operations where four important objectives are viewed: minimising the total expense of the system, maximising the average food quality along with the minimisation of the amount of CO₂ emissions in transportation along with production and total weighted delivery lead time minimisation. A four objective mixed integer linear programming model for intelligent food logistics system is developed in the paper. The optimisation of the formulated mathematical model is proposed using a modified multi-objective particle swarm optimisation algorithm with multiple social structures: MO-GLNPSO (Multi-Objective Global Local Near-Neighbour Particle Swarm Optimisation). Computational results of a case study on a given dataset as well as on multiple small, medium and large-scale datasets followed by sensitivity analysis show the potency and effectiveness of the introduced method. Lastly, there has been a scope for future study displayed which would lead to the further progress of these types of models.     

Multi-objective evolutionary simulated annealing optimisation for mixed-model multi-robotic disassembly line balancing with interval processing time

This paper considers the design and balancing of mixed-model disassembly lines with multi-robotic workstations under uncertainty. Tasks of different models are performed simultaneously by the robots which have different capacities for disassembly. The robots have unidentical task times and energy consumption respectively. Task precedence diagrams are used to model the precedence relations among tasks. Considering uncertainties in disassembly process, the task processing times are assumed to be interval numbers. A mixed-integer mathematical programming model is proposed to minimise the cycle time, peak workstation energy consumption, and total energy consumption. This model has a significant managerial implication in real-life disassembly line systems. Since the studied problem is known as NP-hard, a metaheuristic approach based on an evolutionary simulated annealing algorithm is developed. Computational experiments are conducted and the results demonstrate the proposed algorithm outperforms other multi-objective algorithms on optimisation quality and computational efficiency.     

Cross-docking truck scheduling with product unloading/loading constraints based on an improved particle swarm optimisation algorithm

Cross-docking is a very useful logistics technique that can substantially reduce distribution costs and improve customer satisfaction. A key problem in its success is truck scheduling, namely, decision on assignment and docking sequence of inbound/outbound trucks to receiving/shipping dock doors. This paper focuses on the problem with the requirement of unloading/loading products in a given order, which is very common in many industries, but is less concerned by existing researches. An integer programming model is established to minimise the makespan. An improved particle swarm optimisation (ωc-PSO) algorithm is proposed for solving it. In the algorithm, a cosine decreasing strategy of inertia weight is designed to dynamically balance global and local search. A repair strategy is put forward for continuous search in the feasible solution space and a crossover strategy is presented to prevent the algorithm from falling into local optimum. After algorithm parameters are tuned using Taguchi method, computational experiments are conducted on different problem scales to evaluate ωc-PSO against genetic algorithm, basic PSO and GLNPSO. The results show that ωc-PSO outperforms other three algorithms, especially when the number of dock doors, trucks and product types is great. Statistical tests show that the performance difference is statistically significant.     

Industrial robot layout based on operation sequence optimisation

The robot layout is one of the primary problems in the robot work cell design. An optimal layout not only makes the robotic end-effector reach the desired position with the desired orientation, but also minimises the robot cycle time for completing a given task with collision avoidance. There may be many feasible robot operation sequences for a given task. The optimisation of operation sequence for a robot placed in a fixed location is NP-complete. There may be different optimal operation sequences as the robot is placed in the different locations. Hence the problem of robot layout is quite complex. This paper presents a method of industrial robot layout based on operation sequence optimisation. The robot motion control model with velocity, acceleration and jerk is established. The feasible space of robot base is calculated and then divided into discrete grids before the optimisations. Then the ant colony algorithm is applied to optimise the operation sequence for each grid. The adjacent grids are merged or divided enough times to form the bigger grids. The pattern search algorithm is adopted to solve the local optimal position and orientation of the robot base in each big grid, and the global optimal layout is found through the comparison of the local solutions. The industrial robot layout method has been applied to the work cell design for car door welding.     

Tool path optimisation for flank milling ruled surface based on the distance function

This paper deals with optimised tool path generation for five-axis flank milling using signed point-to-surface distance function. The main idea is that the geometrical deviations between the design surface and the machined surface are minimised by fine tuning the cutter locations. Based on the tangency conditions in envelope theory, the analytic representation of the envelope surface of a cutter undergoing five-axis motion is first obtained. Then the geometrical deviations between the envelope surface (i.e. machined surface) and the design surface are calculated. Optimisation of the five-axis tool path is modeled as the fine tuning of the initial cutter locations under the minimum zone criterion recommended by ANSI and ISO, which requires minimisation of the maximum geometrical deviation between the design surface and the envelope surface. Using the signed point-to-surface distance function, tool path optimisation for finish milling is formulated as a constrained optimisation problem. Based on the first-order Taylor approximation of the signed distance function, two sequential approximation algorithms for the Minimax and Least Square optimisations are developed. Numerical examples, in which a conical tool is chosen as a special case of flank machining ruled surface, verify the proposed strategy.     

An efficient camera calibration and optimisation method based on orthogonal vanishing points

A more efficient camera calibration and optimisation method based on orthogonal vanishing points is proposed. After extracting corner points on the calibration images by using the Harris algorithm, some parallel lines are fitted via the least square method. The Levenberg–Marquard (L–M) algorithm is used to find out the optimum vanishing points. The intrinsic parameters are solved on the basis of the orthogonal vanishing point characters. The enhanced Tsai’s method is designed to solve the extrinsic parameters. The minimum residue optimisation function is introduced to calculate the distortion coefficients and optimise all parameters globally. The feasibility and efficiency of the proposed method is illustrated by an experimental study. The method can be used to obtain high precision calibration parameters without the need of sophisticated mechanical devices. It has great potential to calibration cameras in the field of computer vision, 3D measurement, etc.     

Clamping-sequence optimisation based on heuristic algorithm for sheet-metal components

The traditional clamping-sequence optimisation of sheet-metal parts requires many complicated finite element analyses, and clamping-sequence planning does not account for the springback from clamp-release. Therefore, this paper proposes a new optimisation method based on a heuristic algorithm. We first propose a new contact model of parts, clamps and supporting locators to analyse assembly deformation. Then, we use the distance between the actual and nominal positions to evaluate the clamp layout. Finally, we apply the heuristic algorithm to optimise the clamping sequence. We illustrate the proposed method with a case study of a taillight bracket, whose results show that the method of clamping-sequence optimisation can effectively decrease the deformation of sheet metal from clamping.     

企业产品包装竞争的价值链分析

着重论述了价值链的内涵,价值链与产品包装的竞争优势,企业产品包装设计的基本要求,价值链与产品包装竞争策略,对现代企业产品包装竞争的价值链分析具有重要的决策指导意义.     

A MCVRP-based model for PCB assembly optimisation on the beam-type placement machine

The beam-type placement machine is capable of picking up multiple components simultaneously from the feeders in printed circuit board (PCB) assembly. Simultaneous pickup occurs only if the heads in the beam are aligned with the feeders and the nozzle-types on these heads match with the component-types on the feeders. In order to minimise the assembly cycle time, the optimisation problem is decomposed into two sub-problems, the pickup combination and sequencing problem, and the placement cluster and sequencing problem. These two sub-problems are simultaneously solved by the proposed hybrid genetic algorithm (HGA). The pickup combination and sequencing problem is similar to the popular multi-compartment vehicle routing problem (MCVRP); a genetic algorithm (GA) for the MCVRP is therefore modified and applied to solving the pickup combination and sequencing problem. A greedy heuristic algorithm is used to solve the placement cluster and sequencing problem. The numerical experiments reveal that the HGA outperforms the algorithms proposed by previous papers.     

Machining fixture layout optimisation under dynamic conditions based on evolutionary techniques

Optimisation of fixture layout is critical to reduce geometric and form error of the workpiece during the machining process. In this paper the optimal placement of fixture elements (locator and clamp locations) under dynamic conditions is investigated using evolutionary techniques. The application of the newly developed particle swarm optimisation (PSO) algorithm and widely used genetic algorithm (GA) is presented to minimise elastic deformation of the workpiece considering its dynamic response. To improve the performances of GA and PSO, an improved GA (IGA) obtained by basic GA (GA) with sharing and adaptive mutation and an improved PSO (IPSO) obtained by basic PSO (PSO) incorporated into adaptive mutation are developed. ANSYS parametric design language (APDL) of finite element analysis is employed to compute the objective function for a given fixture layout. Three layout optimisation cases derived from the high speed slot milling case are used to test the effectiveness of the GA, IGA, PSO and IPSO based approaches. The comparisons of computational results show that IPSO seems superior to GA, IGA and PSO approaches with respect to the trade-off between global optimisation capability and convergence speed for the presented type problems.     

基于CAN总线的食品包装生产线控制系统的设计

提出了基于CAN 总线技术的食品包装生产线控制系统的设计,给出了系统整体结构,同时阐述了CAN节点控制器的硬、软件设计方法.结果证明:该系统结构简单、运行可靠、易于调整,提高了生产管理水平和工作效率,为企业生产和管理人员及时了解远程生产现场的状况提供了可能.