Tabu search approach based on a similarity coefficient for cell formation in generalized group technology

Both a similarity coefficient method (SCM)-based algorithm and meta-heuristics have been widely applied to various cell formation problems; however, few studies have explored the combination of the two methods. This paper addresses a hybrid algorithm, in which, based on the initial solution produced by a new SCM-based hierarchical clustering method, a fast and effective tabu search approach is presented to solve cell formation in group technology (GT). The proposed algorithm is applied to several problems from literature and a group of the randomly generated instances with alternative process plans and compared with simulated annealing (SA) and other TS; the results demonstrate that the proposed algorithm is available and efficient for cell formation in generalized GT.     

Redesign of production flow lines to postpone product differentiation

Redesign of a multi-product production system to delay product differentiation to final stages of the production is a planning problem. We propose two nonlinear binary optimisation models for determining the optimal stage for differentiating each product. The first model maximises the expected value of profit and the second model maximises the value of options to postpone product differentiation. We use the results from financial basket options pricing theory to formulate and solve the second model. We devise an efficient tabu-constrained randomised search algorithm to obtain models solution, and use parametric analysis of a numerical example to verify the models and gain insight into the product differentiation problem.     

A parallel genetic algorithm for dynamic cell formation in cellular manufacturing systems

Instead of using expensive multiprocessor supercomputers, parallel computing can be implemented on a cluster of inexpensive personal computers. Commercial accesses to high performance parallel computing are also available on the pay-per-use basis. However, literature on the use of parallel computing in production research is limited. In this paper, we present a dynamic cell formation problem in manufacturing systems solved by a parallel genetic algorithm approach. This method improves our previous work on the use of sequential genetic algorithm (GA). Six parallel GAs for the dynamic cell formation problem were developed and tested. The parallel GAs are all based on the island model using migration of individuals but are different in their connection topologies. The performance of the parallel GA approach was evaluated against a sequential GA as well as the off-shelf optimization software. The results are very encouraging. The considered dynamic manufacturing cell formation problem incorporates several design factors. They include dynamic cell configuration, alternative routings, sequence of operations, multiple units of identical machines, machine capacity, workload balancing, production cost and other practical constraints.     

Machine cell formation for production management in cellular manufacturing systems

The formation of machine-part families is an important task in the design of cellular manufacturing systems. Manufacturing cell grouping has the effect of reducing material handing cost and work in process. Among the many methods utilized in machine cells formation, the similarity coefficient method is most widely used. Production sequence and product volumes, if incorporated properly in determining the machine cells, can enhance the quality of solutions and reduce the number of intercellular movements. Measures for cell formation based on operations sequence utilizing ordinal production data are few and have many limitations, such as counting the number of the trips for each individual part instead of counting the weights of the batches. A new ordinal production data similarity coefficient based on the sequence of operations and the batch size of the parts is introduced. Furthermore, a new clustering algorithm for machine cell formation is proposed. The new similarity measure showed more sensitivity to the intercellular movements and the clustering algorithm showed better machine grouping.     

A framework for designing robust supply chains considering product development issues

The primary objective of the design for supply chain (DFSC) is the selection of an appropriate product family. Moreover, it deals with the selection of the optimal combination among the different conflicting criteria while making a trade-off between the supply chain cost, sales profit and the product design complexities. In this research, to address the DFSC issues a product platform approach has been proposed which amalgamates the component modularity as well as the function modularity in the product design. The optimisation model proposed in this paper for the product development and the supply chain design is based on a generic bill of materials (GBOM) representation. The complete framework includes vital decision-making needed for designing a robust supply chain such as locating plants to alleviate the likely dominance of production cost and market mediation cost on product variety and imparting process flexibility of the located plants. The optimisation model proposed in this paper, models the supply chain cost, sales profit and product design complexity as three criteria that altogether determine the robustness of the supply chain and the underlying product development approach. Certain parameters like process flexibility, flow types and drivers of the product variety dominance have been controlled in the design framework. To resolve the complexity of the proposed model a genetic algorithm (GA) technique has been proposed. The proposed GA adopts an arithmetic crossover, a dynamic mutation and a variable penalty strategy to produce optimal results in a very short computational time. To validate the proposed model, a simulated case study of the wiring harness supplier of an AGV manufacturer has been studied.     

Complete and fractional cell formation using Kohonen self-organizing map networks in a cellular manufacturing system

The primary objective of group technology (GT) is to enhance the productivity in batch manufacturing environment. The GT cell formation and fractional cell formation are done by using Kohonen self-organizing map (KSOM) networks. The effectiveness of the cell formation is measured with number of exceptional elements, bottleneck parts and grouping efficiency and the effectiveness of the fractional cell formation is measured by number of exceptional elements and the number of machines in the reminder cell. This method is applied to the known benchmarked problems found in the literature and it is found to be equal or best when compared to the other algorithms in terms of minimizing the number of the exceptional elements. The relative merits of using this method with respect to other known algorithms/heuristics in terms of computational speed and consistency are presented.     

A data-driven approach for the optimisation of product specifications

In order to develop the profit-maximising, market share-maximising or cost-minimising bundle of product engineering specifications with proper performance levels, an optimisation model driven by operating data is proposed. The operating data are input as the sources to conduct the optimisation and a data-based customer satisfaction function can be formed. Then, a customer choice model developed from the customer satisfaction is constructed to estimate the customer choice probability. The expected market share (EMS) then can be derived from the choice probability. After all, a multi-objective model is constructed to maximise the EMS and minimise the total engineering cost. The candidate Pareto-optimal solutions can be obtained by solving the optimisation model. Then a membership function is defined to select the optimal solution from the Pareto-optimal solutions. A case study for optimising the smartphone’s specifications is conducted to demonstrate the effectiveness of the newly developed approach. Compared with the commonly used Conjoint Analysis (CA) method in determining the most desired levels for product specifications, the proposed data-driven method can avoid the situation where the user’s preferences are irrational, making the proposed method be more practical in measuring customer preferences than the utility-based model.     

A tabu search algorithm for the pallet loading problem

This paper presents a new heuristic algorithm for the pallet loading problem, the problem of packing the maximum number of identical rectangular boxes onto a rectangular pallet. The problem arises in distribution and logistics and has many practical applications. We have developed a tabu search algorithm based on new types of moves. Instead of moving individual boxes, we propose moving blocks, sets of boxes with the same orientation. We have tested our algorithm on the whole sets Cover I and Cover II, usually taken as a reference for this problem, and we obtain excellent results in very short computing times.This complete issue was revised and published online in November 2004. The previous version contained a false date. Correspondence to: R. Alvarez-ValdesThis paper has been partially supported by the Project PBC-02-002, Consejeria de Ciencia y Tecnologia, JCCM, the Spanish Ministry of Science and Technology, DPI2002-02553 and the Valencian Science and Technology Agency, GRUPOS03/174     

Analytic hierarchy prioritisation of new product development activities for electronics manufacturing

New product development (NPD) is a managerial process, performed by many activities. The purpose of this paper is to present the application of the analytic hierarchy process (AHP) to prioritise activities of NPD for manufacturing companies of electronic products. Mathematical modelling was the research method adopted. An electronics cluster located in the southeast region of Brazil was studied. Therefore, 10 of the 42 activities from an NPD model were suggested to be excluded for the electronics companies. These activities have less than 0.5% overall priority. The results from the AHP application were welcomed by the companies’ NPD experts.     

Creating targeted initial populations for genetic product searches in heterogeneous markets

Genetic searches often use randomly generated initial populations to maximize diversity and enable a thorough sampling of the design space. While many of these initial configurations perform poorly, the trade-off between population diversity and solution quality is typically acceptable for small-scale problems. Navigating complex design spaces, however, often requires computationally intelligent approaches that improve solution quality. This article draws on research advances in market-based product design and heuristic optimization to strategically construct ‘targeted’ initial populations. Targeted initial designs are created using respondent-level part-worths estimated from discrete choice models. These designs are then integrated into a traditional genetic search. Two case study problems of differing complexity are presented to illustrate the benefits of this approach. In both problems, targeted populations lead to computational savings and product configurations with improved market share of preferences. Future research efforts to tailor this approach and extend it towards multiple objectives are also discussed.     

A guided search genetic algorithm using mined rules for optimal affective product design

Affective design is an important aspect of new product development, especially for consumer products, to achieve a competitive edge in the marketplace. It can help companies to develop new products that can better satisfy the emotional needs of customers. However, product designers usually encounter difficulties in determining the optimal settings of the design attributes for affective design. In this article, a novel guided search genetic algorithm (GA) approach is proposed to determine the optimal design attribute settings for affective design. The optimization model formulated based on the proposed approach applied constraints and guided search operators, which were formulated based on mined rules, to guide the GA search and to achieve desirable solutions. A case study on the affective design of mobile phones was conducted to illustrate the proposed approach and validate its effectiveness. Validation tests were conducted, and the results show that the guided search GA approach outperforms the GA approach without the guided search strategy in terms of GA convergence and computational time. In addition, the guided search optimization model is capable of improving GA to generate good solutions for affective design.     

A three-stage decomposition approach for energy-aware scheduling with processing-time-dependent product quality

Due to increasing concerns about energy and environmental demands, decision-makers in industrial companies have developed awareness about energy use and energy efficiency when engaging in short-term production scheduling and planning. This paper studied a flow-shop scheduling problem consisting of a series of processing stages and one final quality check stage with the aim of minimising energy consumption. In particular, the product quality in the problem depends on its processing time at each stage, and the energy consumption is related to the processing speed, equipment state and product quality. A novel three-stage decomposition approach is presented to solve the proposed energy-aware scheduling (EAS) problem. The decomposition approach can drastically reduce the search space and provide reliable solutions for the EAS problem. The numerical experiments show that the computational results can achieve an optimality gap of less than 4% when compared to the global optimal solutions. The parameter analysis demonstrates the managerial implications of the proposed problem. For example, increasing the number of alternative processing speeds or relaxing the delivery date will increase energy efficiency. The energy-saving potential is illustrated by comparing the scheduling results using the proposed approach and human experience.     

Machine cell formation using a mathematical model and a genetic-algorithm-based heuristic

This paper presents a comprehensive mathematical model and a genetic-algorithm-based heuristic for the formation of part families and machine cells in the design of cellular manufacturing systems. The model incorporates dynamic cell configuration, alternative routings, sequence of operations, multiple units of identical machines, machine capacity, workload balancing among cells, operation cost, subcontracting cost, tool consumption cost, set-up cost and other practical constraints. To solve this model efficiently, a two-phase genetic-algorithm-based heuristic was developed. In the first phase, independent cells are formed which are relatively simple to generate. In the second phase, the solution found during the first phase is gradually improved to generate cells optimizing inter-cell movement and other cost terms of the model. A number of numerical examples of different sizes are presented to demonstrate the computational efficiency of the heuristic developed.     

Standardised product development for technology integration of additive manufacturing

ABSTRACT

The implementation of additive manufacturing (AM) as an industrial production process poses extraordinary challenges to companies due to the far-reaching differences to conventional processes. In addition, there are hardly any standards and guidelines or methodical process models for the relatively new technologies that enable the reproducible and target-oriented use of AM. In order to solve this problem, five industrial companies together with the Paderborn University are researching as part of the ‘OptiAMix’ research project funded by the Federal Ministry of Education and Research (BMBF). This paper focuses on the development of an ideal process chain. Reference processes of the OptiAMix partners were analysed, norms and standards from conventional production were adapted and implemented and procedure models developed OptiAMix were integrated. The resulting AM Product Development Process was then applied and validated with the aid of a previously developed integration methodology using an example component from the automotive industry.     

Product family formation and selection for reconfigurability using analytical network process

The paper develops a conceptual framework for product family formation towards reconfigurability through proposing a product-process reconfiguration link. Different decisive factors affecting product family formation and selection such as manufacturing requirements, market requirements, manufacturing cost and process reconfiguration are investigated. An analytical network process (ANP) model is proposed to incorporate all the outlined decisive factors and major criteria and elements influencing the product family formation and selection. As a consequence of the interactive nature of the product family selection problem, most of the children's’ elements within the same cluster and/ or different clusters are associated with each other. As a result, all the clusters are connected to each other (outer dependencies) as well as their connections to themselves (inner dependencies). The proposed ANP model is examined through a case study in a manufacturing company for validation. Synthesis judgments and sensitivity analysis are carried out for selecting the most appropriate product family along with analysing the influence of the selected critical elements.     

具有时间窗约束累积性车辆路径问题的禁忌搜索优化算法

基于对中国实际物流运输中成本计算方法的研究,考虑到我国高速公路系统计价方式的特殊性,提出了具有时间窗约束的累积性车辆路径问题。以降低实际车辆运输成本为目标,设计了新型的禁忌搜索算法对问题进行有效求解;算法中嵌入多种邻域搜索方法,允许同时在可行和不可行解空间内进行邻域搜索,同时采用Nagata提出的时间窗违反量计算方法[1-2]对解的时间窗约束违反进行评估。针对提出的新型问题的数值试验证明了所采用的时间窗违反量计算方法的时间节约性和有效性;同时由于该问题可以覆盖传统的累积性车辆路径问题,对后者的数值实验以及与其他优化算法的对比验证了所提出算法的优良求解效果。     

A methodology for planning experiments in robust product and process design

Robust design is an important method for improving product manufacturability and life, and for increasing manufacturing process stability and yield. In 1980 Genichi Taguchi introduced his approach to using statistically planned experiments in robust product and process design to U.S. industry. Since then, the robust design problem and Taguchi's approach to solving it has received much attention from product designers, manufacturers, statisticians and quality professionals. Although most agree on the importance of the robust design problem, controversy over some of the specific methods used to solve the problem has made this an active research area. Although the answers are not all in yet, the importance of the problem has led to development of a four-step methodology for implementing robust design. The steps are (1) formulate the problem by stating objectives and then listing and classifying product or process variables, (2) plan an experiment to study these variables, (3) identify improved settings of controllable variables from the experiment's results and (4) confirm the improvement in a small follow-up experiment. This paper presents a methodology for the problem formulation and experiment planning steps. We give practical guidelines for making key decisions in these two steps, including choice of response characteristics, and specification of interactions and test levels for variables. We describe how orthogonal arrays and interaction graphs can be used to simplify the process of planning an experiment. We also compare the experiment planning strategies we are recommending to those of Taguchi and to more traditional approaches.     

A heuristic algorithm for master production scheduling problem with controllable processing times and scenario-based demands

Master production scheduling (MPS) is widely used by manufacturing industries in order to handle the production scheduling decisions in the production planning hierarchy. The classical approach to MPS assumes infinite capacity, fixed (i.e. non-controllable) processing times and a single pre-determined scenario for the demand forecasts. However, the deterministic optimisation approaches are sometimes not suitable for addressing the real-world problems with high uncertainty and flexibility. Accordingly, in this paper, we propose a new practical model for designing an optimal MPS for the environments in which processing times may be controllable by allocating resources such as facilities, energy or manpower. Due to the NP-hardness of our model, an efficient heuristic algorithm using local search technique and theory of constraints is developed and analysed. The computational results especially for large-sized test problems show that the average optimality gap of proposed algorithm is four times lower than that of exact solution using GAMS while it consumes also significantly smaller run times. Also, the analysis of computational results confirms that considering the controllable processing times may improve the solution space and help to more efficiently utilise the available resources. According to the model structure and performance of the algorithm, it may be proposed for solving large and complex real-world problems particularly the machining and steel industries.     

Designing a block stacked warehouse for dynamic and stochastic product flow: a scenario-based robust approach

The problem considered in this paper is motivated from a real life case. Conventional models in designing block stacked warehouse assume uniform and deterministic inflow and outflow of products in specific quantities and time intervals. These assumptions would lead to underestimation of space requirement for the case. Also, the possibility of leasing additional warehouse space during seasonal spikes was found to be unexplored in block stacking design literature. In this study, we develop a scenario-based model to address the decisions of warehouse size, lane depth and hiring of temporary additional warehouse capacity when flow of products is stochastic and dynamic in nature. A sequential decision model is presented with future operating scenarios assumed to branch out in the form of a tree. On comparison with conventional block stacking design methods, the model is found to provide robust solution in stochastic and dynamic environments in terms of risk reduction and infeasibility avoidance. Further, the model gives additional flexibility to the designer to trade-off model and solution robustness with predicted cost.