David Z. Zhang

Simultaneous balancing and sequencing of mixed-model parallel two-sided assembly lines

Growing interests from customers in customised products and increasing competitions among peers necessitate companies to configure their manufacturing systems more effectively than ever before. We propose a new assembly line system configuration for companies that need intelligent solutions to satisfy customised demands on time with existing resources. A mixed-model parallel two-sided assembly line system is introduced based on the parallel two-sided assembly line system previously proposed in the literature. The mixed-model parallel two-sided assembly line balancing problem is illustrated with examples from the perspective of simultaneous balancing and sequencing. An agent-based ant colony optimisation algorithm is proposed to solve the problem. This algorithm is the first attempt in the literature to solve an assembly line balancing problem with an agent-based ant colony optimisation approach. The algorithm is illustrated with an example and its operational procedures and principles are explained and discussed.

Dynamically Integrated Manufacturing Systems (DIMS)—A Multiagent Approach

Manufacturing businesses in todays market are facing immense pressures to react rapidly to dynamic variations in demand distributions across products and changing product mixes. To cope with the pressures requires dynamically integrated manufacturing systems (DIMS) that can manage optimal fulfillment of customer orders while simultaneously considering alternative system structures to suit changing conditions. This paper presents a multiagent approach to DIMS, where production planning and control decisions are integrated with systems reconfiguration and restructure. A multiagent framework, referred to as a hierarchical autonomous agent network, is proposed to model complex manufacturing systems, their structures, and constraints. It allows the hierarchical structures of complex systems to be modeled while avoiding centralized control in classical hierarchical/hybrid frameworks. Subsystems interact heterarchically with product orders to carry out optimal planning and scheduling. An agent coordination algorithm, operating iteratively under the control of a genetic algorithm, is developed to enable optimal planning and control decisions for order fulfillment to be made through interactions between agents. This algorithm also allows the structural constraints of systems to be relaxed gradually during agent interaction, so that planning and control are first carried out under existing constraints, but when satisfactory solutions cannot be found, subsystems are allowed to regroup to form new configurations. Frequently used configurations are detected and evaluated for system restructure. The approach also enables Petri-net models of new system structures to be generated dynamically and the structures to be evaluated through agent-based discrete event simulation.

Type-E parallel two-sided assembly line balancing problem

Display Omitted Type-E parallel two-sided line balancing problem is introduced for the first time.ACO algorithm is proposed as a possible solution approach for the addressed problem.Parameters of the ACO are optimised through response surface methodology.The cycle time and the total number of workstations are minimised at the same time.The performance of the ACO algorithm is tested through well-known test problems. There are many factors which affect the performance of a complex production system. Efficiency of an assembly line is one of the most important of these factors since assembly lines are generally constructed as the last stage of an entire production system. Parallel two-sided assembly line system is a new research domain in academia though these lines have been utilised to produce large sized products such as automobiles, trucks, and buses in industry for many years. Parallel two-sided assembly lines carry practical advantages of both parallel assembly lines and two-sided assembly lines.The main purpose of this paper is to introduce type-E parallel two-sided assembly line balancing problem for the first time in the literature and to propose a new ant colony optimisation based approach for solving the problem. Different from the existing studies on parallel assembly line balancing problems in the literature, this paper aims to minimise two conflicting objectives, namely cycle time and number of workstations at the same time and proposes a mathematical model for the formal description of the problem. To the best of our knowledge, this is the first study which addresses both conflicting objectives on a parallel two-sided assembly line configuration. The developed ant colony optimisation algorithm is illustrated with an example to explain its procedures. An experimental design is also conducted to calibrate the parameters of the proposed algorithm using response surface methodology. Results obtained from the performed computational study indicate that minimising cycle time as well as number of workstations help increase system efficiency. It is also observed that the proposed algorithm finds promising results for the studied cases of type-E parallel two-sided assembly line balancing problem when the results are compared with those obtained from other three well-known heuristics.

A mathematical model and genetic algorithm-based approach for parallel two-sided assembly line balancing problem

Assembly lines are usually constructed as the last stage of the entire production system and efficiency of an assembly line is one of the most important factors which affect the performance of a complex production system. The main purpose of this paper is to mathematically formulate and to provide an insight for modelling the parallel two-sided assembly line balancing problem, where two or more two-sided assembly lines are constructed in parallel to each other. We also propose a new genetic algorithm (GA)-based approach in alternatively to the existing only solution approach in the literature, which is a tabu search algorithm. To the best of our knowledge, this is the first formal presentation of the problem as well as the proposed algorithm is the first attempt to solve the problem with a GA-based approach in the literature. The proposed approach is illustrated with an example to explain the procedures of the algorithm. Test problems are solved and promising results are obtained. Statistical tests are designed to analyse the advantage of line parallelisation in two-sided assembly lines through obtained test results. The response of the overall system to the changes in the cycle times of the parallel lines is also analysed through test problems for the first time in the literature.

Mixed-model parallel two-sided assembly line balancing problem

Display Omitted Mixed-model parallel two-sided assembly line balancing problem (MPTALBP) is studied.Agent based ant colony approach enhanced with 10 heuristics is developed.Ants have opportunity to randomly select one of those heuristic search behaviors.A new modified lower bound formulation is proposed for MPTALBP.Statistical tests prove the benefits of the proposed system and the algorithm. Assembly lines are frequently used as a production method to assemble complex products. Two-sided assembly lines are utilized to assemble large-sized products (e.g., cars, buses, trucks). Locating two lines in parallel helps improve line efficiency by enabling collaboration between the line workers. This paper proposes a mixed-model parallel two-sided assembly line system that can be utilized to produce large-sized items in an inter-mixed sequence. The mixed-model parallel two-sided line balancing problem is defined and the advantages of utilizing multi-line stations across the lines are discussed. A flexible agent-based ant colony optimization algorithm is developed to solve the problem and a numerical example is given to explain the method systematically. The proposed algorithm builds flexible balancing solutions suitable for any model sequence launched. The dynamically changing workloads of workstations (based on specific product models during the production process) are also explored. A comprehensive experimental study is conducted and the results are statistically analyzed using the well-known paired sample t-test. The test results indicate that the mixed-model parallel two-sided assembly line system reduces the workforce need in comparison with separately balanced mixed-model two-sided lines. It is also shown that the proposed algorithm outperforms the tabu search algorithm and six heuristics often used in the assembly line balancing domain.

Dynamic reconfiguration and simulation of manufacturing systems using agents

Purpose – Conventionally, various levels of decisions with regard to production are made in a number of sequential stages such as system design, production/process planning, production scheduling, system reconfiguration and system restructure. This paper aims to present an integrated approach for modelling, restructuring and simulating manufacturing systems to suit changing manufacturing situations as quickly as possible.Design/methodology/approach – An agent‐based approach is employed where each manufacturing resource is represented by an agent. Simply speaking, the approach enables the machines in a manufacturing system to manage themselves efficiently and effectively.Findings – The agent‐based modelling and interaction approach enables manufacturing resources to be allocated dynamically in an optimal manner. The modelling approach also enables alternative system configurations to be identified and evaluated using distributed discrete event simulation system. Resource allocation and manufacturing system...

A parallel parking system for a car-like robot with sensor guidance

Abstract An automated parallel parking strategy for a car-like mobile robot is presented. The study considers general cases of parallel parking for a rectangular robot within a rectangular space. The system works in three phases. In scanning phase the parking environment is detected by ultrasonic sensors mounted on the robot and a parking position and manoeuvring path is produced if the space is sufficient. Then in the positioning phase the robot reverses to the edge of the parking space, avoiding potential collisions. Finally, in manoeuvring phase the robot moves to the parking position in the parking space in a unified pattern, which may require backward and forward manoeuvres depending on the dimensions of the parking space. Motion characteristics of this kind of robot are modelled, taking into account the non-holonomic constraints acting on the car-like robot. On the basis of the characteristics, a collision-free path is planned in reference to the surroundings. The strategy has been integrated into an automated parking system and implemented in a modified B12 mobile robot capable of safe parking in tight situations. The system is developed for an automated parking device to help vehicle drivers. It also shows the potential to be integrated into automobiles.

Manufacturing Feasibility and Forming Properties of Cu-4Sn in Selective Laser Melting

Copper alloys, combined with selective laser melting (SLM) technology, have attracted increasing attention in aerospace engineering, automobile, and medical fields. However, there are some difficulties in SLM forming owing to low laser absorption and excellent thermal conductivity. It is, therefore, necessary to explore a copper alloy in SLM. In this research, manufacturing feasibility and forming properties of Cu-4Sn in SLM were investigated through a systematic experimental approach. Single-track experiments were used to narrow down processing parameter windows. A Greco-Latin square design with orthogonal parameter arrays was employed to control forming qualities of specimens. Analysis of variance was applied to establish statistical relationships, which described the effects of different processing parameters (i.e., laser power, scanning speed, and hatch space) on relative density (RD) and Vickers hardness of specimens. It was found that Cu-4Sn specimens were successfully manufactured by SLM for the first time and both its RD and Vickers hardness were mainly determined by the laser power. The maximum value of RD exceeded 93% theoretical density and the maximum value of Vickers hardness reached 118 HV 0.3/5. The best tensile strength of 316–320 MPa is inferior to that of pressure-processed Cu-4Sn and can be improved further by reducing defects.

Lexicographic bottleneck mixed-model assembly line balancing problem

Lexicographic bottleneck mixed-model assembly line balancing problem is studied.Artificial bee colony and tabu search algorithms are proposed.Parameters of the proposed algorithms are optimised using response surface method.Test problems are solved to assess the performance of the proposed methods.It is observed that both algorithms provide promising results in reasonable times. The lexicographic bottleneck assembly line balancing problem is a recently introduced problem which aims at obtaining a smooth workload distribution among workstations. This is achieved hierarchically. The workload of the most heavily loaded workstation is minimised, followed by the workload of the second most heavily loaded workstation and so on. This study contributes to knowledge by examining the application of the lexicographic bottleneck objective on mixed-model lines, where more than one product model is produced in an inter-mixed sequence. The main characteristics of the lexicographic bottleneck mixed-model assembly line balancing problem are described with numerical examples. Another contribution of the study is the methodology used to deal with the complex structure of the problem. Two effective meta-heuristic approaches, namely artificial bee colony and tabu search, are proposed. The parameters of the proposed meta-heuristics are optimised using response surface methodology, which is a well-known design of experiments technique, as a unique contribution to the expert and intelligent systems literature. Different from the common tendency in the literature (which aims to optimise one parameter at a time), all parameters are optimised simultaneously. Therefore, it is shown how a complex production planning problem can be solved using sophisticated artificial intelligence techniques with optimised parameters. The methodology used for parameter setting can be applied to other metaheuristics for solving complex problems in practice. The performances of both algorithms are assessed using well-known test problems and it is observed that both algorithms find promising solutions. Artificial bee colony algorithm outperforms tabu search in minimising the number of workstations while tabu search shows a better performance in minimising the value of lexicographic bottleneck objective function.

Production planning in additive manufacturing and 3D printing

Production planning problem in additive manufacturing and 3D printing is introduced.The mathematical model of the problem is developed and coded in CPLEX.Two heuristics are proposed and explained through a numerical example.Optimal and heuristic solutions are provided for the newly generated test problems.Experimental tests exhibit the requirement of planning in additive manufacturing. Additive manufacturing is a new and emerging technology and has been shown to be the future of manufacturing systems. Because of the high purchasing and processing costs of additive manufacturing machines, the planning and scheduling of parts to be processed on these machines play a vital role in reducing operational costs, providing service to customers with less price and increasing the profitability of companies which provide such services. However, this topic has not yet been studied in the literature, although cost functions have been developed to calculate the average production cost per volume of material for additive manufacturing machines.In an environment where there are machines with different specifications (i.e. production time and cost per volume of material, processing time per unit height, set-up time, maximum supported area and height, etc.) and parts in different heights, areas and volumes, allocation of parts to machines in different sets or groups to minimize the average production cost per volume of material constitutes an interesting and challenging research problem. This paper defines the problem for the first time in the literature and proposes a mathematical model to formulate it. The mathematical model is coded in CPLEX and two different heuristic procedures, namely best-fit and adapted best-fit rules, are developed in JavaScript. Solution-building mechanisms of the proposed heuristics are explained stepwise through examples. A numerical example is also given, for which an optimum solution and heuristic solutions are provided in detail, for illustration. Test problems are created and a comprehensive experimental study is conducted to test the performance of the heuristics. Experimental tests indicate that both heuristics provide promising results. The necessity of planning additive manufacturing machines in reducing processing costs is also verified. Display Omitted