Studying the impact of sequence-dependent set-up times in integrated process planning and scheduling with E-ACO heuristic

In job-shop scheduling, the importance of set-up issues is well known and has been considered in many solution approaches. However, in integrated process planning and scheduling (IPPS) involving flexible process plans, the set-up times are often ignored, or absorbed into processing times in IPPS domain, with the purpose to reduce the complexity. This is based on the assumption that set-up times are sequence-independent, or short enough to be ignored compared to processing times. However, it is not uncommon to encounter sequence-dependent set-up times (SDSTs) in practical production. This paper conducts a detailed investigation on the impact of SDSTs on the practical performance of the schedule: a comparative study is made for different cases where set-up times are (1) separately considered, (2) absorbed into processing times, or (3) totally ignored. An enhanced version of ant colony optimisation (E-ACO) algorithm is used to solve the IPPS problem, with the objective to minimise the total makespan. The following four types of set-up issues are considered: part loading/unloading, fixture preparation, tool switching and material transportation. Situations with various set-up time lengths have been studied and compared. A special case of IPPS problem involving a large number of identical jobs has been specifically studied and discussed. The results have shown that, set-up times should be carefully dealt with under different circumstances.     

用混合型蚂蚁群算法求解TSP问题

介绍了求解TSP问题的混合型蚂蚁群算法,并以att532(美国532个城市)为例给出了计算实验结果,说明了混合型蚂蚁群算法能改进标准蚂蚁群算法的计算效率和计算结果的质量。     

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.     

Ant colony optimisation algorithm for distribution-allocation problem in a two-stage supply chain with a fixed transportation charge

In a fixed charge transportation problem, each route is associated with a fixed charge (or a fixed cost) and a transportation cost per unit transported. The presence of the fixed cost makes the problem difficult to solve, thereby requiring the use of heuristic methods. In this paper, an algorithm based on ant colony optimisation is proposed to solve the distribution-allocation problem in a two-stage supply chain with a fixed transportation cost for a route. A numerical study on benchmark problem instances has been carried out. The results obtained for the proposed algorithm have been compared with that for the genetic algorithm-based heuristic currently available in the literature. It is statistically confirmed that the proposed algorithm provides significantly better solutions.     

Scheduling job shop associated with multiple routings with genetic and ant colony heuristics

The general job shop problem is one of the well known machine scheduling problems, in which the operation sequence of jobs are fixed that correspond to their optimal process plans and/or resource availability. Scheduling and sequencing problems, in general, are very difficult to solve to optimality and are well known as combinatorial optimisation problems. The existence of multiple job routings makes such problems more cumbersome and complicated. This paper addresses a job shop scheduling problem associated with multiple job routings, which belongs to the class of NP hard problems. To solve such NP-hard problems, metaheuristics have emerged as a promising alternative to the traditional mathematical approaches. Two metaheuristic approaches, a genetic algorithm and an ant colony algorithm are proposed for the optimal allocation of operations to the machines for minimum makespan time criterion. ILOG Solver, a scheduler package, is used to evaluate the performance of the proposed algorithms. The comparison reveals that both the algorithms are capable of providing solutions better than the solution obtained with ILOG Solver.     

Performance of an ant colony optimisation algorithm in dynamic job shop scheduling problems

The goal of the current study is to identify appropriate application domains of Ant Colony Optimisation (ACO) in the area of dynamic job shop scheduling problem. The algorithm is tested in a shop floor scenario with three levels of machine utilisations, three different processing time distributions, and three different performance measures for intermediate scheduling problems. The steady-state performances of ACO in terms of mean flow time, mean tardiness, total throughput on different experimental environments are compared with those from dispatching rules including first-in-first-out, shortest processing time, and minimum slack time. Two series of experiments are carried out to identify the best ACO strategy and the best performing dispatching rule. Those two approaches are thereafter compared with different variations of processing times. The experimental results show that ACO outperforms other approaches when the machine utilisation or the variation of processing times is not high.     

Development of a hybrid metaheuristic to minimise earliness and tardiness in a hybrid flowshop with sequence-dependent setup times

This paper presents an efficient hybrid metaheuristics for scheduling jobs in a hybrid flowshop with sequence-dependent setup times. The problem is to determine a schedule that minimises the sum of earliness and tardiness of jobs. Since this problem class is NP-hard in the strong sense, there seems to be no escape from appealing to metaheuristic procedures to achieve near-optimal solutions for real life problems. This paper proposes the hybrid metaheuristic algorithm which comprises three components: an initial population generation method based on an ant colony optimisation, a simulated annealing algorithm as an evolutionary algorithm that employs certain probability to avoid becoming trapped in a local optimum, and a variable neighbourhood search which involves three local search procedures to improve the population. A design of experiments approach is employed to calibrate the parameters of the algorithm. Results of computational tests in solving 252 problems up to 100 jobs have shown that the proposed algorithm is computationally more effective in yielding solutions of better quality than the adapted random key genetic algorithm and immune algorithm presented previously.     

动力锂电池模组多工位多电性能参数测试调度方法研究

针对多组动力锂电池多电性能参数测试过程存在测试通道闲置,整体测试时间较长、测试效率低的问题,研究动力锂电池模组多工位测试系统,并提出动力锂电池模组多工位多电性能参数任务调度方法.通过对电性能参数测试任务进行拆分并构建任务单元测试路径集,基于测试路径集求解任务调度总时间;基于蚁群算法(ant colony algorit...     

A bi-objective metaheuristic approach to unpaced synchronous production line-balancing problems

Synchronous production lines are those in which the timing of the job movement between stations is coordinated in such a way that all of the jobs are indexed simultaneously. Unpaced synchronous lines are synchronous lines that advance only when all the stations have completed their tasks. Numerous industrial applications have used unpaced synchronous lines, but little research has been conducted regarding unpaced synchronous production line-balancing problems. This research studies the unpaced synchronous production line-balancing problem with stochastic task-completion times, and develops a tabu-search metaheuristic approach to solve the problem using two methods of analysis: extreme value theory and simulation. Through the analysis of the computational results, we discover that unpaced synchronous production lines are quite efficient relative to paced lines.     

Tool path optimisation method for large thin-wall part of spacecraft

Roughing tool path of panel machining, which is a bottleneck of spacecraft production, should be optimised rapidly to shorten process time. This problem has a large solution space, and surface quality should be taken into account. The decision variables are cavity machining order, feed point and cutting direction of each cavity. Our problem is presented as an asymmetric general travelling salesman problem (AGTSP). A cluster optimisation-based hybrid max–tmin ant system (CO-HMMAS) is proposed, which solves two sub-problems as a whole. The oriented pheromone and dynamic heuristic information calculating methods are designed. We analyse the differences between one-stage and two-stage AGTSP local search heuristics and combine CO-HMMAS with them properly. An improved Global 3-opt heuristic suitable for both symmetric and asymmetric cases is proposed with sharply reduced time complexity. Comparison experiments verified that, two-stage local search heuristics decrease solution error significantly and rapidly when the error is great, and one-stage ones improve a near-optimal solution costing much more computing time. Benchmarks tests show that, CO-HMMAS outperforms the state-of-the-art algorithm on several technical indexes. Experiments on typical panels reveal that all algorithm improvements are effective, and CO-HMMAS can obtain a better tool path than the best algorithm within less CPU time.     

Ant colony optimisation with parameterised search space for the job shop scheduling problem

The job-shop scheduling problem (JSSP) is known to be NP-hard. Due to its complexity, many metaheuristic algorithm approaches have arisen. Ant colony metaheuristic algorithm, lately proposed, has successful application to various combinatorial optimisation problems. In this study, an ant colony optimisation algorithm with parameterised search space is developed for JSSP with an objective of minimising makespan. The problem is modelled as a disjunctive graph where arcs connect only pairs of operations related rather than all operations are connected in pairs to mitigate the increase of the spatial complexity. The proposed algorithm is compared with a multiple colony ant algorithm using 20 benchmark problems. The results show that the proposed algorithm is very accurate by generating 12 optimal solutions out of 20 benchmark problems, and mean relative errors of the proposed and the multiple colony ant algorithms to the optimal solutions are 0.93% and 1.24%, respectively.     

Heuristic approaches for mixed-model sequencing problem with stochastic processing times

Despite many pioneering efforts and works over the past decades, stochastic events have not been studied extensively in mixed-model assembly lines thus far. For a mixed-model sequencing problem with stochastic processing times, this paper aims to minimise expected total work overload. It also focuses on the most critical workstation of the line. In practice, this assumption is useful when the whole or a big portion of the assembly line is considered as a single station. In order to tackle the problem, a dynamic programming (DP) algorithm as well as two greedy heuristics from the literature is employed. However, it is realised that the DP cannot guarantee the optimal sequence neither for stochastic nor deterministic problems. It is because the calculation of work overload is involved in a recursive procedure that affects the states’ value functions. Therefore, by the use of network representation, the problem is modelled as a shortest path problem and a new heuristic, inspired by Dijkstra’s algorithm is developed to deal with it. Numerical results show that the proposed method outperforms other algorithms strongly. Finally, some discussion is provided about why one should consider stochastic parameters and why the proposed heuristic performs well in this regard.