A comparative evaluation of assembly line balancing Heuristics

A comparative evaluation is presented of six popular assembly line balancing heuristics, namely, ranked positional weight, Kilbridge and Wester, Moodie and Young, Hoffman precedence matrix, immediate update first fit, and rank and assign heuristic. The evaluation criteria used are the number of excess stations given, line efficiency, smoothness index and CPU time. The trade and transfer phase of the Moodie and Young method is applied to all heuristics, then the results are compared based on the evaluation criteria. Twenty problems each with 5 different cycle times are used for the comparative evaluation of heuristics. All six heuristics are coded in C++ language. Among the six considered heuristics the Hoffmann enumeration procedure performs best; but, the execution time for the Hoffmann procedure is longer because this procedure enumerates all the feasible alternative sets of tasks for the stations. An erratum to this article is available at .     

Fixed-quantity dynamic lot sizing using simulated annealing

In this paper, simulated annealing (SA) is applied to the deterministic dynamic lot-sizing problem with batch ordering and backorders. Batch ordering requires orders that are integer multiples of a fixed quantity that is larger than 1. The performance of the developed SA heuristic is compared to that of a genetic algorithm (GA) and a modified silver-meal (MSM) heuristic developed in the literature, based on the frequency of obtaining the optimum solution and the percentage average deviation from the optimum solution. In addition, the effects of three factors on the performance of the SA, GA, and the MSM are investigated in a 2³ factorial experiment. The investigated factors are the demand pattern, the batch size, and the length of the planning horizon. Results indicate that the SA heuristic has the best performance, followed by GA, in terms of the frequency of obtaining the optimum solution and the average deviation from the optimum solution. SA is also the most robust of the investigated heuristics as its performance is only affected by the length of the planning horizon.     

Multilevel Lot Sizing with a Genetic Algorithm Under Fixed and Rolling Horizons

This paper proposes a genetic algorithm (GA) as a heuristic for multilevel lot sizing, combined with the Bit Mod heuristic developed in this context, and invoking adaptive probabilities for crossover and mutation. The influence of various parameters under fixed and rolling horizons is detailed. Design of experiments methodology is used in this connection. In the rolling horizon, the behaviour of the GA and other rules are compared with and without freezing the plan. The performance of the GA is compared with the cost-modified Wagner Whitin algorithm and cost-modified silver meal methods. The superiority of the proposed method is discussed, and case studies are given.This revised version with a corrected online cover date was published online in April 2004.     

A Petri net approach for simple assembly line balancing problems

A simple assembly line balancing problem of type-1 (SALBP-1) aims to minimize the number of workstations for a given cycle time. In the relevant literature, several heuristics based on a branch-and-bound procedure, tabu search, and genetic algorithms (GAs) were proposed to solve SALBP-1. In this paper, an algorithm based on the reachability analysis of Petri nets is developed for SALBP-1. The proposed algorithm searches enabled transitions (or assignable tasks) in the Petri net model of precedence relations between tasks, and then the task minimizing the idle time is assigned to the station under consideration. The algorithm is coded in MATLAB, and its efficiency is tested on Talbot’s and Hoffmann’s benchmark datasets according to some performance measures and classifications. A computational study validates its effectiveness on Tonge’s 70-task problem by comparison with optimal solutions of traditional heuristics and a GA.     

An improved tool path discretization method for five-axis sculptured surface machining

The genetic algorithm (GA) is a heuristics, and commonly used to solve combinational problems. It has been proven to have high effectiveness and efficiency in many application areas. However, a successful GA application requires adapting the algorithm to the characteristics of a problem. In the past decades, various articles on single machine earliness and tardiness (SET) problem using a heuristic approaching have been published. Yet, there are few research addressing the area of the SET problem with distinct due dates and ready times for jobs. In this paper, a designed GA, modified optimal timing procedure, which starts the searching with a feasible solution obtained by applying the EXP-ET rule developed by Ow and Morton is developed for the SET problem. Computational results in the provided experiment show that the designed GA improves the SET solution in both quality and efficiency.     

A P-invariant-based algorithm for simple assembly line balancing problem of type-1

A simple assembly line balancing problem of type-1 (SALBP-1) concerns minimizing the number of workstations on an assembly line for a given cycle time. In this problem only a single product with deterministic task times is considered. Since the SALBP-1 is known as an NP-hard, considerable research effort has been spent to develop heuristic approaches. In this study we develop a different heuristic approach based on the P-invariants of Petri nets. The algorithm is coded in MATLAB, and its efficiency is tested on Talbot’s and Hoffmann’s benchmark datasets according to some performance measures and classifications. A computational study validates its effectiveness on Tonge’s 70-task problem by comparison with solutions of traditional heuristics and a genetic algorithm reported to perform well.     

An efficient memetic algorithm for the multi-stage supply chain network problem

A supply chain is dynamic and involves the constant flow of information, production, services, and funds from suppliers to customers between different stages. In this paper, a memetic algorithm (MA, a hybrid genetic algorithm) is developed to find the strategy that can give the lowest cost of the physical distribution flow. The proposed MA is combined with the genetic algorithm (GA), a multi-greedy heuristic method (GH), three local search methods (LSMs): the pairwise exchange procedure (XP), the insert procedure (IP), and the remove procedure (RP), the Fibonacci number procedure, and the linear programming technique (LP) to improve the tradition genetic algorithm (GA). Preliminary computational experiments demonstrate the efficiency and performance of the proposed MA.     

Heuristic search algorithms for lot streaming in a two-machine flowshop

The objective of this paper is to propose and evaluate heuristic search algorithms for a two-machine flowshop problem with multiple jobs requiring lot streaming that minimizes makespan. A job here implies many identical items. Lot streaming creates sublots to move the completed portion of a production lot to second machine. The three heuristic search algorithms evaluated in this paper are Baker’s approach (Baker), genetic algorithm (GA) and simulated annealing (SA) algorithm. To create neighborhoods for SA, three perturbation schemes, viz., pair-wise exchange, insertion and random insertion are used, and the performance of these on the final schedule is also compared. A wide variety of data sets is randomly generated for comparative evaluation. The parameters for GA and SA are obtained after conducting sensitivity analysis. The genetic algorithm is found to perform well for lot streaming in the two-machine flowshop scheduling.     

Non-identical parallel machine scheduling with fuzzy processing times using genetic algorithm and simulation

This paper addresses non-identical parallel machine scheduling problem with fuzzy processing times (FPMSP). A genetic algorithm (GA) approach embedded in a simulation model to minimize maximum completion time (makespan) is proposed. The results are compared with those obtained by using longest processing time rule, known as the most appropriate dispatching rule for such problems. This application illustrates the need for efficient and effective heuristics to solve FPMSPs. The proposed GA approach yields good results and reaches them fast and several times in one run. Moreover, due to its advantage of being a search algorithm, it can explore alternative schedules providing the same results.     

遗传调度算法的研究

提出一种基于２算法进行生产调度优化的算法，该算法通过ＧＡ，启发式调度以及评价算法的有机结合，在调度效率较高的情况下，实现调度方案的全局优化。特别是这种方法为ＧＡ在调度系统中的应用提供了经验，也为解决工艺计划与生产调度的集成提供了一种有效的优化手段。