Genetic Programming with Delayed Routing for Multiobjective Dynamic Flexible Job Shop Scheduling

Dynamic Flexible Job Shop Scheduling (DFJSS) is an important and challenging problem, and can have multiple conflicting objectives. Genetic Programming Hyper-Heuristic (GPHH) is a promising approach to fast respond to the dynamic and unpredictable events in DFJSS. A GPHH algorithm evolves dispatching rules (DRs) that are used to make decisions during the scheduling process (i.e., the so-called heuristic template). In DFJSS, there are two kinds of scheduling decisions: the routing decision that allocates each operation to a machine to process it, and the sequencing decision that selects the next job to be processed by each idle machine. The traditional heuristic template makes both routing and sequencing decisions in a non-delay manner, which may have limitations in handling the dynamic environment. In this article, we propose a novel heuristic template that delays the routing decisions rather than making them immediately. This way, all the decisions can be made under the latest and most accurate information. We propose three different delayed routing strategies, and automatically evolve the rules in the heuristic template by GPHH. We evaluate the newly proposed GPHH with Delayed Routing (GPHH-DR) on a multiobjective DFJSS that optimises the energy efficiency and mean tardiness. The experimental results show that GPHH-DR significantly outperformed the state-of-the-art GPHH methods. We further demonstrated the efficacy of the proposed heuristic template with delayed routing, which suggests the importance of delaying the routing decisions.

An effective hybrid ant lion algorithm to minimize mean tardiness on permutation flow shop scheduling problem

This article aimed to develop an improved Ant Lion algorithm. The objective function was to minimize the mean tardiness on the flow shop scheduling problem with a focus on the permutation flow shop problem (PFSP). The Hybrid Ant Lion Optimization Algorithm (HALO) with local strategy was proposed, and from the total search of the agent, the NEH-EDD algorithm was applied. Moreover, the diversity of the nominee schedule was improved through the use of swap mutation, flip, and slide to determine the best solution in each iteration. Finally, the HALO was compared with some algorithms, while some numerical experiments were used to show the performances of the proposed algorithms. It is important to note that comparative analysis has been previously conducted using the nine variations of the PFSSP problem, and the HALO obtained was compared to other algorithms based on numerical experiments.

Dynamic due date assignment method

Purpose The purpose of this paper is to determine the interaction between dynamic due date assignment methods and scheduling decision rules in a typical dynamic job shop production system in which setup times are sequence dependent. Two due date assignment methods and six scheduling rules are considered for detailed investigation. The scheduling rules include two new rules which are modifications of the existing rules. The performance of the job shop system is evaluated using various measures related to flow time and tardiness. Design/methodology/approach A discrete-event simulation model is developed to describe the operation of the job shop. The simulation results are subjected to statistical analysis based on the method of analysis of variance. Regression-based analytical models have been developed using the simulation results. Since the due date assignment methods and the scheduling rules are qualitative in nature, they are modeled using dummy variables. The validation of the regression models involves comparing the predictions of the performance measures of the system with the results obtained through simulation. Findings The proposed scheduling rules provide better performance for the mean tardiness measure under both the due date assignment methods. The regression models yield a good prediction of the performance of the job shop. Research limitations/implications Other methods of due date assignment can also be considered. There is a need for further research to investigate the performance of due date assignment methods and scheduling rules for the experimental conditions that involve system disruptions, namely, breakdowns of machines. Practical implications The explicit consideration of sequence-dependent setup time (SDST) certainly enhances the performance of the system. With appropriate combination of due date assignment methods and scheduling rules, better performance of the system can be obtained under different shop floor conditions characterized by setup time and arrival rate of jobs. With reductions in mean flow time and mean tardiness, customers are benefitted in terms of timely delivery promises, thus leading to improved service level of the firm. Reductions in manufacturing lead time can generate numerous other benefits, including lower inventory levels, improved quality, lower costs, and lesser forecasting error. Originality/value Two modified scheduling rules for scheduling a dynamic job shop with SDST are proposed. The analysis of the dynamic due date assignment methods in a dynamic job shop with SDST is a significant contribution of the present study. The development of regression-based analytical models for a dynamic job shop operating in an SDST environment is a novelty of the present study.

PENJADWALAN PRODUKSI DI CV. JATI AGUNG FURNITURE

This research is done for particular company in furniture scope which is all of order produced by job order system. For the time being the company oftenly couldn’t fulfil the order from the consument on time. It was because the company scheduled the products based on the biggest product volume if the order came on the same day without paid any attention to due date which are agreed both by consument and the company. This research is done by improving the set up method with SMED method, finishing time model, a better scheduled with EDD and SPT method for regular job and MWKR rule. The result showed that by scheduling proposal was gotten of decreasing mean tardiness from 0,667 the postpone day (delayed day) become no delayed.

A Novel MOEA/D for Multiobjective Scheduling of Flexible Manufacturing Systems

This paper considers the multiobjective scheduling of flexible manufacturing systems (FMSs). Due to high degrees of route flexibility and resource sharing, deadlocks often exhibit in FMSs. Manufacturing tasks cannot be finished if any deadlock appears. For solving such problem, this work develops a deadlock-free multiobjective evolutionary algorithm based on decomposition (DMOEA/D). It intends to minimize three objective functions, i.e., makespan, mean flow time, and mean tardiness time. The proposed algorithm can decompose a multiobjective scheduling problem into a certain number of scalar subproblems and solves all the subproblems in a single run. A type of a discrete differential evolution (DDE) algorithm is also developed for solving each subproblem. The mutation operator of the proposed DDE is based on the hamming distance of two randomly selected solutions, while the crossover operator is based on Generalization of Order Crossover. Experimental results demonstrate that the proposed DMOEA/D can significantly outperform a Pareto domination-based algorithm DNSGA-II for both 2-objective and 3-objective problems on the studied FMSs.

Computational Intelligence Approach for Flow Shop Scheduling Problem

Flow shop scheduling is an important tool for manufacturing and engineering, where it can have a major impact on the productivity of a process. Because the resources used in manufacturing activities are very limited, flow shop scheduling becomes a very important concept in managerial decision-making. It deals with the allocation of resources to tasks over given time periods with a view to optimize one or more objective functions like makespan or mean tardiness resulting in reduced production time and costs. During recent years, effective computational intelligent algorithms are developed and successfully employed for achieving the optimum output of the flow shop problems. This chapter provides a computational intelligence approach for flow shop scheduling.

Minimization of Mean Tardiness in Hybrid Flow Shop with Missing Operations Using Genetic Algorithm

A hybrid flow shop (HFS) scheduling is characterized of “[Formula: see text]” jobs “[Formula: see text]” machines with “[Formula: see text]” stages by unidirectional flow of work with a variety of jobs being processed sequentially in a single-pass manner. The HFS scheduling problem is known to be strongly NP-hard in nature. Hence, the essential complexity of the problem necessitates the application of meta-heuristics to solve HFS scheduling problems. A population-based genetic algorithm (GA) and a simulated annealing (SA) algorithm have been proposed to solve the multi-stage HFS scheduling problem with missing operations to minimize the mean tardiness. The computational results observed that the GA is efficient in finding out good quality solutions.