Average Tardiness Research Articles

A bi-objective integrated procurement, production, and distribution problem of a multi-echelon supply chain network design: A new tuned MOEA

Efficient management of supply chain (SC) requires systematic considerations of miscellaneous issues in its comprehensive version. In this paper, a multi-periodic structure is developed for a supply chain network design (SCND) involving suppliers, factories, distribution centers (DCs), and retailers. The nature of the logistic decisions is tactical that encompasses procurement of raw materials from suppliers, production of finished product at factories, distribution of finished product to retailers via DCs, and the storage of raw materials and end product at factories and DCs. Besides, to make the structure more comprehensive, a flow-shop scheduling model in manufacturing part of the SC is integrated in order to obtain optimal delivery time of the product that consists of the makespan and the ship time of the product to DCs via factories. Moreover, to make the model more realistic, shortage in the form of backorder can occur in each period. The two objectives are minimizing the total SC costs as well as minimizing the average tardiness of product to DCs. The obtained model is a bi-objective mixed-integer non-linear programming (MINLP) model that is shown to belong to NP-Hard class of the optimization problems. Thus, a novel algorithm, called multi-objective biogeography based optimization (MOBBO) with tuned parameters is presented to find a near-optimum solution. As there is no benchmark available in the literature, the parameter-tuned multi-objective simulated annealing algorithm (MOSA) and the popular non-dominated sorting genetic algorithm (NSGA-II) are developed to validate the results obtained and to evaluate the performance of MOBBO using randomly generated test instances.

Simulation-Based Sequencing Algorithm for MRO Operations of Train Couplings

Sequencing rules are often used in production control to improve key performance indicators, such as cycle time, mean tardiness and on-time delivery. However, for Maintenance, Repair and Overhaul (MRO) processes, there is a development need for sequencing rules, which effectively improve these indicators since specific requirements have to be considered and recent studies have mainly been using standard rules and do not allow a clear conclusion. This paper describes the modeling and validation of a real-case scenario, the MRO of train couplings by means of an event-driven simulation. The simulation model is used in order to derive and test a new sequencing rule, the Fix-and-continue algorithm (FACA), as well as to compare its results with standard sequencing rules concerning average cycle time, mean tardiness and on-time delivery. Thereby, the potential of the algorithm is revealed.

Combination effects analysis of bottleneck-load order review/release and the dispatching rule: Application to a printed circuit board manufacturing line with multiple bottlenecks

The purpose of this paper is to consider the interaction among shop floor control strategies (order review/release and dispatching, ORR) and its effect on the performance of the printed circuit board manufacturing system with multiple bottleneck workstations. For that purpose, we suggest the pull from ORR strategies based on bottleneck-load and simulation tests and TOPSIS analysis are performed to compare various combinations of bottleneck-load ORR mechanisms and dispatching rule at each bottleneck workstation. An experiment is performed based on a real case in Korea and used for data from simulation test. The performance of proposed ORR strategies is improved when the strategies are combined with a suitable dispatching rule at each bottleneck workstation. The experimental results demonstrate that the interaction between the bottleneck-load ORR and the optimal combination of the dispatching rule at each bottleneck workstation is highly relevant with respect that the performance indicators have a characteristic of “lower is better”, such as average tardiness, cycle time, and WIP level.

Comparison of SCONWIP, DCONWIP, TOC and CWIPL II in Job Shop

Critical WIP Loop II (CWIPL II) is a control mechanism previously proposed for unbalance flow line. The purpose of this paper is show how to apply CWIPL II in job shop and then comparing it with Theory of Constraint (TOC), Dedicated CONWIP (DCONWIP) and Shared CONWIP (SCONWIP). The Performance measures considered are average throughput, average lead time, average tardiness time and percent of tardy. Two set of examples are simulated. At First set, each job has a bottleneck that is not bottleneck for other jobs. Simulation results and statistical tests demonstrates that CWIPL II has better lead time and throughput compared to TOC and CWIPL II has better or the same due date compared to TOC. TOC works better than DCONWIP for all performance measures and DCONWIP is better than SCONWIP for average throughput lead time and tardiness time while DCONWIP and SCONWIP are the same for percent of tardy. At second set, single machine is bottleneck for all jobs. So the Job shop has one bottleneck. In this set, TOC has better lead time, tardiness and percent of tardy compared to CWIPLII, DCONWIP and SCONWIP with the same throughput.

A Deadline based Task Scheduling Algorithm for Heterogeneous Grid Environments

Grid computing is the framework of computer systems that provides high performance computing environment. The challenging issue in grid computing is to design efficient and reliable task scheduling algorithms for efficient utilization of grid computing. In this paper, we are proposing a new Improvised Prioritized Deadline (IPD) based scheduling algorithm for efficient task execution with deadline constraints of users’ tasks. The proposed algorithm considers the processing power of the resources while scheduling the tasks. Performance comparison of the algorithm has been done with the other task scheduling algorithms such as Earliest Deadline First (EDF) and Prioritized Based Deadline Scheduling Algorithm (PDSA). The proposed algorithm improves 45%-70% with respect to the average tardiness over the PDSA algorithm. The proposed algorithm also shows good results with respect to the number of non-delayed tasks. In the cases the purposed improvised algorithm has shown good results.

Periodical capacity setting methods for make-to-order multi-machine production systems

The paper presents different periodical capacity setting methods for make-to-order, multi-machine production systems with stochastic customer required lead times and stochastic processing times to improve service level and tardiness. These methods are developed as decision support when capacity flexibility exists, such as, a certain range of possible working hours a week for example. The methods differ in the amount of information used whereby all are based on the cumulated capacity demand at each machine. In a simulation study the methods’ impact on service level and tardiness is compared to a constant provided capacity for a single and a multi-machine setting. It is shown that the tested capacity setting methods can lead to an increase in service level and a decrease in average tardiness in comparison to a constant provided capacity. The methods using information on processing time and customer required lead time distribution perform best. The results found in this paper can help practitioners to make efficient use of their flexible capacity.

Comparative Study of Well Known Sequencing Methodologies and Differential Evolution: A Case Study of Thailand Garment Industries

The objective of this research is to study the problem and efficiency improvement of the clean room garment factory. The methods of production schedule were performed by heuristic technique of well-known scheduling methodologies including Shortest Processing Time, , Earliest Due Date, First Come First Serve method and Differential Evolution Method (DE). All methods can help to reduce the costs of delay shipment and time loss in various case studies. They were applied to the production order in 2 months after flooding problems in Thailand .The demand has a lot of quantities over production capacity. The results showed that the rules of Earliest Due Date method (EDD) is a powerful methodology for increasing production efficiency and minimizing make span. Whereas, the First Come First Serve (FCFS) method is the best method for finding a minimum value of total late job. Finally, Differential Evolution Method (DE) is the best method for finding a minimize value of average tardiness job.

A dispatching algorithm for flexible job-shop scheduling with transfer batches: an industrial application

In this study, we present a dispatching algorithm to solve a real-world case of the flexible job-shop scheduling problem with transfer batches and the objective of minimising the average tardiness of production orders. The proposed algorithm considers two variants: (i) an ordered variant, where the priority dispatching rules are applied in a predefined order, and (ii) a randomised variant, where the user can assign probabilities (weights) to the priority rules. Using the information of the number of units and due dates requested by the only customer, the algorithm provides the sequence of operations that must be performed on each machine, as well as the start and completion times of operations. In order to reduce the impact of unexpected events on a generated schedule, several robustness rules are considered. The obtained results substantially improve the former method used in the company in terms of minimising average tardiness. Additionally, other important benefits are obtained, including significant saving in the time spent on scheduling, simplicity of use of the proposed procedure, robustness against unexpected events, reduction of idle times, improvement of decision-making information, and improvement of on-time delivery performance.

Study of stochastic sequence-dependent flexible flow shop via developing a dispatching rule and a hybrid GA

A flexible flow shop is a generalized flow shop with multiple machines in some stages. This system is fairly common in flexible manufacturing and in process industry. In most practical environments, scheduling is an ongoing reactive process where the presence of real time information continually forces reconsideration of pre-established schedules. This paper studies a flexible flow shop system considering non-deterministic and dynamic arrival of jobs and also sequence dependent setup times. The problem objective is to determine a schedule that minimizes average tardiness of jobs. Since the problem class is NP-hard, a novel dispatching rule and hybrid genetic algorithm have been developed to solve the problem approximately. Moreover, a discrete event simulation model of the problem is developed for the purpose of experimentation. The most commonly used dispatching rules from the literature and two new methods presented in this paper are incorporated in the simulation model. Simulation experiments have been conducted under various experimental conditions characterized by factors such as shop utilization, setup time level and number of stages. The results indicate that methods proposed in this study are much better than the traditional dispatching rules.

Optimizing fuzzy makespan and tardiness for unrelated parallel machine scheduling with archived metaheuristics

This research presents two simulated annealing (SA) and a greedy randomized adaptive search procedure (GRASP) to solve unrelated parallel machine scheduling problems (UPMSPs) with two fuzzy optimization objectives—makespan and average tardiness. Few studies have employed fuzzy approach to solve multi-objective UPMSPs. In the research, several schemes are incorporated into the algorithm, including (1) matching-based decoding; (2) acceptance rule based on Pareto dominance, objective fitness, or Pareto reference point distance; (3) random or fixed weighted direction search. The matching-based decoding scheme has two phases: first max–min matching and then Hungarian method. Experiments were conducted to evaluate the algorithms’ performance for moderate to large problem size instances. The results indicate that matching-based decoding scheme significantly improves solution quality, but will require more computation time. GRASP with path relinking performs slightly worse than objective fitness-based multi-objective simulated annealing algorithms (MOSA), but better than Pareto dominance-based MOSA in terms of several Pareto performance measures.

A multi-objective comparison of dispatching rules in a drum–buffer–rope production control system

The advantages of the TOC (Theory of Constraints) philosophy have been extensively documented in the literature since its introduction during the 1980s. At the operational level, TOC is implemented by means of the well-known DBR (drum–buffer–rope) production control system. In a multiproduct manufacturing environment, the performance of DBR is greatly affected by the dispatching rules employed in front of the bottleneck station. Furthermore, it has been proved that no single dispatching rule (DR) performs globally better than any others. Therefore, for systems usually influenced by variability conditions, the selection of a robust DR could help practitioners to reach a good system performance. In this paper we propose a methodology to obtain a robust DR (by means of Taguchi signal-to-noise ratio) from a set of previously selected rules according to the performance measures of the system pursued by the practicing managers. We study the performance of different dispatching rules for several conflicting objectives (namely average tardiness, maximum tardiness, and WIP) from a robustness viewpoint and for a range of manufacturing scenarios in a shop floor formed by five stations in line and three different products. Different variability sources, such as processing times, breakdowns and set-ups, are discussed. The results obtained are of special interest for practitioners.

Real time selection of scheduling rules and knowledge extraction via dynamically controlled data mining

A new scheduling system for selecting dispatching rules in real time is developed by combining the techniques of simulation, data mining, and statistical process control charts. The proposed scheduling system extracts knowledge from data coming from the manufacturing environment by constructing a decision tree, and selects a dispatching rule from the tree for each scheduling period. In addition, the system utilises the process control charts to monitor the performance of the decision tree and dynamically updates this decision tree whenever the manufacturing conditions change. This gives the proposed system the ability to adapt itself to changes in the manufacturing environment and improve the quality of its decisions. We implement the proposed system on a job shop problem, with the objective of minimising average tardiness, to evaluate its performance. Simulation results indicate that the performance of the proposed system is considerably better than other simulation-based single-pass and multi-pass scheduling algorithms available in the literature. We also illustrate knowledge extraction by presenting a sample decision tree from our experiments.

A New Artificial Immune System Algorithm for Multiobjective Fuzzy Flow Shop

In this paper a new artificial immune system (AIS) algorithm is proposed to solve multi objective fuzzy flow shop scheduling problems. A new mutation operator is also described for this AIS. Fuzzy sets are used to model processing times and due dates. The objectives are to minimize the average tardiness and the number of tardy jobs. The developed new AIS algorithm is tested on real world data collected at an engine cylinder liner manufacturing process. The feasibility and effectiveness of the proposed AIS is demonstrated by comparing it with genetic algorithms. Computational results demonstrate that the proposed AIS algorithm is more effective meta-heuristic for multi objective flow shop scheduling problems with fuzzy processing time and due date.

Penjadwalan Produksi pada Mesin Paralel dalam Proses Pembuatan Produk Botol Kemasan

PT Abadi Adimulia is a company that work in plastic container production. The job arrival is static and the difference of processing time between the main process and the other process is very long. Until now the company use the First Come First Serve (FCFS) method to schedule their production activities, but with this method often the company finish their job over the due. The objective of this research was to obtain the production scheduling that give the minimum value in maximum lateness, makespan, number of late job, and the average tardiness on the blow mouldings machine. The production scheduling method that used in this research were Longest Processing Time method (LPT), Shortest Processing Time method (SPT), and Earliest Due Date method (EDD). The result from the scheduling using the three method indicate that LPT method is the best method to apply in this case which reduce the maximum lateness for 112.27 hours, makespan for 101.93 hours, number of late job for two jobs, and the average tardiness for 4.67 hours/job on June, maximum lateness for 60.07 hours, makespan for 123.19 hours, number of late job for one jobs, and the average tardiness for 2.5 hours/job on June, and maximum lateness for 53.42 hours, makespan for 38.1 hours, number of late job for one jobs, and the average tardiness for 2.05 hours/job on June.

A fuzzy multi-objective programming for scheduling of weighted jobs on a single machine

This paper considers a classic model of weighted single machine scheduling problem and aims to improve it to a real-world application through fuzzy set theory. For this purpose, processing times and due dates of jobs are defined as fuzzy numbers. In the proposed model, two objectives are considered to be minimized: average tardiness and number of tardy jobs. The objectives are converted into fuzzy statement through fuzzy arithmetic. Because the problem is NP-hard, it is proposed to solve the model through three well-known meta-heuristic algorithms as Simulated Annealing, Tabu Search, and Genetic Algorithm albeit with some modifications. Comparative analysis of algorithms is attainable through different experimented results on generated benchmark problems with different sizes and difficulties. Efficiency of the developed algorithms is analyzed on the different versions of the model which come from the assumption of different parameters and/or components. Algorithms' behaviors to managerial insights are also considered in some sensitivity analysis experiments. The obtained results show the applicability of the proposed model in real-world scheduling problems.

A New Artificial Immune System Algorithm for Multiobjective Fuzzy Flow Shop Problems

In this paper a new artificial immune system (AIS) algorithm is proposed to solve multi objective fuzzy flow shop scheduling problems. A new mutation operator is also described for this AIS. Fuzzy sets are used to model processing times and due dates. The objectives are to minimize the average tardiness and the number of tardy jobs. The developed new AIS algorithm is tested on real world data collected at an engine cylinder liner manufacturing process. The feasibility and effectiveness of the proposed AIS is demonstrated by comparing it with genetic algorithms. Computational results demonstrate that the proposed AIS algorithm is more effective meta-heuristic for multi objective flow shop scheduling problems with fuzzy processing time and due date.

Waiting profiles: An efficient protocol for enabling distributed planning of container barge rotations along terminals in the port of Rotterdam

We consider the problem of aligning barge rotations with quay schedules of terminals in the port of Rotterdam. Every time a barge visits the port, it has to make a rotation along, on average, eight terminals to load and unload containers. A central solution, e.g., a trusted party that coordinates the activities of all barges and terminals, is not feasible for several reasons. We propose a multi-agent based approach of the problem, since a multi-agent system can mirror to a large extent the way the business network is currently organized and can provide a solution that is acceptable to each of the parties involved. We examine the value of exchanging different levels of information and evaluate the performance by means of simulation. We compare the results with an off-line scheduling algorithm. The results indicate that, in spite of the limited information available, our distributed approach performs quite well when compared to the central approach. In addition, our experiments indicate that an information exchange based on waiting profiles reduces the average tardiness per barge with almost 80% when compared to the situation with no information exchange. We therefore think that waiting profiles provide a promising protocol to tackle this problem.

Minimizing the average tardiness: the case of outsource machines

Two important factors that affect outsourcing decisions are the production cost and the potential loss of customer goodwill due to late orders. This article deals with the problem of finding outsourcing strategies or solutions that consider trade-offs between outsourcing cost and average tardiness, an important measure of lost customer goodwill. Assumptions include that outsource costs are a function of machine use and that the planning organization owns no production equipment; instead, the production function is completely outsourced. Furthermore, the planning organization has flexibility in terms of the quantity of parallel production resources to outsource and it controls the assignment of jobs to these parallel production resources. The article presents lower bounds for the problem, which are used for comparisons. Several approaches to generate trade-off solutions are proposed and compared under a variety of experimental factors.

Multi-objective genetic algorithm for single machine scheduling problem under fuzziness

This paper presents a new multi-objective approach to a single machine scheduling problem in the presence of uncertainty. The uncertain parameters under consideration are due dates of jobs. They are modelled by fuzzy sets where membership degrees represent decision maker’s satisfaction grade with respect to the jobs’ completion times. The two objectives defined are to minimise the maximum and the average tardiness of the jobs. Due to fuzziness in the due dates, the two objectives become fuzzy too. In order to find a job schedule that maximises the aggregated satisfaction grade of the objectives, a hybrid algorithm that combines a multi-objective genetic algorithm with local search is developed. The algorithm is applied to solve a real-life problem of a manufacturing pottery company.

Analysis of order review/release methods in a flexible flow shop to minimise work-in-process inventory and average tardiness

This paper evaluates the effects of Order Review/Release (ORR) mechanisms, priority rules, and loading mechanisms on jobs in a flexible flow shop. Simulation is used to investigate the effect of two ORR mechanisms, three priority rules, and two loading mechanisms on average Work-in-Process (WIP) costs and job tardiness. The results indicate that the loading mechanisms, along with appropriate ORR mechanism, can significantly affect the flow of jobs through the shop.