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Comparison of Integrated and Sequential Decisions on Production and Distribution Activities
Published in Turan Paksoy, Çiğdem Koçhan, Sadia Samar Ali, Logistics 4.0, 2020
Ece Yağmur, Saadettin Erhan Kesen
The sequential problem is comprised of two separate problems: (i) production scheduling and (ii) vehicle routing. Production scheduling is to determine order sequences on each machine setup in series along with their production starting times so as to minimize makespan (Cmax). The reason lying behind the selection of makespan as objective is to use machines as efficiently as possible. Completion time of order i (denoted by ri) on the last machine found by solving the production scheduling problem (or interpreted as release time) is given as a parameter for the vehicle routing problem. The vehicle can only start the delivery of order i after ri. Contrary to the integrated problem, in the sequential problem, production and distribution sequences are not necessarily the same, meaning that orders are not grouped into the batches.
A neighborhood search algorithm for the unrelated parallel machine scheduling problem
Published in Jimmy C.M. Kao, Wen-Pei Sung, Civil, Architecture and Environmental Engineering, 2017
We select the minimization of the maximum completion time or makespan (Cmax) as the optimization criterion. The makespan is determined by the maximum workload among parallel machines, where the workload of a machine is the sum of processing times of all jobs assigned to it. The goal of schedule is to distribute workload among parallel machines as equally as possible so as to minimize the makespan. According to the three-field classification scheme, the scheduling problem studied in this paper can be denoted by Pm/Mj/Cmax.
Models and Algorithms for Machine Scheduling with Setup Times
Published in Cornelius Leondes, Computer-Aided Design, Engineering, and Manufacturing, 2019
Problems incorporating makespan or flowtime objectives reflect practical situations where the primary consideration is the efficiency of material flow. For a given set of jobs to be processed, each with a prespecified processing time, minimization of makespan is directly equivalent to minimization of the total time spent on activities other than job processing, that is, non-productive time. For the problems to be addressed, this non-productive time is due to setup times between jobs; and for dynamic problems, inserted idle time as well.
The model of maintenance planning and production scheduling for maximising robustness
Published in International Journal of Production Research, 2019
The work that is closest in spirit to the work presented in this paper is that in Cui, Lu, and Pan (2014). They also use the QR and SR for optimisation. The SR criterion allows one to measure the sensitivity of activity start times to variations in input data. The QR criterion allows one to measure the sensitivity of schedule efficiency (maximum tardiness from due dates) with respect to disruptions. The disadvantage of the model is that the authors optimise the performance criteria only for a single-machine scheduling problem with maintenance. Liping et al. (2013) used the weighted function of two objectives, makespan and schedule stability to improve the performance of a production system. The advantage of the application of makespan is that the smaller the makespan is, the higher the machine utilisation is. The disadvantage is the assumption of the same reliability characteristics, MTBF and MTTR for all machines on the shop floor. One machine fails in each simulation, but there is no explanation if the key machine is randomly selected or if it is the bottleneck.
Minimisation of non-machining times in operating automatic tool changers of machine tools under dynamic operating conditions
Published in International Journal of Production Research, 2018
Adil Baykasoğlu, Fehmi Burcin Ozsoydan
In a single machine environment, makespan can simply be calculated by the sum of machining (cutting operations) times and non-machining (ATC indexing & tool switching) times. It’s clear that machining times here can be considered as constants in the objective function. Non-machining times, on the other hand, have explicit effects on the makespan because they vary due to an applied plan such as part sequence assignment or allocation policies of the used cutting tools. Furthermore, ATC indexing and ToSP should be handled simultaneously if the aim is to minimise the total non-machining time.
A generalised makespan estimation for shop scheduling problems, using visual data and a convolutional neural network
Published in International Journal of Computer Integrated Manufacturing, 2019
Arent W. De Jong, Jose I. U. Rubrico, Masaru Adachi, Takayuki Nakamura, Jun Ota
Makespan is one of the most important and commonly used performance indicators for Shop Scheduling problems (SSp’s) (De Jong et al. 2017). It is defined by the total time a plant needs to finish processing all jobs in a batch. This is directly related to the schedule/timetable used for processing those jobs in the plant. Shorter makespan translates to reduced costs and higher productivity and output. For these reasons, it is subject to important research (Rossi, Nagano, and Neto 2016; Marichelvam and Tosun 2016).