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In a production environment, scheduling decides job and machine allocations and the operation sequence. In a job shop production system, the wide variety of jobs, complex routes, and real-life events becomes challenging for scheduling activities. New, unexpected events disrupt the production schedule and require dynamic scheduling updates to the production schedule on an event-based basis. To solve the dynamic scheduling problem, we propose a multi-agent system with reinforcement learning aimed at the minimization of tardiness and flow time to improve the dynamic scheduling techniques. The performance of the proposed multi-agent system is compared with the first-in–first-out, shortest processing time, and earliest due date dispatching rules in terms of the minimization of tardy jobs, mean tardiness, maximum tardiness, mean earliness, maximum earliness, mean flow time, maximum flow time, work in process, and makespan. Five scenarios are generated with different arrival intervals of the jobs to the job shop production system. The results of the experiments, performed for the 3 × 3, 5 × 5, and 10 × 10 problem sizes, show that our multi-agent system overperforms compared to the dispatching rules as the workload of the job shop increases. Under a heavy workload, the proposed multi-agent system gives the best results for five performance criteria, which are the proportion of tardy jobs, mean tardiness, maximum tardiness, mean flow time, and maximum flow time.

In the dairy processing, the rapid quality decay of milk-based intermediate mixture to make and pack restricts productivity and, forces organizations to carefully plan and schedule their production. Hereby, in this study, we consider a planning and scheduling problem encountered in the dairy industry and propose a chance-constrained programming model accounting for uncertainty in quality decay of intermediate mixture. The aim of the model is to find the optimal lot sizes and production schedule with minimum makespan (total time needed to finish the daily production). The proposed schedule allows the storage duration of intermediate mixture to be within a stochastic lifetime. A case study is presented to illustrate the typical structure of the two-stage semi-continuous make-and-pack production process. The numerical study reflects real settings from a set (Balkan type) yoghurt production process. Accordingly, a simulation of the production process is introduced to evaluate the proposed production plan and schedule in terms of product waste. The model is examined with 32 scenarios consisting of different distribution parameters, confidence levels and demand patterns. Overall in the scenarios, the proposed plan and schedule result in decreasing 26931 of product waste with 3.24 h increase of makespan in total. (C) 2017 Elsevier Inc. All rights reserved.

Buçalışmada, bir üretim çizelgeleme problem ele alınmaktadır. Bu çizelgelemeproblemine atölye tipi bir üretim tipinde karşılaşılmaktadır. Üretim sistemisürekli iş gelişlerinin söz konusu olduğu kesikli dinamik sistemdir.Çizelgeleme kurallarının birbirinden bağımsız şekilde kullanılmasının gerektiğimakine bozulmaları ve değişen teslim süreleri gibi bazı durumlarındeğerlendirilmesi için bir simülasyon modeli sunulmaktadır. En erken teslimsüresi, en kısa işlem süresi ve ilk giren ilk çıkar kuralı olmak üzere üççizelgeleme kuralı bu simülasyon modeline dahil edilmiştir. Dinamik sistemdekibelirsiz çizelgeleri ortaya koymak için tavlama benzetimi sezgiseli tabanlı birsimülasyon optimizasyonu yöntemi önerilmektedir. Sayısal analizlerdeçizelgeleme kurallarının ve önerilen tavlama benzetimi sezgiselininperformansları simülasyon deneyleri kullanılarak kıyaslanmıştır. Ortalama akış süresini ve ortalama gecikmesüresini en küçükleyen amaç fonksiyonları farklı seviyelerdeki atölye kullanımoranı ve teslim süresi durumlarında incelenmiştir. Genel bir sonuç olarak,önerilen tavlama benzetimi sezgiselinin en erken teslim zamanı ve ilk giren ilkçıkar kurallarından daha iyi sonuç verdiği, en kısa işlem süresi kuralının eniyi sonuçları sağladığı gözlenmiştir. Fakat tavlama benzetimi sezgiseli en kısaişlem süresi kuralına çok yakın sonuçlara erişmektedir ve çözüm zamanınınkritik olduğu uygulamalarda kabul edilebilir bir hesaplama yükü getirmektedir., Inthis study, we address a production scheduling problem. The scheduling problemis encountered in a job-shop production type. The production system is discrete and dynamic system in which jobs arrive continually. We introduce asimulation model (SM) to identify several situations such as machine failures,changing due dates in which scheduling rules (SRs) should be selectedindependently. Three SRs, i.e. the earliest due date rule (EDD), the shortestprocessing time first rule (SPT) and the first in first out rule (FIFO), areincorporated in a SM. A simulated annealing heuristic (SA) basedsimulation-optimisation approach is proposed to identify the unknown schedulesin the dynamical system. In the numerical analysis, the performance of SRsand SA are compared using the simulation experiments. The objective functionsminimising the mean flowtime and the mean tardiness are examined with varyinglevels of shop utilization and due date tightness. As an overall result, weobserve that the proposed SA heuristic outperforms EDD and FIFO, the well-knownSPT rule provides the best results. However, SA heuristic achieves very closeresults to the SPT and offers a reasonable computational burden intime-critical applications.