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Üretim planlaması, üretim kaynaklarının ekonomik kullanımı ile verimliliği artırır. Tarımsal ürünlerin üretim miktarları ekim alanlarına göre belirlenmekte ve tarımsal planlama ile üretim verimliliği öngörülebilmektedir. Buradaki problem ekonomik olarak karlı ve ülke ihtiyacını en yüksek düzeyde karşılayan yıllık tarımsal üretim planını rasyonel olarak yapmaktır. Bu çalışmada, Türkiye’de bakliyat üretimi üzerine bir tarımsal planlama yapmak amacıyla doğrusal programlama modeli geliştirilmiştir. Model ile Kuru fasulye, Nohut, Mercimek ürünlerinin 2019 yılına ait üretim ve maliyet verileri kullanılarak ulusal ölçekte verimlilik ve iktisadilik analizleri gerçekleştirilmiştir. Analizler neticesinde, bakliyat üretimi için ayrılan tarım alanlarının %6.14 artırılarak ile 1.24 oranında bir iktisadilik ile ülke ihtiyaçlarının %100 yeterlilik düzeyinde karşılanacağı sonucuna varılmıştır. Önümüzdeki yıllarda gerçekleştirilecek tarımsal faaliyetlerin ve bu faaliyetleri düzenleyen ulusal politikaların planlanmasında ortaya koyulan bu karar destek modelinden istifade edilebilecektir.

In this study, using natural gas instead of coke gas in the reheating furnace in a steelmaking company is investigated in terms of economic, social, and environmental impacts. A sample projection is prepared, and economic analyses are conducted in line with the production plan target for future planning periods of 144 months. The natural gas usage increases the production quantity by 914 tonnes and allows the company to produce 5,979,334 kWh of additional electric power from the metallurgical gases monthly. In the economic analysis, we use engineering economics techniques to examine the economic impacts of the modernization investment for reheating furnaces. Accordingly, a positive return for each month shows the feasibility of the renovation project. The self-paying time of the investment is calculated as a short period of 11 months. Besides, the social and environmental impacts are notable; the renovation project decreases occupational health and safety risks by using natural gas as a substitute fuel, preventing a potential explosion or poisoning risk in the production, storage, and distribution. The renovation project decreases the global warming potential of blast furnace gas constituents and carbon emissions by 0.84% per month.

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.

This paper addresses a green capacitated vehicle routing problem that accounts for transportation emissions. A Dynamic Programming approach has been used to formulate the problem. Although small-sized problems can be solved by Dynamic Programming, this approach is infeasible for larger problems due to the curse of dimensionality. Therefore, we propose a Dynamic Programming based solution approach that involves the ideas of restriction, simulation and online control of parameters to solve large-sized problems. The added values of the proposed decision support tool have been shown on a small-sized base case and relatively larger problems. Performance comparisons of the proposed heuristic against other existing Dynamic Programming based solution approaches reveal its effectiveness, as in most of the instance-setting pairs, the proposed heuristic outperforms the existing ones. Accordingly, the proposed heuristic can be used as an alternative decision support tool to tackle real routing problems confronted in sustainable logistics management.

The Iron and Steel Industry is a process industry and subject to continuous production. In the iron and steel sector, many companies producing solid steel form using raw iron ore work for full-day shifts. Production process is costly and laborious. Liquid iron ore must be continuously transported between the casting operations. Transportation problems cause high financial losses. In this study, we aim to increase the efficiency of torpedoes transferring the liquid steel between the casting operations in an iron and steel company. We introduce an event-based simulation model and propose not using the torpedo causing bottleneck in production. The torpedo used in the production is decided by Topsis method which is a multi-criteria decision-making approach. Comparing to existing system and the proposed approach using the simulation model, it has been observed that production quantity increases by %9,06 (13.215.33 tons of liquid iron on average per month) while the waste of time decreases by %66,35.

Most companies have alogistics network and come with a carbon footprint. It’s a requirement in termsof sustainability of the transport tools are routing allaround in this networkand the providing of a low carbon footprint. In this study, the routing problemof personnel services of a company has been mentioned. And the total carbondioxicle emission is tryed to reduce. Application of the austerity algorithm,one of the common solution methods, has been perfomed by calculating carbonfootprint with Tier-1 approaching. At the of the application, there is a %24,7,reduction in carbon dioxide emission, %24,5 reduction in the cost in thiscompany with the recommended route., Çoğu firmada lojistikağı bulunmakta ve beraberinde bir karbon ayak izi meydana gelmektedir. Taşımaaraçlarının bu ağ çerçevesinde uygun bir şekilde rotalanması ve düşük bir karbonayak izinin sağlanması sürdürülebilirlik açısından bir gerekliliktir. Buçalışmada, bir firmanın personel servislerinin rotalama problemi ele alınmıştırve toplam CO2 salınımı azaltılmaya çalışılmıştır. Yaygın çözümyöntemlerinden biri olan tasarruf algoritmasının uygulaması, karbon ayak izininTier-1 yaklaşımı ile hesaplanması vasıtasıyla gerçekleştirilmiştir. Uygulamasonucunda önerilen rota ile belirlenen firmada CO2 salınımı % 24,7 vemaliyet unsurunda ise % 24,5 oranında azalma meydana gelmiştir.

In this study, we address a production scheduling problem. The scheduling problem is encountered in a job-shop production type. The production system is discrete and dynamic system in which jobs arrive continually. We introduce a simulation model (SM) to identify several situations such as machine failures, changing due dates in which scheduling rules (SRs) should be selected independently. Three SRs, i.e. the earliest due date rule (EDD), the shortest processing time first rule (SPT) and the first in first out rule (FIFO), are incorporated in a SM. A simulated annealing heuristic (SA) based simulation-optimisation approach is proposed to identify the unknown schedules in the dynamical system. In the numerical analysis, the performance of SRs and SA are compared using the simulation experiments. The objective functions minimising the mean flowtime and the mean tardiness are examined with varying levels of shop utilization and due date tightness. As an overall result, we observe that the proposed SA heuristic outperforms EDD and FIFO, the well-known SPT rule provides the best results. However, SA heuristic achieves very close results to the SPT and offers a reasonable computational burden in time-critical applications.

In this study, we design a catering supply chain involving production and service management. The production management involves food production lot-sizing and delivery scheduling decisions, while the service management is associated with balancing the food service lines. If production is in excess of demand, the food waste occurs along the catering chain. If the quantity produced is less than demand, this leads to a shortage. To address this problem, we develop a stochastic programming model accounting for the key performance indicators of total waste, total shortage and total cost of production and distribution. The selected indicators enable to assess the sustainability performance of the catering supply chain. The numerical study reflects real settings from catering operations of a university cafeteria in Turkey. For a sustainable development of catering supply chains, the analyses reveal the potential benefits of outsourcing (e.g., decreasing total costs by 36% and waste costs by 23%), employing qualified staff and increasing capacity through process improvement.

This chapter addresses vehicle routing problems with simultaneous pickups and deliveries, and time-dependent vehicle speeds, particularly confronted in the soft-drink distribution in urban areas. The conducted review provides a state-of-the-art assessment of the literature on soft-drink supply chain to reveal the recent developments. Furthermore, a real-life case is examined to address a common reverse logistics problem of collecting/reusing the reusable empty soda bottles encountered in the soft-drink industry. The problem has been formulated and solved by means of an adaptation of a recent Approximate Dynamic Programming based optimisation algorithm. The study highlights a great potential of sustainable supply chain management practices in the field. The study could be useful not only for researchers studying the topic, but also for practitioners in the soft-drink industry.

Purpose: In this study, the aim is to discuss the relationship between the supplier selection process and sustainability issues with developing technology and transportation systems. Methodology: In the literature review, based on the years 2010-2020, the studies published in the journals indexed in TR-Dizin and SCI-Expanded are examined using supplier selection, recycling and sustainability keywords by DergiPark and the Web of Science platforms.Findings: As a result, it is observed that the common supplier selection criteria of recycling companies are quality, price, cost, and deadline. The criteria accounting for the environmental concerns that are so-called green criteria have less attraction in the literature. Originality: This study contributes to the multi-criteria decision making literature due to the insufficient number of studies in which green and sustainable criteria are taken into account in the supplier selection process, and since companies need environmental and social policies., Amaç: Bu çalışma, gelişen teknoloji ve ulaşım sistemleri ile firmalar için önemli bir kriter haline gelen tedarikçi seçim sürecinin, sürdürülebilirlik kriterleri ile olan ilişkisini ortaya koymayı amaçlamıştır. Yöntem: Literatür taraması, 2010-2020 yılları baz alınarak DergiPark ortamında TR-Dizin’de taranan dergilerde ve Web of Science ortamında SCI-Expanded indeksli dergilerde yayımlanan tedarikçi seçimi, geri dönüşüm ve sürdürülebilirlik anahtar kelimelerini konu alan makaleler ele alınarak yapılmıştır. Bulgular: İnceleme sonucunda, geri dönüşüm malzemeleri temin etmek üzerine yapılacak bir tedarikçi seçimi için sıklıkla tercih edilen kriterlerin, kalite, fiyat, maliyet, ve termin süresi olduğu görülmüştür. Bunun yanında çevresel kaygıları dikkate alan ve yeşil kriterler olarak adlandırabileceğimiz kriterlerin literatürde daha az yer bulduğu gözlenmiştir. Özgünlük: Bu çalışma, tedarikçi seçim sürecinde yeşil ve sürdürülebilir kriterlerin dikkate alındığı çalışma sayısının yeterli olmaması, şirketlerin çevreci ve sosyal politikalara ihtiyaç duymaları sebebiyle, çok kriterli karar verme literatürüne katkı sağlamaktadır.

In a closed-loop supply chain, where a Vendor Managed Inventory system is executed, the Closed-Loop Inventory Routing Problem is one of the main problems confronted by logistics decision-makers. This study addresses a Closed-Loop Inventory Routing Problem under a mixed fleet of electric and conventional vehicles. The problem is formulated as a Mixed-Integer Linear Programming model and a Fix&Optimize algorithm is developed to tackle larger problem instances. The proposed decision support models incorporate comprehensive estimation approaches for energy consumption of both electric and conventional vehicles that allow to better estimate fuel and electric cost and transportation emissions. The models respect uncertain reverse returnable transport items flow from customers as well. The numerical analyses demonstrate the benefits that could be obtained by means of the provided models. The Fix&Optimize heuristic yields in 5.72% lower costs within 59.23% shorter computation times on average compared to the Mixed-Integer Linear Programming model. The proposed models are capable to provide trade-off analyses for sustainable logistics management.

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.

This study addresses the production and distribution planning problem in the soft drink industry. The problem involves the allocation of production volumes among the different production lines in the manufacturing plants, and the delivery of products to the distribution centers (DCs). A mixed integer linear programming (MILP) model is developed for the problem. In this paper, we present a hybrid solution methodology combining simulation and mixed integer programming (MIP) based Fixed and Optimize heuristic to solve the considered problem. First, MIP based Fix and Relax (F&R), Fix and Optimize (F&O) heuristics are proposed. The solution quality and performance of the proposed heuristics are analyzed with the randomly generated demand figures for the three granularity categories and various capacity load scenarios. Computational performances of these heuristic procedures are compared with the standard MIP results. The computational experiments carried out on a large set of instances have shown that the F&O heuristic algorithm provides good quality solutions in a reasonable amount of time. Second, simulation model is introduced to represent the problem with stochastic machine failures. Hybrid methodology combining the MIP based F&O heuristic and simulation model is implemented. The optimization model uses an F&O heuristic to determine the production and delivered quantity. Subsequently the simulation model is applied to capture the uncertainty in the production rate. Numerical studies from the data which have a tight production capacity and high demand granularity demonstrate that the developed hybrid approach is capable of solving real sized instance within a reasonable amount of time and demonstrate the applicability of the proposed approach.

PINARBASI, MEHMET/0000-0003-3424-2967; ALAKAS, Haci Mehmet/0000-0002-9874-7588; Sel, Cagri/0000-0002-8657-2303 WOS: 000325261400024 In this paper, flexible manufacturing systems (FMS) are studied. Firstly, an FMS design approach is proposed using integrated definition for function methodology. A systematic layout design and performance evaluation scheme is presented and detailed using this modeling framework. Then, the proposed approach is carried out with a case study from an aircraft industry to convert an existing traditional production system to FMS. To improve the system performance, a simulation-based method with Taguchi approach consisting of multiproducts is utilized. The objective is to find the machine and the product mix that achieves the maximum utilization while minimizing the cycle time. FMS system performance has been greatly improved by determining the most advantageous level of system components. It has also shown that FMS is a practicable production system in aircraft industry.

The topic of supply chain management in dairy industry has received a great deal of attention in recent years due to its structural characteristics, such as long sequence dependent setup times, large changeover costs, numerous flavoured and coloured product types with the complicated changeover rules, limited shelf life restricting the storage duration and delivery conditions for each perishable raw material, intermediate and final product. This paper is intended to provide a critical review on quantitative supply chain models within the dairy industry. A number of problem variants are investigated in terms of: 1) solution approaches; 2) problem and model characteristics; 3) decision levels. Through the analysis of the review, a framework is developed for the existing literature to reveal major trends, explore research opportunities, and give several directions for future research.