Bi-objective supply chain problem using MOPSO and NSGA-II

The increase competition and decline economy has increased the relevant importance of having reliable supply chain. The primary objective of many supply chain problems is to reduce the cost of services and, at the same time, to increase the quality of services. In this paper, we present a multi-level supply chain network by considering multi products, single resource and deterministic cost and demand. The proposed model of this paper is formulated as a mixed integer programming and we present two metaheuristics namely MOPSO and NSGA-II to solve the resulted problems. The performance of the proposed models of this paper has been examined using some randomly generated numbers and the results are discussed. The preliminary results indicate that while MOPSO is able to generate more Pareto solutions in relatively less amount of time, NSGA-II is capable of providing better quality results

Resolving forward-reverse logistics multi-period model using evolutionary algorithms

© 2016 Elsevier Ltd In the changing competitive landscape and with growing environmental awareness, reverse logistics issues have become prominent in manufacturing organizations. As a result there is an increasing focus on green aspects of the supply chain to reduce environmental impacts and ensure environmental efficiency. This is largely driven by changes made in government rules and regulations with which organizations must comply in order to successfully operate in different regions of the world. Therefore, manufacturing organizations are striving hard to implement environmentally efficient supply chains while simultaneously maximizing their profit to compete in the market. To address the issue, this research studies a forward-reverse logistics model. This paper puts forward a model of a multi-period, multi-echelon, vehicle routing, forward-reverse logistics system. The network considered in the model assumes a fixed number of suppliers, facilities, distributors, customer zones, disassembly locations, re-distributors and second customer zones. The demand levels at customer zones are assumed to be deterministic. The objective of the paper is to maximize the total expected profit and also to obtain an efficient route for the vehicle corresponding to an optimal/near optimal solution. The proposed model is resolved using Artificial Immune System (AIS) and Particle Swarm Optimization (PSO) algorithms. The findings show that for the considered model, AIS works better than the PSO. This information is important for a manufacturing organization engaged in reverse logistics programs and in running units efficiently. This paper also contributes to the limited literature on reverse logistics that considers costs and profit as well as vehicle route management

Facility location decisions within integrated forward/reverse logistics under uncertainty

In this paper, a stochastic mixed integer linear programming (SMILP) model is proposed to optimize the location and size of facilities and service centres in integrated forward and reverse streams under uncertainty. The objective of the model is to minimize establishment, transportation and inventory management costs and simultaneously maximize customer satisfaction with sustainable perspective. The model incorporates different elements and features of distribution networks including inventory management, transportation and establishment of new facilities as well as existing centres. The presented model is the streamlined approach for multi-objective, multi-period, multi-commodity distribution system, and it is supported by a real case study in automobile after sales network. Genetic algorithm is implemented to solve the model in reasonable time. The performance of the model and the effects of uncertainty on provided solution are studied under different cases. Competitive result of the stochastic model compared to deterministic model ensures that the proposed approach is valid to be applied for decision making under uncertainty.Scopu

A Conceptual Framework for Reverse Logistics Performance and Innovation

Green Supply Chain Management has been receiving a lot of attention in recent times. Attention on Reverse Logistics (RL) area has also been steadily growing. Reverse Logistics involves reduce-reuse-recycle as well as redesign, remanufacture, etc. It is a closed loop of the Supply Chain for value creation. Carter and Ellram (1998) discuss that Reverse Logistics can result in savings for an organization. Daugherty, Richey, Genchev, and Chen (2005) analyze where the resources in Reverse Logistics could be committed to achieve good Reverse Logistics Performance. The Automotive after-market in India is also in its nascent stages and has a huge scope for growth. Thus, a study in this area is definitely warranted. This paper gives a Conceptual framework for the RL Performance and Innovation in Automotive after-market with an emerging economy backdrop. The authors of this paper find fewer papers in these areas and this paper aims to fill this gap

Modeling of Promising Interaction Between a Timber Industry Enterprise and a Commodity Exchange in Russia

The relevance of the study lies in the absence of works in the literature devoted to the formation of supply chains of materials in volumes sufficient for production using the apparatus of commodity exchanges. The aim of the work is to conduct an empirical study to assess the prospects for the interaction of a timber industry enterprise with a commodity exchange. For the study, a mathematical model was chosen to assess the effectiveness of the purchase of raw materials from the forestry department of the commodity exchange by an enterprise in the timber industry. The hypothesis is that the interaction of the timber industry complex can be beneficial for the enterprise. To ensure the feasibility of purchasing raw materials from the exchange, simulation modeling was chosen. For each individual simulation iteration, a linear integer programming mathematical model was used. To generate some input data, like price, demand, etc., the Monte Carlo method was used. The complexity of the problem lies in the following aspects: polynomial growth of the number of numbers; a large number of restrictions on the increase in the degree of complexity of finding the first feasible solution to the model; search for a solution within the framework of integer optimization; a fairly large number of independent simulation iterations. The practical significance of the study is to prove the expediency of purchasing raw materials by the enterprise from the commodity and raw materials exchange of Russia. The theoretical significance of the study lies in the development of a model for assessing the feasibility of purchasing materials using the exchange apparatus. The scientific novelty is based on the constructed mathematical model of the formation of supply chains and the volume of production, taking into account the demand in the market and the volume of materials. The model was tested on data from one forestry enterprise in the Primorsky Territory. Optimization is carried out in terms of the volume of products produced, the volume of purchased materials from each region and the stock of raw materials in the production warehouse. Based on the testing of data models of the exchange and the forestry enterprise, an analysis was performed of the possibilities for cooperation between the company and the commodity exchange. The work reflects the behavior in the long term of accumulated profit, the nature of changes in stock in the warehouse and the volume of products produced.Актуальность исследования заключается в отсутствии работ в литературе, посвященных формированию цепочек поставок сырья в достаточных для производства объемах с применением аппарата товарно-сырьевых бирж. Целью работы является проведение эмпирического исследования по оценке целесообразности взаимодействия лесопромышленного предприятия с товарно-сырьевой биржей. Для проведения исследования была разработана математическая модель по оценке целесообразности закупа сырья с лесного отдела товарно-сырьевой биржи предприятием лесопромышленной отрасли. Гипотеза заключается в том, что взаимодействие лесопромышленного комплекса с биржей может быть выгодным для предприятия. Для проведения анализа целесообразности проведения закупок сырья с биржи было выбрано имитационное моделирование. Для каждой отдельной имитационной итерации использовалась математическая модель линейного целочисленного программирования. Для генерации некоторых входных данных таких, как цена, спрос и др. использовался метод Монте-Карло. Сложность задачи заключается в следующих аспектах: полиномиальный рост количества переменных; большое количество ограничений увеличивает степень сложности поиска первого допустимого решения модели; поиск решения в рамках целочисленной оптимизации; достаточно большое количество независимых имитационных итераций. Практическая значимость исследования заключается в доказательстве целесообразности закупа предприятием сырья с товарно-сырьевой биржи России. Теоретическая значимость исследования заключается в разработке новой модели по оценке целесообразности проведения закупок сырья с применением аппарата биржи. Научная новизна заключается в построенной математической модели формирования цепочек поставок и объемов производства с учетом спроса на рынке и доступных объемов сырья. Апробация модели проведена на данных одного из предприятий лесной отрасли Приморского края. Оптимизация ведется по объему производимой продукции, объему закупаемого сырья из каждого региона и по запасам сырья на складе производства. На основе апробации модели на данных биржи и предприятия лесной отрасли был проведен анализ целесообразности сотрудничества компании и товарно-сырьевой биржи. В работе отражены поведение в долгосрочной перспективе накопленной прибыли, характер изменения запасов сырья на складе и объем производимой продукции.Актуальность исследования заключается в отсутствии работ в литературе, посвященных формированию цепочек поставок сырья в достаточных для производства объемах с применением аппарата товарно-сырьевых бирж. Целью работы является проведение эмпирического исследования по оценке целесообразности взаимодействия лесопромышленного предприятия с товарно-сырьевой биржей. Для проведения исследования была разработана математическая модель по оценке целесообразности закупа сырья с лесного отдела товарно-сырьевой биржи предприятием лесопромышленной отрасли. Гипотеза заключается в том, что взаимодействие лесопромышленного комплекса с биржей может быть выгодным для предприятия. Для проведения анализа целесообразности проведения закупок сырья с биржи было выбрано имитационное моделирование. Для каждой отдельной имитационной итерации использовалась математическая модель линейного целочисленного программирования. Для генерации некоторых входных данных таких, как цена, спрос и др. использовался метод Монте-Карло. Сложность задачи заключается в следующих аспектах: полиномиальный рост количества переменных; большое количество ограничений увеличивает степень сложности поиска первого допустимого решения модели; поиск решения в рамках целочисленной оптимизации; достаточно большое количество независимых имитационных итераций. Практическая значимость исследования заключается в доказательстве целесообразности закупа предприятием сырья с товарно-сырьевой биржи России. Теоретическая значимость исследования заключается в разработке новой модели по оценке целесообразности проведения закупок сырья с применением аппарата биржи. Научная новизна заключается в построенной математической модели формирования цепочек поставок и объемов производства с учетом спроса на рынке и доступных объемов сырья. Апробация модели проведена на данных одного из предприятий лесной отрасли Приморского края. Оптимизация ведется по объему производимой продукции, объему закупаемого сырья из каждого региона и по запасам сырья на складе производства. На основе апробации модели на данных биржи и предприятия лесной отрасли был проведен анализ целесообразности сотрудничества компании и товарно-сырьевой биржи. В работе отражены поведение в долгосрочной перспективе накопленной прибыли, характер изменения запасов сырья на складе и объем производимой продукции

Elektrikli ve elektronik atıklar için tersine lojistik ağ tasarımı: İstanbul örneği

Bu çalışmada, Çevre ve Şehircilik Bakanlığı AEEE Kontrolü Yönetmeliği’ne göre İstanbul ilinde toplanması gereken Atık Elektrikli ve Elektronik Eşyalar (AEEE) için çok amaçlı karma tamsayılı programlama modeli oluşturulmuştur. Model, sürdürülebilirlik kavramının üç temel hedefinden (ekonomik, çevresel ve sosyal) yola çıkarak, yönetmeliğin gerektirdikleri ve sorumlu paydaşların yükümlülükleri baz alınarak tasarlanmıştır. Fakat yönetmeliğe rağmen yasa dışı olarak AEEE toplayan ve ayrıştıran hurdacılar mevcuttur. Bu tip atıklar insan sağlığına oldukça zararlı olabildiğinden, gerekli ekipmanları olmayan hurdacılar için risk oluşturmaktadır. Buradan yola çıkarak modelde sosyal amaç, AEEE toplayan hurdacıların, yapılacak devlet yatırımı ile istihdam edilerek sağlıklarının garanti altına alınması ve sosyal statülerinin arttırılmasıdır. Modelin diğer amacı da tersine lojistik aktivitelerin çevresel etkilerinin ve maliyetinin enküçüklenmesidir. Önerilen model epsilon kısıt yöntemi ile çözülmüş ve açılacak elektronik atık işleme tesislerine ve yatırım yapılarak AEEE geri kazanım sistemine dahil edilecek hurda depolarına karar verilmiştir

A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return

A closed-loop supply chain (CLSC) network consists of both forward and reverse supply chains. In this paper, a CLSC network is investigated which includes multiple plants, collection centres, demand markets, and products. To this aim, a mixed-integer linear programming model is proposed that minimizes the total cost. Besides, two test problems are examined. The model is extended to consider environmental factors by weighed sums and ε-constraint methods. In addition, we investigate the impact of demand and return uncertainties on the network configuration by stochastic programming (scenario-based). Computational results show that the model can handle demand and return uncertainties, simultaneously

A Nondominated Sorting Genetic Algorithm for Sustainable Reverse Logistics Network Design

Abstract Determining the appropriate strategy in recycling method that will has less pollution besides the economical process, has been signified as one of the basic principles for achieving sustainability in the supply chain due to the new legislation, environmental concerns' growth, increase volume of returns, etc. in recent years. Here will be considered a design of reverse network development for recycling waste products besides the reduction of harmful emissions due to storage, transportation and various processes. The purpose of this study investigates a multi objective optimization problem of sustainable Reverse logistics network design with the aim of management in allocating customers by coordinating facilities to ensure minimum cost as well as the environmental impact. In this work is presented a dynamic linear programming model which examines the important aspects for the design and strategic planning of a sustainable chain. Due to the complexity and differences in the nature of the model objectives, is proposed a heuristic method based nondominated sorting genetic algorithm to solve the problem. Finally, the results are discussed and analyzed

A proposed mathematical model for closed-loop network configuration based on product life cycle

Products may be returned over their life cycle. Industrial experiences show that there are three main return–recovery pairs. Commercial returns are repaired. End-of-use returns often are remanufactured. In addition, end-of-life returns are recycled. However, up to now, no optimization model is proposed for closed-loop configuration based on three return–recovery pairs. The repaired and remanufactured products can be sold in the same or secondary market. In this paper, we design and configure a general closed-loop supply chain network based on product life cycle. The network includes a manufacturer, collection, repair, disassembly, recycling, and disposal sites. The returned products are collected in a collection site. Commercial returns go to a repair site. End-of-use and end-of-life returns are disassembled. Then, end-of-life returns are recycled. The manufacturer uses recycled and end-of-use parts and new parts to manufacture new products. The new parts are purchased from external suppliers. A mixed-integer linear programming model is proposed to configure the network. The objective is to maximize profit by determining quantity of parts and products in the network. We also extend the model for the condition that the remanufactured products are sent to the secondary market. The mathematical models are validated through computational testing and sensitivity analysis