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About this book

This book presents the latest developments in optimization and optimal control models; exact, approximate and hybrid methods; and their applications in lean and green supply chains. It examines supply chain network design and modeling, closed loop supply chains, and lean, green, resilient and agile or responsive networks, and also discusses corporate social responsibility and occupational health and safety. It particularly focuses on supply chain management under uncertainty – employing stochastic or nonlinear modeling, simulation based studies and optimization – multi-criteria decision-making and applications of fuzzy set theory, and covers various aspects of supply chain management such as risk management, supplier selection or the design of automated warehouses. Lastly, using experimental applications and practical case studies, it shows the impact of lean and green applications on vehicle/fleet management and operations management.

Table of Contents

Frontmatter

Lean and Green Supply Chain Management: A Comprehensive Review

Companies and enterprises are becoming more and more aware of reducing their environmental impact of supply chains due to the pressures of various stakeholders; i.e. government and law enforcement bodies, customers and employees. Recently, the deployment of lean and green practices in supply chains, either sequential or consolidated, has attracted attention among academicians and practitioners. This attention is evident by the growing number of publications in scientific journals on lean and green supply chain management (LGSCM), which have been published in recent years. However the number of review papers in the literature is insufficient to present existing literature on LGSCM and provide future directions for researchers and practitioners. Hence, the aim of this paper is to review recently published papers on LGSCM in scientific journals. For this review, a total of 41 papers published between 2000 and 2017 were selected and reviewed. The papers are analyzed and categorized to present the current literature and highlight the future directions for academicians and practitioners.
Batuhan Eren Engin, Maren Martens, Turan Paksoy

A New Model for Lean and Green Closed-Loop Supply Chain Optimization

The dynamics of Supply Chain Management (SCM) have changed over the years; new paradigms are added into the SCM as a response to changes related with increasing environmental concerns and pressures. Therefore, lean and green practices in a company belong to its most important factors to enhance the company’s performance. In this study a new model, inspired by an automotive supply chain, is proposed for lean and green closed-loop supply chain management. In this model, we deal with lean and green drivers to set the objectives of the decision makers (DMs) as follows; (1) Construction: The amount of emitted CO 2 depends on the size of the potential facilities; (2) Production: Higher equipment or techniques in a production system means a higher environmental investment and leads to lower CO 2 emissions; (3) Handling: The usage of a proper forklift is an important decision to increase the productivity and reduce CO 2 emissions; (4) Transportation: Three different options in the transportation process which DMs can choose are considered: small-sized, medium-sized, and heavy-sized trucks. All of the truck types differ in transportation cost and CO 2 emissions with respect to the engines. The benefits of the heavy-sized trucks are obvious: less transportation cost with bigger lot size deliveries but more environmental pollution as well; (v) On Time Deliveries: Lean manufacturing needs suppliers to comply with delivery times, which directly affect the buyer’s manufacturing lead times, operational performances and competitiveness. Thus, late deliveries of suppliers aimed to be minimized. Under these circumstances, there are few trade-offs which need to be optimized simultaneously. The developed model consists of six different objectives: minimization of transportation cost, purchasing and operational cost, fixed facility cost, environmental cost, handling cost and late deliveries. In order to validate the proposed model, a numerical example is implemented and analyzed by using fuzzy weighted additive method where the weights are determined via Fuzzy Analytic Hierarchy Process (Fuzzy AHP) method.
Turan Paksoy, Ahmet Çalik, Alexander Kumpf, Gerhard Wilhelm Weber

Risk Management in Lean & Green Supply Chain: A Novel Fuzzy Linguistic Risk Assessment Approach

In today’s world, the pressure of competition considerably changed because of the increasing pressure of market competition, changes in customer expectations due to global warming and the globalization of the economy. Growing pressures in supply chain activities urge to companies to adopt new approaches by reducing waste and planning environmental friendly processes, such as recycling, reuse, and remanufacturing. There have been several studies proposed that lean activities can help make the case for environmental impact reduction to companies. Thus, most companies have reorganized their supply chain by the integrate the green activities through the balance of lean and green models simultaneously. According to this idea, they started to transfer some of their business process activities to external companies to be leaner & greener. Outsourcing can lead to cost reductions, meeting customer demand, reducing waste, gaining more flexibility and sharing of risks. Although outsourcing in the lean & green supply chain management brings benefits, companies can confront various risks. From this viewpoint, we propose a new fuzzy linguistic risk assessment approach to assess suppliers’ risks according to several criteria such as experience level of suppliers, criticality level of parts supplied, manufacturing technical requirements and complexity of parts supplied, effect of the deviations on the final product function, flexibility of suppliers, green activity level of suppliers, occupational health and safety risk, environmental risk and cooperation level of suppliers. For this purpose, an integrated solution approach that consists of four stages is applied. At the first stage, the relative weights of the criteria are calculated by asking the decision makers with the help of the pair-wise comparison matrix. At the second stage, suppliers’ scores are evaluated according to the selected criteria using linguistic variables. At the third stage, risk levels of suppliers are calculated. At the fourth stage, suppliers are assigned to risk groups according to their risk level action plans are determined. Finally, a numerical example is presented to demonstrate the effectiveness of the proposed approach.
Turan Paksoy, Ahmet Çalik, Abdullah Yildizbaşi, Sandra Huber

A New Multi Objective Linear Programming Model for Lean and Green Supplier Selection with Fuzzy TOPSIS

Supplier selection is a critical task for the success of an organization. The goal in supplier selection is to select the “right” suppliers based on some criteria such as cost, quality and environmental performance. Lean thinking has been increasingly adopted among the suppliers to stay ahead of the competition by achieving faster delivery, higher quality and lower cost. On the other hand, governmental legislations put pressure on the companies to lower their environmental impact. Therefore, environmental impact of a supplier is as important as other criteria; i.e. cost, quality and faster delivery when evaluating the suppliers. Therefore, companies also adopt environmental management practices in supply chain management (referred to as green supply chain) to lower their environmental impact. In this context, lean and green supplier selection has attracted ever-growing attention recently, which is a multi-criteria decision-making problem that requires consideration of many qualitative and quantitative factors. Hence, this study presents an integrated approach for the selection of the appropriate suppliers in a supply chain, aiming to maximize lean and green value in a supply chain network, using fuzzy AHP-TOPSIS and fuzzy multi-objective linear programming. The applicability of the developed approach is demonstrated using a case study in Turkey.
Belkız Torğul, Turan Paksoy

The Impact of Routing on CO2 Emissions at a Retail Grocery Store Chain: A GIS-Based Solution Approach

With a world-wide increasing concern for the environment, logistics service providers and freight carriers should be in need of paying more attention to the CO2 emissions of their operations. This paper investigates the combined impact of multi-trip, fleet composition and vehicle routing decisions on CO2 emissions and fuel consumptions at one of the major retail grocery store chain. The objective is to minimize the total routing cost which can be defined with respect to the cost of fuel consumption and CO2 emissions. The CO2 emissions are proportional to the amount of fuel consumed by the fleet which depends on several parameters. A GIS-based solution approach, uses a tabu search heuristic, applied to a real dataset of the supermarket store chain operates in Turkey. The paper sheds light on the trade-offs between various performance indicators such as number of vehicles and shop demand. Several managerial and policy insights are also presented.
Çağrı Koç, Mehmet Erbaş, Eren Özceylan

A Simulated Annealing Algorithm Based Solution Method for a Green Vehicle Routing Problem with Fuel Consumption

This chapter presents a new G-VRP model that aims to reduce the fuel consumption of the vehicle’s gas tank. The fuel consumption of a vehicle is related to total vehicle weight through route and thus, this changes the CO2 levels as a result of the changes of total weight and distance for any arc {i, j} in the route. To minimize CO2 levels, a simulated annealing-based algorithm is proposed. About the experiments, firstly, we applied small-VRP problem set for defining the proposed algorithm and then, the Christofides et al. (Combinatorial optimization. Wiley, 1979) small/medium scale C1–C14 datasets are used with proposed G-VRP model and a convex composition solution with two objective functions. The proposed methods are compared with statistical analysis techniques to explain the statistical significance of solutions. The procedures are also tested using additional examples previously analyzed in the literature. The result has shown good solutions for minimizing the emitted CO2 levels.
Kenan Karagul, Yusuf Sahin, Erdal Aydemir, Aykut Oral

Development of a Web-Based Decision Support System for Strategic and Tactical Sustainable Fleet Management Problems in Intermodal Transportation Networks

This paper presents a web-based decision support system (DSS) which uses a fuzzy-stochastic mathematical programming based model for strategic and tactical intermodal fleet management. Indeed, several sub-problems such as load planning, fleet sizing and composition, fleet allocation, vehicle inventory control, fleet expansion/reduction and empty vehicle repositioning decisions are incorporated into the proposed DSS. Therefore, it has a modular structure to support these interactive decisions in an integrated manner. In the model component of the proposed DSS, in addition to optimize overall transportation costs, users are able to provide environmentally conscious and customer-oriented freight and fleet plans by minimizing total transit times and CO2 emissions. In the data component, an object-relational database management system namely Oracle was utilized. The LINGO 15.0 optimization code of the proposed model is run over C# and object-oriented matching was utilized for connection among Oracle database and C# programs. The web-based user interface is designed by using .Net and C# programs on Microsoft Visual Studio. The proposed system is tested on a real-life application in an international logistics company of Turkey. By making use of such a DSS, effective and efficient fleet and freight plans can be generated under different types of uncertainties and risk-levels.
Adil Baykasoğlu, Kemal Subulan, A. Serdar Taşan, Nurhan Dudaklı, Murat Turan, Erdin Çelik, Özgür Ülker

Integrated Production Scheduling and Distribution Planning with Time Windows

Ensuring timely product deliveries in supply chains depends on the decisions made at various stages of the supply chain, including the production stage where commodities are made available, and the distribution stage where shipments are made to customers within requested time windows. Delivery times depend on the distribution plans, which are inherently linked to the production schedules, as a commodity must first be produced or procured before being sent onwards in the supply chain. One way to ensure that the delivery times are respected is to perform direct shipments, but this is often costly. In contrast, products can be consolidated whereby several customers are visited on a given vehicle route, but this may result in either early or late deliveries. The challenge is then to devise lean production and distribution schedules that eliminate any redundancy in delivery times. In this chapter, we present an integrated production and outbound distribution scheduling problem with time windows arising in a supply chain. The problem involves jointly deciding on production and distribution operations where a manufacturer is committed first to processing a given set of orders and then to distributing them to the respective customers in different locations. The orders first undergo single processing through a set of identical parallel machines. Once completed, they are delivered by a fleet of vehicles in such a way so as to meet the customer time windows. The objective is to improve the timeliness of the deliveries, which is achieved by minimizing the earliness or tardiness of each order in reaching the customer. The chapter formally introduces the problem, describes integer linear programming formulations for two variants of the problem, and presents computational results on solving randomly generated instances with the proposed formulations.
Saadettin Erhan Kesen, Tolga Bektaş

Achieving Shojinka by Integrated Balancing of Multiple Straight Lines with Resource Dependent Task Times

The concept of Shojinka, which is an important element of Just in Time production system, is attaining flexibility on the number of workers in a workshop due to demand variations.
Even though Just in Time production system adopts U-shaped line layout, traditional straight lines can be combined and balanced in an integrated manner to obtain the advantages of Shojinka. In this case, the problem, which is called Integrated Balancing of Multiple Straight Lines, arises.
On the other hand, the basic assumption of assembly line balancing is that every task’s time is fixed. However, in practice, different resource alternatives such as equipment or assistant worker may be available to process a task with different times. The problem in this case is to assign tasks and resources to stations that minimise total cost.
In this study, a binary integer mathematical model is presented for integrated balancing of multiple straight lines, assuming that task times are resource dependent. The proposed model is illustrated and validated using several examples. An experimental analysis is also conducted to emphasize the advantages of the integrated balancing concept, compared to the situation in which the lines are balanced independent from each other. The experimental results show that, when multiple straight assembly lines are balanced in an integrated manner with resource dependent task times, an average total cost improvement of 23.105% can be obtained compared to the independent balances of the lines. This is a significant improvement that emphasizes the importance of balancing multiple straight lines in an integrated manner, to take the advantages of shojinka and to adhere to the main philosophy of Just in Time.
Yakup Atasagun, Yakup Kara, Gözde Can Atasagun
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