Eren Özceylan

A MULTI OBJECTIVE MODEL FOR OPTIMIZATION OF A GREEN SUPPLY CHAIN NETWORK

This study develops a model of a closed‐loop supply chain (CLSC) network which starts with the suppliers and recycles with the decomposition centers. As a traditional network design, we consider minimizing the all transportation costs and the raw material purchasing costs. To pay attention for the green impacts, different transportation choices are presented between echelons according to their CO2 emissions. The plants can purchase different raw materials in respect of their recyclable ratios. The focuses of this paper are conducting the minimizing total CO2 emissions. Also we try to encourage the customers to use recyclable materials as an environmental performance viewpoint besides minimizing total costs. A multi objective linear programming model is developed via presenting a numerical example. We close the paper with recommendations for future researches.

A mixed integer programming model for a closed-loop supply-chain network

In this paper, a new mixed integer mathematical model for a closed-loop supply-chain network that includes both forward and reverse flows with multi-periods and multi-parts is proposed. The proposed model guarantees the optimal values of transportation amounts of manufactured and disassembled products in a closed-loop supply chain while determining the location of plants and retailers. Finally, computational results are presented for a number of scenarios to show and validate the applicability of the model.

Swarm intelligence approaches to estimate electricity energy demand in Turkey

This paper proposes two new models based on artificial bee colony (ABC) and particle swarm optimization (PSO) techniques to estimate electricity energy demand in Turkey. ABC and PSO electricity energy estimation models (ABCEE and PSOEE) are developed by incorporating gross domestic product (GDP), population, import and export figures of Turkey as inputs. All models are proposed in two forms, linear and quadratic. Also different neighbor selection mechanisms are attempted for ABCEE model to increase convergence to minimum of the algorithm. In order to indicate the applicability and accuracy of the proposed models, a comparison is made with ant colony optimization (ACO) which is available for the same problem in the literature. According to obtained results, relative estimation errors of the proposed models are lower than ACO and quadratic form provides better-fit solutions than linear form due to fluctuations of the socio-economic indicators. Finally, Turkeys electricity energy demand is projected until 2025 according to three different scenarios.

Fuzzy multi-objective linear programming approach for optimising a closed-loop supply chain network

With the urgency of remanufacturing and environmental concerns, closed-loop supply chain (CLSC) networks have drawn the attention of researchers. Although there are many CLSC network models in the literature, most of them do not consider uncertainty in general terms. However, practical situations are often not well defined and thus cannot be described precisely in real world CLSCs. In this paper a mixed integer fuzzy mathematical model is proposed for a CLSC network which includes both forward and reverse flows with multiple periods and multiple parts. A fuzzy multi-objective model (FMOM) approach is applied to take into account the fuzziness in the capacity, objectives, demand constraints and also in the reverse rates. Computational results are presented for a number of scenarios to show and validate applicability and flexibility of the model. Results show that the proposed model presents a systematic framework which enables the logistics manager (LM) to adjust the search direction during the solution procedure to obtain a desired satisfactory solution.

Interactive fuzzy programming approaches to the strategic and tactical planning of a closed-loop supply chain under uncertainty

In this paper, a closed-loop supply chain (CLSC) network model consisting of various conflicting decisions of forward and reverse facilities is considered. The proposed model integrates the strategic and tactical decisions to avoid the sub-optimalities led from separated design in both chain networks. The strategic-level decisions relate to the amounts of goods flowing on the forward and reverse chains whereas the tactical-level decisions concern balancing disassembly lines, collection and refurbishing activities in the reverse chain. First, a fuzzy multi-objective mixed-integer non-linear programming model that considers the imprecise nature of critical parameters such as cost coefficients, capacity levels, market demands and reverse rates is proposed. Then, proposed fuzzy model is converted into an auxiliary crisp multi-objective mixed-integer non-linear programming (MOMINP) model by applying two different approaches. Finally, different fuzzy interactive programming approaches are applied to solve this MOMINP model to find a satisfactory solution for the network that is considered. The proposed model with the solution approaches is validated through a realistic numerical example. Computational results indicate that our proposed model and solution approaches can effectively be used in CLSC network problems.

Mixed model disassembly line balancing problem with fuzzy goals

The collection of used products is the driving force of remanufacturing systems and enterprises can gain significant economic, technical and social benefits from recycling. All products are disassembled up to some level in remanufacturing systems. The best way to disassemble returned products is valid by a well-balanced disassembly line. In this paper, a mixed integer programming (MIP) model is proposed for a mixed model disassembly line balancing (MMDLB) problem with multiple conflicting objectives: (1) minimising the cycle time, (2) minimising the number of disassembly workstations and (3) providing balanced workload per workstation. In most real world MMDLB problems, the targeted goals of decision makers are frequently imprecise or fuzzy because some information may be incomplete and/or unavailable over the planning horizon. This study is the first in the literature to offer the binary fuzzy goal programming (BFGP) and the fuzzy multi-objective programming (FMOP) approaches for the MMDLB problem in order to take into account the vague aspirations of decision makers. An illustrative example based on two industrial products is presented to demonstrate the validity of the proposed models and to compare the performances of the BFGP and the FMOP approaches.

A genetic algorithm approach for optimising a closed-loop supply chain network with crisp and fuzzy objectives

This paper proposes a mixed integer programming model for a closed-loop supply chain (CLSC) network with multi-periods and multi-parts under two main policies as secondary market pricing and incremental incentive policies. In the first policy, customers order and receive products from distribution centres, but at next period, they can trade among themselves with used products that are returned in a secondary market. Financial incentives are offered to the customers to influence the returns, and the correct amount of collections at different prices is determined by the second policy. In addition to the base case (crisp) formulation, a fuzzy multi-objective extension is applied to solve CLSC network problem with fuzzy objectives to represent vagueness in real-world problems. Then, developed genetic algorithm approach is applied to solve real size crisp and fuzzy CLSC problems. The effectiveness of the proposed meta-heuristic approach is investigated and illustrated by comparing its results with GAMS-CPLEX on a set of crisp/fuzzy problems with different sizes.

Fuzzy Multi-Objective Optimization of a Green Supply Chain Network with Risk Management that Includes Environmental Hazards

ABSTRACT Among the leading environmental risks, global climate alteration has become one of the most important controversial issues. Greenhouse gas emissions (CO2, methane, etc.) and air pollution have motivated a need to develop and improve environmental management strategies. As a consequence, environmental sanctions are forcing commercial enterprises to re-consider and re-design supply chain processes in a green way. This article provides a multi-objective model to design a closed-loop supply chain (CLSC) network in a green framework. Our first and second objectives are to minimize all the transportation costs for the supply chains forward and reverse logistics; the third objective is to minimize total CO2 emissions; the fourth objective is to encourage customers to use recyclable materials as an environmental practice. To provide more realistic modeling by treating the uncertainty in decision-makers’ objectives, fuzzy modeling is used in this study. The model is explained and tested via fulfilling a numerical example. In scenario analyses, analytic hierarchy process (AHP), fuzzy AHP (F-AHP), and fuzzy TOPSIS (F-TOPSIS) approaches were applied and compared to evaluate different objectives to guide decision-makers.

Reverse supply chain optimisation with disassembly line balancing

Due to responding environmental issues, conforming governmental legislations and providing economic benefits, there has been a growing interest in recycling activities through the supply chains. Reverse supply chain (RSC) optimisation problem has a great potential as an efficient tactic to achieve this goal. While disassembly, one of the main activities in RSC, enables reuse and recycling of products and prevents the overuse, disassembly line balancing problem involves determination of a line design in which used products are partially/completely disassembled to obtain available components. The aim of this study is to optimise a RSC, involving customers, collection/disassembly centres and plants, that minimises the transportation costs while balancing the disassembly lines, which minimises the total fixed costs of opened workstations, simultaneously. A non-linear mixed-integer programming model, which simultaneously determines: (i) optimal distribution between the facilities with minimum cost, (ii) the number of disassembly workstations that will be opened with minimum cost, (iii) the cycle time in each disassembly centre and (iv) optimal assignment of tasks to workstations, is developed. A numerical example is given to illustrate the applicability of the proposed model. Different scenarios have been conducted to show the effects of sensitivity analyses on the performance measures of the problem.

Supply chain optimisation with assembly line balancing

Supply chain management operates at three levels, strategic, tactical and operational. While the strategic approach generally pertains to the optimisation of network resources such as designing networks, location and determination of the number of facilities, etc., tactical decisions deal with the mid-term, including production levels at all plants, assembly policy, inventory levels and lot sizes, and operational decisions are related to how to make the tactical decisions happen in the short term, such as production planning and scheduling. This paper mainly discusses and explores how to realise the optimisation of strategic and tactical decisions together in the supply chain. Thus, a supply chain network (SCN) design problem is considered as a strategic decision and the assembly line balancing problem is handled as a tactical decision. The aim of this study is to optimise and design the SCN, including manufacturers, assemblers and customers, that minimises the transportation costs for determined periods while balancing the assembly lines in assemblers, which minimises the total fixed costs of stations, simultaneously. A nonlinear mixed-integer model is developed to minimise the total costs and the number of assembly stations while minimising the total fixed costs. For illustrative purposes, a numerical example is given, the results and the scenarios that are obtained under various conditions are discussed, and a sensitivity analysis is performed based on performance measures of the system, such as total cost, number of stations, cycle times and distribution amounts.