Solving closed-loop supply chain problems using game theoretic particle swarm optimisation

© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. In this paper, we propose a closed-loop supply chain network configuration model and a solution methodology that aim to address several research gaps in the literature. The proposed solution methodology employs a novel metaheuristic algorithm, along with the popular gradient descent search method, to aid location-allocation and pricing-inventory decisions in a two-stage process. In the first stage, we use an improved version of the particle swarm optimisation (PSO) algorithm, which we call improved PSO (IPSO), to solve the location-allocation problem (LAP). The IPSO algorithm is developed by introducing mutation to avoid premature convergence and embedding an evolutionary game-based procedure known as replicator dynamics to increase the rate of convergence. The results obtained through the application of IPSO are used as input in the second stage to solve the inventory-pricing problem. In this stage, we use the gradient descent search method to determine the selling price of new products and the buy-back price of returned products, as well as inventory cycle times for both product types. Numerical evaluations undertaken using problem instances of different scales confirm that the proposed IPSO algorithm performs better than the comparable traditional PSO, simulated annealing (SA) and genetic algorithm (GA) methods

Modelling Freight Allocation and Transportation Lead-Time

The authors have investigated sustainable environment delivery systems and identified transportation lead-time investigation cases. This research study aimed to increase freight delivery lead-time and minimize distance in transportation. To reach the goal, the paper\u27s authors, after analysis of the hierarchy of quantitative methods and models, proposed the framework for modeling freight allocation and transportation lead-time and delivered a study that includes discrete event simulation. During the simulation, various scenarios have been revised. Following the simulation mentioned above analysis, around 3.8 % of distance could be saved during freight delivery if lead-time for transportation were revised by choosing five days criteria for modeling freight allocation. The savings depend on the number of received orders from different geographic locations

Role of Green Supply Chain Management Practices on Manufacturing Company Performance: A Moderating Role of Supply Chain Traceability and Institutional Pressures

Abstract- Purpose: This research paper has analysed the role of green supply chain management practices on organisational performance in the manufacturing sector of Malaysia. The moderating role of supply chain traceability and institutional pressure are considered as the moderators. Method: Primary data was collected via Likert scale based survey questionnaire. The sample of this study was considered to be 369 employees and managers from Malaysian manufacturing companies. SEM has been used as the data analysis technique in this paper to assess the role of moderators in the model. Findings: The results have indicated that institutional pressures moderates the relationship of green purchasing and organisational performance and there is also a direct impact of internal environment management on organisational performance. However, the results have also indicated that traceability is not as efficient moderator in the model as in its presence none of the predictors have a significant impact on organisational performance. Limitations: This paper is centric on the manufacturing firms only. Therefore, other sectors of business are not entertained in this study. Consequently, the outcome of this study cannot be implemented in any sector other than manufacturing. Secondly, this study has emphasized on Malaysia as the core geographical domain of investigatio

Optimizing Integrated Municipal Solid Waste Management System under Multiple Uncertainties

To define a holistic and systematic approach to municipal waste management, an integrated municipal solid waste management (IMSWM) system is proposed. This system includes functional elements of waste generation, source handling, and processing, waste collection, waste processing at facilities, transfer, and disposal. Multi-objective optimization algorithms are used to develop an optimum IMSWM that can satisfy all main pillars of sustainable development, aiming to minimize the total cost of the system (economic), and minimize the total greenhouse gas emissions (environmental), while maximizing the total social suitability of the system (social). For the social objective, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used to identify the main parameters that affect the social suitability of the system. This research focuses on developing an optimized holistic model that considers all four main components of a modern IMSWM namely transfer, recycling, treatment, and disposal. The model is formulated as a mixed-integer linear programming (MILP) problem and solved using the epsilon constraint handling method. A metaheuristic method is developed using non dominated sorting genetic algorithm (NSGA) to deal with larger problems. A solution repair function is developed to handle several equality constraints included in the proposed IMSWM model. Sensitivity analyses are conducted to identify the effect of changes in parameters on the objective functions. Based on the results, the proposed metaheuristic algorithm based on NSGA-II performed better than other algorithms. The interval-parameter programming (IPP) methods are used to consider various uncertainties that exist in the system. The model is applied to the case study of the Australian capital territory (ACT). The data is gathered from several resources including Australian national waste reports, and ACT government transport Canberra and city services (TCCS). Based on the waste characteristic and city map several feasible scenarios are recommended. Several non-dominated solutions are identified for the model that the decision-maker can choose the most desirable solution based on the preferences. Based on the importance of any objective function at any time the decision-maker can choose a solution to suit the needs

A holonic framework for managing the sustainable supply chain in emerging economies with smart connected metabolism

Since their origins, human societies have integrated into the natural environment, where social metabolism that identified the interactions between society and nature was established. This social metabolism enables the flows of energy and materials between social and natural environments to be analyzed and quantified. However, in the last century, many societies have undergone a transformation from an agricultural to an industrial system. Thus, labour, as a generator of economic capital through the supply chain, has provoked a loss of natural and social capital, especially in emerging economies, thereby generating the metabolic rift. This situation can be mitigated and reversed through a circular economy, the use of digital and technological enablers of Industry 4.0 and the incorporation of an organizational enabler such as the holonic paradigm. The integration of these enablers has given rise to the development of the cyber-physical holon, which incorporates inherently sustainable concepts and allows the analysis of distributed complex systems. This paper proposes a holonic framework for multiscale and multilevel Adaptive and Integrated Sustainable Supply Chain Management (AISSCM). This framework supports a smart connected social metabolism integrated within the natural environment and oriented towards mitigation and reversal of the metabolic rift, through the processes of adaptation and integration to enable the co-evolution of the supply chain within the environment. The framework developed is applied to a family of products through their sustainable supply chain based on circularity. This proposal is developed to enable the necessary transition towards sustainable societies

Mathematical Methods and Operation Research in Logistics, Project Planning, and Scheduling

In the last decade, the Industrial Revolution 4.0 brought flexible supply chains and flexible design projects to the forefront. Nevertheless, the recent pandemic, the accompanying economic problems, and the resulting supply problems have further increased the role of logistics and supply chains. Therefore, planning and scheduling procedures that can respond flexibly to changed circumstances have become more valuable both in logistics and projects. There are already several competing criteria of project and logistic process planning and scheduling that need to be reconciled. At the same time, the COVID-19 pandemic has shown that even more emphasis needs to be placed on taking potential risks into account. Flexibility and resilience are emphasized in all decision-making processes, including the scheduling of logistic processes, activities, and projects

A Bibliometric Review of Two Decades’ Research on Closed-Loop Supply Chain: 2001-2020

The closed-loop supply chain (CLSC) is generally regarded as an environmentally friendly approach that can help in reducing environmental impacts and achieving sustainable development of society and economics. In recent years, the popularity research of CLSC has been widely concerned by both business and academia practitioners. It is observed that most of the literatures have focused only on a particular journal or field; there is a distinct lack of comprehensive bibliometric review of two decades' research on CLSC. This study contributes in fulfilling this gap. A comprehensive bibliometric analysis was conducted based on 1,155 articles in Web of Science Core Collection Database from 2001 to 2020. In order to track research frontiers and hotspots, visualization software VOSviewer and CiteSpace are used for analysis. Initially, a descriptive analysis was carried out to identify the trends of number of publications, the leading journals, top authors and regions. A thematic cluster analysis was then carried out to identify the research domains. Subsequently, based on the analyses of co-keywords, dominant categories and co-citation, hot issues and research trends are summarized. “game theory”, and “remanufacturing” are emerging research trends for CLSC. “Dual channel”, “quality” and “circular economy” had become hot topics. This review also finds the landmark nodes and pivot nodes in the research of CLSC. Finally, some research gaps are revealed to shed light on future directions

A hybrid multi-objective evolutionary algorithm-based semantic foundation for sustainable distributed manufacturing systems

Rising energy prices, increasing maintenance costs, and strict environmental regimes have augmented the already existing pressure on the contemporary manufacturing environment. Although the decentralization of supply chain has led to rapid advancements in manufacturing systems, finding an efficient supplier simultaneously from the pool of available ones as per customer requirement and enhancing the process planning and scheduling functions are the predominant approaches still needed to be addressed. Therefore, this paper aims to address this issue by considering a set of gear manufacturing industries located across India as a case study. An integrated classifier-assisted evolutionary multi-objective evolutionary approach is proposed for solving the objectives of makespan, energy consumption, and increased service utilization rate, interoperability, and reliability. To execute the approach initially, text-mining-based supervised machine-learning models, namely Decision Tree, Naïve Bayes, Random Forest, and Support Vector Machines (SVM) were adopted for the classification of suppliers into task-specific suppliers. Following this, with the identified suppliers as input, the problem was formulated as a multi-objective Mixed-Integer Linear Programming (MILP) model. We then proposed a Hybrid Multi-Objective Moth Flame Optimization algorithm (HMFO) to optimize process planning and scheduling functions. Numerical experiments have been carried out with the formulated problem for 10 different instances, along with a comparison of the results with a Non-Dominated Sorting Genetic Algorithm (NSGA-II) to illustrate the feasibility of the approach.The project is funded by Department of Science and Technology, Science and Engineering Research Board (DST-SERB), Statutory Body Established through an Act of Parliament: SERB Act 2008, Government of India with Sanction Order No ECR/2016/001808, and also by FCT–Portuguese Foundation for Science and Technology within the R&D Units Projects Scopes: UIDB/00319/2020, UIDP/04077/2020, and UIDB/04077/2020

Discrete Event Simulations

Considered by many authors as a technique for modelling stochastic, dynamic and discretely evolving systems, this technique has gained widespread acceptance among the practitioners who want to represent and improve complex systems. Since DES is a technique applied in incredibly different areas, this book reflects many different points of view about DES, thus, all authors describe how it is understood and applied within their context of work, providing an extensive understanding of what DES is. It can be said that the name of the book itself reflects the plurality that these points of view represent. The book embraces a number of topics covering theory, methods and applications to a wide range of sectors and problem areas that have been categorised into five groups. As well as the previously explained variety of points of view concerning DES, there is one additional thing to remark about this book: its richness when talking about actual data or actual data based analysis. When most academic areas are lacking application cases, roughly the half part of the chapters included in this book deal with actual problems or at least are based on actual data. Thus, the editor firmly believes that this book will be interesting for both beginners and practitioners in the area of DES