Low-carbon multi-objective location-routing in supply chain network design

Traditional supply chain modelling tends to focus on singular objectives, with a predominant focus on cost. Within this discipline location-routing problems are one of the most researched categories in recent years. This study extends this paradigm to consider the multi-objective of cost and environmental impact in the form of carbon emissions. The focus of this study is on the design of a low-cost low-carbon structure for the demand side of supply chain networks. This research has developed two-layer and three-layer multi-objective 0-1 mixedinteger AHP-integrated location-routing models. Disparate multi-objective Genetic Algorithm, Particle Swarm, and Simulated Annealing-based optimisers are used to execute these developed models. The main execution platform used is modeFRONTIER®, a multi-objective optimisation and design environment. The main contributions from this research are 1) the modelling extension to include low carbon emissions; costs; demand as an objective function component; and the inclusion of the decision makers’ priority as a green constraint, 2)with regard to implementing these specific NP-hard models, a DoE-guided solution approach is used. Various heuristics/meta-heuristics are adopted and compared in terms of their efficiency, with the three-layer model being solved in two phases, 3) both sets of developed models are applied to the demand side of a dairy supply chain in Ireland

Sustainable distribution system design: a two-phase DoE-guided meta-heuristic solution approach for a three-echelon bi-objective AHP-integrated location-routing model

This article introduces a sustainable integrated bi-objective location-routing model, its two-phase solution approach and an analysis procedure for the distribution side of three-echelon logistics networks. The mixed-integer programming model captures several real-world factors by introducing an additional objective function and a set of new constraints in the model that outbound logistics channels find difficult to reconcile. The sustainable model minimises CO2 emissions from transportation and total costs incurred in facilities and the transportation channels. Design of Experiment (DoE) is integrated to the meta-heuristic based optimiser to solve the model in two phases. The DoE-guided solution approach enables the optimiser to offer the best stable solution space by taking out solutions with poor design features from the space and refining the feasible solutions using a convergence algorithm thereby selecting the realistic results. Several alternative solution scenarios are obtained by prioritising and ranking the realistic solution sets through a multi-attribute decision analysis tool, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The robust model provides the decision maker the ability to take decisions on sustainable open alternative optimal routes. The outcomes of this research provide theoretical and methodological contributions, in terms of integrated bi-objective location-routing model and its two-phase DoE-guided meta-heuristic solution approach, for the distribution side of three-echelon logistics networks

An evaluation of three DoE-guided meta-heuristic-based solution methods for a three-echelon sustainable distribution network

This article evaluates the efficiency of three meta-heuristic optimiser (viz. MOGA-II, MOPSO and NSGA-II)-based solution methods for designing a sustainable three-echelon distribution network. The distribution network employs a bi-objective location-routing model. Due to the mathematically NP-hard nature of the model a multi-disciplinary optimisation commercial platform, modeFRONTIER®, is adopted to utilise the solution methods. The proposed Design of Experiment (DoE)-guided solution methods are of two phased that solve the NP-hard model to attain minimal total costs and total CO2 emission from transportation. Convergence of the optimisers are tested and compared. Ranking of the realistic results are examined using Pareto frontiers and the Technique for Order Preference by Similarity to Ideal Solution approach, followed by determination of the optimal transportation routes. A case of an Irish dairy processing industry’s three-echelon logistics network is considered to validate the solution methods. The results obtained through the proposed methods provide information on open/closed distribution centres (DCs), vehicle routing patterns connecting plants to DCs, open DCs to retailers and retailers to retailers, and number of trucks required in each route to transport the products. It is found that the DoE-guided NSGA-II optimiser based solution is more efficient when compared with the DoE-guided MOGA-II and MOPSO optimiser based solution methods in solving the bi-objective NP-hard three-echelon sustainable model. This efficient solution method enable managers to structure the physical distribution network on the demand side of a logistics network, minimising total cost and total CO2 emission from transportation while satisfying all operational constraints

A solution method for a two-layer sustainable supply chain distribution model

This article presents an effective solution method for a two-layer, NP-hard sustainable supply chain distribution model. A DoE-guided MOGA-II optimiser based solution method is proposed for locating a set of non-dominated solutions distributed along the Pareto frontier. The solution method allows decision-makers to prioritise the realistic solutions, while focusing on alternate transportation scenarios. The solution method has been implemented for the case of an Irish dairy processing industry׳s two-layer supply chain network. The DoE generates 6100 real feasible solutions after 100 generations of the MOGA-II optimiser which are then refined using statistical experimentation. As the decision-maker is presented with a choice of several distribution routes on the demand side of the two-layer network, TOPSIS is applied to rank the set of non-dominated solutions thus facilitating the selection of the best sustainable distribution route. The solution method characterises the Pareto solutions from disparate scenarios through numerical and statistical experimentations. A set of realistic routes from plants to consumers is derived and mapped which minimises total CO2 emissions and costs where it can be seen that the solution method outperforms existing solution methods

A case analysis of a sustainable food supply chain distribution system—A multi-objective approach

Sustainable supply chain management is a topical area which is continuing to grow and evolve. Within supply chains, downstream distribution from producers to customers plays a significant role in the environmental performance of production supply chains. With consumer consciousness growing in the area of sustainable food supply, food distribution needs to embrace and adapt to improve its environmental performance, while still remaining economically competitive. With a particular focus on the dairy industry, a robust solution approach is presented for the design of a capacitated distribution network for a two-layer supply chain involved in the distribution of milk in Ireland. In particular the green multiobjective optimisation model minimises CO2 emissions from transportation and total costs in the distribution chain. These distribution channels are analysed to ensure the non-dominated solutions are distributed along the Pareto fronts. A multi-attribute decision-making approach, TOPSIS, has been used to rank the realistic feasible transportation routes resulting from the trade-offs between total costs and CO2 emissions. The refined realistic solution space allows the decision-makers to geographically locate the sustainable transportation routes. In addition to geographical mapping the decision maker is also presented with a number of alternative analysed scenarios which forcibly open closed distribution routes to build resiliency into the solution approach. In terms of model performance, three separate GA based optimisers have been evaluated and reported upon. In the case presented NSGA-II was found to outperform its counterparts of MOGA-II and HYBRID

Intelligent Decision Support Systems in Supply Chain Management

Increasingly complex supply chains have to adapt to the uncertain and dynamic environment in which they operate. Efficient decision-making in such an environment is a necessity and affects the supply chain performance significantly. Research shows that conventional approaches to decision-making are no-longer an efficient way of dealing with problems in supply chains. Artificial Intelligence or Knowledge-Based techniques are used increasingly as efficient alternatives to more conventional techniques to decision making. Use of Decision Support Systems and Artificial Intelligent techniques has a long history in management of Information Systems, yet literature review reveals limited use of AI techniques in decision making and managing supply chains. AI techniques are recognised as complex and dynamic approaches through which complicated situations can be dealt with. Ideally, a Knowledge-based decision support system within the supply chain should behave like a smart (human) consultant; gather and analyse data, identify problems throughout the supply chain, find and evaluate the solutions and propose and monitor actions. This paper is based on an ongoing interdisciplinary research on the applications of Artificial Intelligence techniques in Supply Chain Management. The focus of this paper is specifically on Decision Support Systems and the contribution of AI in this field to efficient management of supply chains. It reviews the existing literature on research and publications in AI, Decision Support Systems and IDSS to date. Through the content analysis, research gaps in the subject area are identified and proposed for future research

Supply chain risk management and artificial intelligence:state of the art and future research directions

Supply chain risk management (SCRM) encompasses a wide variety of strategies aiming to identify, assess, mitigate and monitor unexpected events or conditions which might have an impact, mostly adverse, on any part of a supply chain. SCRM strategies often depend on rapid and adaptive decision-making based on potentially large, multidimensional data sources. These characteristics make SCRM a suitable application area for artificial intelligence (AI) techniques. The aim of this paper is to provide a comprehensive review of supply chain literature that addresses problems relevant to SCRM using approaches that fall within the AI spectrum. To that end, an investigation is conducted on the various definitions and classifications of supply chain risk and related notions such as uncertainty. Then, a mapping study is performed to categorise existing literature according to the AI methodology used, ranging from mathematical programming to Machine Learning and Big Data Analytics, and the specific SCRM task they address (identification, assessment or response). Finally, a comprehensive analysis of each category is provided to identify missing aspects and unexplored areas and propose directions for future research at the confluence of SCRM and AI.</p