Modelling sustainable supply networks with adaptive agents

© 2018 IEEE. This paper proposes a multi-agent modelling approach that supports supply network configuration decisions towards sustaining operations excellence in terms of economic, business continuity and environmental performance. Two types of agents are employed, namely, physical agents to represent supply entities and auxiliary agents to deal with supply network configuration decisions. While using the evolutionary algorithm, Non-dominated Sorting Genetic Algorithm-II to optimize both cost and lead time at the supply network level, agents are modelled with an architecture which consists of decision-making, learning and communication modules. The physical agents make decisions considering varying situations to suit specific product-market profiles thereby generating alternative supply network configurations. These supply network configurations are then evaluated against a set of performance metrics, including the energy consumption of the supply chain processes concerned and the transportation distances between supply entities. Simulation results generated through the application of this approach to a refrigerator production network show that the selected supply network configurations are capable of meeting intended sustainable goals while catering to the respective product-market profiles

Multiagent Optimization Approach to Supply Network Configuration Problems With Varied Product-Market Profiles

IEEE This article demonstrates the application of a novel multiagent modeling approach to support supply network configuration (SNC) decisions toward addressing several challenges reported in the literature. These challenges include: enhancing supply network (SN)-level performance in alignment with the goals of individual SN entities; addressing the issue of limited information sharing between SN entities; and sustaining competitiveness of SNs in dynamic business environments. To this end, a multistage, multiechelon SN consisting of geographically dispersed SN entities catering to distinct product-market profiles was modeled. In modeling the SNC decision problem, two types of agents, each having distinct attributes and functions, were used. The modeling approach incorporated a reverse-auctioning process to simulate the behavior of SN entities with differing individual goals collectively contributing to enhance SN-level performance, by means of setting reserve values generated through the application of a genetic algorithm. A set of Pareto-optimal SNCs catering to distinct product-market profiles was generated using Nondominated Sorting Genetic Algorithm II. Further evaluation of these SNCs against additional criteria, using a rule-based approach, allowed the selection of the most appropriate SNC to meet a broader set of conditions. The model was tested using a refrigerator SN case study drawn from the literature. The results reveal that a number of SNC decisions can be supported by the proposed model, in particular, identifying and evaluating robust SNs to suit varied product-market profiles, enhancing SC capabilities to withstand disruptions and developing contingencies to recover from disruptions

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

A fuzzy rough sets-based multi-agent analytics framework for dynamic supply chain configuration

Considering the need for more effective decision support in the context of distributed manufacturing, this paper develops an advanced analytics framework for configuring supply chain networks. The proposed framework utilizes a distributed multi-agent system architecture to deploy fuzzy rough sets-based algorithms for knowledge elicitation and representation. A set of historical sales data, including network node-related information, is used together with the relevant details of product families to predict supply chain configurations capable of fulfilling desired customer orders. Multiple agents such as data retrieval agent, knowledge acquisition agent, knowledge representation agent, configuration predictor agent, evaluator agent and dispatching agent are used to help execute a broad spectrum of supply chain configuration decisions. The proposed framework considers multiple product variants and sourcing options at each network node, as well as multiple performance objectives. It also captures decisions that span the entire supply chain simultaneously and, by implication, represents multiple network links. Using an industry test case, the paper demonstrates the effectiveness of the proposed framework in terms of fulfilling customer orders with lower production and emissions costs, compared to the results generated using existing tools

An exploratory study on the commercialisation of heat pump-fluidised bed drying technology

Fluidised bed-heat pump drying technology offers distinctive advantages over the existing drying technology employed in the Australian food industry. However, as is the case with many other examples of innovations that have had clear relative advantages, the rates of adoption and diffusion of this technology have been very slow. "Why does this happen?" is the theme of this research study that has been undertaken with an objective to analyse a range of issues related to the market acceptance of technological innovations. The research methodology included the development of an integrated conceptual model based on an extensive review of literature in the areas of innovation diffusion, technology transfer and industrial marketing. Three major determinants associated with the market acceptance of innovations were identified as the characteristics of the innovation, adopter information processing capability and the influence of the innovation supplier on the adoption process. This was followed by a study involving more than 30 small and medium enterprises identified as potential adopters of fluidised bed-heat pump drying technology in the Australian food industry. The findings revealed that judgment was the key evaluation strategy employed by potential adopters in the particular industry sector. Further, it was evidenced that the innovations were evaluated against a predetermined criteria covering a range of aspects with emphasis on a selected set of attributes of the innovation. Implication of these findings on the commercialisation of fluidised bed-heat pump drying technology was established, and a series of recommendations was made to the innovation supplier (DPI/FT) enabling it to develop an effective commercialisation strategy

Modeling supply network configuration problems with varying demand profiles

© 2018 IEEE. In this paper, we develop a novel multi-objective modeling approach to support supply network configuration decisions, while considering varying demand profiles. In so doing, we illustrate how such an approach could contribute to building supply network robustness and resilience. The proposed model entails two key objectives; minimizing lead time and cost across the supply network. The solution approach first employs a bidding mechanism to select a set of supply network entities that match with a given demand profile from a candidate pool of entities. It then applies the popular technique known as N on-dominated Sorting Genetic Algorithm-II to generate a set of Pareto-optimal solutions representing alternative supply network configurations. The proposed model is tested on a case study of a refrigerator supply network to draw delivery time and cost comparisons under static and dynamic demand profiles

A utility-driven approach to supplier evaluation and selection: empirical validation of an integrated solution framework

Supplier evaluation and selection (SES) problems have long been studied, leading to the development of a wide range of individual and hybrid models for solving them. However, the lack of widespread diffusion of existing SES models in the industry points to a need for simpler models that can systematically evaluate both qualitative and quantitative attributes of potential suppliers while enhancing the flexibility decision-makers need to account for relevant situational factors. Furthermore, empirical validations of existing models in SES have been few and far between. With a view to addressing these issues, this paper proposes an integrated solution framework that can be used to evaluate both tangible and intangible attributes of potential suppliers. The proposed framework combines three individual methods, namely the fuzzy analytic hierarchy process, fuzzy complex proportional assessment and fuzzy linear programming. The framework is validated through application in a Turkish textile company. The results generated using the proposed framework is compared with the actual historical data collected from the company. Additionally, a feasibility assessment is conducted on the sample supplier selection criteria employed, as well as assessment of the results generated using the proposed model

Selenium content in meals consumed for lunch by Sri Lankans and the effect of cooking on selenium content

The selenium (Se) content in meals consumed by Sri Lankans for lunch, composed of fixed and random menus, was determined using Hydride Generation Atomic Absorption Spectrometer. The samples were obtained from five districts in Sri Lanka. The Se content (μg/kg) in meals of fixed and random menus was in the range of 48-70 and 53-60 respectively. These values are comparable to the daily requirement of Se (55 μg/kg) prescribed by the World Health Organization and Food and Agriculture Organization of the United Nations. There is no significant difference in Se content in meals consumed by people in the districts of Kandy, Gampaha, Kurunegala, Rathnapura, and Colombo, as well as among individual households in each district. The effects of different cooking methods on the Se content indicate that the level of Se (μg/kg) in fried chicken (30.45 - 52.49) is less than that in a chicken curry (61.38 - 84.25). The percentage loss of Se during cooking for chicken, dahl (Lens culinaris) and Gotukola (Centella asiatica) were 89.6%, 84.1%, and 79.9% respectively. The present study revealed that Se content in Sri Lankan menus provides the required Se for people. However, the different methods of cooking indicate that there is a loss of Se during cooking