Solving a Supply Chain Optimization Problem Collaboratively

We propose a novel algorithmic framework to solve an integrated planning and scheduling problem in supply chain management. This problem involves the integration of an inventory management problem and the vehicle routing problem with time windows, both of which are known to be NP-hard. Under this framework, algorithms that solve the underlying sub-problems collaborate rigorously yet in a computationally efficient manner to arrive at a good solution. We will then present two algorithms to solve the inventory management problem: a complete mathematical model integrating integer programming with constraint programming, and an incomplete algorithm based on tabu search. We present experimental results based on extended Solomon benchmark vehicle routing problems

Designing sustainable cold chains for long-range food distribution: Energy-effective corridors on the Silk Road Belt

Modern food production-distribution processes represent a critical stressor for the environment and for natural ecosystems. The rising flows of food across growing and consumption areas couple with the higher expectations of consumers for the quality of products and compel the intensive use of refrigerated rooms and transport means throughout the food supply chain. In order to aid the design of sustainable cold chains that incorporate such aspects, this paper proposes a mixed integer linear programming model to minimize the total energy consumption associated with the cold operations experienced by perishable products. This model is intended for food traders, logistics practitioners, retail managers, and importers collaboratively called to design and plan a cost and environmentally effective supply strategy, physical channels, and infrastructures for cold chains. The proposed model is validated with a case study inspired by the distribution of two example food products, namely fresh apples and ice cream, along the New Silk Road connecting Europe and China. The illustrated analysis investigates the effect of alternative routes and transport modes on the sustainability of the cold chain. It is found that the most energy-efficient route for ice cream is via rail over a northern route and, for apples, is via a southern maritime route, and, for these two routes, the ratios of the total energy consumed to the energy content of the food are 760 and 913, respectively. By incorporating the energy lost due to the food quality decay, the model identifies the optimal route to adopt in accordance with the shelf life and the conservation temperature of each product

A Holistic Algorithm for Materials Requirement Planning in Collaborative Networks

[EN] Collaboration has increasingly been considered a key topic within the small and medium-sized enterprises, allowing dealing with the intense competitiveness of today¿s globalised markets. The European H2020 Cloud Collaborative Manufacturing Networks Project proposes mechanisms to encourage collaboration among enterprises, through the computation of collaborative plans. Particularly, this paper focuses on the proposal of a holistic algorithm to deal with the automated and collaborative calculation of the Materials Requirement Plan. The proposed algorithm is validated in a collaborative network belonging to the automotive industry.The research leading to these results is in the frame of the “Cloud Collaborative Manufacturing Networks” (C2NET) project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 636909.Andres, B.; Poler, R.; Sanchis, R. (2017). A Holistic Algorithm for Materials Requirement Planning in Collaborative Networks. IFIP Advances in Information and Communication Technology. 560:41-50. https://doi.org/10.1007/978-3-319-65151-4_4S4150560CORDIS Europa: Factories of the Future. H2020-EU.2.1.5.1. - Technologies for Factories of the Future (2014)H2020 Project C2NET (2015). http://cordis.europa.eu/project/rcn/193440_en.htmlAndres, B., Sanchis, R., Poler, R.: A cloud platform to support collaboration in supply networks. Int. J. Prod. Manag. Eng. 4(1), 5–13 (2016)Andres, B., Sanchis, R., Lamothe, J., Saari, L., Hauser, F.: Integrated production-distribution planning optimization models: a review in collaborative networks context. Int. J. Prod. Manag. Eng. 5(1), 31–38 (2017)Camarinha-Matos, L.M., Afsarmanesh, H.: Collaborative networks: a new scientific discipline. J. Intell. Manuf. 16(4–5), 439–452 (2005)Andres, B., Poler, R.: Models, guidelines and tools for the integration of collaborative processes in non-hierarchical manufacturing networks: a review. Int. J. Comput. Integr. Manuf. 2(29), 166–201 (2016)Sanchis, R., Poler, R., Lario, F.C.: Identification and analysis of Disruptions: the first step to understand and measure Enterprise Resilience. In: International Conference on Industrial Engineering and Engineering Management, pp. 424–431 (2012)Andres, B., Saari, L., Lauras, M., Eizaguirre, F.: Optimization algorithms for collaborative manufacturing and logistics processes. In: Zelm, M., Doumeingts, G., Mendonça, J.P. (eds.) Enterprise Interoperability in the Digitized and Netwroked Factory of the Future, iSTE 2016, pp. 167–173 (2016)Orbegozo, A., Andres, B., Mula, J., Lauras, M., Monteiro, C., Malheiro, M.: An overview of optimization models for integrated replenishment and production planning decisions. In: Building Bridges Between Researchers and Practitioners. Book of Abstracts of the International Joint Conference CIO-ICIEOM-IISE-AIM (IJC 2016), p. 68 (2016

Collaborative planning in non-hierarchical networks - an intelligent negotiation-based framework

In today’s competing business market, companies are constantly challenged to dynamically adapt to customer expectations by diminishing the time response that goes from the beginning of the business opportunity to the satisfaction of the customer need. Simultaneously, there is increased recognition of the advantages that companies obtain in focusing on their core business and seeking other competencies through partnerships with other partners by forming collaborative networks. These new collaborative organizational structures require a new set of methods and tools to support the management of manufacturing processes across the entire supply chain. The present paper addresses the collaborative production planning problem in networks of non-hierarchical, decentralized, and independent companies. By proposing a collaborative planning intelligent framework composed of a web-based set of methods, tools, and technologies, the present study intends to provide network stakeholders with the necessary means to responsively and efficiently address each one of the market business opportunities. Through this new holistic framework, the managers of the networked companies can address the challenges posed during collaborative network formation and supply chain production planning.The research leading to these results received funding from the European Union’s Seventh Framework Program (FP7/2007-2013) under grant agreement No. 260169. This work was also financed by national funds through the Portuguese funding agency, FCT—Fundação para a Ciência e a Tecnologia, within project LA/P/0063/2020

Particle swarm optimization for bi-level pricing problems in supply chains

With rapid technological innovation and strong competition in hi-tech industries such as computer and communication organizations, the upstream component price and the downstream product cost usually decline significantly with time. As a result, an effective pricing supply chain model is very important. This paper first establishes two bi-level pricing models for pricing problems with the buyer and the vendor in a supply chain designated as the leader and the follower, respectively. A particle swarm optimization (PSO) based algorithm is developed to solve problems defined by these bi-level pricing models. Experiments illustrate that this PSO based algorithm can achieve a profit increase for buyers or vendors if they are treated as the leaders under some situations, compared with the existing methods. © 2010 Springer Science+Business Media, LLC

AN AGENT-BASED COOPERATIVE MECHANISM FOR INTEGRATED PRODUCTION AND TRANSPORTATION PLANNING

This paper presents a decentralized cooperative economic scheduling mechanism for a supply chain environment. For this purpose, we design autonomous agents that minimize the production or transportation costs and outsourcing costs incurred by the external execution of a task. The decentralized cooperative scheduling approach comprises two parts: the individual optimization an agent\u27s local schedule and the cooperative contract optimization, either by outsourcing the task or by (re-)contracting the release time and due time with the contract partners aiming to maximize their total profits. A negotiation mechanism based on trust accounts is employed to protect the agents against systematic exploitation by their partners