Decision support for build-to-order supply chain management through multiobjective optimization

This paper aims to identify the gaps in decision-making support based on multiobjective optimization for build-to-order supply chain management (BTOSCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting multiobjective optimization (MOO) techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted and future research directions to better exploit the decision support capabilities of MOO are proposed

A framework for smart production-logistics systems based on CPS and industrial IoT

Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems

Decision support for build-to-order supply chain management through multiobjective optimization

This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.Brunel Research Initiative and Enterprise Fund (BRIEF

Food supply chain network robustness : a literature review and research agenda

Today’s business environment is characterized by challenges of strong global competition where companies tend to achieve leanness and maximum responsiveness. However, lean supply chain networks (SCNs) become more vulnerable to all kind of disruptions. Food SCNs have to become robust, i.e. they should be able to continue to function in the event of disruption as well as in normal business environment. Current literature provides no explicit clarification related to robustness issue in food SCN context. This paper explores the meaning of SCN robustness and highlights further research direction

Multi Agent Systems in Logistics: A Literature and State-of-the-art Review

Based on a literature survey, we aim to answer our main question: “How should we plan and execute logistics in supply chains that aim to meet today’s requirements, and how can we support such planning and execution using IT?†Today’s requirements in supply chains include inter-organizational collaboration and more responsive and tailored supply to meet specific demand. Enterprise systems fall short in meeting these requirements The focus of planning and execution systems should move towards an inter-enterprise and event-driven mode. Inter-organizational systems may support planning going from supporting information exchange and henceforth enable synchronized planning within the organizations towards the capability to do network planning based on available information throughout the network. We provide a framework for planning systems, constituting a rich landscape of possible configurations, where the centralized and fully decentralized approaches are two extremes. We define and discuss agent based systems and in particular multi agent systems (MAS). We emphasize the issue of the role of MAS coordination architectures, and then explain that transportation is, next to production, an important domain in which MAS can and actually are applied. However, implementation is not widespread and some implementation issues are explored. In this manner, we conclude that planning problems in transportation have characteristics that comply with the specific capabilities of agent systems. In particular, these systems are capable to deal with inter-organizational and event-driven planning settings, hence meeting today’s requirements in supply chain planning and execution.supply chain;MAS;multi agent systems

Interaction between intelligent agent strategies for real-time transportation planning

In this paper we study the real-time scheduling of time-sensitive full truckload pickup-and-delivery jobs. The problem involves the allocation of jobs to a fixed set of vehicles which might belong to dfferent collaborating transportation agencies. A recently proposed solution methodology for this problem is the use of a multi-agent system where shipper agents other jobs through sequential auctions and vehicle agents bid on these jobs. In this paper we consider such a multi-agent system where both the vehicle agents and the shipper agents are using profit maximizing look-ahead strategies. Our main contribution is that we study the interrelation of these strategies and their impact on the system-wide logistical costs. From our simulation results, we conclude that the system-wide logistical costs (i) are always reduced by using the look-ahead policies instead of a myopic policy (10-20%) and (ii) the joint effect of two look-ahead policies is larger than the effect of an individual policy. To provide an indication of the savings that might be realized with a central solution methodology, we benchmark our results against an integer programming approach

Integrated network design for forest bioenergy value chain - decisions support system for the transformation of the Canadian forest industry

Les usines de bioénergie devraient jouer un rôle important dans la production d'énergie verte à partir de la biomasse forestière. Pour intégrer l'usine de bioénergie dans la chaîne d'approvisionnement forestière, l'industrie a besoin de nouveaux investissements ainsi que de la conception et de la gestion de la chaîne de valeur. D'un autre côté, les incertitudes associées aux nouveaux produits sur le marché peuvent ajouter des risques supplémentaires à un investissement aussi important dans la chaîne d'approvisionnement forestière instable. Par conséquent, l'objectif principal de cette thèse est d'étudier la conception du réseau de bioénergie forestière dans un contexte déterministe et stochastique. La première partie de la thèse propose une plate-forme expérimentale pour intégrer la conception et le pilotage de la chaîne de valeur puisque le nouveau design ne sera réalisable que s'il considère au préalable la planification. La plateforme a inclus plusieurs actions collaboratives entre tous les partenaires impliqués dans la chaîne d'approvisionnement. Cette plateforme est la base d’un nouvel outil éducatif appelé jeu de transport. Ensuite, la plate-forme a été utilisée pour concevoir un réseau optimisé de bioénergie forestière. La chaîne d'approvisionnement forestière de Terre-Neuve, composée de quatre acteurs majeurs de l’industrie forestière, a été considérée comme notre étude de cas. La rentabilité de l'ajout de nouvelles installations de bioénergie ainsi que de nouveaux terminaux dans plusieurs emplacements potentiels ont été évalués. Enfin, à la troisième partie de la thèse, nous repensons le réseau bioénergétique en tenant compte de l'incertitude de la demande et des prix de tous les produits finaux de la nouvelle chaîne de valeur. Plusieurs bioprocédés potentiels avec différentes technologies ont été évalués dans notre étude de cas. Pour fournir une solution tenant compte du risque, nous avons développé deux nouveaux modèles de gestion des risques. Les résultats dans les trois parties ont clairement démontré l'impact de la planification intégrée, des usines de bioénergie et de la collaboration sur l'amélioration de la performance de la chaîne d'approvisionnement forestière. En général, le travail accompli dans ce projet permettra une transformation en douceur de la chaîne d'approvisionnement forestière en tenant compte des risques d'investissement. En ce qui concerne les résultats obtenus grâce aux études de cas, nous croyons que la plateforme et les approches proposées dans cette thèse peuvent être considérées comme des outils novateurs et pratiques pour le problème de la conception des réseaux de bioénergie forestière.Bioenergy plants are expected to play an important role in green energy production from forestry biomass. To incorporate bioenergy plant in the forest supply chain, the industry requires new investments as well as new value chain design and management. On the other side, the uncertainties associated with demand and price of new products in the market may add risks to such large investment in current forest supply chain. Hence, the main objective of this thesis is to analyze and to propose new design of the forest bioenergy network in both a deterministic and a stochastic context. The first part of the thesis has proposed four optimization models for strategic, tactical and operational planning levels of the supply chain. The models have included several collaborative actions between all involved stakeholders of the supply chain. They have been integrated in a new educational tool called hierarchical transportation game. In the second part of the thesis, we have integrated the developed optimization models to propose an integrated value chain design and value chain management optimization model. This model has been used to analyze a forest bioenergy network in Newfoundland. Newfoundland forest supply chain comprising four major stakeholders was considered as our case study. The profitability of adding a new bioenergy plant as well as new terminals in several potential locations have been evaluated. Finally, in a third part of the thesis we have proposed the bioenergy network taking into account uncertainty on demand and price of all final products of a new value chain. Several potential bioprocesses with different technologies have been evaluated for our case study. To provide a risk-averse solution, we have proposed two risk management models. The results from the three parts of the thesis have demonstrated the impact of integrated planning, bioenergy plants and collaboration on improvement of forest value chain. In general, the work in this thesis can support an efficient transformation of the forest supply chain considering investment risks. The optimization models and approaches proposed in this thesis are novel and practical for the forest bioenergy network design problem

Supply chain management: An opportunity for metaheuristics

In today’s highly competitive and global marketplace the pressure on organizations to find new ways to create and deliver value to customers grows ever stronger. In the last two decades, logistics and supply chain has moved to the center stage. There has been a growing recognition that it is through an effective management of the logistics function and the supply chain that the goal of cost reduction and service enhancement can be achieved. The key to success in Supply Chain Management (SCM) require heavy emphasis on integration of activities, cooperation, coordination and information sharing throughout the entire supply chain, from suppliers to customers. To be able to respond to the challenge of integration there is the need of sophisticated decision support systems based on powerful mathematical models and solution techniques, together with the advances in information and communication technologies. The industry and the academia have become increasingly interested in SCM to be able to respond to the problems and issues posed by the changes in the logistics and supply chain. We present a brief discussion on the important issues in SCM. We then argue that metaheuristics can play an important role in solving complex supply chain related problems derived by the importance of designing and managing the entire supply chain as a single entity. We will focus specially on the Iterated Local Search, Tabu Search and Scatter Search as the ones, but not limited to, with great potential to be used on solving the SCM related problems. We will present briefly some successful applications.Supply chain management, metaheuristics, iterated local search, tabu search and scatter search

An optimal-control based integrated model of supply chain

Problems of supply chain scheduling are challenged by high complexity, combination of continuous and discrete processes, integrated production and transportation operations as well as dynamics and resulting requirements for adaptability and stability analysis. A possibility to address the above-named issues opens modern control theory and optimal program control in particular. Based on a combination of fundamental results of modern optimal program control theory and operations research, an original approach to supply chain scheduling is developed in order to answer the challenges of complexity, dynamics, uncertainty, and adaptivity. Supply chain schedule generation is represented as an optimal program control problem in combination with mathematical programming and interpreted as a dynamic process of operations control within an adaptive framework. The calculation procedure is based on applying Pontryagin’s maximum principle and the resulting essential reduction of problem dimensionality that is under solution at each instant of time. With the developed model, important categories of supply chain analysis such as stability and adaptability can be taken into consideration. Besides, the dimensionality of operations research-based problems can be relieved with the help of distributing model elements between an operations research (static aspects) and a control (dynamic aspects) model. In addition, operations control and flow control models are integrated and applicable for both discrete and continuous processes.supply chain, model of supply chain scheduling, optimal program control theory, Pontryagin’s maximum principle, operations research model,

Disaster preparedness in humanitarian logistics:A collaborative approach for resource management in floods

The logistical deployment of resources to provide relief to disaster victims and the appropriate planning of these activities are critical to reduce the suffering caused. Disaster management attracts many organisations working alongside each other and sharing resources to cope with an emergency. Consequently, successful operations rely heavily on the collaboration of different organisations. Despite this, there is little research considering the appropriate management of resources from multiple organisations, and none optimising the number of actors required to avoid shortages or convergence. This research introduces a disaster preparedness system based on a combination of multi-objective optimisation and geographical information systems to aid multi-organisational decision-making. A cartographic model is used to avoid the selection of floodable facilities, informing a bi-objective optimisation model used to determine the location of emergency facilities, stock prepositioning, resource allocation and relief distribution, along with the number of actors required to perform these activities. The real conditions of the flood of 2013 in Acapulco, Mexico, provided evidence of the inability of any single organisation to cope with the situation independently. Moreover, data collected showed the unavailability of enough resources to manage a disaster of that magnitude at the time. The results highlighted that the number of government organisations deployed to handle the situation was excessive, leading to high cost without achieving the best possible level of satisfaction. The system proposed showed the potential to achieve better performance in terms of cost and level of service than the approach currently employed by the authorities