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

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

Decision support systems for large dam planning and operation in Africa

Decision support systems/ Dams/ Planning/ Operations/ Social impact/ Environmental effects

Overview of the PlanWise application and examples of its use

There are many demands on forests today, such as producing wood and bioenergy, maintaining biodiversity, providing attractive recreational settings, and mitigating climate. These objectives are partly in conflict with each other, and management strategies differ in how much they contribute to each of these objectives. Therefore, there is a need to assess the long-term consequences of different management strategies on e.g. indicators for different ecosystem services and biodiversity. One important tool to do such assessments are forest decision support systems (DSS), i.e. ‘computer-based systems that help decision makers to analyse and solve ill-structured problems’ (Vacik et al. 2015). Methodologically, DSS can be classified into three groups: DSS based on simulation, DSS based on optimization, and DSS used for multi-criteria decision analysis (MCDA). In this context, simulation means that forest management rules are specified, and the outcome is based on an application of these rules (Nobre et al. 2016). The simulator thus projects the likely development of the forest, and the resulting ecosystem services under pre-defined management rules. Simulators are useful for answering “what if” questions, i.e., for assessing the consequences of a limited set of pre-defined management alternatives. The advantage of simulation approaches lies in the relative ease of formulating the problem and interpreting the output. Simulation approaches are useful for projecting the consequences of a limited set of predefined scenarios. DSS based on optimization, in contrast, generate a large set of alternatives from which the best alternative is selected using an optimising algorithm based on the goals and constraints of the planning problem. These kinds of DSS can be used for answering “How to” questions, i.e., for finding the optimal way to reach certain objectives. Optimisation problems thus require that the user defines forest management goals and constraints rather than strict management rules. Both simulation and optimization approaches can be used to generate a number of scenarios, which can be used in a MCDA approach to identify the solution that best fits decision makers’ preference’s for different objectives. MCDA is the collective term for a set of mathematical methods and approaches used to find solutions to decision problems with multiple conflicting objectives. In Sweden, the forest DSS most widely used in research, education and at forest companies for producing long-term plans and making analysis related to forest and forestry is Heureka. The Heureka forest DSS was developed at SLU and the first 1. Introduction 7 version was released in 2009 (Wikström et al. 2011). The system includes three applications that are designed to be used for different types of analysis and at different spatial levels and one application that helps compare scenarios (such as different long-term forest management plans) using MCDA. StandWise is an interactive simulator for stand-level analysis. PlanWise, which we focus on in this report, is a system for analyzing a large set of forest management options in order to identify the best alternative using optimization based on user-defined objectives and constraints. RegWise, on the other hand, is based on a simulation approach where users pre-define the management for e.g. different forest types and landowners through management rules. The advantage of using PlanWise is the possibility to find the most cost-effective solution among a nearly continuous scale of possible alternatives. On the other hand, problems with a high degree of stochasticity are difficult to formulate and solve with in the PlanWise application. For such problems, RegWise could be a better alternative. Finally, PlanEval is a MCDA application designed to evaluate and rank forest plans or scenarios created in PlanWise or RegWise. PlanEval is also available as a web version intended for participatory planning processes. The aim of the report is to present how the Heureka PlanWise application can be used in different types of analysis for mapping and valuation of the future state of the forest, and forest-related indicators for ecosystem services and biodiversity. More specifically, we show which indicators can be assessed, how the type of input data determines what kind of analysis can be done, and how to assess trade-offs between conflicting objectives. We give several examples from recent research projects

A decision support system for fostering smart energy efficient districts

The role of ICT is becoming prominent in tackling some of the urban societal challenges such as energy wastage and increasing carbon emissions. In this context, the concept of DAREED aims to deliver an integrated decision support system (DSS) to drive energy efficiency and low carbon activities at both a building and district level. The main aim of this paper is to present the technical concept of the Best Practices recommendation component of the DAREED system. This component seeks to compare and identify existing best practices to recommend practical actions to various stakeholders (e.g. building managers, citizens) in order to improve energy performance considering the global needs of a building. This paper also discusses the context of the three field trial sites (based in UK, Spain and Italy) in which the DAREED platform along with the best practices tool is to be tested and validated.This work evolved in the context of the project DAREED (Decision support Advisor for innovative business models and useR engagement for smart Energy Efficient Districts), www.dareed.eu, a project cofunded by the EC within FP7, Grant agreement no: 609082

Multi-Criteria Optimal Planning for Energy Policies in CLP

In the policy making process a number of disparate and diverse issues such as economic development, environmental aspects, as well as the social acceptance of the policy, need to be considered. A single person might not have all the required expertises, and decision support systems featuring optimization components can help to assess policies. Leveraging on previous work on Strategic Environmental Assessment, we developed a fully-fledged system that is able to provide optimal plans with respect to a given objective, to perform multi-objective optimization and provide sets of Pareto optimal plans, and to visually compare them. Each plan is environmentally assessed and its footprint is evaluated. The heart of the system is an application developed in a popular Constraint Logic Programming system on the Reals sort. It has been equipped with a web service module that can be queried through standard interfaces, and an intuitive graphic user interface.Comment: Accepted at ICLP2014 Conference as Technical Communication, due to appear in Theory and Practice of Logic Programming (TPLP

Multi-Sectoral Uses of Water & Approaches to DSS in Water Management in the NOSTRUM Partner Countries of the Mediterranean

Agriculture contributes an average of about 10% to the GDP of the partner countries of the Mediterranean involved in the project NOSTRUM. On the other hand, industry contributes an average of about 30% in these countries. It is to remark that in almost all countries the weight of industry accounts between 20% and 30% of the national economy, with the exception of Algeria, where this weight is at about 60%, mainly imputable to the great development of oil extraction and energy sector. In the majority of participating countries, agriculture sector is the greatest consumer of water (more than 65% of total water consumption). Although the case from France where agriculture water use is only about 10% of total water consumption and Italy with around 45%, but this may be due to the fact that most countries reporting for their agricultural water consumption do not include the amount of rain-fed to cultivated lands as a part of their agriculture water use. Most agriculture water use is limited to irrigation water from streams/rivers and groundwater. Rain-fed cultivated-lands in France is almost 90% of its total cultivated area. For Croatia, data given in National Report indicate a 0% of water use for agriculture. The average of water use for agriculture for all the basin is of 62.3% but with a great scatter expressed by a high standard deviation (26.8%) that reflects a wide variation range of water use for agriculture among different countries. The average of water use for agriculture is weakly less on northern countries (52.7%) than on southern countries (75.2) but the twice values are still on the range of the average of the all basin and cannot be taken as indication of difference between north and south. Integrated Water Resources Management (IWRM) plans are currently developed and implemented by various countries to organize the multi-sectoral water uses. On the other hand, the need for Decision Support System (DSS) as a tool in developing and implementing Integrated Water Resources Management (IWRM) is in growing demand. In spite of the great potential for the research and the development of DSS, the utilization of DSS in water management is not widely spread in the partner countries. In some countries, DSS was planned and developed at the scale of territorial integrated water management. Integration of DSS application to the existing IWRM systems at the partner countries would assist in satisfying the water related Millennium Development Goals (MDGs).Integrated Water Resources Management, Decision Support Systems, Mediterranean Basin

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