1- Introduction

2- Related works

3- Ramp-up Decision Support System

4- Experimental procedure and results

5- Discussion

6- Conclusions

References

Abstract

Production ramp-up is a key phase during the introduction or changeover of a production system. Process calibration and tuning are inevitably required to make such a system fully operational and let it reach its maximum production yield. A complex decision-making process takes place in order to optimally tune the system and requires a long time for testing and experimenting that will determine the system behaviour. This work considers the sequential nature of ramp-up and proposes a Cyber-Physical Systems approach based on data capturing, learning mechanisms and knowledge extraction, leading to an Industry 4.0 compliant Decision Support System (DSS) for human operators. The proposed system is implemented as an online DSS and also supports offline learning using previously gathered knowledge. A number of experiments have been carried out on a micro scale assembly station, validating the expected benefits of the proposed DSS. Results show a reduction of over 40% in the number of ramp-up steps required when using the DSS.

Introduction

Nowadays, production systems (PSs) are subject to rapid technological changes, which led to very short product lifecycles and high variety of products, to meet the growing demand of product customization (Kalir and Rozen, 2018). In addition to that, PSs becomes more modular and able to change from one configuration to another (Colledani et al., 2018). The rapid introduction of new products, mostly complex, and changeover cause a prolonged and more frequent production ramp-up (Letmathe and Rößler, 2019). Ramp-up phase is very important for PSs because having high outputs with good quality and fast cycle time are the key factors for increasing profits, where at the beginning of product life-cycle product prices are the heights and competition is scarce (Kalir and Rozen, 2018). Production ramp-up is a decision-making process where human experts decide on the best actions to fine-tune the process. It is a highly complex parameter tuning process with many interrelated factors leading to a well-defined goal. Studies on ramp-up report long lead times of up to a few months from the initial sys-tem built to full production (Terwiesch and Bohn, 2001). Since the product, PS, and the supply chain are new, uncertainty is very high, making the ramp-up process very difficult to manage (Hansen and Grunow, 2015). Also, ramp-up process is unstable, expressed in the unpredictability of the system at this stage, which makes planning proactive steps to avoid some problems ineffective for many ramp-up problems (Schuh et al., 2015).

Undoubtedly, high demands for food from the world-wide growing population are impacting the environment and putting many pressures on agricultural productivity. Agriculture 4.0, as the fourth evolution in the farming technology, puts forward four essential requirements: increasing productivity, allocating resources reasonably, adapting to climate change, and avoiding food waste. As advanced information systems and Internet technologies are adopted in Agriculture 4.0, enormous farming data, such as meteorological information, soil conditions, marketing demands, and land uses, can be collected, analyzed, and processed for assisting farmers in making appropriate decisions and obtaining higher profits. Therefore, agricultural decision support systems for Agriculture 4.0 has become a very attractive topic for the research community. The objective of this paper aims at exploring the upcoming challenges of employing agricultural decision support systems in Agriculture 4.0. Future researchers may improve the decision support systems by overcoming these detected challenges. In this paper, the systematic literature review technique is used to survey thirteen representative decision support systems, including their applications for agricultural mission planning, water resources management, climate change adaptation, and food waste control. Each decision support system is analyzed under a systematic manner. A comprehensive evaluation is conducted from the aspects of interoperability, scalability, accessibility, usability, etc. Based on the evaluation result, upcoming challenges are detected and summarized, suggesting the development trends and demonstrating potential improvements for future research.

Introduction

Human beings have cultivated lands and breed animals to obtain food for their survival since ancient times. This practice, known as agriculture, has evolved following a long-term and progressive process (Tekinerdogan, 2018), going from Agriculture 1.0 to 4.0, as shown in Fig. 1. In Fig. 1, Agriculture 1.0 refers to the traditional agricultural era, mainly replying on the manpower and animal forces. In this stage, though simple tools like sickles and shovels were used in agricultural activities, humans still cannot get rid of heavy manual labor, so productivity remained at a low level. Until the 19th century, steam engines were improved and widely used to provide new powers in all walks of life and industries, including agriculture. It came to the era of Agriculture 2.0 when various agricultural machineries were operated by farmers manually and plenty of chemicals were used. Obviously, Agriculture 2.0 significantly increased the efficiency and productivity of farm works. Nevertheless, this substantial improvement brought too harmful consequences: field chemical contaminations, destruction of the ecological environment, excessive consumption of powers, and waste of natural resources.

In complex systems, decision makers encounter uncertainty from various sources. In this paper, a new hybrid grey-based Multi-Criteria Decision Analysis (MCDA) approach is proposed to optimize the evaluation space in decision problems that are subject to subjective and objective uncertainty over different types of interrelated criteria. The four-phase methodology begins with the formulation of a decision problem through the analysis of the system of concern, its functionality, and substantial connections among evaluation criteria. The second phase involves the development of grey linguistic scales to handle the uncertainty of human judgements. The third phase integrates the grey linguistic scale, concepts of grey systems theory, and principles of Analytical Network Process to prioritize criteria. Finally, to evaluate and rank alternatives in such a complex setting, Preference Ranking Organization METHod for Enrichment Evaluation II is extended using a grey linguistic scale to articulate subjective uncertainty, grey numbers to account for objective uncertainty, grey operating rules to normalize evaluation measures, and the proposed approach of prioritizing evaluation criteria to establish relative preferences. To demonstrate the viability of the methodology, a case study is presented, in which a strategic decision is made within the context of innovation. To validate the methodology, a comparative analysis is provided.

Introduction

Decision makers usually encounter large amount of complex information. The complexity of decision problems increases when different evaluation criteria of different nature (e.g., qualitative and quantitative), different scales, and different values (e.g., continuous, discrete, and linguistic) are involved. Multi-Criteria Decision Analysis (MCDA) is therefore considered one of the most fruitful sub-disciplines of operations research. The main role of MCDA is to aid Decision Makers (DMs) in establishing a coherent picture about complex decision problems (Kurka and Blackwood, 2013). However, in many cases uncertainty-related aspects (i.e., uncertainty associated with limited objective information and uncertainty associated with subjective expert knowledge) are present. This adds to the complexity of analyzing the decision problems as the conventional MCDA approaches presume the availability of precise information (Kuang et al., 2015; Li et al., 2007). Various methods have been proposed to deal with different types of uncertainty-related aspects. Grey systems theory is recommended for decision problems with a relatively small amount of data (i.e., small samples) and poor information, which cannot be described by a probability distribution (Li et al., 2012; Li and Yuan, 2017; Liu and Lin, 2006). Accordingly, different researchers, which are presented in the next section, have considered the grey systems theory to address uncertainty in decision problems. The existing approaches assumed that DMs are able to assign the weights of the evaluation criteria precisely, did not consider the interrelationships among evaluation criteria, or did not consider the relations among evaluation criteria of different clusters, hence a better method is needed to address the existing research gaps.

We present an optimization procedure based on a hybrid version of an evolutionary multiobjective decision-making algorithm for its application in urban freight transportation planning problems. This tool is intended to solve the planning problems of a merchandise distribution firm that dispatches small volume fractional loads of fresh foods on daily schedules. The firm owns a network of distribution centers supplying a large number of small businesses in Buenos Aires and its surroundings. The recombination operator of the evolutionary algorithm used here has been designed specifically for this problem. It is intended to embody a strategy that takes into account constraints like temporary closeness, closeness time window and connectivity in order to improve its performance in the clustering phase. The representation allows incorporating specific information about the actual instances of the problem and uses adaptive control of the parameters in the calibration stage. The performance of the proposed optimizer was tested against the results obtained by two evolutionary algorithms, NSGA II and SPEA 2, widely used in similar problems. We use hypervolume as a measure of convergence and dispersion of Pareto fronts. The statistical analysis of the results obtained with the three algorithms uses the Wilcoxon rank sum test, which yields evidence that our procedure provides good results.

Introduction

Decision-making tools based on bio-inspired algorithms have been successfully used in logistics during the last decades. They have been continuously improved in the context of urban freight transport (UFT). The goal has always been increasing the efficiency and competitiveness of the firms, an objective usually hampered by the atomization of the sector and the complexity of logistic management at this stage of supply chains. A frequent issue involves taking into account in the decision-making process the needs of third parties since externalities over the relations with other agents may lead to quality and competitiveness losses in merchandise deliverance. We seek here to overcome those limitations by changing to a multi-objective cooperative objective approach, taking into account the interests of all the parties involved in the process, ranging from managers of distribution centers to the final customers. We proceed by developing a hybrid version of an evolutionary multi-objective algorithm addressing the problem of a firm delivering perishable fresh goods from several distribution centers, carrying relatively small fractional volumes to a large number of grocery stores in Buenos Aires

Introduction

Farming, for the most part being a mantle passed down from generation to generation, farmers feel obliged to stick to age old patterns of farming right from the seasons of working the ground to ritualistic harvest practices. Only recently in the past decade have farmers started adopting informed crop cycle patterns (Soleri and Cleveland (2004). In general, farmers have an opinion that more fertilizer equals more yields yet it cannot be farther from the truth. The role of chemical fertilizers is very important and acceptable, because it plays an important role in increasing soil fertility and increasing crop production (Jallah et al., 1991; Simonne et al., 2017). However, long term usage of inappropriate chemical fertilizers will decrease the quality of soil and increase the soil degradation and effects ecological pollution (Ayoub, 1999; Patnaik, 2010). Sometimes the fertility and quality of the soil is heavily affected due to the knowledge void of the farming community. The impact of the chemicals is the most severe among the various others. Savci (2012) and Ning et al. (2016) have mentioned the vulnerabilities of chemical fertilizers and the contamination of various types of environmental pollutions. Aziz et al. (2015) have reported in detail about the need of fertilizers and their impacts on environment. On this basis, we have come to know the effects of chemical agriculture and the adverse effects it has on our agricultural lands. So every attempt at remediating the damage done is being attempted by mindful and discerning farmers/agricultural engineers. But the efforts to shift from modern agricultural practices to primitive yet more effective methods of cultivation presents its own set of complications, as our lands have lost the bounce back ability to cope up with organic and natural cultivation methods to produce a similar quantum of agricultural produce upon termination of chemical inputs. Fertilizer usage and crop production are heavily interlinked processes, especially in horticultural crops (Fageria, 2001). Soil and Water quality, fertilizer quality and quantity, micro nutrients, and climatic factors, are all equally important, and if even one of them is not optimum, then the production is affected (Lal and Moldenhauer, 1987). Several models have been proposed by many researchers for the issue of fertilizer consumption considered through various points of view. Zhang et al. (2016) using the platform of Network of Science and Technology Backyards (STB), have examined fertilizer consumptions during the span of crop cultivation. As per their findings, the yield of wheat increased by 11% with addition of 1.5% Nitrogen alone. Nájera et al. (2015) evaluated soil fertility from soil samples of 31 maize cultivating farmers by testing micronutrient and macronutrients. They found that the Maize does fairly well in neutral- alkaline soils along with high inputs of NPK and Zn. Delzeit et al. (2017) presented a concept of trade-off between crop production and crop diversity, along with crop land expansion for improving food security, in the context of possible food shortage scenario in the decades to come (Natarajan et al., 2016) have analysed sugarcane yields in Coimbatore, Tamil Nadu and have suggested optimum parameters for both soil nutrients as well as environmental factors for cultivating sugarcane. Toyonaga et al. (2005) addressed the problem of crops chosen seasonally in Japan. Apart from their formulated problem, they also introduced the concept of fuzzy based profit coefficients for a more efficient and effective farming model. Fan et al. (2011) concentrates on the use of minimum amount of fertilizer to get efficient outcome with respect to environmental pollution in China. Papadopoulos et al. (2011) analysed the nitrogen fertilization requirement for cotton through a fuzzy based decision support system, which analysed 30 years of rainfall data and 8 years of temperature data.

Introduction

As data mining, decision support systems, and artificial intelligence are quickly entering mainstream technologies, we have to account for the human nature that is innate in people. The recent interest in the field of User Experience (UX) is an important demonstration that software and hardware need to be easily and quickly understood by the users 8. As the user interface is only a filter that mediates between a very subjective user and a very matter-of-fact series of algorithms, we also need to identify ways to let the underlying processes understand and account for the very human concept of imprecision. Keeping the human nature in mind is not just a concept that is applicable to user interfaces, but also to the processes powering decision support systems 4. The vague and often imprecise nature of humans is an essential element to take into account also in the back-end of analytical software. In particular, we have to account for the arbitrary nature of linguistic variables and quantifiers 12, such as ”People purchase more umbrellas when it is very cloudy,” and temporal associations 2, such as ”Sales in football jerseys spike in the days following a team’s victory.” The need for a more elastic reasoning model is even more significant when we put the two concepts together, for example when we warn a friend that ”traffic gets very intense on highway XYZ shortly after it starts raining, even if it is just a light rain. This paper presents a practical approach to creating an association analysis system that leads to a more meaningful and actionable set of recommendations. The workshop-oriented nature of the venue in which this paper is presented lends a great opportunity to explore the inner-workings of this analysis methodology as well as some of the fundamental concepts on which it is based.

Introduction

Metal casting is considered as a 7000-year-old technology which offers a large range of routes in order to produce the components with a range of sizes, shapes, quantities, quality and metal requirements (Akarte et al., 1999). Metal casting is the simplest process in which mostly gives a direct routing to the shape of the product and it gives least expensive. The process of metal casting requires a mould cavity with the desired shape and metal that is molten is poured over the mould cavity. The main objective of the metal casting is produced beneficialimplements for the human consumption and also for the beautiful work of art. From the ancient art casting, it is clear that modern industrial casting has their production which has significant skill and technological skill. In the ancient times learned and traditional skill has been used for the ages and experiences in order to produce acceptable casting. The quality of the metal casting is affected by a number of parameters which are found at the different process stages. For example, defects that may result from improper parameters of the moulding and core sand include pinholes due to the presence of hydrogen, sand deformation, gas fractures, and shape imperfections. Defects that may result from improper construction or improper assembly of the pattern andmouldsare fractures, shape imperfections, sand presence, is runs, cracks, gas presence, surface defects, mechanical damage, knob, flash, mismatch, pushing up, warping. Gases tend to dissolve in the liquid steel at all stages of the production of castings, i.e. during melting in the furnace, during tapping, during pouring of moulds, and even after pouring of the mouldsbefore complete solidification of the casting. Therefore, reducing or eliminating casting defects such as blowholes, voids in the cast structure, pinholes, non-metallic presence or porosity, and scaling on the surface of casting requires strict control of the whole process of melting and casting. The control of charge and compliance with the technological regime during melting of alloys in a furnace for casting are particularly important in the absence of vacuum treatment of liquid metal (in an induction furnace or ladle). Possible defects caused by incorrect melting include is runs, slag presence, tears (caused by excessively high temperature), gas presence, incorrect chemical composition, and pinholes(Wilk-Kołodziejczyk et al., 2014). The systematic review of the designer and robust in a way for evaluating the options that are available for identifying the best. The process selection is a manufacturing task in order to choose a method to transform the materials in to the one or more processes. The process considered to be more economical, and they are subjected for the meeting of technical constraints(Dieter, 1991). The manufacturing and material processing is a selection process problem in which multi-attribute decision-makingproblem. The decision is made during the preliminary design of an environment that is uncertain in the parameters, relationship and requirements. Material and process selection (MPS) occurs before the beginning of design for manufacturing (DFM) (Karthik et al., 2003).The study indicates that the product cost is around 5% of the total product cost, and the design is made of the stage effect of the product cost (Chen and Feng, 2000).

The development of modern science and technology tends to accompany environmental crisis and ecological degradation. And the evaluation of the security condition of the forestry ecosystem has become an urgent task. This paper details the development of a decision support system (the FESEDSS), which was built to provide a range of actors with an effective tool for the evaluation and governance of forestry ecological security in China. The FESEDSS considers multiple factors through a general decision-support framework, making possible the calculation of ecological security indexes and relative indexes for the forestry ecosystem and its three subsystems (forest, wetlands and desert) at both the national and provincial level. The system integrates econometric models, mathematical methods, geographic information systems and a dynamic database, providing users with an understanding of spatiotemporal patterns and regional differences in the ecological security of the forestry ecosystem and its three subsystems in China’s 31 provinces. Results generated using the FESEDSS and addressing the period 1999–2012 demonstrate a positive change in forest ecological security condition and a negative change in wetland and desert ecological security. Spatially, the ecological security of the forest subsystem was shown to have improved significantly in eastern and central China, while in the western region it experienced degradation. The ecological security of wetland ecosystems performed well in the southwest region and the middle and lower reaches of the Yangtze River; and desert ecological security was shown to be of a higher standard in south of Qinling-Huaihe Line than in the northern regions of the country. The overall forestry ecological security condition (which integrates the three subsystems) demonstrated an upward trend with some fluctuations, with high values generally being located in the south and low values in the north of the country. Finally, the paper also discusses performance, uncertainty, and implementation challenges, as well as detailing potential extensions of the FESEDSS. The paper lays a foundation to the national forestry ecological security evaluation and monitoring.

Introduction

China is a populous country with a vast territory and a tremendous diversity of biological resources. In recent years, however, its forest ecosystems have begun to suffer severe degradation as a result of human disturbances of differing durations, intensities, frequencies and types (Dai et al., 2006). A large proportion of the country’s wetlands have been converted to industrial, agricultural and construction land uses; this, in combination with environmental pollution and an excessive utilisation of resources, has in turn led to reductions in the quality of wetlands and a marked decrease in biodiversity. Human activities such as the overexploitation made possible by land reclamation, practices of overgrazing and the degradation of water resources have all further contributed to advancing land degradation and soil erosion, a trend ultimately linked to a soaring rate of desertification. In order to resolve the ecological problems addressed above, China’s central government has put forward a series of policies and schemes to improve ecological security. Environmental protection forms one of the country’s basic national policies, and the government has also mapped out a number of important strategies to realise sustainable economic development. Ecological security is one of the most important aspects of environmental protection. Maintaining ecological security has become a critical task for human societies, in our attempt to achieve sustainable development in the 21st century (Wang et al., 2014; Li et al., 2014). The “forestry ecosystem” – a term put forward here to describe the management of not only forest ecosystems but also wetland and desert ecosystems – guarantees the biological function and terrestrial eco-environment of the earth (Lu et al., 2002). The higher the level of stability and security within the forestry ecosystem, the greater the level of protection it in turn can offer in relation to the environment as a whole. The evaluation of the security condition of the forestry ecosystem has thus become a hot topic in the research community. An urgent task exists in establishing a decision support system to effectively evaluate the ecological security of forests, wetlands and deserts and scientifically extends the results of such evaluations via practical applications.

Tourism is rapidly becoming a sustainable pathway toward economic prosperity for host countries and communities. Recent advances in information and communications technology (ICT), the smartphone, the Internet and Wi-Fi have given a boost to the tourism industry [1–5]. In the last thirty years, the energetic growth and development of the tourism industry has been mirrored by the growth of ICT [6], which adds value to tourism services and products throughout the value chain. Nowadays, all tourism-related activities, from hotel reservations and air tickets to visa applications, are pre-planned online (see Fig. 1). The capacity of mobile technology in assisting travelrelated decisions has substantially improved with respect to that offered by the early prototype of mobile tour-guides. From GPS-supported systems and camera phones to today’s smartphones with enhanced information and communication capabilities [7], we have witnessed an ICT escalation that has provided a very fertile ground to promote tourism. Sightseeing in cities is an indispensable part of almost all tourists’ plans. A large body of research is dedicated to identify the most attractive places and major landmarks for tourists to visit. Once identified, these places, usually referred to as places of interest (PoI), need to be reached and visited by the tourists, which is the problem we focus on in this study. City bus tour (CBT) service, that is, a specific bus service providing pre-planned tours to a number of PoI, is an alternative commonly preferred to public transportation. Thus, the problem becomes the design of CBT routes that allow the tourists to save time while reducing the operational costs of the city bus company. Due to the surge of interest shown by experts of different disciplines, city tour planning tools have proliferated, incorporating an array of useful services [8]. However, the existing tools still fail to address several practical aspects, hence compromising their utility in realistic tourist scenarios [9]. Formally speaking, the problem belongs to the class of minimum cost network flow problems [10, 11] that are in general intractable (NP-hard) and involve optimal and/or heuristic algorithms and methods characterized by different types of time complexity [12–19]. This is particularly true when dealing with networks of extremely large size. Note that while this can also be the case of urban networks, the road networks connecting PoI are usually small in size or composed of small size blocks. Thus, the main issue is to formulate a mathematical model that makes the problem tractable. From a practical viewpoint, the goal is to guarantee that a solution (minimum cost flow) exists and can be obtained using a specific optimization software with relatively good performance (time complexity: reasonably fast).

Flood incidents can endanger human life, cause extensive property damage, and result in significant harm to the environment. To attenuate the risk and reduce the loss caused by flood accidents, flood forecasting has been studied and developed throughout human history. Although global or nationwide flood forecasts and warnings are available through mass media, the comparatively low accuracy of prediction for a certain region causes false alarms, improper responses, and therefore unnecessary loss of property and/or life. One conventional method to improve the accuracy is to increase the resolution or decrease the based cluster size. Either way, the occupancy of computational resources must be increased enormously. The higher the resolution and the smaller the cluster size, the more computing power is needed. Another alternative method is to develop stand‐alone systems only for small regions. Recent examples include using ensemble numerical weather prediction systems for medium‐range flood forecasting (Cloke & Pappenberger, 2009); applying data‐driven approaches, such as traditional artificial neural networks, adaptive neuro‐fuzzy inference systems, wavelet neural networks, and hybrid adaptive neuro‐fuzzy inference systems with multiresolution analysis using wavelets to develop models for hourly run‐off forecasting at Casino station on the Richmond River in Australia (Badrzadeh, Sarukkalige, & Jayawardena, 2015); coupling meteorological observations and forecasts with a distributed hydrological model to advance flood forecasting in Alpine watersheds (Jasper, Gurtz, & Lang, 2002); coupling HEC‐HMS with atmospheric models for predicting watershed run‐off in California (Anderson, Chen, Kavvas, & Feldmand, 2002); and combining multimodels for operational forecasting for river basins in the Western United States (Najafi & Moradkhani, 2015). Although the models or systems listed above provided overall better performance for the whole river basins or catchments examined in the cited studies, the accuracy of forecasting for a small place such as a small town, a little community, and a specific house was not mentioned or was completely ignored. The reason is the same: To obtain accurate forecasting for a comparatively small place, the resolution of the entire studied region of the river basin or catchment must be higher (Luo, Xu, Jamont, Zeng, & Shi, 2007). More detailed local situations must be collected and considered, more memory space must be allocated, and a heavier computational burden must be loaded onto the models that already have vast amounts of meteorological, hydrologic, and hydraulic data to analyse through complicated calculations (Cloke & Pappenberger, 2009; Fang, Xu, Zhu, et al., 2014b). In fact, most incidents begin and end locally and are managed at the local level (DHS, 2013). The most useful data are locally collected, although it is correlated with the data from outside the specific region.

The debate over the effectiveness of integrated water resources management (IWRM) in practice has lasted for years (Jeffrey and Gearey, 2006; Biswas, 2008; Quevauviller, 2010; Giordano and Shah, 2014). As the complexity and scope of IWRM increases, the difficulties of hydrological modeling are shifting from the model itself to the links with other cognate sciences and to understand the interactions among water, earth, ecosystem and humans. This paper presents the development and implementation of a decision support system (DSS) that links the outputs of hydrological models with real-time decision making on social-economic assessments and land use management. The DSS assists the decision-making in a qualitative manner based on the outputs of hydrological models and knowledge of experts in cross-disciplinary fields. There are currently many DSS models with economic assessments (Wenkel et al., 2013; West and Turner, 2014; Yue et al., 2014; Pedro-Monzonís et al., 2016), focusing on single topic (e.g., climate change, land use, agriculture, water account), and many socio-economic models (Flörke et al., 2013; Arnell and Lloyd-Hughes, 2014; Herrero et al., 2014; Visconti et al., 2015) without simulation of hydrological processes. Few model is capable of integrating meteorological, geographical, ecological, social and economic factors, which is based on the simulation of hydrological models. Little research has been done to show the interactions of so many crossdisciplinary researches on IWRM to understand ecosystem services (ESS) and integrate them into land and water management. Due to model complexity issues, many DSS models have to consider less issues within a specific research framework (McCown, 2002; Basso and Ritchie, 2015). However, the model complexity of DSS is worth the effort (Chenoweth et al., 2004; Power et al., 2015) to increase model accuracy on the comprehensive analysis in IWRM. Since the last decade the Chinese government has been promoting the development of western China. The demographic development and socio-economic change has led to a rapid change of land use systems in the Tarim River Basin, where it is the habitat for more than 80% of Populus euphratica trees in China, and has substantially affected the quantity and quality of arable soil, surface water, and groundwater (Zhao et al., 2009; Zhang et al., 2014; Rumbaur et al., 2015). These changes in soil and water have large impacts on the crop production and natural vegetation (Chen et al., 2015; Zhang et al., 2015), such as the P. euphratica trees. Since 2011, the German Ministry of Science and Education Bundesministeriums fuer Bildung und Forschung (BMBF) established the Sino-German SuMaRiO (www.sumario.de) project for sustainable management of river oases along the Tarim River. A cross-disciplinary consortium of 11 German and 9 Chinese universities and research institutes joint together for the research on SuMaRiO and the DSS. Project SuMaRiO focus on realizable management strategies, considering social, economic and ecological criteria. This will have positive effects for nearly 10×106 inhabitants of different ethnic groups. The DSS is the main outcome of SuMaRiO. The overall goal of the DSS is to integrate all crucial research results of SuMaRiO, including stakeholder perspectives, into a model based decision support system, which allows a sustainability impact assessment within regional planning on land and water management in the Tarim River Basin to understand ESS and interactions with water, earth and humans. The DSS is an indicator based tool that enables stakeholders and decision-makers to evaluate the consequences of their intended actions, which helps to implement sustainable land and water management measures in the upcoming development plans (Disse, 2016).

Logistics is vital to the success of supply chains and inevitably linked with the overall organizational performance. The activities may involve freight transport, materials handling, storage and inventory management. Logistics has the potential of bringing tremendous impact on local as well as global trade by aiming to supply the right items at the right place and right time. In the past two decades, there has been a growing concern for environmental and social impacts of logistics besides economic aspects. In this regard, the term ‘green logistics’ and ‘sustainable logistics’ has been used by organizations and academic researchers. According to Chang and Qin (2009), “The green logistics refers to plan, control, management and implementation of the logistics system through the advanced logistics technology and environmental management, aiming to reduce the pollutant emission.” Zhao et al. (2009) further explained that “the meaning of green logistics is to improve resource utilization, reduce resource consumption and waste, and minimize environmental pollution when implementing logistics activities, through rational planning, optimize resource allocation and use environmental technology”. Mckinnon et al. (2012) defined ‘green logistics’ as the study of the environmental effects of all the activities involved in logistics in both forward and reverse directions. Sbihi and Eglese (2010) suggested that “green logistics is concerned with producing and distributing goods in a sustainable way, taking account of environmental and social factors.” The most widely accepted definition of sustainable development was given by the World Commission on Environment and Development which is concerned with the fulfilment of the needs of the present without compromising the ability of future generations to meet their own needs (WCED, 1987). Elkington (1997) suggested the consideration of three closely interrelated economic, ecological (environmental) and social aspects of sustainability in a “triple-bottom line”. The scope of sustainability in supply chain and logistics has also been borrowed from these concepts. However, there is still a lack of consensus among researchers over a unified definition of green and sustainable logistics, and both of these terms have been used interchangeably in the literature with a different implied scope. Moreover, the formal definition of ‘sustainable logistics’ is relatively scarce in literature compared to ‘green logistics’. It is evident that environmental issues are extensively addressed in the literature in comparison to that of social issues, which is partially covered under green logistics. The lack of research addressing social issues in sustainable logistics gives the field a direction to develop further considering sustainability in logistics as a whole giving equal importance to economic, environmental and social aspects. In the context of this study, we are limiting our scope to those papers that have addressed economic as well as at least either the environmental or social dimension of sustainability.

Nowadays smart waste management is an integral part of Smart Cities (Schaffers et al., 2011; Anagnostopoulos et al., 2017). In October 2013, an article published by the (World Bank, 2013) states that ‘By 2100,…, the growing global urban population will be producing three times as much waste as it does today’. This means that the need for an efficient waste management system is exceptionally high. For example, current practice of garbage collection in Melbourne, Australia (www.melbourne.vic.gov.au, 2017) requires the municipality to hire special workers, i.e. garbage truck drivers, for collecting garbage, and they still need to go through all the streets where the bins are located on a weekly basis, and receive the same salary regardless of the garbage bins in their area being full or partially empty. This creates extra cost to the municipality and produces additional air pollution as a result of unnecessary driving. This research aims to improve environmental sustainability using sensor-based technology in waste management. The goal of the research is to facilitate information exchange between citizens, dispatchers of the garbage truck fleet and drivers themselves by employing modern sensor technology and information system infrastructure for decision support. This paper describes an innovative decision support system (DSS) architecture to support this process as an artefact of the design science research. This DSS is intended to aid a dispatcher in solving the optimal route problem for garbage collection based on the capacity and type of the trucks, and the start and end locations of the path. Using sensor technology, system collects all the notifications from the bins needed to be emptied, and shows them on the dispatcher’s map, proposing the route to follow for garbage collection. We also consider a different type of vehicles designed to serve the needs of the citizens. Since the research goal is to find an optimal path for a particular truck, this is a modified heterogeneous vehicle routing problem (VRP), that is a modification of the classical VRP, which is capable to consider different garbage truck capacities.