Editorial: Smart and sustainable supply chain and logistics - trends, challenges, methods and best practices

The COVID-19 pandemic has created a lot of disturbances in supply chains around the globe leading to disturbances in the availability of logistics infrastructure (e.g., ports closure, boarders’ control, etc.) and workforce absence. Companies have decided to digitalize their logistics processes and business operations in order to establish the resilience to changing market conditions and enable increased agility and higher performance. The increasing digitalization of the processes in logistics and the need for more integrated and seamless cooperation in logistics, supply chain, inventory and production management currently belong to the dominant trends in the business world. Moreover, the pressure for CO2 emissions reduction and more resource-efficient business and waste management models strongly influence the organization of logistics operations on both a local and global scale. The integration of physical and cyber systems is necessary in order to achieve more environmentally friendly, efficient, safe logistics, supply chain, inventory and production operations

The continuously expanding pools of managerial data and the sets of environmental and technical constraints have triggered the development of new mathematical models that ought to be the foundations of most advanced simulation and decision making techniques that are just what the business and economic areas of logistics, supply chain, inventory and production management urgently require. All of these emerging problems are facing the high obstacle of uncertainty that has become the striking characteristic of the many items included in the operations aforementioned, at every stage and every moment. Here, we also mention stochastic disruption and regime switching. All the smart processes of learning, improvement, optimization and control under uncertainty are core purposes of modern operations research, data mining, analytics, machine learning, and artificial intelligence (AI) that will be discussed, used and refined in this special issue.

This special issue covers a wide range of topics related to logistics, supply chain management and related areas where advanced operational research (OR) and management science (MS) methods, theories, and methods are used and further developed. The scope of the special issue study includes the following areas, methodologies and challenges: logistics, supply chain management (SCM), inventory management (IM), and production management (PM). We wish to provide a short overview of the contents of this special volume.

Sustainability in logistics and supply management (SiLSSM) studies included the following topics

• Circular economy and closed-loop supply chain.

  The Circular Economy (CE) concept encourages the redesign of industrial activities and social practices to decouple economic growth from negative environmental impacts. The emphasis is on creating more resource-efficient supply chain that enable reduction, reuse and recycling, thus supporting the transition to a zero-waste economy. Closing the loop for materials flows in a supply chain is challenging due to the high uncertainty, thus the new modelling and optimization approach contribute to more efficient decision making.

  Ishizaka et al. in the article “Supplier selection in closed loop pharma supply chain: a novel BWM–GAIA framework” employ a three-stage research method to define a hierarchical structure of criteria for evaluating supplier performance using the best-best method (BWM). Additionally, they apply a geometric analysis plane for interactive assistance (GAIA) on the BWM results.

  Bakhshi and Heydari in the paper “An optimal put option contract for a reverse supply chain: case of remanufacturing capacity uncertainty” propose a decentralized and centralized decision making model, which helps a remanufacturer to deal with uncertainty in two echelon supply chain.

  Huang et al. in the article “Supplier–remanufacturing and manufacturer–remanufacturing in a closed-loop supply chain with remanufacturing cost disruption” apply Stackelberg game to compare supplier–remanufacturing and manufacturer–remanufacturing with respect to equilibrium strategies and profits, under different disruptions.

  Zhang and Zhang in the paper “Optimal pricing and greening decisions in a supply chain when considering market segmentation” study the optimal pricing and the remanufactured product’s greening decisions in a supply chain with one manufacturer and one retailer. They apply Stackelberg games for three remanufacturing systems, (centralized, decentralized manufacturer, and decentralized retailer-remanufacturing) to demonstrate the conditions under which the manufacturer or retailer should engage in remanufacturing. They consider traditional and green consumers to find the optimal solution.

  Ke et al. in their work “Pricing new and remanufactured products under patent protection and government intervention” consider a three-period closed-loop supply chain model framework. They investigate the basic model in which companies determine prices to maximize profit according to consumers’ strategic behavior and green preference. Furthermore, they extend the model and investigate the impact of patent protection and government intervention on company’s operational strategies, profitability, and consumer surplus.

• Reverse logistics and waste management.

  From the managerial perspective Reverse Logistics (RL) includes the planning, implementing, and controlling the flow of materials, and finished goods from the point of production, distribution, or use to the point of recovery or proper disposal. Designing and managing an appropriate reverse logistics network is challenging, as often the reverse flows are distributed and thus not cost-efficient.

  Egri et al. in the work “Robust facility location in reverse logistics” study a facility location problem with the collection and transportation of wood to designated processing facilities. They develop mathematical models to achieve the economy of scale. A novel approach with bilevel optimization is used to compute the exact solutions of the robust problem in smaller instances. A local search and a tabu search method are used to solve problems of realistic size.

  Tirkolaee et al. in the paper “A novel model for sustainable waste collection arc routing problem: Pareto-based algorithms” develop a novel mixed integer linear programming (MILP) model to formulate the sustainable periodic capacitated arc routing problem (PCARP) for municipal waste management (MSW). Their solution allows for simultaneously minimizing the total cost, workload deviation, and total environmental emission, They design a novel hybrid multi-objective optimization algorithm, with simulated annealing algorithm (MOSA) and multi-objective invasive weed optimization algorithm (MOIWOA).

  Hosseini-Motlagh et al. in the paper “Reverse supply chain management with dual channel and collection disruptions: supply chain coordination and game theory approaches” study the effect of collection competition between the collector channel and the remanufacturer channel on the acquisition prices offered to consumers as incentive schemes for returning used products. They search for an equilibrium for the sales prices of remanufactured products, the acquisition prices, and the transfer price in the settings of decentralization, centralization, and coordination.

• Carbon footprint management, low carbon supply chain design, and optimization.

  In recent years, policies and regulations with focus to reduce carbon emissions worldwide (e.g., adoption of the Paris Agreements in 2015) have changed the gravity of the decision making in supply chain management. Carbon regulatory mechanisms can take different forms, such as: Carbon cap policy (CCP), Carbon tax policy (CTP), Carbon cap-and-trade, policy (CCTP), and Carbon offset policy (COP). The carbon footprint measures in a cumulative way the total amount of carbon dioxide emissions that are directly and indirectly created by operations and processes throughout the product’s life cycle. The need to include the aspect related to the carbon footprint in the design and management of logistics operations creates a demand for new methods and tools. The carbon footprint in a supply chain can be reduced by applying more sustainable modes of transportation transport, related routing decisions, but also by redefining the design of whole supply chains (low carbon design, low carbon routing, etc.).

  Ali et al. in the article “Developing a hybrid evaluation approach for the low carbon performance on sustainable manufacturing environment” explore the enablers of green sustainable practices of procurement, logistics, product and process design, and regulatory frameworks for reducing the carbon footprint.

  Das et al. in the paper “Multi-objective solid transportation-location problem with variable carbon emission in inventory management: a hybrid approach” propose a new heuristic with alternating locate-allocate heuristic and the intuitionistic fuzzy programming to get the Pareto-optimal solution.

  He et al. in two papers “To pool or not to pool in carbon quotas: analyses of emission regulation and operations in supply chain supernetwork under cap-and-trade policy” and “Differential game theoretic analysis of the dynamic emission abatement in low-carbon supply chains” apply game models, and study the impact of different carbon regulatory mechanism on a supply chain performance and carbon dioxide reduction.

  Homayouni et al. in the article “A robust-heuristic optimization approach to a green supply chain design with consideration of assorted vehicle types and carbon policies under uncertainty” apply the multi-choice goal programming model to study sustainability strategies for carbon regulations mechanisms. They propose a novel robust-heuristic optimization approach which support the supply chain managers in decision-making in selecting of carbon emission policies in supply chains with different vehicle types, demand and economic uncertainty in large scale-problems.

  Li and Lai in the paper “The abatement contract for low-carbon demand in supply chain with single and multiple abatement mechanism under asymmetric information” propose a novel principal-agent model to reduce carbon emissions through improved manufacturer’s abatement efficiency through more accurate information about demand and customer’s preferences.

• Sustainability assessment and its impact on sustainable supply design.

  Sustainability practices in a supply chain are predominantly investigated through the triple bottom line (TBL) framework, which distinguishes three dimensions of sustainability, such as: economic, environmental, and social. To implement and monitor a more sustainable performance, companies need to define a set of performance measures, which provide decision-makers with a transparent, and a good understanding about what happens in a supply chain at present, and to direct them towards optimal future actions.

  Ali et al. in the article “Evaluating sustainability initiatives in warehouse for measuring sustainability performance: an emerging economy perspective” model a case study from Saudi Arabia to rank the sustainable practices in the warehousing. This three-phase study applies the contingency theory and triple bottom line approach to investigate sustainable practices of warehousing with fuzzy Delphi and the Best Worst Method.

  Fulzele and Shankar in their work on “Performance measurement of sustainable freight transportation: a consensus model and FERA approach” propose an integrated approach to develop a performance index that supports decision making in a supply chain. They innovatively combine the Consensus Model (CM) for assessing the degree of importance of Key Performance Indicators (KPIs) with the Fuzzy Evidential Reasoning Algorithm (FERA) for the aggregation of subjective judgments with crisp quantitative values.

  Jauhari et al. in their paper on “Sustainable inventory management with hybrid production system and investment to reduce defects” analyze a vendor which performs a regular production and a green production. They propose a model for cost reduction, which allows them to find the optimal shipment quantity, production allocation, number of shipments, safety factor, defective rate, and production.

  Moreno-Camacho et al. in their paper “Sustainable supply chain network design: a study of the Colombian dairy sector” propose mixed-objective linear programming model with four decisions and three sustainable criteria. The proposed model supports decision making in food sector by determining the optimal location and capacity of the processing and distribution facility and mode of transportation to comply with environmental and social rules without neglecting economic performance.

  Tao et al. in their paper “Optimal channel structure for a green supply chain with consumer green-awareness demand” analyze four different settings: a manufacturer’s dual-channel supply chain, a retailer’s dual-channel supply chain, a manufacturer-online and retailer-offline (hybrid I) structure, and a manufacturer-offline and retailer-online (hybrid II) structure. They assess the of impact of consumers’ green awareness and shopping habits (online or offline) on the level of green technology, profits, and retail prices.

  Koppiahraj et al. in the article “Optimal sustainability assessment method selection: a practitioner perspective” evaluate ten sustainability assessment (SA) methods, and 20 critical factors of sustainable manufacturing (SM). They apply a fuzzy MULTIMOORA (Multi-Objective Optimization on the basis of a Ratio Analysis plus the full multiplicative form) to rank SA methods with the weights of the critical factors of SM practice obtained using a fuzzy analytic hierarchy process (AHP).

  Ciardiello et al. in the paper “A game-theoretic multi-stakeholder model for cost allocation in urban consolidation centres” design mechanisms that support the economic and financial sustainability of UCC systems. They consider the problems of responsibility and cost allocation among stakeholders of a sustainable urban freight network. Applying the Shapley Value concept, they study the economic viability under different scenarios, including the outsourcing of the last-mile deliveries.

  Kwon et al. in their work on “Dynamic interplay of environmental sustainability and corporate reputation: a combined parametric and nonparametric approach” propose a novel PROCESS-Neural network to explore the holistic effect of environmental sustainability and corporate reputation on the performance of a company.

• Green mobility and green routing.

  Despite advancements in battery technology, the range anxiety of electric vehicles (EVs) remains a major obstacle to their adoption in logistics operations. The need to frequently recharge the battery adds complexity to the operational planning when dispatching last-mile vehicles. The energy consumption of electric vehicles may vary depending on various factors, for example ambient temperature, weight, speed, and road conditions.

  Aghalari et al. in the work “Electric vehicles fast charger location-routing problem under ambient temperature” propose two innovative heuristics with the two-phase Tabu Search-modified Clarke and Wright algorithm and the Sweep-based Iterative Greedy Adaptive Large Neighborhood algorithm. Their approach supports decision making on the design of DC fast charging stations for EVs in cities with high temperature fluctuations.

  Rastani and Çatay in their paper “A large neighborhood search-based matheuristic for the load-dependent electric vehicle routing problem with time windows” take into account the role of the weight of a cargo in energy consumption and related routing decisions for a fleet of EVs. They propose a matheuristic approach that integrates an optimal repair procedure into the large neighborhood search method.

  Tirkolaee et al. in their work on “A novel two-echelon hierarchical location-allocation-routing optimization for green energy-efficient logistics systems” aim to minimize the total cost (shipping, environmental pollution, travelling, vehicle usage, and fuel consumption) with Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms.

• Sustainable humanitarian logistics operations.

  Today, logistics is one of the most important instruments for disaster relief. Natural disasters cause devastating effects, and major problems with supplying commodities such as food, medicine, etc. Logistics planning is an essential component in addressing the initial needs immediately after any disaster. Planning and coordination of transportation and distribution of emergency supplies in a resource efficient way is a challenge. Research is needed on methods and tools for logistics operation planning, optimization of location of the key points in the logistics network for supply storage, and coordination mechanisms of the teams involved in the relief operations.

  Eligüzel et al. in the paper “Location-allocation analysis of humanitarian distribution plans: a case of United Nations Humanitarian Response Depots” apply three different location-allocation models to analyze the current distribution plan in the UNHRD distribution network, including maximum coverage, P-median, and set covering. The focus is placed on the maximization of the number of covered people, and reductions in total distance travelled, time, and costs.

  Ergün et al. in their article “A game theoretical approach to emergency logistics planning in natural disasters” analyze the case of Turkey, where a significant part of the population lives in the earthquake-prone areas. They apply a cooperative game theoretical model for emergency logistic planning with a flow problem to support decision making on the maximization of the transferred commodity.

Recent advances in operational research, data mining, analytics, machine learning, and AI in development of logistics network and strategies

• Smart technologies.

  The dynamic development of information technology has promoted the digital transformation of supply chains and logistics operations. Stakeholders can collect, store, and analyze data to enable achieving improved performance due to better coordination and reduction of uncertainties resulting from information asymmetry.

  Ben-Daya et al. in the paper “Optimal pricing in the presence of IoT investment and quality-dependent demand” propose a model based on the Stackelberg game to analyze the impact of the Internet of Things (IoT) on the retailer and distributor performance. They use the model to justify the investment in IoT and to decide where to deploy IoT technologies in the supply chain.

  Liu et al. in the article “The influence of leadership and smart level on the strategy choice of the smart logistics platform: a perspective of collaborative innovation participation” consider a two-tier supply chain with value-added service innovation. They apply a game model consisting of a smart logistics platform and a smart logistics provider. They study the strategic choice of the platform about whether to collaborate with the provider in order to co-innovate depending on the leadership and smart level.

  Seddigh et al. in their work “Approaching towards sustainable supply chain under the spotlight of business intelligence” develop a theoretical model to explain how business intelligence capabilities influence the company’s supply chain sustainability. Through an empirical survey in the Iranian pharmaceutical industry, they investigate the relationships between business intelligence and sustainability dimensions in supply chains.

  Wang et al. in the paper “Decision optimization in service supply chain: the impact of demand and supply-driven data value and altruistic behavior” develop five analytical models with two service providers (SPs) and one service integrator (SI). They discuss the paradox and effects which appear in data-driven supply chain under different settings and find an “optimal purchasing area” which allows us to obtain higher-level value-added service at a lower price.

• Design of logistics network and supply chain coordination.

  The modelling and optimization of the logistics network play crucial role in achieving cost-efficient and competitive supply chains. The uncertainty which is inherent in the economic environment make companies search for new methods and tools which allows them to establish the robustness and resilience in supply chains.

  Amin-Tahmasbi et al. in the paper “A multi-objective integrated optimisation model for facility location and order allocation problem in a two-level supply chain network” design a mixed-integer multi-objective integrated mathematical model for a cost-efficient dynamic decision-making within a multi-period time horizon. They explore the concept of factoryless production to find the optimal replenishment number of production facilities by the multi-objective functions (minimisation of the total cost, rejected and late delivery units, and maximisation of the assessment score of suppliers). For solving large-scale instances, they apply the Multi-Objective Particle Swarm Optimisation (MOPSO) and Multi-Objective Vibration Damping Optimisation (MOVDO) heuristic.

  Chatterjee in the paper “Multivariate supplier selection for asymmetric specification region: using price and quality” designs the multivariate process capability index (MPCI) based approach with the conventional phase wise hypothesis testing procedure. The multivariate capability-price comparison (MCPC) chart is used for graphical presentation. The proposed approach contribute to the theory by handling simultaneously quality and price constraints in decision making on the supplier selection.

  Gerami et al. in the work on “A novel network DEA-R model for evaluating hospital services supply chain performance” propose the model to overcome the underestimation of efficiency and pseudo-inefficiency scores in logistics management. They extend the network data envelopment analysis (NDEA) model with the ratio data (NDEA-R) to evaluate the performance with the given internal structure of a supply chain, relationships among different divisions of an SC with two assumptions of free-links and fixed-links. The results are provided from the testing of the new model in a supply chain of 19 hospitals in Iran over six months.

  Panchal et al. in the paper “Supply chain network redesign problem for major beverage organization in ASEAN region” analyze multi-echelon supply chains under future economic integration. The authors apply case-based modeling to design the network with minimum gross cost while considering the impacts of barriers to resource accessibility and external economic decisions on infrastructure. A developed mixed-integer linear programming model explores location and capacity selection for factories, warehouses, suppliers and transportation, to achieve the optimal flow of products in the network.

  Pourmohammadi et al. in their work “Solving a hub location-routing problem with a queue system under social responsibility by a fuzzy meta-heuristic algorithm” aim to minimize the total transportation cost and to maximize the employment, regional development, and social responsibility. They apply the queuing system to estimate the waiting time at hub nodes and maximize the responsiveness, and a fuzzy queuing to model the uncertainties. For problem solving, they develop a new evolutionary meta-heuristic algorithm based on fuzzy invasive weed optimization, variable neighborhood search, and game theory.

  Ribeiro and Barbosa-Póvoa in the paper “A responsiveness metric for the design and planning of resilient supply chains” develop an optimization model, to maximize economic and responsiveness objectives. They analyze a case study where supply chain managers in decision-making avoid adopting universal strategies and search for the best plan for their SC operations, including the impact on customers.

  Hosseini-Motlagh et al. in their paper on “Recall management in pharmaceutical industry through supply chain coordination” provide a real life case study and investigate the impacts of production disruption, recall of defective products, and decentralization among the members on the performance of multi-echelon pharmaceutical SC. They propose an altered revenue sharing contract to support decision making on channels coordination in a supply chain in order to mitigate risks.

  Kashav et al. in the paper “Ranking the strategies to overcome the barriers of the maritime supply chain (MSC) of containerized freight under fuzzy environment” provide an effective, and structured framework for policy making. Their framework applies the FAHP (Fuzzy Analytical Hierarchy Process) for evaluating weights and ranking the identified barriers, then the Fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) technique is used to facilitate the ranking of the strategies to overcome these barriers.

• Development of strategies for supply chain.

  The growing competition on procurement and sales markets, the increasing complexity, and dynamics of business environment, combined with the rapid development of information and communications technology, and pressure for more cooperation in logistics networks lead companies to seek new methods and tools that support decision making at the strategic level in a supply chain.

  Laguir et al. in the article “Performance effects of analytics capability, disruption orientation, and resilience in the supply chain under environmental uncertainty” based on a survey of 405 respondents, test hypotheses on the links between Analytics Capability of an Organization (ACO), Supply Chain Disruption Orientation (SCDO), and Supply Chain Resilience (SCR) to enable operational performance in case of environmental uncertainty.

  Li et al. in the paper “The optimal advertising strategy with differentiated targeted effect consumers” develop four mutually exclusive models for two retailers, which advertise their goods to customers and engage in Nash game to carry out price competition and then in Stackelberg game to carry out advertising strategy competition under two different game sequences.

  Özmen et al. in the article “Robust multivariate adaptive regression splines under cross-polytope uncertainty: an application in a natural gas market” investigate the parametric uncertainties in future scenarios. They expand the multivariate adaptive regression splines (MARS), through the usage of robust optimization techniques. To cope with complexity of the underlying model, they apply so-called weak robustification to exploit a geometrical and combinatorial approach by formulating Robust MARS (RMARS) under cross-polytope uncertainty.

  Shakouhi et al. in their work “A competitive pharmaceutical supply chain under the marketing mix strategies and product life cycle with a fuzzy stochastic demand” investigate the Nash and Stackelberg games. They consider two supply chains with exclusive retailer and manufacturer under different levels of price, quality, access, and promotion (i.e., marketing mix) across different stages of product life cycles.

  Waqas et al. in the paper “Influence of supply chain risk management and its mediating role on supply chain performance: perspectives from an agri-fresh produce” present the results of the survey among 430 fresh fruit and vegetable agropreneurs in Malaysia. They apply SmartPLS 3.0, which uses partial least squares structural equation modelling (PLS-SEM), to test the correlation between supply chain risks and supply chain performance, investigate the mediating effect of supply chain risk management on the correlation between supply chain risks and supply chain performance. Furthermore, they evaluate the moderating role that knowledge management plays between supply chain risks and supply chain risk management.

  Yun et al. in their work “Contract design under asymmetric demand information for sustainable supply chain practices” apply a game-theoretic model with the most crucial factors in market entry stage, such as market uncertainty, competition, contract design, distribution channel, and sustainability. They investigate a supply chain consisting of one supplier and two retailers (an incumbent and an entrant). They analyze how the supplier optimally designs the contract for the retailers under asymmetric information to make the supply chain more efficient and sustainable. They consider the preference between a franchise contract (FC) and a two-part tariff contract (TTC), from the perspectives of the supplier, the incumbent retailer, and the whole supply chain.

• Modelling, simulation, and optimization of production and logistics operations.

  Before some reasonable decision making can be done in any field of broader logistics, including production, the available data have to be carefully arranged, processed, and analyzed. In fact, not seldom do new data have to be found or even made, with all the needed and, if possible, optimal statistical design. The resulting models, e.g., in terms of clustering, regression, or classification, have to be evaluated and compared based on their statistical performance criteria. Nowadays, more AI-based methods of ANNs and SVMs have joined the more classic methods, which are based on mathematics and leading to their corresponding models. Eventually, models become part of and reflected by optimization problems and their methods, not least as heuristics of various kinds. When decision making has been made, the quality of the results is tested, especially by fine sensitivity analyses and eventually simulations. During these efforts, the role of the data and of the modeling are never forgotten; rather sometimes the decision maker along with his or her team has to return to them with new measurements and further preparations to improve the overall results.

  Ketkov et al. in the paper “Planning of life-depleting preventive maintenance activities with replacements” develop a general repair-replacement problem with an infinite time horizon. They search for an optimal number of periodic maintenance (PM) and the optimal time interval between them. They discuss global optimization techniques for problem solving under assumptions on the reward and lifetime depletion rates.

  Dovramadjiev and Mrugalska in the paper “Real-time planning and monitoring of the steel pipes towards life cycle sustainability management” propose how to design products in a computer environment in accordance with sustainability concept. They show calculations of environmental impact of their models. The knowledge of such data as carbon footprint, energy consumption, air acidification, and water eutrophication allows the management of a life cycle of the exemplary products.

  Pitakaso et al. in the article “A novel variable neighborhood strategy adaptive search for SALBP-2 problem with a limit on the number of machine’s types” propose a new method for assembly line balancing with a limitation of multi-skilled employees. By applying the artificial intelligence methods, they aim to minimize the cycle time while considering the limited number of types of machines in a particular workstation.

  Shao et al. in their work “A multi-period inventory routing problem with procurement decisions: a case in China” propose a novel mixed linear programming model for MIRP-PD to minimize the total cost of procurement, inventory holding, and transportation. Furthermore. They propose a hybrid two-level heuristic (with tabu search and adaptive threshold acceptance) to solve problems with large-scale instances.