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Many studies have addressed supply chain that shows the importance of the subject and the competition in the supply chain consisting of several companies. The previous studies address the issue of reducing the Bullwhip Effect in the supply chain, which is possible by predicting the correct amount of customer demand. This paper improves the accuracy of the model prediction and reduces the existing error in the previous models to attain an accurate and very close-to-reality forecast and also to reduce the Bullwhip Effect in the supply chain. Literature shows the absence of research on the presentation of a metaheuristic algorithm consisting of a neural network and bat algorithm to forecast supply chain demand in manufacturing companies; therefore, this article is innovative. On the other hand, no researchers have addressed Bullwhip Effect reduction using mixed metaheuristic algorithms. Therefore, this article improves the previous models, reduces the number of errors in demand forecasting, and reduces the Bullwhip Effect. For this purpose, the scalable gradient algorithm method is used for better network training. The results indicate the optimal performance of neural network training with a comparable gradient and bat algorithm on reducing the Bullwhip Effect. [ABSTRACT FROM AUTHOR]

Nowadays, Energy has become one of the most important issues. Negative environmental effects of fossil fuels lead countries to use renewable and sustainable energy sources day by day. In addition, energy supply and security have been an motivator factor in this field. This study focus on biomass that renewable and sustainable energy source. According to studies in literature, energy production from biomass resource less than other energy sources. The most important reason for this is the logistics costs. Therefore, biomass supply chain optimization is an important issue. In this study, mathematical models of biomass supply chain are reviewed. When the studies are evaluated, there are three approaches in modeling biomass supply chains. These approaches are as follows: 1) Collection and Distirubiton, 2) Selection, 3) Clustering. In additon, researchers generally focus on single-aim mixed integer mathematical models. However, recently, it is seen that there are multi-aim models in the literature for minimizing emissions as well as supply chain costs. In this study, general information about biomass and biomass supply chain, studies of in this area, mathematical models, details of published papers are given. Objective functions, cost/income items, methods of these models are discussed in detail. [ABSTRACT FROM AUTHOR]

Nowadays, the lack of natural resources and reserves of raw materials, besides the increased production costs and the problems with burying and destroying industrial waste, have attracted attention to product recovery and waste recycling in the framework of a closed-loop and sustainable supply chain, also able to create a competitive advantage. On the other hand, researches indicate that taking resilience approaches into account in supply chain design may protect buyers against disruptions such as natural, human, or technological disasters. The present paper presents a multi-objective mathematical model of a single-period, multi-product, and multi-level closed-loop supply chain, considering the dimensions of resilience and sustainability under conditions of uncertainty. Hence, a deterministic mixed integer linear programming model is initially presented; subsequently, to eliminate the uncertainty of the demand parameters and costs, its stochastic counterpart was presented based on Pishvaee’s robust possibilistic programming (RPP) model. In order to solve the model, the Augmented Epsilon Constraint method was employed in the GAMS software environment. Ultimately, the model was solved and evaluated by a numerical example. [ABSTRACT FROM AUTHOR]

This research work aims to develop the mathematical modeling for a class of dynamic supply chains. Demand fluctuation corresponds to product demand volatility, which increases or decreases over a given time frame. Industrial engineering practitioners should consider the function that applied mathematical modeling plays in providing approximations of solutions that may be used in simulations and technical implementations at the strategic, tactical, and operational levels of an organization. In order to achieve proper results, two mathematical models are presented in this paper: In addition to a finite-dimensional system of Ordinary Differential Equations (ODEs) for coupled dynamic pricing, production rate, and inventory level, which properly integrates Lyapunov stability analysis of the dynamical system and simulations, there is an infinite-dimensional Partial Differential Equation (PDE) production level modeling system available. Infinite and finite-dimensional systems incorporate a dynamic pricing approach in the mathematical modeling. The main research goal of this work is to explore the dynamic nature of supply chains applying PDE and ODE methods, with proper analytical analysis and simulations for both systems. [ABSTRACT FROM AUTHOR]

Purpose: This paper studies a 3-dimensional pallet loading problem considering interlock stacking pattern, box dimensions, humidity, and storage time, where overlapping and overhanging are not allowed. Despite the importance of this problem in the literature, our work provides the first method that considers the environmental conditions such as 1) storage time and 2) humidity, and their tremendous impacts on the strength of the boxes, as has been observed widely in the DHL supply chain. Design/methodology/approach: This paper proposes a two-phase heuristic algorithm to solve a 3-dimensional pallet loading problem under real conditions (relative humidity, and storage time) considering interlock stacking patterns, where overlapping and overhanging are not allowed. In phase 1, the horizontal layer configuration is determined by block techniques. Three types of horizontal layers are created based on box dimensions perpendicular to the base. In phase 2, a novel mathematical model is propounded to improve the pallet volume utilization, and stability considering the pallet's maximum allowable height and weight, and the dynamic compression strength of boxes. The dynamic compression strength of boxes is calculated by the modified McKee formula. Two performance measures, pallet volume utilization, and stability (load height) are utilized to evaluate the performance of the proposed heuristic algorithm in real-world instances (DHL Supply Chain). Findings: The results illustrated that the dynamic compression strength of boxes decreases as the relative humidity and storage time increase. The load height changes dynamically along with box dimensions, box alignment, direction, relative humidity, and storage time. Increasing relative humidity and storage time and applying an interlock stacking pattern reduce the pallet utilization, however, enhance the pallet stability. Finally, the proposed heuristic algorithm's efficacy increases as the identical box dimensions' heterogeneity increases. Originality/value: It is believed in the supply chain where these characteristics are observed, the implementation of the heuristic algorithm will help them improve the pallet volume utilization and stability. [ABSTRACT FROM AUTHOR]

Considering carbon emissions when making supply chain decisions has been an essential contributor for keeping this world more sustainable. This paper presents a mixed-integer linear programming (MILP) model to optimize production and transportation decision where the transportation activities involve a multimodal combination. The proposed mathematical model was aimed at mini- mizing the total costs incurred from supply chain activities as well as the emissions generated. Carbon cap is used to ensure that the emissions produced in the whole activities do not exceed the allowable limit. In this research, we address a multi- product, multi-plant, multi-departure, and arrival stations where multiple customers are to be served for multiple periods. The numerical tests show that the demand, carbon tax, and distance significantly affected the total emissions and the total costs. Interestingly, we observed that the decisions are much more affected by the logistical costs rather than the emission costs. The model presented in this paper can assist the decision makers to make production, delivery, and inventory decisions when multi-modal transportation is involved. [ABSTRACT FROM AUTHOR]

Supply chain clusters achieve viability by exploiting geographical concentration to gain efficiencies in matching supply and demand. Although the benefits of operating in these environments are understood, little is known about how supply chain clusters form and adapt. From a systems theory perspective, causal events lead to this formation and adaptation and facilitate sustained operation in modern business settings. This paper extends this understanding by theorizing key antecedents responsible for supply chain cluster formation and adaptation, conceptualizing individual firms as agents. Through a characterization of agents as either passive or active, we construct propositions and a conceptual model. The modelling, supported by causal loop diagrams, details the interactions between agents that lead to cluster formation and adaptations to contextual changes in supply chain clusters. These findings can guide policy design that is more effective in enabling the emergence and sustained existence of supply chain clusters through facilitated causal interactions. [ABSTRACT FROM AUTHOR]

Nowadays, supply chain (SC) decentralised decision making is the most usual situation in SC operations planning. In this context, different companies can collaboratively plan to achieve a certain level of individual and SC performance. However in many cases, there is reluctance to collaborate because it is not known a priori which benefits will be reported. This paper aims to develop a mathematical programming-based methodology for the evaluation of different supply chain collaborative planning scenarios (MPM-SC-CP). It is assumed that different SC decision centres (DCs) make decisions based on mixed and integer linear programming models. Two main inputs feed the proposed MPM-SC-CP, a framework and associated methodology that support the integrated conceptual and analytical modeling of the SC-CP process in which several DCs make decisions according to spatio-temporal integration. Finally, an application to a real ceramic SC was conducted. [ABSTRACT FROM AUTHOR]

A system's approach depends on the low malfunction of the equipment and processes of that system, and maintenance plays an essential role in achieving this goal. In addition, over time, the equipment quality decreases, and a quality transfer from controlled to uncontrolled mode may occur, characterized by an increase in the rate of return of the product and the tendency to fail. One of the methods that researchers have widely used in analyzing the risk of net operations is the analysis of the effect and failure modes to identify critical failure modes and focus planning and net resources on them. In analyzing the effect and failure modes, one of the essential steps is prioritizing the equipment to determine the critical equipment, as well as determining the fundamental failure modes and prioritizing them to plan the net operation purposefully. This paper aims to dynamically rank equipment in intuitionistic fuzzy environments with interval values to identify and prioritize critical equipment and present a mathematical model for combining optimization of preventive maintenance intervals and control parameters. For this purpose, a model is presented that calculates the dynamic weights of each piece of equipment according to the conditions of each piece of equipment in the indicators of failure probability, failure consequence, and lack of fault detection power. Therefore, dynamic ranking is provided for the equipment. In this research, for dynamic prioritization of equipment, the method of analysis of the ratio of intuitionistic fuzzy gradual weighting with quantitative values (IVIF-SWARA) was presented. Then, a mathematical model was presented for the identified critical equipment. The proposed model can determine the optimal value of each of the four decision variables, i.e., sample size, inspection rotation time, control limit coefficient, and preventive repair intervals of each of the critical equipment of the Northern Oil Pipeline and Telecommunication Company and the total expected cost of integration per unit. Minimize time. The results show that the proposed model is much more flexible in calculating equipment's weight and dynamic rating and provides more logical rating results. [ABSTRACT FROM AUTHOR]

This paper provides a synthesis of the concept of supply chain optimization. The aim of the research is to identify a supply chain optimization process, the key aspects in carrying out this process, and at the same time to emphasize the advantages that the optimization of companies' processes brings through the mathematical modeling of logistic processes that can be useful in the optimization of supply chains. In the research methodology it is used the Simplex transport algorithm. In a practical situation of a company with 3 warehouses and 4 clients, it allowed optimizing the transport routes and getting a minimal total cost for logistical delivery. [ABSTRACT FROM AUTHOR]

In the supply chain environment, agile distribution refined the division of operations, responded quickly to the customer needs of the mobile terminal, and promoted the construction of an intelligent logistics distribution system. In order to integrate the distribution resources of the distribution system of the logistics industry and maximize the overall benefits of distribution, this paper designs an intelligent logistics agile distribution model and optimizes the distribution strategy. Firstly, this paper puts forward a rationalization evaluation system based on the established logistics agile distribution model. Make full use of the fuzzy subsets of each level to quantify the fuzzy indexes to evaluate, and then comprehensively clarify the index attributes through the fuzzy transformation criteria to obtain the evaluation results. Secondly, aiming at the networked agile distribution organization form, the scientific location mathematical model of agile distribution center and the path mathematical model of the agile distribution are established. Finally, the uncertain influence of many objective conditions on distribution agility has been analyzed, and the dynamic optimization model of an agile distribution path with the concept of time window under random demand is constructed. Through the experimental analysis, the rationalization evaluation strategy and index of verification test results show that the model has significant optimization advantages, which not only improve the rationality and economic benefits of distribution, but also meet the time requirements of all users. [ABSTRACT FROM AUTHOR]

Nowadays, rapid changes in customers' demands have redoubled the importance of new concepts such as supply chain flexibility and its application. The extent to which flexibility should be built into supply chains requires full consideration. Flexibility is defined as firms' quick and efficient response to changes. This paper quantifies the positive effects of adding different flexibility dimensions to a production planning bi-objective mathematical model. Four flexibility dimensions are proposed according to the needs of a production plant chosen as the case study. The objective functions are to minimize total costs along with the delivery time of the product, respectively. We also employ two metaheuristic algorithms, NSGA-II and MOPSO, to solve our proposed NP-hard model. Afterwards, we compare both solution strategies based on five criteria to achieve optimal results. Moreover, we compare the performance of both flexible and inflexible models in terms of costs. The results show that applying the flexible model causes a reduction of %22 in costs. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to optimize a two-level perishable product supply chain by increasing its revenue with the Internet of Things (IoT). It particularly investigates how radio-frequency identification (RFID) technology impacts the revenue of the supply chain.Design/methodology/approach In this paper, two revenue models were built to calculate the revenue of perishable product supply chain before and after the application of IoT to analyze the influences of IoT on perishable product supply chain. In the case study, particular data of an aquatic product supply chain were analyzed through these models which were later solved by a computer simulation method based on Colonial Competitive Algorithm, a new heuristic algorithm inspired by imperialistic competition in human society.Findings Using these revenue models to compare the revenues of supply chain before and after the application of IoT, this paper concludes that the application of IoT can efficiently optimize a perishable product supply chain by balancing its wholesale profits and its total costs including logistics costs, therefore, increasing its overall revenue. However, this conclusion is only applicable for large enterprises, while small enterprises are not supposed to introduce IoT due to its high cost.Originality/value The revenue models built in this paper can be used to evaluate the profits of supply chain and help enterprises determine how to maximize their profits and whether they should introduce IoT in a perishable product supply chain. In addition, through the analysis of case study, this paper gives several valuable suggestions to help enterprises reduce their logistics costs and increase their overall revenue. [ABSTRACT FROM AUTHOR]

With the increasing importance of the platform service supply chain (PSSC) in creating economic value, academic research is paying more and more attention to it. The current literature's research topics and problems cover broad areas. This review adopts bibliometric analysis and thematic analysis to review the related literature systematically and comprehensively. We divided the literature about PSSC into six groups according to the literature's research topic and research question. Each literature's research problem and research method are categorized and summarized. Our review results demonstrate that the supply chain's members' operational decisions and the supply chain's coordination are two main types of research questions. Pricing decisions have received the most attention. In terms of the research method, game models are the most common method used in research to achieve the optimization of the PSSC. [ABSTRACT FROM AUTHOR]

Sustainable Supply Chain Management (SSCM) is a topic of growing interest among researchers. Many works have investigated the negative impact of CO2 emission, but have put less emphasis on other emission types. Many works have focused on the economic and environmental dimensions of sustainability but lacked content on the social dimension. Since SSCM is concerned with all aspects of sustainability, this work undertakes the three dimensions of sustainability and models particulate pollution (PM2.5) which has been cited in the literature to have a negative impact on human health. The paper constructs a mathematical programming model that minimizes the transportation costs and the undesirable social medical costs for remedying the release of PM2.5 into the atmosphere. The paper solves a model that evaluates different transportation modes, transportation costs, carbon emissions, PM2.5, medical costs, and capturing options. Moreover, the work utilizes a Monte Carlo simulation to best present the inherent variations in the model. Largely, the work provides stakeholders with transportation mode choices and greening opportunity investments to minimize the overall transportation costs while respecting physical and operational constraints. [ABSTRACT FROM AUTHOR]

In this paper, a multi-echelon, multi-period, decentralized supply chain (SC) with a single manufacturer, single distributor and single retailer is considered. For this setting, a two-phase planning approach combining centralized and decentralized decision-making processes is proposed, in which the first-phase planning is a coordinated centralized controlled, and the second-phase planning is viewed as independent decentralized decision-making for individual entities. This research focuses on the independence and equally powerful behavior of the individual entities with the aim of achieving the maximum profit for each stage. A mathematical model for total SC coordination as a first-phase planning problem and separate ones for each of the independent members with their individual objectives and constraints as second-phase planning problems are developed. We introduce a new solution approach using a goal programming technique in which a target or goal value is set for each independent decision problem to ensure that it obtains a near value for its individual optimum profit, with a numerical analysis presented to explain the results. Moreover, the proposed two-phase model is compared with a single-phase approach in which all stages are considered dependent on each other as parts of a centralized SC. The results prove that the combined two-phase planning method for a decentralized SC network is more realistic and effective than a traditional single-phase one. [ABSTRACT FROM AUTHOR]

As the people are becoming conscious about protection of the environment from pollutant caused by human beings, businesses are adopting green technology to procure green products to save the environment from pollution. Consequently, it is a challenging task at the firm manager to capture the market providing best green quality at fair price in a given economy. The paper plans to discuss two situations in two models. In model 1, the optimal green quality and sales prices of the manufacturer and the retailer in both decentralize and centralize systems of a two-echelon supply chain system are investigated. The profit functions of the manufacturer and the retailer include procurement costs, selling prices and cost for green level development and then it is analyzed by calculus method to obtain the optimal values of the decision variables. The model 2 focuses on price competition of two substitute products where demand of the end customers depends on price and quality of green product. Both the firms of green and regular products manufacturer are corporate social responsible. In this model, profit functions of the firms 1 & 2 are formulated separately considering revenues from sales items, cost of green quality and contribution for activities of social responsibility. The main objective is to find out optimal prices and green quality in order to maximize the profit functions of individual and integrated systems. The proposed models are analyzed mathematically and numerical examples are illustrated to justify the feasibility of the model in reality. [ABSTRACT FROM AUTHOR]

This paper, presents a mathematical model of a four-level supply chain under hyperchaos circumstances. The analysis of this model shows that the hyper-chaotic supply chain has an unstable equilibrium point. Using Lyapunov's theory of stability, the problem of designing a hyperchaotic supply chain control is investigated. The design of the nonlinear controller is performed first to synchronize two identical hyper-chaotic systems with different initial conditions and then to eliminate the chaotic behavior in the supply chain and move to one of unstable equilibrium points, as well as different desired values at different times. A different supply chain is predicted to demonstrate the performance of the controller. In the next part of numerical simulation, with the control of the distributor as the center of gravity of the model, the stability of the entire chaotic supply chain can be achieved. The most important point in designing a control strategy is the ability to implement it in the real world. Numerical simulation results in all stages show that the applied nonlinear control policy can provide supply chain stability in a short period of time, also, the behavior of control signals has low amplitude and oscillations. In other words, it represents a low cost to control the hyperchaotic supply chain network. [ABSTRACT FROM AUTHOR]

This paper proposes a novel multi-objective mathematical model to design a multi-product agile supply chain (ASC) in which one of the most useful utilities in the field of no waste system is considered. The proposed ASC network include different levels of plants, warehouse facilities, distribution facilities, customers in the forward logistics flow, and collection, repair, recycling and disposal facilities for the reverse logistics flow. The objective functions are to minimize the total cost, minimization of lead time, minimization of risk and maximization of flexibility. As the model is multi-objective, the fuzzy goal programming (FGP) method is then applied to deal with the multi-objectiveness of the model. To validate the proposed model, the GAMS software and CPLEX solver is used to solve several test problem instances in different sizes. These problems are then analysed under different conditions using sensitivity analyses. It is revealed that the proposed model has the appropriate performance to obtain efficient optimal solutions. [ABSTRACT FROM AUTHOR]

This paper focuses on the coordination and optimization between a manufacturer and multiple retailers in a supply chain. The manufacturer makes product quotes and delivery deadlines for all retailers, and each retailer selects product offers and delivery deadlines based on their own needs. Manufacturers maximize their own total profits by setting optimal quotes and delivery deadlines. This paper constructs the mathematical model of the optimal quotation and delivery deadline and proposes a scheduling algorithm that is different from the general M/M/1 and then studies the production scheduling problem and explores the effective implementation of quotation policy in management practice. [ABSTRACT FROM AUTHOR]

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• The model allocates safety stocks in supply chains at minimum cost. • An MIQCP reformulation with piecewise approximation greatly improves computational efficiency. • The piecewise function yields an improved estimation for the fill rates. • The GSM is extended to handle non-normally distributed demands. • Real-world case studies solved to optimality within few seconds of computational time. This paper presents a Multi-Echelon Inventory Optimization (MEIO) framework, based on the Guaranteed-Service Model (GSM), to allocate safety stocks across a supply chain with several locations and products, minimizing costs while meeting service level objectives. Extending previous work by Achkar et al. (2023), this paper enhances the Mixed-Integer Quadratically Constrained Program (MIQCP) with a highly efficient solution approach. The model introduces a piecewise linear approximation, significantly improving computational efficiency and the accuracy of the approximation for the fill rate function. It also introduces a different and more efficient approach to account for stochastic lead times using a discrete function. Moreover, an extension of the approach to account for non-normally distributed demands is proposed. The model is applied to several instances of a real-world case study from a pharmaceutical company, with more than 7300 product-location combinations, showing that optimal solutions can be obtained within few seconds of computational time. [ABSTRACT FROM AUTHOR]

A green supply chain with a well-designed network can strongly influence the performance of supply chain and environment. The designed network should lead the supply chain to efficient and effective management to meet the efficient profit, sustainable effects on environment and customer needs. The proposed mathematical model in this paper identifies locations of productions and shipment quantity by exploiting the trade-off between costs, and emissions for a dual channel supply chain network. Due to considering different prices and customers zones for channels, determining the prices and strategic decision variables to meet the maximum profit for the proposed green supply chain is contemplated. In this paper, the transportation mode as a tactical decision has been considered that can affect the cost and emissions. Lead time and lost sales are considered in the modeling to reach more reality. The developed mathematical model is a mixed integer non-linear programming which is solved by GAMS. Due to NP-hard nature of the proposed model and long run time for large-size problems by GAMS, artificial immune system algorithm based on CLONALG, genetic and memetic algorithms are applied. Taguchi technique is used for parameter tuning of all meta-heuristic algorithms. Results demonstrate the strength of CLONALG rather than the other methods. [ABSTRACT FROM AUTHOR]

Supply chains suffer from serious vulnerabilities and disruptions with increasing global crises, including pandemics and natural disasters. Dynamic and complex supply chain environments have constantly led companies to modern management approaches such as resilience to address disruptions. Besides, the sustainability approach enhances the strength of the supply chain in disruptions by considering economic, social, and environmental aspects. This paper develops a mathematical model for designing a supply chain network considering resilience and sustainability. In this model, suppliers were exposed to disruption with different probabilities. The model has three objectives: minimizing total costs and maximizing suppliers' social and environmental scores. A robust scenario-based stochastic programming approach has been used for potential disruption scenarios. The multiobjective model is solved by the ε -constraint method in GAMS software. The numerical results show the performance of the model in a different situation. Also, the robust scenario-based stochastic programming approach allows the average performance of the supply chain in each objective to improve. [ABSTRACT FROM AUTHOR]

In this paper, a new mathematical model is presented for a cellular manufacturing system into tactical planning of a closed-loop supply chain to build a sustainable manufacturing enterprise. On the manufacturing side of the model, a comprehensive cellular manufacturing system is designed considering dynamic cell configuration, alternative process routings, lot splitting, sequence of operations, multiple units of identical machines, machine capacity, machine adjacency requirements, and cell size limits. On the closed-loop supply chain side of the model, different activities are considered including acquiring returned products, setting up the system for the implementation of disassembly operations, inventory holding of the returned products, remanufacturing the parts having high quality, and disposing of the returned products that cannot be economically recovered. The mathematical model in this paper, to the best of our knowledge, is the first model reducing the total costs of the cellular manufacturing system while considering the alternative process routings and subcontracting of the part demands. A detailed economic analysis is done on the large-sized example problem of the mathematical model to investigate the impacts of adopting different production policies such as internal production, inventory holding, and subcontracting as well as different manufacturing attributes such as dynamic reconfiguration and alternative process routings. The mathematical model is also solved for different instances to investigate the effects of incorporating subcontracting, alternative process routings, and dynamic reconfigurations in the model. Sensitivity analyses are also conducted to investigate the effects of the recovery rate of returned products on the objective function value and the number of returned products to be acquired. The effect of taking alternative process routings into consideration on the objective function value is also investigated. [ABSTRACT FROM AUTHOR]

Port terminals processing large cargo vessels play an important role in bulk material supply chains. This paper addresses the question of how to allocate vessels to a location on a berth and the sequence in which the vessels should be processed in order to minimize delays. An important consideration in the berth allocation is the presence of tidal constraints that limit the departure of fully loaded vessels from the terminal. We show how the berth allocation problem can be modeled as an integer program and discuss a number of ways to tighten the formulation in order to make it computationally tractable. In addition, a two-phase method is developed for solving these problems. Empirical computational results demonstrate an order of magnitude improvement in performance. The two new approaches can solve significantly larger instances, producing faster solutions for small instances and much tighter bounds for large instances. [ABSTRACT FROM AUTHOR]

In most supply chains (SCs), transaction relationships between suppliers and customers are commonly considered to be an extrapolation from a linear perspective. However, this traditional linear concept of an SC is egotistic and oversimplified and does not sufficiently reflect the complex and cyclical structure of supplier-customer relationships in current economic and industrial situations. The interactional relationships and topological characteristics between suppliers and customers should be analyzed using supply networks (SNs) rather than traditional linear SCs. Therefore, this paper reconceptualizes SCs as SNs in complex adaptive systems (CAS), and presents three main contributions. First, we propose an integrated framework of CAS network by synthesizing multi-level network analysis from the network-, community- and vertex-perspective. The CAS perspective enables us to understand the advances of SN properties. Second, in order to emphasize the CAS properties of SNs, we conducted a real-world SN based on the Japanese industry and describe an advanced investigation of SN theory. The CAS properties help in enriching the SN theory, which can benefit SN management, community economics and industrial resilience. Third, we propose a quantitative metric of entropy to measure the complexity and robustness of SNs. The results not only support a specific understanding of the structural outcomes relevant to SNs, but also deliver efficient and effective support to the management and design of SNs. [ABSTRACT FROM AUTHOR]

Holistic supply chain network (H-SCN) systems are a set of supply chains that have inter-dependence relations. Design and management of H-SCN systems are largely ignored in the literature. Modeling of H-SCN systems will provide a computational facility for design and management of H-SCN systems. A model for disruption of the H-SCN system was proposed by us before, which consists of two parts: a model for disruption of a single firm and a model for propagation of disruption. The present paper mainly discusses how these models can be implemented especially with Petri nets. Guidelines with templates of how the Petri net models for H-SCN systems can be constructed in industrial setting are also given to facilitate the use of our model. Since H-SCN systems are complex network systems, this paper may also contribute to the field of the Petri net technique for network systems modeling in the aspect of reduction of complexity arising from vast amounts of construct instances-a common problem that existed in a Petri net model. Indeed, the proposed Petri net model is suitable for any type of supply chains or supply chain networks. [ABSTRACT FROM AUTHOR]

A number of factors, including environmental legislations and increasing global uncertainties, have led many companies to consider engaging in remanufacturing. In this paper, we mainly address a topical issue of choosing the appropriate reverse channel format for collecting used products from customers. We suppose a manufacturer who has three reverse hybrid collection channel structures to choose: (1) manufacturer and retailer collecting used products at the same time, (2) manufacturer contracting the collection of used products to a retailer and a third party, and (3) manufacturer and third party collecting used products at the same time. To generate managerial insights into this important issue, we develop a structural model to explore the appropriate reverse channel structure for the manufacturer. Numerical examples are presented to demonstrate the applicability of the presented model. The results show that, ceteris paribus, the manufacturer and the retailer hybrid collection channel is the most effective reverse channel structure for the manufacturer. We also find that, under the same conditions, the manufacturer and the retailer hybrid collection approach is obviously superior to the single channel collection approach. Finally, we explore the channel coordination mechanism to implement channel cooperation for the manufacturer and the retailer. Our results indicate that the strategic alliance between the manufacturer and the retailer can effectively achieve the desired channel coordination. Furthermore, the Nash bargaining model is utilized to implement profit sharing for the manufacturer and the retailer in the channel coordination of strategic alliance. [ABSTRACT FROM AUTHOR]

In this paper, a bi-objective mixed-integer linear programming model is formulated for designing a perishable pharmaceutical supply chain network under demand uncertainty. The objectives of the proposed model are to simultaneously minimize the total cost of the network and lost demand amount. The proposed model is multi-product and multi-period and includes simultaneous facilities location, vehicle routing, and inventory management; hence, it is considered an operational-strategic model. Procurement discounts, the lifetime of products, storing products for future periods, lost demand, and soft and hard time windows are the main assumptions of the proposed model. A novel hybrid approach, based on fuzzy theory, chance constrained programming, and goal programming approach, is developed for solving the proposed bi-objective model. The validity of the proposed model and developed solution approach is evaluated using data from Avonex, a prefilled syringe distribution chain serving 11 health centers in Tehran. The proposed model indicates that some lost sales exist, and to overcome the lost sales, the case company needs to invest a little more in addition to the initial investment of around 2 billion tomans. The results obtained from implementing the model and the sensitivity analysis, using real-world data, confirm the efficiency and validity of the proposed mathematical model and solution approach. [ABSTRACT FROM AUTHOR]

In traditional Indian agro-fresh food supply chain (AFSC), authors identify the following four shortcomings through the literature survey: (1) unorganized supply chain structure; (2) low profitability of farmers; (3) high wastage of agricultural products; and (4) a large number of small-farm-holding farmers. According to the fourth shortcoming, 85% of farmers have less than 2 hectares of farming land, and these farmers transport their products independently into the market to sell. Owing to this, a higher transportation cost is incurred in traditional AFSC, which leads to low profit for farmers. To overcome these shortcomings, authors propose aggregation of products by forming clusters of farmers and its transportation from these cluster centers to market. This paper formulates multi-period, multi-product, mixed-integer nonlinear programming model to design a four-echelon supply chain with considering the clustering of farmers and perishability of products. A real case study problem of Mandsaur District (India) of vegetable distribution is solved in LINGO 17.0 to check the validity of the formulated model. The results revealed that 85% of the total distribution cost incurred in the transportation of products from farmers to the market. Hence, the major focus should be to design an efficient transportation plan for the minimization of transportation cost from farmers to the market. Further, sensitivity analysis shows that the proposed model is robust and sensitive to changes in maximum distance traveled by a farmer to reach a cluster center and number of hubs to be opened, respectively. [ABSTRACT FROM AUTHOR]

This paper develops the game models of two symmetric supply chains, each consisting of one manufacturer and one retailer, while both retailers compete in the market with a linear function. The disclosure mechanism is designed when the information of the disrupted demand is asymmetric between supply side and retail side. We first study the model with the full information as a benchmark to explore the effect of asymmetric information on the system. In the case, each manufacturer maximizes her profit while the downstream retailer only obtains the reservation profit. For the case of asymmetric information, each manufacturer can obtain the real information of the disrupted demand by using a menu of contract bundles. For each information structure, there are always robust regions for each manufacturer's original trading quantity scheme. That is, when the disrupted amount of the demand is sufficiently small, the trading quantity will be unchanged. However, some special measures, e.g., the higher unit wholesale price, should be taken to prevent the retailer from deviating the trading quantity scheme. The high-disruption retailer gets the higher profit due to the information rent. Compared with a single supply chain, Cournot competition results in the less retail price and the lower performance for the whole system. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to present an analysis of research on lean production (LP) since the concept was developed at the end of the 1980s with the aim of developing a model that permits an extended and comprehensive understanding of LP. Design/methodology/approach – A literature survey of peer reviewed journal articles and paradigmatic books with managerial impact is employed as the research methodology. Findings – The findings derived from the evaluation of the publications analysed have led to the creation of an extended model of LP. Specifically, two new groups of factors to be taken into account in order to achieve a comprehensive understanding of LP are presented. Apart from internal aspects at the shop floor level and value chain elements, the model provided includes work organisation and the impact that the geographical context has on LP. In addition, the critical assessment of publications has allowed a number of specific aspects to be identified for which there is no empirical evidence. Originality/value – This paper puts forward a new classification of literature identifying key aspects that should be included for LP development and management. It might represent new opportunities for rigorous and relevant research that would contribute to more transparent knowledge of LP being gained. [ABSTRACT FROM AUTHOR]

This paper considers large-scale stochastic simulations with correlated inputs having normal-to-anything (NORTA) distributions with arbitrary continuous marginal distributions. Examples of correlated inputs include processing times of workpieces across several workcenters in manufacturing facilities and product demands and exchange rates in global supply chains. Our goal is to obtain mean performance measures and confidence intervals for simulations with such correlated inputs by accounting for the uncertainty around the NORTA distribution parameters estimated from finite historical input data. This type of uncertainty is known as the parameter uncertainty in the discrete-event stochastic simulation literature. We demonstrate how to capture parameter uncertainty with a Bayesian model that uses Sklar's marginal-copula representation and Cooke's copula-vine specification for sampling the parameters of the NORTA distribution. The development of such a Bayesian model well suited for handling many correlated inputs is the primary contribution of this paper. We incorporate the Bayesian model into the simulation replication algorithm for the joint representation of stochastic uncertainty and parameter uncertainty in the mean performance estimate and the confidence interval. We show that our model improves both the consistency of the mean line-item fill-rate estimates and the coverage of the confidence intervals in multiproduct inventory simulations with correlated demands. [ABSTRACT FROM AUTHOR]

This paper addresses the multiobjective, multiproducts and multiperiod closed‐loop supply chain network design with uncertain parameters, whose aim is to incorporate the financial flow as the cash flow and debts' constraints and labor employment under fuzzy uncertainty. The objectives of the proposed mathematical model are to maximize the increase in cash flow, maximize the total created jobs in the supply chain, and maximize the reliability of consumed raw materials. To encounter the fuzzy uncertainty in this model, a possibilistic programming approach is used. To solve large‐sized problems, the multiobjective simulated annealing algorithm, multiobjective gray wolf optimization, and multiobjective invasive weed optimization are proposed and developed. The numerical results demonstrate that these algorithms solve the problems within about 1% of the required solving time for the augmented ε‐constraint and have similar performance and even better in some cases. The multiobjective simulated annealing algorithm with a weak performance takes less time than the other two algorithms. The multiobjective gray wolf optimization and multiobjective invasive weed optimization algorithms are superior based on the multiobjective performance indices. [ABSTRACT FROM AUTHOR]

Sustainability in the supply chain means pushing the supply chain to focus on social, economic and environmental aspects, and addressing the existing problems in the traditional supply chain. Considering the importance of evaluating supply chain networks, especially in the field of perishable commodities, this paper aimed to design a mathematical model for the reverse supply chain of perishable goods, taking into account the sustainable production system. In this research, four objective functions were considered to maximize profitability and the level of satisfaction with the use of technology, minimize costs and measure environmental impacts. The results of the implementation of the proposed model for a manufacturing company show that objective functions are sensitive to demand, so the change in demand changes the objective functions, in particular the profitability function. [ABSTRACT FROM AUTHOR]

This paper aims to develop a recovery planning approach in a three-tier manufacturing supply chain, which has a single supplier, manufacturer, and retailer under an imperfect production environment, in which we consider three types of sudden disturbances: demand fluctuation, and disruptions to production and raw material supply, which are not known in advance. Firstly, a mathematical model is developed for generating an ideal plan under imperfect production for a finite planning horizon while maximizing total profit, and then we re-formulate the model to generate the recovery plan after happening of each sudden disturbance. Considering the high commercial cost and computational intensity and complexity of this problem, we propose an efficient heuristic, to obtain a recovery plan, for each disturbance type, for a finite future period, after the occurrence of a disturbance. The heuristic solutions are compared with a standard solution technique for a considerable number of random test instances, which demonstrates the trustworthy performance of the developed heuristics. We also develop another heuristic for managing the combined effects of multiple sudden disturbances in a period. Finally, a simulation approach is proposed to investigate the effects of different types of disturbance events generated randomly. We present several numerical examples and random experiments to explicate the benefits of our developed approaches. Results reveal that in the event of sudden disturbances, the proposed mathematical and heuristic approaches are capable of generating recovery plans accurately and consistently. [ABSTRACT FROM AUTHOR]

The paper proposes a structured model for evaluating vendor selection using the analytical hierarchy process (AHP) and fuzzy AHP. The model is developed using evidence from an empirical study. The paper aims to demonstrate how the model can help in solving such decisions in practice. A usability evaluation of the AHP-based model with three vendors is discussed. It also examines the structure of the decision hierarchy, whether it can represent vendor selection decisions in reality and whether it covers all key factors affecting vendor selection choices. The effectiveness of the AHP model is illustrated using a company in the southern part of India and the results validated using fuzzy AHP. [ABSTRACT FROM AUTHOR]

Due to increasing air pollution, which is a consequence of the environmental effects of production in various industries, green supply chain management (GSCM) has attracted the attention of both scholars and practitioners. Green supplier selection is an important problem in GSCM and seeks to satisfy a firm's environmental goals as well as its economic targets. In this paper, for the first time, a green supplier selection problem considering both green and non-green evaluation criteria in a closed-loop supply chain is studied, and a cap-and-trade mechanism as a way of controlling the air pollution caused by manufacturers is proposed. To solve this particular problem, we propose a multi-objective robust optimization (RO) model. This specific model is an effective approach to handle uncertainty. A numerical example using randomly generated data, accompanied by subsequent discussion of the proposed approach, is deployed to validate the model. The results prove that the developed model for green supplier selection is able to effectively enhance the decision-making process of the experts. By illustrating the trade-off in robustness between the model and proposed solutions, as well as the effect of the deviation penalty on the closeness of results to the achieved solution, we show how firms can make optimal decisions when assigning the parameters. Furthermore, analyses show that decreasing the allowance amount (cap) and increasing the allowance prices in the cap-and-trade system escalate the firms' costs, but lower the amount of carbon released. Finally, we show that the cap-and-trade mechanism results in a better solution in terms of the total utility of the supply chain compared to the penalty-based system for a specific range of carbon allowance. • This paper generalizes the green supplier selection and order allocation problem. • A mathematical model for the problem is developed. • A robust optimization approach to handle the model uncertainty is developed. • The impact of changes in model parameters on the objective functions is analyzed. • The cap-and-trade mechanism is compared with a penalty-based mechanism. [ABSTRACT FROM AUTHOR]

Consider a supply chain consisting of two independent agents, a supplier (e.g., a manufacturer) and its customer (e.g., a retailer), the latter in turn serving an uncertain market demand. To reconcile manufacturing/procurement time lags with a need for timely response to the market, such supply chains often must commit resources to production quantities based on forecasted rather than realized demand.The customer typically provides a planning forecast of its intended purchase, which does not entail commitment. Benefiting from overproduction while not bearing the immediate costs, the customer has incentive to initially overforecast before eventually purchasing a lesser quantity. The supplier must in turn anticipate such behavior in its production quantity decision. This individually rational behavior results in an inefficient supply chain.This paper models the incentives of the two parties, identifying causes of inefficiency and suggesting remedies. Particular attention is given to the Quantity Flexibility (QF) contract, which couples the customer's commitment to purchase no less than a certain percentage below the forecast with the supplier's guarantee to deliver up to a certain percentage above. Under certain conditions, this method can allocate the costs of market demand uncertainty so as to lead the individually motivated supplier and customer to the systemwide optimal outcome. We characterize the implications of QF contracts for the behavior and performance of both parties, and the supply chain as a whole. [ABSTRACT FROM AUTHOR]

In this paper, we develop a quantitative reactive mitigation approach for managing supply disruption for a supply chain. We consider a three-tier supply chain system with multiple raw material suppliers, a single manufacturer and multiple retailers, where the system may face sudden disruption in its raw material supply. First, we develop a mathematical model that generates a recovery plan after the occurrence of a single disruption. Here, the objective is to minimize the total cost during the recovery time window while being subject to supply, capacity, demand, and delivery constraints. We develop an efficient heuristic to solve the model for a single disruption. Second, we also consider multiple disruptions, where a new disruption may or may not affect the recovery plans of earlier disruptions. We also develop a new dynamic mathematical and heuristic approach that is capable of dealing with multiple disruptions, after the occurrence of each disruption as a series, on a real-time basis. We compare the heuristic solutions with those obtained by a standard search algorithm for a set of randomly generated disruption test problems, which shows the consistent performance of our heuristic. Finally, a simulation model is developed to analyze the effect of randomly generated disruption events that are not known in advance. The numerical results and many random experiments are presented to explain the usefulness of the developed models and methodologies. [ABSTRACT FROM AUTHOR]

Most of the product life cycle costs and environmental impacts are determined during product design stage. A product supply chain network can be seen as a system dealing with product life cycle processes. Ideally, a product should be designed to have a structure yielding a good life cycle performance in its existing supply chain network. However, there is a lack of research related to modeling supply chain effects in the product design stage. This paper aims at investigating the supply chain effects on product modular design. The life cycle performance of a modular structure (life cycle cost and life cycle environmental impact) at various supply chain states is evaluated by a supply chain evaluation model and analyzed by statistical techniques. The preliminary results indicate that the supply chain-related effects on product life cycle performance are significant and modular structure optimization is effective in reducing the surge of life cycle cost and life cycle environmental impact caused by various supply chain states. The main supply chain effects are identified for use in product design improvement. [ABSTRACT FROM AUTHOR]

Purpose: Optimal order allocation on the part of the buyer in a multi-supplier environment has become a major concern in supply chains. There are numerous articles that analyze and present models for optimizing order allocation from a given panel of suppliers. The purpose of this paper is to provide an analysis on this topic which considers: (i) aims, (ii) results, (iii) model complexity, and (iv) resolution procedures. Design/methodology/approach: The paper reviews twenty-eight articles, twenty-one of them published since 2007 in journals indexed by Journal Citation Reports (in ISI Web of Knowledge) on this topic. Findings: This review reveals four main aspects mentioned as determinant in generating mathematical models. The analysis of these four points does not allow for a single, overarching model. Rather, all analyzed solutions reflect and respond to a specific company environment. Originality/value: A global analysis on several recent papers, describing main aspects wich determines optimal order allocation in multi-supplier environment. [ABSTRACT FROM AUTHOR]

In this paper, the impacts brought by the carbon footprint tax on fashion supply chain systems are analytically examined. First, based on various industrial practices in fashion apparel, we construct the two-echelon manufacturer-retailer analytical fashion supply chain model. Second, by exploring the fashion supply chain with a highly fashionable product, we study how the carbon footprint tax can affect the retailer's optimal choice of sourcing. We further investigate the significance of carbon footprint tax on fashion supply chain management under two commonly adopted contracts, namely the pure wholesale pricing (WP) contract and the markdown money (MM) contract. Our analytical findings illustrate that: (1) under the WP contract case: A properly set carbon footprint tax, which depends on the product's manufacturing and shipping costs, and manufacturer's profit margin, can successfully entice the retailer to source locally. (2) Under the MM contract case where the MM contract is set in a way that the supply chain is coordinated: (a) If the carbon footprint tax is equal to the difference between the product costs from the local and offshore sourcing, then the optimal MM rates and the optimal supply chain product quantities from the local and offshore sourcing modes are the same. (b) Similar to the WP contract case, we prove analytically that a properly determined carbon footprint tax can always entice the retailer to source locally. Further analysis with extended models under the consideration of having a stochastically larger demand with the local sourcing mode is conducted. [ABSTRACT FROM AUTHOR]

The collection of agricultural goods is a very dynamic process which involves the coordination of hundreds of transport routes and machines with respect the processing capacities at a factory. Numerous fluctuations in the number of transport vehicles, malfunctioning on engaged machines and weather conditions make the process of planning and maximizing the utilization of all resources very difficult. In this paper we present a mathematical model and a heuristic algorithm that in a short period of time finds nearly optimal solutions, which enables a dispatcher to re-plan and update the collection plan according to new constraints. [ABSTRACT FROM AUTHOR]

This paper proposes a production and differential pricing decision model in a two-echelon supply chain that involves a demand from two or more market segments. In this framework, the retailer is allowed to set different prices during the planning horizon. While integrated production-marketing management has been a key research issue in supply chain management for a long time, little attention has been given to set prices and marketing expenditures in integrated multi-site (parallel) manufacturing systems and multiple demand classes. Generally, the presence of multiple demand classes induced by different market segments may impose demand leakage and then change production plan and ordering policies throughout the supply chain system. To tackle this problem, this paper develops a novel approach in order to provide an optimal aggregate production and marketing plan by interconnecting the sales channels of the retailer and demand. A non-linear model is established to determine optimal price differentiation, marketing expenditures and production plans of manufacturing sites in a multi-period, multi-product and multi-sale channels production planning problem by maximizing total profit of the supply chain. To handle the model and obtain solutions, we propose an efficient analytical model based upon convex hulls. Finally, we apply the proposed procedure to a clothing company in order to show usefulness and significance of the model and solution method. [ABSTRACT FROM AUTHOR]

Supply chain network designing and programming is a momentous issue that many practitioners have focused on and contributed numerous novelties for this prompt. This paper puts forward a fuzzy multi-agent system according to which compatible with the decision makers' interests and environmental survey, identifies the parameters of the mathematical model. An embedded optimization party including evolutionary-based optimizer intelligent agents, obtains non-dominated potential solutions. The output of these optimizer agents during the calibration process is an underpinning for evaluating the performance of the party. The system makes the policy of optimization complying with the results evaluation as well as the decision makers' elaborated desires. Afterwards, in step with this policy, it sets a pool from obtained Pareto Fronts and aggregates them to extract a set of the best individuals. It interactively represents this set to the decision makers and catches their desired circumstance amongst these optional solutions. Proposing the network graph and program-which its generic morphography is determined-for decision makers is contrived as the system last stage. The main competencies of this system could be contemplated regarding the facts that it interactively fulfills the decision makers' utilities relying on its robustness in optimization, self-tuning, training loop, ambient intelligence and consciousness toward the changes in environment. [ABSTRACT FROM AUTHOR]

In this paper, we have proposed a dual-channel supply chain model in uncertain environment to analyze the demand of manufacturer and retailer demand in which the profit being maximized. Linguistic terms are also utilized to establish two fuzzy systems for estimating the demand in direct and retail channels. In order to do that, a mathematical model is proposed based on decentralized situation of supply chain. To solve the model, we have developed a hybrid solution method of genetic algorithm, fuzzy system, and L-P metric. Finally, several test problems are first generated; then, the computational results are analyzed. [ABSTRACT FROM AUTHOR]

Logistics and the supply chain are areas of great importance within organizations. Due to planning gaps, an increase in extra and unnecessary transport costs is usually observed in several companies due to their commercial commitments and need to comply with the delivery time and the batch quantity of products, leading to a negative economic impact. Thus, the objective of this work was to adjust an optimization model to maximize the shipments usually carried out by the companies. To validate the model, an automotive components manufacturer was selected, allowing us to apply the model to a real case study and evaluate the advantages and drawbacks of this tool. It was found that the company to validate the model exports most of its products, and most pallets sent are not fully optimized, generating excessive expense for the company in terms of urgent transport. To solve this problem, two mathematical optimization models were used for the company's current reality, optimizing the placement of boxes per pallet and customer. With the use of the new tool, it was possible to determine that five pallets should be sent to the customer weekly, which correspond to their needs, and that have the appropriate configurations so that the pallet is sent completely. [ABSTRACT FROM AUTHOR]

Recently, industry, suppliers, distributors, academia, governments, and even consumers have focused on agri-food supply chain sustainability, environmental concerns, and managing energy and other resources essential for human survival. A supply chain model is a network of facilities and operations involving processes related to procuring raw materials from suppliers, producing and developing products on production sites, and ultimately distributing products at final consumption destinations. This study aims to propose a multi-stage model for sustainable supply chain network design. After an overview of operations research methods for sustainable supply chain network design, this study proposed a hybrid method based on multi-criteria decision-making (MCDM) and optimization techniques in operations research. The criteria extracted from library resources were selected using the Delphi method in the first step. Then, the criteria for selecting suppliers, transformer sites, and critical distribution hubs were weighted using the best–worst method. After weighing the criteria using the Complex Proportional Assessment of alternatives (COPRAS) technique, eight raw material suppliers, three potential transformer sites, and five main distribution hubs were selected for supply chain network design. The second part presented a multi-objective mixed-integer linear programming model to optimize the designed supply chain network. All three sustainability dimensions, i.e., economic, social, and environmental, were considered in developing the supply chain network. In the economic dimension, we sought to minimize total costs consisting of transportation costs, the cost of construction, maintenance, and closure of transformer and distribution sites, the cost of the capacity change of transformer and distribution sites, and the cost of production. In the social dimension, we sought to maximize the number of job opportunities created in each facility. We sought to minimize carbon and nitrous oxide footprints and water consumption in the environmental dimension. Furthermore, a fourth objective function was presented to minimize product delivery time in addition to the three dimensions of sustainability. Then the proposed mathematical programming model was solved using the LP-metric method, and the necessary comparisons were made between the results. Finally, agri-food industry executives were given a decision-making tool by generating Pareto frontier graphs. [ABSTRACT FROM AUTHOR]