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This study considers a channel with a retailer who satisfies demand by ordering from two suppliers: a low‐cost primary supplier whose production is subject to failure and an expensive reliable backup supplier. We develop a call option contract and evaluate the preference of each party through two negotiable scenarios: (i) the backup supplier only decides on option price and (ii) the backup supplier determines both option and exercise prices. We find that higher authority and flexibility of the backup supplier in making decisions in the second scenario results in a lower option price, yet more possibility of achieving channel coordination compared with the first scenario. While the backup supplier prefers the second scenario, the retailer would rather adopt the other one. The results also shed light on when/how the call option contract can be beneficial for each participant. Specifically, when the primary supplier is unable to supply the whole demand, the proposed option contract can be a win‐win Pareto‐improving profit‐sharing mechanism only if the likelihood of failure in the primary supplier's production is sufficiently low. Otherwise, the backup supplier may be a detrimental option for the retailer. [ABSTRACT FROM AUTHOR]

The research examines the factors affecting the growth of new technology-based firms (NTBFs) in Iran as a developing country. The regional, economic, and social characteristics of developing countries are different from developed ones. In the research, 87 NTBFs that were able to pass the initial growth stage within 3 years are analyzed by structural equation modeling (SEM) method. The findings show that among the four dimensions of growth construct, corporate governance and environmental factors have the most effective role in the growth of NTBFs. In addition, business knowledge with path coefficient of 0.241 has the greatest impact on NTBFs growth. This explain that although environmental factors are very important for NTBFs, there are other factors which could be better managed by NTBFs to gain sustainable growth. Furthermore, findings reveal that if governance and managers make efforts to improve business knowledge among NTBFs, they are more likely to experience growth. [ABSTRACT FROM AUTHOR]

The increased environmental awareness of consumers has led supply chains (SC) to green their operations. To extract a higher portion from the expanded demand due to greening activities, SC parties may hide key information regarding their green activities. This paper investigates the channel coordination problem in a green SC consisting of a manufacturer who sells a green product through a retailer. Both parties may involve in greening operations to expand an environmental-aware market; however, the retailer is privy to the information about his green sales effort. The analysis of the first-best outcome characterizes the conditions for (i) hold-up problem under which the retailer benefits from free ride on the manufacturer's greening operations effort, (ii) commitment strategy from the retailer to cover for the market expansion due to the manufacturer's underinvestment in greening operations, and (iii) synergy in greening efforts. We then solve for the optimal incentive contracts under asymmetric information. Our analysis suggests that the manufacturer can include her greening effort in the contract to work as an incentive-fee; the higher level of greening effort by the manufacturer incentivizes the retailer to increase his green sales effort. We also show that the wholesale price term works as a screening tool to avoid the low efficient retailer from mimicking the high efficient one. Finally, we show that information asymmetry reduces the social welfare in a green market; it leads to a higher market price and a lower greening effort level. [ABSTRACT FROM AUTHOR]

Purpose: Within a framework of supply chain (SC) coordination, this paper analyzes a green SC consisting of a retailer and a manufacturer, under government incentives and legislations and the consumer environmental awareness. To mitigate carbon emissions and promote the sustainability of the SC, a customized carbon emission trading mechanism is developed. Design/methodology/approach: A game-theoretical decision model formulated determines the optimal sustainability level and the optimal quota of carbon credit from the ceiling capacity set by the government. In order to coordinate the SC and optimize environmental decisions, a novel combination of consignment and zero wholesale price contracts is proposed. Findings: Analytical and numerical analyses conducted highlight that the proposed contract generates a Pareto improvement for both channel members, boosts the profit of the green SC, enhances the sustainability level of the channel and contributes to a reduction in the requested carbon emission credit by the manufacturer. Social implications: With the proposed mechanism, governments can protect their industries and, more importantly, comply with European Union (EU) rules on annually reducing emission ceilings allocated to industries. Originality/value: Different from previous studies on cap-and-trade strategies, the proposed mechanism enables companies to select lower emission quota/allowances than the maximum amount set by the government, and in return, companies can benefit from several incentive strategies of the government. [ABSTRACT FROM AUTHOR]

This study analyses the green channel coordination problem in a two-echelon supply chain where demand is a function of the selling price and the product's green quality. The retailer decides on the selling price while the manufacturer regulates the green quality of the product. To initiate the channel coordination and to establish a win-win outcome for both parties, a hybrid of 'greening cost sharing' and 'revenue sharing' contract (HGRS) is developed. This study contributes to the literature by providing an analytical approach to address the channel coordination and pricing issues in a green supply chain under the consumer environmental awareness while the manufacturer has the ability of enhancing, with investments, the product's green quality. Our study reveals that: (a) the proposed HGRS contract is capable of achieving channel coordination while both supply chain members gain more profit than in decentralised decision making, (b) the new suggested contract enhances the product's green quality, reduces the selling price, and stimulates the market demand, and (c) HGRS contract results in more satisfied customers (by offering low prices) as well as more sustainable operations (by increasing greenness level) at the same time. [ABSTRACT FROM AUTHOR]

Reverse supply chain (RSC) management can be implemented to ameliorate environmental and economic goals simultaneously. In 2020, the devastating influences generated by the COVID-19 global pandemic had established high uncertainty in the manufacturers' capacity, which can hinder the fulfillment of such goals. To address such a problem, in this research, we analyze a two-echelon RSC, including a re-manufacturer who, despite facing the remanufacturing capacity uncertainty, remanufactures eligible obsolete products, then re-enters the marketplace, and a collector who accumulates eligible obsolete products from consumers. We survey centralized and decentralized decisions, and also a condition where the collector, as a Stackelberg game leader, offers a put option contract as a risk-sharing approach and decides on both option and exercise prices; in return, the re-manufacturer determines the order quantity. Contrary to previous studies in which the value of option contracts has been analyzed under demand disruptions, this paper aims to address the performance of a put option contract to mitigate the remanufacturing capacity uncertainty in an RSC. Our results demonstrate that by offering the put option contract and determining the option price as nearly low as the marginal refund cost, not only can the collector motivate the re-manufacturer to augment its order quantity but both parties also attain a win–win profit-sharing outcome. Besides, the customized put option contract can achieve Pareto-improving channel coordination in the condition of remanufacturing capacity uncertainty. [ABSTRACT FROM AUTHOR]

With the expansion of urbanization, we are witnessing the growing uncertainty in municipal food demand leading to an increase in urban waste. With the motive of producing organic fertilizers and conserving the environment, expired food can be collected and recycled. This study examines the hypothesis that leasing recycling facilities from peri-urban areas, due to the ban on reproduction operations in the city centers, can manage the recycling system participants' relationship and enhance sustainability in urban communities. The problem has been investigated under two separate sources of uncertainty, namely, quality and capacity. In the first scenario, a recycling system consisting of a commercial food service located in urban areas, a food waste collection agency, and a suburban fertilizer factory is optimized, in which the commercial food service leases the fertilizer factory's facilities for recycling operations. In the second scenario, the two factories' relationship, in which the first factory can rent the second factory's facilities in case of capacity shortage, is managed through hybrid contracts and mathematical programming models. The results show that the whole system optimization and Pareto Improvement results for all members are guaranteed under proposed hybrid contracts. These conclusions can help food recycling system managers have a better relationship with other players in their supply chains and enhance their credibility for caring about the environment, social concerns, and government compliance. [ABSTRACT FROM AUTHOR]

This paper addresses coordination and competition problem in two reverse supply chains each having its own exclusive retailer and manufacturer. The chains have various collecting channel structures so that one of them uses the advantages of dual channels, where the consumer can return their e-waste through direct or traditional channels, while its competitor collects obsolete products only through its traditional channel. The willingness to return in each channel is a function of self- and cross-discounts of the competitors. Four decision scenarios are investigated; the first and second chain respectively select, Decentralised-Decentralised, Centralised-Centralised, Centralised-Decentralised or Decentralised-Centralised scenario. The closed-form optimal solution of each channel is derived based on the Stackelberg game when the second chain acts as a leader. The most economical scenario is determined by using a Non-Zero-Sum game when each chain plays as a single player in the game. To coordinate the members' decisions and to convince unsatisfied members, two coordination contracts are offered. Numerical investigations reveal that direct channel suggests more discount and obtains more share of market. The results show that Centralised-Centralised scenario is the best decision from the SCs' perspective which proposing contracts are able to persuade members to change their strategy to a global decision. [ABSTRACT FROM AUTHOR]

This study analyzes a participative pricing scheme, name‐your‐own‐price, as a practical marketing mechanism for a socially sustainable supply chain. There is an ongoing argument on the profitability of participative pricing. This study introduces a different approach in capturing optimal decisions of a corporate social responsibility practice. In the proposed approach, the manufacturer is willing to donate as long as, (I) business image is improving; thus, the potential market size is expanding and, (II) consumers are also donating through the proposed donation scheme. Results indicate that not only the total earnings are increasing, the market participation is also boosted. Further, operational decisions are coordinated successfully with a revenue‐sharing contract. Findings imply that there is a minimum threshold for manufacturer's participation ratio to ensure the profitability of the proposed scheme, not to mention consumer's donation size. Sensitivity analysis is conducted to prove the applicability of the proposed mechanism in real‐world applications. [ABSTRACT FROM AUTHOR]

As the occurrence of disasters has increased frequently and has resulted in growing concern about their adverse effects on the environment, Sustainable Humanitarian Logistics (SHL) has received great attention recently. SHL aims to reduce disaster damages in an environmentally-friendly manner in the shortest possible time. The terms including 'environmentally-friendly' and 'shortest possible time' refer to the environmental and social aspects of sustainability. This research proposes a stochastic multi-objective mixed-integer programming model to configure an SHL network during the response phase. Having compared to the research literature, this is the first study that considers economic, social, and environmental aspects of sustainability by incorporating relief cost, deprivation cost, and carbon emissions, respectively. Then, the improved multi-choice goal programming approach is applied to solve the proposed multi-objective model. To indicate the validity of the proposed model, an earthquake that occurred in a region of Kermanshah, Iran, in 2017 is investigated as a real case study. Finally, sensitivity analysis is performed and several managerial and theoretical insights are provided. The results show that exerting environmental issues in humanitarian logistics does not necessarily increase the relief costs, but can be in contrast with the social aspect. Furthermore, a minor increase in the budget of the preparedness phase drastically decreases the response costs. [ABSTRACT FROM AUTHOR]

This study analyzes the rescue team assignment problem as a critical issue in disaster management rescue operations. A mathematical model is proposed to minimize the total weighted completion time of the relief operations. This model considers some real-world features, such as precedence constraints, time windows, and different types of vehicles, which have been rarely addressed in the literature collectively. To mitigate the risk of uncertainty, a fuzzy robust optimization method is developed to deal with uncertain parameters such as the severity level of incidents, relief times, and travel times. The proposed model is successfully examined on a real data set from a flood in Agh-Qala, Iran, and the results verify its applicability in real-world conditions. Since the proposed model is an NP-hard problem, a hybrid metaheuristic algorithm (a combination of genetic algorithm and particle swarm optimization) is developed to optimally solve it for large-scale instances. Numerical experiments show that the algorithm can find high-quality solutions in a reasonable time. Several sensitivity analyses are also performed on the main parameters, and some promising managerial insights for disaster management practitioners are derived. [ABSTRACT FROM AUTHOR]

Natural disasters cause heavy casualties and financial losses annually. To reduce these damages, the rescue teams need to be planned effectively. In this regard, in this research, a mixed-integer programming model is offered to allocate and schedule rescue teams in a response phase of disaster management under uncertainty. The objective function minimizes the incident's total weighted completion times. The literature review shows that the uncertain condition and time windows have been less addressed in the previous studies. To cover these gaps, this paper investigates the problem under uncertainty and considers time windows for incidents. Besides, the fatigue effect is considered in this paper. Accordingly, within a planning horizon, incident processing times are not fixed. Since the considered problem is an NP-hard one and exact methods cannot solve it within a reasonable amount of time, this research develops a heuristic-based simulated annealing algorithm. The presented model is solved using the developed algorithm and three known meta-heuristic algorithms. Then, the results obtained by algorithms are compared and analyzed. Finally, the sensitivity analysis is carried out on some crucial parameters of the presented model, and the related results are reported. [ABSTRACT FROM AUTHOR]

Natural or man-made disasters impose destructive effects like human injuries and urban infrastructure damages, which lead to disruptions that affect the entire distribution system. This research addresses the routing-allocation problem in the response phase of disaster management. The related literature shows that the researchers had less attention to some features like sustainability and resiliency in the mentioned problem. Hence, to cover these gaps, this study proposes a scenario-based multi-objective programming model to examine the resilient-sustainable routing-allocation problem considering the concept of fairness. The proposed model aims at minimizing total traveling time, total environmental impacts and total demand loss. The fuzzy robust stochastic optimization approach is utilized to cope with uncertain data arisen in disaster conditions. Then, due to the complexity of the research problem, a hybrid approach based on the multi-choice goal programming method and a heuristic algorithm is developed to solve the problem in a reasonable time. A case study is then selected to demonstrate the efficiency of the proposed model and the developed method. Finally, sensitivity analyses have been conducted in some parameters of the model and also the robustness of the solution has been investigated. [ABSTRACT FROM AUTHOR]

Sustainability and low-carbon manufacturing have been under the scrutiny of the academics and practitioners, along with the governmental and non-governmental organizations. Not to mention the growing awareness and concern of the consumers about the carbon footprint, leading the researchers to the joint problem of sustainable supply chain coordination and emission abatement. This study contributes to the current literature by developing a contract for a dyadic supply chain model under a manufacturer-led Stackelberg game where emission abatement is a key decision. Implementing a greensensitive consumer preference and carbon abatement level into the demand function, a revenue-sharing contract is conducted to solve the channel conflict and double marginalization effect. The analytical results and numerical example prove the profit improvement of both channel members and validate the effectiveness and environmental-friendliness of the constructed supply chain structure. [ABSTRACT FROM AUTHOR]

In this paper, simultaneous coordination of order quantity and sales effort (SE) decisions in a supplier/retailer system with stochastic effort‐dependent demand is investigated. The main aim of the proposed model is to attain an optimal balance that results in a Pareto‐efficient solution for both channel members. A revised sales rebate (RSR) contract is developed to achieve channel coordination. In addition to the usual incentive approach of sales rebate schemes, a punitive approach is designed for the new proposed contract as a stockout penalty. Furthermore, some numerical experiments are examined to analyze the performance of the presented model under three decision‐making scenarios (i.e., decentralized, centralized, and RSR). Additionally, some in‐depth sensitivity analyses are conducted to examine the behavior of the supply chain performance under alteration of different parameters. The results show that the proposed RSR contract leads to channel coordination, while both channel members experience a Pareto improving situation. Moreover, it is proved that the RSR contract has significant potential on neutralizing adverse impacts of demand fluctuations on channel performance indicators. [ABSTRACT FROM AUTHOR]

Purpose: Distribution systems usually utilize both traditional retailing channels in conjunction with e-channels. The purpose of this paper is to investigate a dual-channel supply chain, comprising a traditional retailing channel and an e-channel under disruption. By benchmarking against the centralized decision structure, the authors intend to propose a collaboration model to achieve channel coordination as well as more reliable decisions. Design/methodology/approach: Four different channel disruption scenarios, with customers' reaction toward disruptions, are examined, and then, optimal pricing decisions for both centralized and decentralized decision-making structures are extracted. Next, a collaboration mechanism based on the dominancy power of channel members is developed to entice all channel members to participate in channel coordination. By benchmarking the proposed collaboration model against both the decentralized/centralized structures a win–win solution is guaranteed for all channel members. In addition, the proposed model ensures more reliable decisions than the centralized structure, as it guarantees less fluctuated income levels. Findings: This study shows, as the disruption probability grows, the channel profit decreases while the channel-retailing price increases. Furthermore, the exact alignment of the centralized decision-making approach and the proposed collaboration model is not achievable due to the problem infeasibility. Numerical experiments and sensitivity analyses benchmark the performance of the proposed collaboration mechanism against the centralized structure for the full alignment with centralized decision-making approach. Originality/value: This study contributes to the channel conflict literature as jointly considers pricing decisions, disruptions and coordination. Further, consumers' reaction toward disruption is analyzed through a transshipment agreement. [ABSTRACT FROM AUTHOR]

The procurement phase is known as a major component of engineering, procurement and construction (EPC) projects. One of the main reasons for the lack of timely completion of projects and creating additional costs is the poor performance of companies in this phase. The complexity of the purchasing process arises from a wide variety of project purchasing items and their suppliers. A purchasing portfolio model (PPM) has been developed based on the Kraljic model in the context of multi-criteria decision-making. The proposed hybrid model consists of a combination of DEMATEL and ANP (DANP) and VIKOR. The contribution of the work presents a hybrid MCDM method to clarify the interactive relationships among the criteria, Development of the Application Range of the Kraljic portfolio model, and a managerial tool for solving classification issues. Our study outcomes verified by a real Mall project and can be used by project professionals and academic researchers. [ABSTRACT FROM AUTHOR]

Modern societies face a dilemma called electronic waste (e-waste). This waste, which may contaminate the soil or cause unwanted impacts on human health when treated improperly, is one of the fastest growing waste streams in developed as well as developing countries and has brought great environmental impacts. Developing countries like Iran also face this modern waste management challenge. In order for more appropriate disposal or, if possible, recycling of this waste, more attention has been paid to reverse logistics as the most appropriate way to manage them. The first and most important action to address e-waste and implementation of reverse logistics is to persuade residents to bring back their obsolete electronic products. This paper attempts to understand significant factors affecting residents' incentive dependency to participate in e-waste recycling program. Socioeconomic and demographic information of different residents is discussed by means of logistic regression for the first time in Iran. The results show that about 58.7 % of residents will participate even if no incentive is given. Household income, household size, education, e-waste concern level, and marital status are the significant factors affecting the incentive dependency of respondents. [ABSTRACT FROM AUTHOR]

Improper setting of replenishment decisions can lead to lower service level and therefore reduces the whole supply chain (SC) profitability. In a decentralised SC, each member has the full authority of setting its own replenishment decisions, while these decisions affect the other SC members. In this paper, a single-upstream and single-downstream SC is considered, in which the upstream by proposing an incentive scheme based on delay in payments convinces the buyer to globally optimise his/her reorder point and also order quantity. Demand and also lead times are assumed uncertain in the investigated model. In addition, a truckload limitation is considered in downstream side. The proposed model ensures the global optimum of replenishment decisions under some circumstances. The model has the ability of sharing benefits between members to guarantee their participation. Maximum and minimum acceptable delay-in-payments durations are extracted. The results of numerical experiments show that the model has the ability of coordinating SC in most of the realistic parameter sets. Condition under which the proposed model is not able to coordinate the SC is also reported using a sensitivity analysis on the primary parameters. Extensive numerical experiments show capabilities of the proposed model in coordinating SC. [ABSTRACT FROM AUTHOR]

Supply chain (SC) uncertainties are present in both the demand and supply sides of the chain. If these uncertainties are not countered appropriately, the customer service level (CSL) and customer satisfaction will decrease, and this, in turn, will lead to a loss of sales volume and SC profitability. Maintaining an appropriate level of safety stock as a counteracting solution to SC uncertainties can boost the CSL. In traditional decision making, the retailer determines the CSL by determining its own safety stock level. However, the CSL affects the sales volume and profitability of the upstream members. This paper investigates the joint decision making on CSL in a two-stage SC with a constraint on the retailer's order size to analyze the benefits of coordination and cooperation when determining the CSL. The results of this study show that CSL coordination can significantly increase the profitability of the SC. [ABSTRACT FROM AUTHOR]

Lead time (LT) is a core parameter that varies and affects all supply chain (SC) partners. LT uncertainty is known as a kind of supply uncertainty in the SC literature which attracts attention of many researchers. In this paper, impact of LT variability in a serially connected supply chain with 4 levels is investigated. The study includes two main stages: development of a structural model and testing the structural relations by using simulated data. Simulation makes it possible to remove effects of intervener variables in order to measure pure impact of LT uncertainty on SC. Regarding this matter, a structural relationship model with three hypotheses about direct and indirect impact of LT uncertainty on supply chain inventories have been developed. To test the proposed structural relations, simulated data were used. The results of the study show that by increasing the lead time variance, order variances increase while no impact on the bullwhip is observed, i.e. the order variances increase uniformly throughout the entire SC. Furthermore, results show that the increase in the lead time variance will lead to inventory fluctuations. One practical recommendation of this study is to apply an investment strategy to reduce the lead time uncertainty. [ABSTRACT FROM AUTHOR]

Electrical conductivities and densities of acidic zinc, cadmium and manganese sulphate solutions were measured. Empirical equations have been derived to represent the measured values with high accuracy. Electrical conductivity of solutions has been shown to be expressed as a logarithmic function of temperature, a second order polynomial function of sulphuric acid concentration and a linear function of metal ion concentration. It can be deduced that the density of solution can be described as a linear function of metal ion concentration, temperature and sulphuric acid concentration. These equations could be used to improve the electrowinning or electrorefining process conditions. [ABSTRACT FROM AUTHOR]