Supply chain management (SCM) deals with various strategic, tactical, and operational level decisions in which pricing is of utmost importance to decision makers. Most of the real‐life supply chain ...pricing problems consider competition as a crucial factor in order to expand the market share and tackle emerging competitors. Accordingly, competitive pricing in SCM has attracted great attention by practitioners and academicians in the last four decades. Now after 40 years, it seems necessary to systematically review and classify the previous studies and present the most appealing future research directions. This paper provides a comprehensive review of the state‐of‐the‐art published papers in scientific journals. Here, 166 papers published from 1983 to 2021 are reviewed and classified to introduce the major models and specific considerations in this field. After reviewing the selected competitive pricing models, some research insights and potential subjects for extending the literature are presented to illustrate the most appropriate and challenging future research directions.
This paper presents a fuzzy bi-objective bi-level model with a price-dependent demand for the network design of a closed-loop supply chain in the presences of random disruptions at suppliers. The ...environmental issues resulted in considering two strategies, namely: adding a reverse flow to the supply chain and controlling the amount of CO2 emissions. In this model, the uncertain demand is assumed as a function of price offered to the customers by the supply chain and its rival. The proposed model determines outsourcing strategies, pricing decisions that maximize the total profit in such a competitive situation and minimizes the amount of CO2 emissions by production processes. A hybrid approach combining Karush–Kuhn–Tucker (K-K-T) conditions and possibilistic method was developed to solve the fuzzy bi-level model. Then the ε-constraint method is used to convert the integrated bi-objective model to a single-objective one. Finally, important managerial insights are obtained from an empirical case study of a filter industry.
•Presenting a bi-objective model for green closed-loop supply chain network design.•Considering disruption and operational risks with a fuzzy price-dependent demand.•Addressing environmental issues by minimizing CO2 emission in production process.•Applying a bi-level programming method to model Stackleberg game in the problem.•Developing a solution approach based on fuzzy programming technique.
A robust bi-level model of the single-product multi-period network design problem is proposed for a competitive green supply chain considering pricing and inventory decisions under uncertainty and ...disruption risks. The bi-level programming approach is used through this model to demonstrate the competition among two supply chains; the leader and the follower, respectively. After modelling the competition and applying pricing decisions by defining a price-dependent demand, disruption risks are analysed through the model. The proposed model simultaneously considers demand uncertainty and disruption risks and is capable of dealing with such uncertainties by implementing resilience strategies including, inventory decisions, and having a contract with reliable suppliers. Moreover, to consider the environmental issues, controlling CO2 emissions and managing the reverse flow were added to the model. Our approach to mitigate the problem uncertainties is to use the possibilistic programming method. The Karush-Kuhan-Tucker (K-K-T) optimality conditions are deployed to make a single-level equivalent form. Since the integrated model was bi-objective, the ϵ-constraint method is implemented to make a single objective integrated model. Finally, some managerial implications are discussed through an industrial case example.
This paper presents a multi-objective model for the closed-loop supply chain network design (CLSCND) with a price-dependent demand while considering random disruptions and shortage. This network ...consists of suppliers, production centers, collection/inspection centers, disposal centers, and market zones. In this model, a demand function is assumed to be a function of price offered to the customer by two different linear and exponential forms. Different resilience strategies including lateral transshipment among production centers and multi-source allocation are applied to model the resilient supply chain network design problem. Moreover, the importance of time for costumers is led to consider different transportation methods for fulfilling the customers’ demand. Then, three different objective functions were incorporated when shortage is allowed and the unfulfilled demand will be back ordered. Then, a compromise programming method is used to convert the multi-objective model to a single-objective equivalent form. Furthermore, the application of the model is examined in a real industrial case involved in the production and distribution of glass. Finally, and the numerical results arrive at helpful managerial insights.
In this paper, the problem of sensor selection for energy efficient spectrum sensing in wireless sensor networks is addressed while considering several primary users operating asynchronously in the ...specified frequency band. The basic notion of the paper is to overcome the limited energy budget in wireless sensor networks and also the hidden primary user problem. The original problem of minimizing energy consumption in multi-primary user scenarios considering the detection performance constraints has NP-hard form, so we relaxed it to a tractable form where sub-optimal solutions are extracted based on the convex optimization framework. For the sake of simplicity, energy detector is considered as a practical sensing method. Simulation results show that the proposed algorithms not only decrease energy consumption in comparison with benchmark algorithms, but also improve the detection performance.
In this paper, we address the problem of sensor selection for energy efficient spectrum sensing in multi-band cognitive sensor networks. We formulate the problem of sensor selection in multi-band ...case in order to minimize energy consumption while satisfying the detection performance constraints. Energy detector is used as a simple detector which can be implemented in practice. Due to NP-hardness of the original problem, we relaxed it to a more tractable form and suboptimal solution is extracted based on convex optimization framework. Simulation results show that the proposed algorithm not only consumes the least energy in comparison with benchmark algorithms, but has the most ability in finding solutions which satisfy detection performance constraints.
