Combination of pricing and inventory policies for deteriorating products with sustainability considerations.

Economic, environmental, and social criteria are all being taken into consideration simultaneously when determining pricing policies or inventory levels in sustainable production management. The combination of pricing and inventory policies is an important source of leverage for the efficient management of perishable products. This paper, among the first studies, proposes the problem of devising optimal pricing and inventory management decisions simultaneously where the environmental and social criteria are contributed for perishable complementary products replenished and sold by the same company. This study considers two interrelated price-sensitive linear demand functions to consider the possibility of shortage with both budget and warehouse capacity constraints. Another contribution of the proposed model is to consider an upper bound for environmental pollution and a lower bound for job opportunities as the constraints to the model. As a complex optimization model, the challenge of complexity is addressed by a heuristic algorithm for finding an optimal solution. After an extensive analysis using numerical examples, some managerial insights are concluded from the results. One finding from these analyses confirms that the total capacity of the warehouse, the total available budget, carbon emissions, and variable job opportunities have a high impact on the optimal solution to find a balance between sustainability criteria for making pricing and inventory policies.

A multiobjective model for the green capacitated location-routing problem considering drivers' satisfaction and time window with uncertain demand.

Location-routing problem is a combination of facility location problem and vehicle routing problem. Numerous logistics problems have been extended to investigate greenhouse issues and costs related to the environmental impact of transportation activities. The green capacitated locating-routing problem (LRP) seeks to find the best places to establish facilities and simultaneously design routes to satisfy customers' stochastic demand with minimum total operating costs and total emitted carbon dioxide. In this paper, features that make the problem more practical are: considering time windows for customers and drivers, assuming city traffic congestion to calculate travel time along the edges, and dealing with capacitated warehouses and vehicles. The main novelty of this study is to combine the mentioned features and consider the problem closer to the real-world case uses. A mixed-integer programming model has been developed and scenario production method is used to solve this stochastic model. Since the problem belongs to the class of NP-hard problems, a combination of the progressive hedging algorithm (PHA) and genetic algorithm (GA) is considered to solve large-scale problems. It is the first time, as per our knowledge, that this combination is implemented on a green capacitated location routing problem (G-CLPR) and resulted in satisfactory solutions. Nondominating sorting genetic algorithm II (NSGA-II) and epsilon constraints methods are used to face with the bi-objective problem. Finally, sensitivity analysis is performed on the problem's input parameters and the efficiency of the proposed method is measured. Comparing the results of the proposed solution approach with those of the exact method indicates that the solution approach is computationally efficient in finding promising solutions.

Carbon-efficient closed-loop supply chain network: an integrated modeling approach under uncertainty.

Inspired by a circular economy paradigm, an evolving momentum of policies and legislations aims to close the loop of product lifecycles through improved level of recycling, remanufacturing, and reuse, with the objective of adding value to the economy while not endangering the environment. However, the trade-off between the environmental and economic sustainability of designing business processes is inevitable. To address this trade-off in the supply chain context, competing objectives regarding both cost minimization and reduction in carbon emission should be simultaneously considered and integrated into a comprehensive model. This complexity is however elevated when uncertainty of demand is taken into consideration. In this study, the design of a closed-loop supply chain is investigated where competing objectives of cost and sustainability of supply chain operations are evaluated under demand uncertainty. Augmented Weighted Tchebycheff (AWT) and ε-constraint methods are employed to address the multi-objectivity of the problem while a robust optimization approach is applied to deal with the demand uncertainty. The results confirm that the proposed approach provides efficient solutions for designing a green closed-loop supply chain network.