Sustainable Pricing-Production-Workforce-Routing Problem for Perishable Products by Considering Demand Uncertainty; A Case Study from the Dairy Industry

Abstract

The production routing problem seeks to simultaneously optimize production, routing, and inventory decisions for the plant and the suppliers. In this article an integrated multi-objective sustainable pricing-production-workforce-routing problem is presented for perishable products. Total profit, workforce planning, and vehicle fuel consumption are considered as objective functions due to the importance of operational performance, social, and environmental concerns. The application of the proposed approach is investigated using real case data from a dairy product supply chain. Furthermore, a new solution approach, called Fuzzy Domination Self-Learning Non-Dominated Sorting Algorithm (FDSL-NSGA-II), is developed to solve the problem. The results show that the Pareto solutions of FDSL-NSGA-II outperform those of the classic NSGA-II. Moreover, the findings show that the proposed model can create a surpassing tradeoff between the various aspects of a supply chain, including production, distribution, and workforce planning. In addition, it concurrently optimizes the selling price and protects the environment from the negative impacts of greenhouse gas emissions (GHGs). A comprehensive analysis of the results reveals several managerial insights for decision makers in the logistics industry.

The Noble Quest: Navigating Toward Sustainable Transportation

“…As a derived demand, transportation is arranged based on market information related to both demand and supply. Extant literature provides an extensive but far from exhaustive list of variables that can be used to optimize transportation routing, capacity planning, driver safety, and decisions at the strategic level such as fleet composition (Majidi, Farghadani‐Chaharsooghi, and Mirzapour Al‐e‐Hashem 2022; Miller 2020; Jin et al 2017). Esper and colleagues (2010, 7) proposed a demand‐and‐supply integration (DSI) framework, which involves “the balancing of demand and supply market information and business intelligence through integrated knowledge management processes to strategically manage demand and supply activities for the creation of superior customer value.” Considering transportation’s role in bridging the gap between demand and supply through facilitating material movement, significant opportunities exist to examine how to improve the estimation of transportation demand and therefore the ability to manage it more directly.…”

“…These questions are driven in part by transportation’s increased complexity. For instance, Majidi and colleagues (2022) specifically considered green vehicle routing in a perishables category with results not generalizable to nonperishables category. Variables in their models not only include factors specific to the product category but also considerations for various decision nodes at a transportation manager’s disposal that impact transportation process, routing, and shipment quantity.…”

How Much Did That Cost? A Call for Improved Transportation Cost Transparency

“…However, defining and calculating the carbon footprint of firms has been attempted multiple ways with little agreement among the methods (Pandey et al 2010). Since transportation is one of the largest contributors to carbon emissions and since carbon emissions are correlated to transportation cost, increasing the transparency of transportation cost is an important key to accurately calculating the carbon footprint of the firm (Majidi, Farghadani‐Chaharsooghi, and Mirzapour Al‐e‐Hashem 2022). Indeed, environmental social governance (ESG) have become an increasingly powerful investment objective.…”

Stochastic medical tourism problem with variable residence time considering gravity function

“…Due to complexity theory for NP-hard optimization problems [80]; nowadays, there is a great deal of interest in development of heuristics and meta-heuristics for decision-making problems like online learning [89], scheduling [6,27,49], multi-objective optimization [46], healthcare [20,23], transportation [22,62], etc. In a similar way, this study develops a hybrid meta-heuristic algorithm for solving the stochastic medical tourism tour problem.…”