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The issue of integrated scheduling and sequencing operation of unloading and loading equipment in container ports has been one of the most important issues concerning time efficiency. In addition, with the emergence of green harbor concepts, the inclusion of criteria for minimizing energy consumption, fuel and emission reduction are among the other issues that have been noticed by planners in the field of energy efficiency. Furthermore, due to the complexity and scope of activities of a container terminal, uncertainty in operational parameters such as transportation time, time of readiness and entry of work into the system and the velocity of the transportation fleet are inevitable in this operational environment. Therefore, this research with the aim of sharing trucks among loading and unloading equipment, proposes a robust multi-objective integer programming model for the synchronized scheduling of truck operations with other handling equipment to decrease the fuel consumption of trucks and the flow time of containers, considering the uncertainty in operational parameters as fuzzy numbers. To find the Pareto solutions for this model, the ε-Constraint technique is employed. Finally, the performance of the model in deterministic and uncertain modes is evaluated, compared and analyzed employing the inputs gathered from Shahid Rajaei port. The findings demonstrate that using this model will result in a substantial decrease in both fuel consumption and flow time of containers in comparison to the current procedure. Additionally, results will demonstrate the extent to which the terminal's fuel and time consumption will increase under conditions of uncertainty in operational parameters when the optimal plans derived from the robust model are implemented.

In line with the development of green port terminals and the necessity of environmentally friendly efficient use of handling and transportation equipment, criteria such as emission reduction, energy minimization, and efficiency in a container terminal are prominent issues, which in recent years, have been the main concern of container terminal managers in developing terminal operating systems. Therefore, in this study, a bi-objective mixed integer mathematical programing model of integrated scheduling for ships' loading and unloading operation sequencing in container terminals is proposed, which considers the minimization of containers' flow time, trucks' emission, and energy consumption as well as sharing trucks among quay cranes and decreasing their empty trips. In this model trucks' technical specifications and polluting levels are input parameters for estimating emission and energy consumption of operation, which consequently leads to two uniform parallel machine scheduling models. To derive the exact solution of the model, the epsilon-constraint method is applied. Nevertheless, due to the problem's NP-hardness attribute, two variants of non-dominated sorting genetic algorithm and multiobjective particle swarm optimization metaheuristic algorithms are proposed to identify the approximate Pareto front of solutions. In large-size problems, these algorithms outperform the epsilon-constraint method by finding acceptable results in a reasonable time. This model, which could be embedded in terminal operating software, not only yields a practical optimum operational sequence but also measures the amount of energy and emission from trucks during loading and unloading operations. The results, which utilized the data collected from Shahid Rajaee port in southern Iran, indicate that this practical model as compared to the current procedure in the terminal, results in a significant improvement in energy and emission reduction and terminal flow time. [ABSTRACT FROM AUTHOR]

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. Keywords Sustainable supply chain, production routing, workforce planning, pricing, perishable products, Climate change is not just a phenomenon related to the rising temperature in a specific region; rather, it refers to the average long-term changes such as the increased frequency of [...]

A crucial role in the continuation of economic activities is played by the financing of services and production in supply chains. A key element of optimizing the financial flow of these complex networks is to pay attention to the financial aspects of these complex networks since they are becoming more and more complex and expanding. This study aims to investigate the supply chain of a pharmaceutical company’s holding company and its subsidiaries while using internal resource valuation to develop a new strategy for financing the company’s operations. There is a process of money circulation through the chain, which consists of passing through two treasuries (primary and secondary), which provide liquidity to compensate the deficits of some institutions with the excess liquidity of other institutions. In this article, we present three simulation-based models based on a case study conducted at Shafa Darou Investment Company in Tehran-Iran, a leading pharmaceutical investment company in the country, to examine the impact of implementing this idea in the real world. Considering the study’s results, it has been shown that the supply chain as a whole has improved in terms of its working capital. Using a set of local treasuries is generally associated with reducing risks and a greater level of stability when relying on the excess liquidity of chain members provided that financial independence from external institutions, such as banks, is maintained. In addition, if the members’ excess liquidity is deposited in a set of local treasuries rather than a bank, the profit and internal financial flow within the chain will be circulated throughout the chain, and more added value will be generated.