The system of Distribution of goods and services, along with other economic developments around the world, is rapidly evolving. In the world of Distribution of goods, the main focus is on making Distribution operations more effective. Due to the fact that the cross-dock has the advantage of removing intermediaries and reducing the space required for the warehouse, it is worth considering. Among the methods of cross-docking, the post-Distribution method is important in terms of uncertainty. Due to the importance of the issue of the post-Distribution method in cross-dock, this paper addresses the uncertainty of demand in cross-docking. For this purpose, a linear programming model has been developed for post-Distribution cross-dock, and then solved an example by the use of the meta-heuristic whale algorithm. After that, uncertainty enters the model and the robust counterpart of the model present based on the robust optimization approach with using interval and polyhedral collective inductive uncertainty set. The results shows the model could control the demand uncertainty in distance zero until 20 percent and the model does not let the changing of demand efforts considerably on the Scheduling of the cross-docking.

Many supply chains lack flexibility and adaptability in today's competitive market, resulting in customer dissatisfaction, backorders, and several extra costs for the business. Additionally, the inability to quickly meet the customer's demands andthe unnecessary transportation costs is also one of the significant challenges facedby the fixed facilities' supply chain. To address these challenges, this study analyzedthe mobile facilities supply chain and the production, Distribution, and delivery ofgoods conducted by trucks based on customer preferences. This study proposes abi-objective mixed-integer linear programming model to ensure the mobilefacilities' routing and manufacturing schedules are optimized to meet the customer's needs. Furthermore, this model minimizes production and Distribution costs in the shortest amount of time. An exact decomposition algorithm based on Bendersdecomposition is used to find high-quality solutions in a reasonable amount of timeto tackle the problem efficiently. We present several acceleration strategies forincreasing the convergence rate of Benders' decomposition algorithm, includingPareto optimality cut and warm-up start. The warm-up start acceleration strategyitself is a meta-heuristic based on particle swarm optimization (PSO). Using theBenders decomposition, we demonstrate the superior accuracy of our solutionmethodology for large-scale cases with 10 kinds of products ordered by 30customers using 10 mobile facilities.

With increasing competition in the business world and the emergence and development of new technologies, many companies have turned to integrated production and Distribution for timely production and delivery at the lowest cost of production and Distribution and with the least delay in delivery. By increasing human population and the increase in greenhouse gas emissions and industrial waste, in recent years the pressures of global environmental organizations have prompted private and public organizations to take action to reduce environmental pollutants. This paper presents a nonlinear mixed integer model for the production and Distribution of goods with specified shipping capacity and specific delivery time for customers. The proposed model is applicable to flexible production systems; it also provides routing for the means of transportation of products, as well as the reduction of emissions from production and Distribution. The model is presented, and then by mathematical linearization is transformed into a mixed integer linear model. The data of a furniture company is used to solve the linear model, and then the linear model with the company data is solved by CPLEX software. The numerical results show that as costs increase, delays are reduced and consequently, customer satisfaction increases, and as costs increase the air pollution decreases.

In this research, the Scheduling of loading and Distribution for high consumption oil products from dispersed warehouses is studied. The high consumption of oil products requires direct shipment of orders issued by different customers in different geographical locations. In this paper, an integrated nonlinear model is proposed to minimize the total cost of the supply chain including purchasing cost, loading, transportation, and late delivery costs. Because of the NP-hard nature of problem, a new hybrid solution approach based on simulation and meta-heuristic algorithms is proposed. Operations simulation ensure always feasible salutation and meatheuristic algorithms provide intelligent search within the feasible solutions. The efficiency of proposed solution approach is evaluated through several random test problems. Moreover, the performance of a latest versions of genetic algorithm (GA-MPC) and particles swarm optimization (SPSO 2011) as well as the traditional versions are evaluated within the proposed solution approach to solve the real-world scale problems. The results illustrate the better performance of genetic algorithm for large-scale problems.

One of the obvious reasons for most disorders in network service provisioning is network path congestion. Congestion avoidance in today's networks is too costly and sometimes impossible. With the introduction of SDN, centralizing the equipment's control plane has become possible. This paper presents an enhanced method named ESV-DBRA to avoid congestion in multi-tenant SDN networks. At first, ESV-DBRA monitors the traffic load and delay of all network paths for each tenant individually. Then, by merging the parameters obtained from the monitoring, the Service Level Agreements (SLA), and a novel proposed cost function, it calculates the cost of the network paths per tenant. As a result, traffic for each tenant is routed through the path/paths at the lowest possible cost from the tenant's perspective. Next, the bandwidth quotas will be calculated and assigned to the tenants over their optimal routes. Afterward, whenever congestion is likely to occur in a path, ESV-DBRA automatically changes the route or bandwidth of the tenants' traffic related to this path to avoid congestion. Related algorithms are also proposed.Eventually, simulations show that the proposed method effectively increases bandwidth utilization by 10.76%.

The Scheduling of electricity Distribution networks has changed dramatically by integrating renewable energy sources (RES) as well as energy storage systems (ESS). The sizing and placement of these resources have significant technical and economic impacts on the network. Whereas the utilization of these resources in the active Distribution network (ADN) has several advantages, accordingly, the undesirable effects of these resources on ADN need to be analyzed and recovered. In this paper, a hybrid ADN, including wind, PV, and ESS, is investigated in 33 buses IEEE standard system. First of all, optimal energy management and sizing of the RES and ESS are the purposes. Secondly, as demand response (DR) is another substantial option in ADNs for regulating production and demand, an incentive-based DR program is applied for peak shaving. Forasmuch as this method has uncertainty, due to its dependence on customer consumption patterns, the use of inappropriate incentives will not be able to stimulate customers to reduce their consumption at peak times. Accordingly, the climatic condition uncertainty, which is another factor of variability on the production side, is minimized in this paper by relying on the Monte Carlo estimation method. Besides, the optimization problem, which is formulated as optimal programming, is solved to calculate the optimal size and place of each RESs and ESS conditions regarding power loss, voltage profile, and cost optimization. Furthermore, a geometric, energy source and network capacity, and cost constraints, are considered. The results confirm the effectiveness of proposed energy management and cost reduction in the studied test system.

In this paper, we studied a new integrated production Scheduling, vehicle routing, inventory and outsourcing problem. The production phase considers parallel machine Scheduling including setup times with outsourcing allowed and the Distribution phase considered batch delivery by a fleet of homogenous vehicles with respect to holding cost of completed jobs. The objective of the Mixed Integer Linear Programming (MILP) formulated model is to minimize the total costs including production, outsourcing, holding, tardiness and Distribution fixed and variable costs. Due to the nondeterministic polynomial time (Np)-hardness of the problem, we derive a number of dominance properties for the optimal solution and combine them with a Genetic Algorithm (GA) to solve the problem. To assess the efficiency and effectiveness of the proposed hybrid algorithm, we conduct the computational study on randomly generated instances. Sensitivity analyses showed the impacts of the parameters on the objective function were incorporated. In order to evaluate the significance of the differences among the results obtained by GA and GADP one-tailed paired t tests were performed and interval plots were depicted.

This study is concerned with how the quality of perishable products can be improved by shortening the time interval between production and Distribution. As special types of food such as dairy products decay fast, the integration of production and Distribution Scheduling (IPDS) is investigated. An integrated Scheduling of both processes improves the performance and costs because the separated Scheduling of these processes without considering mutual requirements leads to non-optimal solutions.An optimal solution to IPDS requires simultaneously solving of the production Scheduling and vehicle routing problems. This article deals with a variation of IPDS that contains a short shelf-life product; hence, there is no inventory of the product in the process. Once an amount of products is produced, they must be transported with no n negligible transportation time directly to various customer locations. The objective is to determine the minimum cost of the make span and number of vehicles required to complete the Distribution of the products to satisfy the demand of a given set of customers over a wide geographic region. The overall problem consists of permutation flow shop Scheduling with m machines, n jobs and vehicles with different speeds and transportation capacities which transport n jobs from the manufacturing company to c customers distributed in various zones by determining the vehicle routes and number of vehicles. After developing an Integer Linear Programming (ILP) model of the problem, because it is NP-hard, a new graph-based heuristic method is proposed to efficiently solve the problem.