mixed integer programming

To reduce the environmental Pollution emissions caused by market activities, cold chain logistics companies also considered the emission of harmful gases for better service in satisfying customers’ demands. In the cold supply chain, goods are supplied and distributed that become corrupt and degraded over time. Therefore, to keep such goods fresh, the temperature must be constantly and continuously controlled, which in turn requires more fuel consumption. Also, in vehicle routing problem, the travel time of a route and fuel consumption does not only depend on the distance traveled, but also on the speed and time of day when that route is traveled. In this study, a new mixed-integer optimization model of the vehicle routing problem in a cold supply chain concerning congestion is presented with the aim is to minimize costs of Pollution emissions. In this model, in addition to the cost of the environmental Pollution emissions, other costs are considered, including the vehicle operating cost, transportation, loss of quality, product freshness, and penalty cost for arriving outside the customer's time window. In continuing, a solution method based on Benders decomposition is applied to solve the proposed model for large size networks. The computational results showed that the presented model provides the optimal route and travel time of the vehicle by considering the reduction of pollution and the appropriate speed. Also, the implementation of the solution algorithm on several test instances with different sizes showed the efficiency of the algorithm in reducing the solution time and obtaining a good solution.

In the current research, a new approach has been used to optimize the performance of the intersection. The combination of time variables and geometric is enhanced optimization of intersection capacity. Traffic lanes and traffic light settings are considered as decision variables, and the limitation of short lines is also included in the modeling. short are very common and can be seen in many intersections. Intersection capacity and performance have a very significant positive and negative relationship with the variables of short lines. In the present research, with three new approaches: 1- Considering the geometrical variables of the intersection along with the timing and phasing variables of the lights; 2- Considering the short line and its effect; 3- Using mixed integer programming, zero-one as methodology; The capacity of the intersection of Jalal Al-Ahmad highway and Arianfar Boulevard was maximized and the efficiency of the intersection increased by about fourteen percent. In order to validate the model and check the behavior of the intersection, the model was simulated in Synchro software, and the capacity, travel time and delay of the intersection were estimated.

The availability of resources such as workforce and materials at each level of a high-rise construction project just before the commencement of building tasks is a crucial issue that might have direct impacts on project progress. To avoid delays caused by lack of human resources and construction materials, a construction management team always tries to find a better way to facilitate supply chain process specifically for construction projects facing a significant number of simultaneous and repetitive tasks. The other challenge in a high-rise construction project is vertical transportation that requires detailed machinery e.g. cranes or lifts, and maximizing their utilities is another challenge. In this paper, we tried to automate vertical transportation planning process in high-rise construction projects by introducing a platform that handles the entire lifting process. This platform considers (i) tasks attributes (e.g. required resources, location and commencement time) from the project schedule, (ii) lifting system specifications (e.g. travel speed, weight, and volume capacity) and (iii) project geometrics (e.g. current height of the project). In details, the introduced platform provides an optimized daily-based delivery plan by developing a Mixed-Integer Programming (MIP) model that covers workforce and construction materials. In this paper, we also test the proposed platform with field data obtained from a 34-story construction project in Mashhad, Iran. The model could find a solution with 0.000% optimality gap in approximately one hour, which is an acceptable amount of computational cost for the problem.

This study is concerned about how the quality of perishable products can be improved by shortening the time interval between production and distribution. Since special types of food, such as dairy products decay fast, the integration of production and distribution scheduling (IPDS) is investigated. 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 a specific amount of products are produced, they must be transported with the least transportation time directly to various customer positions within its limited lifespan to minimize delivery and tardy costs required to complete producing and distributing the product to satisfy the demand of customers to the limited deadline.
After developing a mixed-integer nonlinear programming model of the problem, because it is NP-hard, an Improved Particle Swarm Optimization (IPSO) is proposed. IPSO performance is compared with a commercial optimization software for small-sized and large-sized problems. The model has developed as an Integer- nonlinear model and it solved by using an improved particle swarm optimization which is improved by 1-opt and 2-opt operator and is coded in Matlab program. The model has been investigated through the real data gathered from Pegah company and the outcomes has been compared to the previous order system.Computational experiments show the efficiency and effectiveness of the proposed IPSO in terms of both the quality of the solution and the time of achieving the best solution.

Optimization of Airports, gate scheduling is a vital activity. The main purpose of this optimization is to find a proper allocation for the flights arriving and departing by considering all of limitations. Studies show that the gate scheduling is applied for minimizing the gate flights delay and maximizing the gate efficiency and productivity. The optimization is formulated as a mixed-integer programming (MIP). The paper is studied data from Mehrabad International Airport. Our results show that the model can be solved by using NSGA-II algorithm in a short time. The set of Pareto solutions are ranked by TOPSIS method and the first 10 solutions are reported. The results show that although adding the first objective function (crowd congestion) leads to %8 reduction of the third objective function (the total gate assignment preference); the first objective function has a positive effect on the satisfaction of passengers.

The MIP technique in combination with Piecewise linear function has lead to a method of stope design, which uses a one, two or three-dimensional discretisation of the ore zone (block model). An optimal economic stoping boundary is developed by optimizing the starting and ending locations for mining within each row or column of blocks (a mining panel). To determine these locations, two piecewise linear, cumulative functions are used for each row. The stope boundary model is optimized using a MIP approach that employs a special kind of variables named “special ordered sets type 2”. This paper presents a step by step explanation of the model construction. The optimizing problem is solved using the MIP approach. GAMS/Cplex11 software tool is employed to numerical examples. The model is validated by comparison with similar cases.