EIA-CNDP: An exact iterative algorithm for critical node detection problem

Mixed Integer Linear Programming Model for Vehicle Routing Problem for Hazardous Materials Transportation

Due to booming economic development over the past decades, energy demands in most of China’s provincial power grids have increased sharply, and it has become challenging to guarantee the energy balance at peak periods. In many provincial electric systems of China, gas-fired generators are one of the most important peaking power sources to respond the load change at peak periods. To meet this practical necessity, a novel mixed integer linear programming model is proposed in this paper for the peak operation of gas-fired generating units with disjoint-prohibited operating zones. In the developed model, the objective function is chosen to minimize the peak-valley difference of the remaining load series that is obtained by subtracting the total generation of all the gas-fired units from the original load curve. The real-world simulations in several cases show that the developed model is able to generate satisfying scheduling results by reasonably allocating the power outputs of all the gas-fired generators in the scheduling horizon. Then, the management implications obtained lie in the fact that it is necessary to increase the share of peak power sources in the mid- to long-term planning of an electrical power system; and in the daily operation of the power grid, greater flexibility should be given to the gas-fired units to reduce peak pressure.

Cellular manufacturing systems (CMS) are production systems that typically comprise a number of manufacturing cells served by a centralized material handling system. Designing such systems includes three major decisions; cell formation (CF), group layout (GL), and group scheduling (GS). Traditionally, these three decisions have been dealt with separately, which has usually lead to less than optimal system performance. In this paper, a new mixed integer linear programming (MILP) model is proposed for the integrated CF, GL and GS problem, to efficiently design and operate CMSs. The model solves the integrated problem, taking into consideration intercellular and intracellular transportation times to determine the optimal cell formation, layout of machines and schedule of parts on the machines, simultaneously. Sequence-dependent set up times are also considered in the model. The performance of the model is tested by solving problems previously introduced in the literature considering two objectives; minimizing the makespan or minimizing the mean flow time in the system. The results show that the proposed model is efficient in solving small to medium-sized problems.

This paper presents multiple readings to solve a vehicle routing problem with pickup and delivery (VRPPD) based on a real-life case study. Compared to theoretical problems, real-life ones are more difficult to address due to their richness and complexity. To handle multiple points of view in modeling our problem, we developed three different Mixed Integer Linear Programming (MILP) models, where each model covers particular constraints. The suggested models are designed for a mega poultry company in Tunisia, called CHAHIA. Our mission was to develop a prototype for CHAHIA that helps decision-makers find the best path for simultaneously delivering the company’s products and collecting the empty boxes. Based on data provided by CHAHIA, we conducted computational experiments, which have shown interesting and promising results.

The increasing level of customer awareness and application of environmental laws by governments, on the one hand, and the increasing number of competitors, on the other hand, has obliged industry owners to include green activities in the design of the supply chain network. Green activities include any type of action that reduces environmental degradation. Hence, this study seeks to develop a bi-objective, mixed integer linear programming (MILP) model for designing a green supply chain network. In the proposed model, the minimisation of costs and detrimental environmental effects are discussed. LP metric method is used to solve the bi-objective model and the proposed model is run by using simulated data in small and medium sizes in GAMS software. Finally, the model sensitivity analysis is measured in order to evaluate the validity and performance of the proposed model by using demand-driven scenarios. The results of this model indicate the effectiveness of the proposed model.

This paper presents a mixed integer linear programming (MILP) model to operate a dual source heat pump (DSHP) optimally. The DSHP draws environmental energy from an underground ice storage tank (IST) or wind infrared sensitive collectors (WISC). WISC are further used to regenerate the IST. Thermal energy storage (TES) stores the thermal output of the DSHP. A single-objective optimisation approach is applied to minimise the operational cost of the DSHP over a scheduling horizon of 24h. The developed framework is tested on various days, which are representative for each season of the year. Furthermore, it is investigated how the variable electricity price market data influence the dynamic behaviour of the DSHP.

Regular security controls on a day by day basis are an essential and important mechanism to prevent theft and vandalism in business buildings. Typically, security workers patrol through a set of objects where each object requires a particular number of visits on all or some days within a given planning horizon, and each of these visits has to be performed in a specific time window. An important goal of the security company is to partition all objects into a minimum number of disjoint clusters such that for each cluster and each day of the planning horizon a feasible route for performing all the requested visits exists. Each route is limited by a maximum working time, must satisfy the visits’ time window constraints, and any two visits of one object must be separated by a minimum time difference. We call this problem the Districting and Routing Problem for Security Control. In our heuristic approach we split the problem into a districting part where objects have to be assigned to districts and a routing part where feasible routes for each combination of district and period have to be found. These parts cannot be solved independently though. We propose an exact mixed integer linear programming model and a routing construction heuristic in a greedy like fashion with variable neighborhood descent for the routing part as well as a districting construction heuristic and an iterative destroy & recreate algorithm for the districting part. Computational results show that the exact algorithm is only able to solve small routing instances and the iterative destroy & recreate algorithm is able to reduce the number of districts significantly from the starting solutions.

Nowadays, intelligent vehicles have be used for transporting jobs in many manufacturing plants; thus, routing vehicles properly is very important for processing jobs. This paper studied a job shop scheduling problem considering intelligent vehicle transport. The problem is difficult to be handled because it needs to optimize the route of a vehicle and the processing sequences of jobs on machines simultaneously. To address this problem, a multi-attribute directed graph is proposed, in which a determined vehicle path associated with the attributes of job and machine is defined as a node and an undetermined vehicle path is defined as an arc; thereby, a mixed integer linear programming model is established. Numerical experiments show the effectiveness of the proposed model.

This paper proposes a deterministic mixed integer linear programming model for the optimal operation of an energy system providing thermal and electrical energy for a residential and commercial nearly zero energy building. The space heating and space cooling demand of the buildings is simulated using a resistive-capacitive model within a quadratic program respectively. Thermal energy for space heating, space cooling and domestic hot water is buffered in thermal energy storage systems. A dual source heat pump provides thermal energy for space heating and domestic hot water, whereas space cooling is covered by an underground ice storage. The environmental energy sources of the heat pump are ice storage or wind infrared sensitive collectors. The collectors are further used to regenerate the ice storage. Further space heating demands are covered by a combined heat and power unit, which also produces electricity. Photovoltaic panels produce electrical energy which can be stored in a battery storage system. The electrical energy system is capable of selling and buying electricity from the public power grid. A mixed integer linear programming model is developed to minimise the operation cost of the combined commercial and residential nearly zero energy building over a scheduling horizon of 24h. The developed model is tested on two typical days, which are representative for the summer and winter season. Furthermore, it is investigated how external incentives such as varying electricity prices impact the optimal scheduling of the energy system.

In this paper, a mixed integer linear programming model for the multi-item uncapacitated lot-sizing problem is presented. The considered factors for formulating the proposed model are the monthly demand of the selected product from the case study company, type of parts used in the product and their consumption coefficients based on the bill of materials, lead time to receive parts from outsourcing suppliers, the costs of ordering, purchasing, and holding, and the amount of safety stock for each part. Accordingly, several forecasting techniques are tried to determine future demands. The prices of the selected parts are estimated using linear regression method. The optimal safety stock for each part is calculated based on variance in demand, lead time and the target service level. Material requirements planning are also performed to obtain the economic purchasing schedule of parts. LINGO and GAMS software are used to solve and validate the suggested model respectively. The results show that the proposed model can find the optimum order quantity for each part per period, which minimises the total cost.

Objectives Facility service districting problem(FSDP) refers to the design of facility service areas according to the facility locations and service capacities in a geographical region. The design criteria for service areas include the balance of service demand and supply, the highest service accessibility, and the spatial contiguity. The spatial contiguity is a necessity for satisfying some policies on public services, such as school districting and medical districting. However, solving the FSDP with contiguity constraints is challenging. We present a hybrid algorithm framework for solving the FSDP. Methods The algorithm is proposed based on the general local search algorithm and furtherly enhanced by multi-start, ruin-recreate, set-partitioning and other optimization strategies. The basic modules such as initial solution generation method, local search operators, solution perturbation, model building and solving, and search strategies, are provided in the framework. Using these building blocks, some exact, metaheuristic and hybrid algorithms could be implemented effectively. Five algorithms are implemented for the FSDP: Exact method by solving a mixed integer linear programming model, simulated annealing (SA) algorithm, iterative local search (ILS) algorithm, SA with set-partitioning, and ILS with set-partitioning. In addition, four instances are designed to test the algorithms. The instance size, supply-demand ratio, and geographic environment are considered in the instance design. Results There are several findings from computing experimentation: Optimal or near-optimal solutions of the instances could be obtained by CPLEX optimizer; high-quality solutions could be found by SA and ILS algorithms much more efficiently; the set-partitioning procedure is capable of improving the solution quality. Conclusions Based on the proposed algorithm framework, it is easy and flexible to design local-search based matheuristics and hybrid algorithms for the FSDP. High quality solutions could be obtained by coupling the local search with ruin-recreate perturbation and set-partitioning.

Job scheduling and maintenance activities scheduling are two important issues in production management, which are often addressed separately. In this paper, the non-permutation flow shop scheduling problem with learning effects and flexible maintenance activities is studied. Each machine has a number of preventive maintenance activities that should be finished within specific time intervals. The aim is to simultaneously determine the sequence of jobs and the finish time of maintenance activities for minimising the sum of tardiness costs and maintenance costs. A mixed integer linear programming model is proposed to formulate the problem. Owing to the high complexity of the problem, an improvement heuristic method and a hybrid meta-heuristic algorithm based on the simulated annealing algorithm and firefly algorithm is presented to find nearly optimal solutions for medium and large problems. To obtain better and more robust solutions, the Taguchi method is used in order to calibrate the hybrid algorithm parameters. Finally, the computational results are provided for evaluating the performance and effectiveness of the proposed solution approaches.

With increasing the efforts towards modernization of residential buildings and changing their functionality towards more proactive players in the networks, energy management systems can be regarded as an important contributing solution. Although several approaches have been developed in the literature for energy scheduling of buildings, the effect of occupants comfort as an objective function on the decision making process and its influence on the energy exchange between dwelling units requires more investigations. This study proposes a multi-objective energy management strategy for a group of active buildings (ABs) that form a residential microgrid (RMG), making a trade-off between operational cost of community and comfort level of its occupants. A building-to-building strategy is developed which benefits from rooftop photovoltaic output to supply the RMG loads. Also, the significance of electric vehicles in helping the community to achieve its techno-economic goals is investigated, taking into account the driving behaviour of users. A mixed integer linear programming model is introduced, benefiting from lower computation time and achieving a global optimal solution. The simulation results show the necessity of considering the cost and comfort simultaneously. Besides, the effect of building collaboration and electric vehicles in achieving the community's goal is observed.

Integrated scheduling of the drying process in a sawmill

The generalized rollon-rolloff vehicle routing problem and savings-based algorithm