Solving an Integrated Sales-Leasing Problem With Remanufacturing and Inventory Shortage Using Differential Evolution

2018 ◽  
Vol 9 (3) ◽  
pp. 1-26
Author(s):  
Masoud Rabbani ◽  
Sina Keyhanian ◽  
Mojtaba Aryaee ◽  
Esmat Sangari
Keyword(s):  
Differential Evolution ◽  
Large Scale ◽  
High Sensitivity ◽  
Third Party ◽  
Mixed Integer ◽  
Heuristic Solution ◽  
Optimal Amount ◽  
Solution Approach ◽  
De Algorithm ◽  
Non Linear

In this article, an integrated sales and leasing company is considered. This company remanufactures leased products at the end of operating lease contracts to make them as good as new ones and sell them to the customers. In order to satisfy customers' demand, required products are provided from a third-party when the company meets inventory shortage. Non-linear competitive demand functions are used which are sensitive to manufacturer suggested retail price (MSRP) and inflation rate. A mixed integer non-linear mathematical model (MINLP) is developed to determine optimal price of selling products, optimal amount of monthly payments in leasing contracts, and optimal inventory control planning, i.e. the optimal amount of manufacturing and remanufacturing products and optimal inventory levels. The main objective is to maximize net profit of the company. Small, medium and large-scale sizes of the model are solved to show the applicability of the model. To solve the large-scale problem, differential evolution (DE) algorithm is applied as a meta-heuristic solution approach. Numerical results show high sensitivity of model to demands. Also, optimal trend behaviors of some main variables of the problem seem similar to the competitive behavior of demands.

scholarly journals Routing Optimisation of Urban Medical Waste Recycling Network considering Differentiated Collection Strategy and Time Windows

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jiajing Gao ◽  
Haolin Li ◽  
Jingwen Wu ◽  
Junyan Lyu ◽  
Zheyi Tan ◽  
...  
Keyword(s):  
Large Scale ◽  
Programming Model ◽  
Time Windows ◽  
Computation Time ◽  
Waste Recycling ◽  
Medical Waste ◽  
Third Party ◽  
Mixed Integer ◽  
Routing Problem ◽  
Solution Approach

The increasing gap between medical waste production and disposal stresses the urgency of further development of urban medical waste recycling. This paper investigates an integrated optimisation problem in urban medical waste recycling network. It combines the vehicle routing problem of medical facilities with different requirements and the collection problem of clinics’ medical waste to the affiliated hospital. To solve this problem, a compact mixed-integer linear programming model is proposed, which takes account of the differentiated collection strategy and time windows. Since the medical waste recycling operates according to a two-day pattern, the periodic collection plan is also embedded in the model. Moreover, we develop a particle swarm optimisation (PSO) solution approach for problem-solving. Numerical experiments are also conducted to access the solution efficiency of the proposed algorithm, which can obtain a good solution in solving large-scale problem instances within a reasonable computation time. Based on the results, some managerial implications can be recommended for the third-party recycling company.

scholarly journals Dynamic Gaming Case of the R-Interdiction Median Problem with Fortification and an MILP-Based Solution Approach

2020 ◽  
Vol 12 (2) ◽  
pp. 581 ◽  
Author(s):  
Yiyong Xiao ◽  
Pei Yang ◽  
Siyue Zhang ◽  
Shenghan Zhou ◽  
Wenbing Chang ◽  
...  
Keyword(s):  
Large Scale ◽  
Dynamic Equilibrium ◽  
Cost Effective ◽  
Mixed Integer ◽  
Single Pair ◽  
Median Problem ◽  
Solution Approach ◽  
Maximum Loss ◽  
Large Scale Problems ◽  
Objective Model

This paper studies the cyclic dynamic gaming case of the r-interdiction median problem with fortification (CDGC-RIMF), which is important for strengthening a facility’s reliability and invulnerability under various possible attacks. We formulated the CDGC-RIMF as a bi-objective mixed-integer linear programming (MILP) model with two opposing goals to minimize/maximize the loss from both the designer (leader) and attacker (follower) sides. The first goal was to identify the most cost-effective plan to build and fortify the facility considering minimum loss, whereas the attacker followed the designer to seek the most destructive way of attacking to cause maximum loss. We found that the two sides could not reach a static equilibrium with a single pair of confrontational plans in an ordinary case, but were able to reach a dynamically cyclic equilibrium when the plan involved multiple pairs. The proposed bi-objective model aimed to discover the optimal cyclic plans for both sides to reach a dynamic equilibrium. To solve this problem, we first started from the designer’s side with a design and fortification plan, and then the attacker was able to generate their worst attack plan based on that design. After that, the designer changed their plan again based on the attacker’s plan in order to minimize loss, and the attacker correspondingly modified their plan to achieve maximum loss. This game looped until, finally, a cyclic equilibrium was reached. This equilibrium was deemed to be optimal for both sides because there was always more loss for either side if they left the equilibrium first. This game falls into the subgame of a perfect Nash equilibrium—a kind of complete game. The proposed bi-objective model was directly solved by the CPLEX solver to achieve optimal solutions for small-sized problems and near-optimal feasible solutions for larger-sized problems. Furthermore, for large-scale problems, we developed a heuristic algorithm that implemented dynamic iterative partial optimization alongside MILP (DIPO-MILP), which showed better performance compared with the CPLEX solver when solving large-scale problems.

scholarly journals Micro Grid Energy Management using DE Algorithm with BDC theorem

2019 ◽  
Vol 8 (4S2) ◽  
pp. 334-338
Keyword(s):  
Power Plants ◽  
Large Scale ◽  
Benders Decomposition ◽  
Reactive Power ◽  
Differential Evolution Algorithm ◽  
Decomposition Theorem ◽  
Drop Out ◽  
Mixed Integer ◽  
De Algorithm ◽  
Micro Grid

this paper evaluates combination of DE algorithm and benders decomposition theorem of VMG is used to solving the large scale mixed integer programming problems. DE algorithm is implemented in Village area Micro grids. Village area micro grid and the required load is calculated regarding the sold out power or purchase power with the help of DE algorithm. Differential evolution algorithm is applied in the village area micro grid and measure the real power, reactive power of various power plants. . In this DE algorithm is implemented in village area micro grid and the survey period is two years. Final survey shows which month produce more power and sold out power in nearest city area electricity board, But in power shortage in village area micro grid ,it purchase the power from nearest electricity board.DE algorithm determine the one month power survey and benders decomposition determine the individual value of the power plants. But the benders decomposition theorem is not accept the non linearity items. To overcome this problem BDCT and DEA is implemented in village area micro grid. This combination is used to maintain the drop out voltage of any power plants.

scholarly journals Modelling and Optimizing an Open-Pit Truck Scheduling Problem

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Yonggang Chang ◽  
Huizhi Ren ◽  
Shijie Wang
Keyword(s):  
Programming Model ◽  
Valid Inequalities ◽  
Upper Bounds ◽  
Open Pit ◽  
Mixed Integer ◽  
Scheduling Problem ◽  
Heuristic Solution ◽  
Solution Approach ◽  
Truck Scheduling ◽  
Improvement Strategies

This paper addresses a special truck scheduling problem in the open-pit mine with different transport revenue consideration. A mixed integer programming model is formulated to define the problem clearly and a few valid inequalities are deduced to strengthen the model. Some properties and two upper bounds of the problem are proposed. Based on these inequalities, properties, and upper bounds, a heuristic solution approach with two improvement strategies is proposed to resolve the problem and the numerical experiment demonstrates that the proposed solution approach is effective and efficient.

scholarly journals A Novel Enhanced Differential Evolution Algorithm for Outbound Logistics of the Poultry Industry in Thailand

2022 ◽  
Vol 8 (1) ◽  
pp. 15
Author(s):  
Karn Moonsri ◽  
Kanchana Sethanan ◽  
Kongkidakhon Worasan
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Transportation Cost ◽  
Mixed Integer ◽  
Computational Time ◽  
Poultry Industry ◽  
Routing Problem ◽  
De Algorithm ◽  
Evolution Algorithm

Outbound logistics is a crucial field of logistics management. This study considers a planning distribution for the poultry industry in Thailand. The goal of the study is to minimize the transportation cost for the multi-depot vehicle-routing problem (MDVRP). A novel enhanced differential evolution algorithm (RI-DE) is developed based on a new re-initialization mutation formula and a local search function. A mixed-integer programming formulation is presented in order to measure the performance of a heuristic with GA, PSO, and DE for small-sized instances. For large-sized instances, RI-DE is compared to the traditional DE algorithm for solving the MDVRP using published benchmark instances. The results demonstrate that RI-DE obtained a near-optimal solution of 99.03% and outperformed the traditional DE algorithm with a 2.53% relative improvement, not only in terms of solution performance, but also in terms of computational time.

Optimal Power Flow Using an Improved Hybrid Differential Evolution Algorithm

2017 ◽  
Vol 11 (1) ◽  
pp. 177-192
Author(s):  
Gonggui Chen ◽  
Zhengmei Lu ◽  
Zhizhong Zhang ◽  
Zhi Sun
Keyword(s):  
Differential Evolution ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Differential Evolution Algorithm ◽  
Local Optimum ◽  
Power Losses ◽  
De Algorithm ◽  
Optimal Power ◽  
Evolution Algorithm

Objective: In this paper, an improved hybrid differential evolution (IHDE) algorithm based on differential evolution (DE) algorithm and particle swarm optimization (PSO) has been proposed to solve the optimal power flow (OPF) problem of power system which is a multi-constrained, large-scale and nonlinear optimization problem. Method: In IHDE algorithm, the DE is employed as the main optimizer; and the three factors of PSO, which are inertia, cognition, and society, are used to improve the mutation of DE. Then the learning mechanism and the adaptive control of the parameters are added to the crossover, and the greedy selection considering the value of penalty function is proposed. Furthermore, the replacement mechanism is added to the IHDE for reducing the probability of falling into the local optimum. The performance of this method is tested on the IEEE30-bus and IEEE57-bus systems, and the generator quadratic cost and the transmission real power losses are considered as objective functions. Results: The simulation results demonstrate that IHDE algorithm can solve the OPF problem successfully and obtain the better solution compared with other methods reported in the recent literatures.

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