simulated annealing algorithm

Developing and optimizing effective inventory systems considering realistic constraints and practical assumptions can help managers remarkably decrease inventory and consequently supply chain costs. In this research, we propose a new variant of the multi-item inventory model taking into account warehouse capacity, on-hand budget constraints, imperfect products in supply deliveries and partial backordering where the products can be converted into perfect products by a local repair shop. To deal with the proposed model, three solution approaches, including interior-point technique, as an exact method, and two metaheuristics based on Simulated Annealing (SA) and Water Cycle Algorithm (WCA), are proposed. Extensive computational experiments are conducted on different sets of instances. Using different measures such as RPD, PRE, and computational time, the performance of the solution approaches is evaluated within different test instances. The results show that the WCA outperforms the two other approaches and leads to the best solutions in the proposed problem.

Digital marketing has become vital to businesses' marketing strategies in today's technology and social media era. However, the effectiveness of digital marketing campaigns largely depends on accurately identifying the target audience. This study aims to implement the simulated annealing initiative algorithm for digital marketing, as well as audience classification and optimum target audience selection. Traditional methods of target audience identification, such as demographic, geographic, and psychographic segmentation, are only sometimes effective in identifying the most responsive audience. Therefore, advanced techniques such as clustering, genetic, and simulated annealing algorithms have been proposed to identify the optimum target audience. The heuristic simulated annealing algorithm is one of the most promising techniques for optimum target audience identification. It is widely used in combinatorial optimization problems and applied in various fields such as engineering, economics, management, and computer science. In this research, a digital marketing campaign is implemented for a new line to sell training courses in empowerment and competency in human resource management within the mining industry. After conducting market research, we have identified five critical segments: age, gender, income group, place of residence, and level of university education. The number of customers at each customer journey stage was 740 people in brand development, email, and advertising campaigns, of which 620 people are in the "Awareness" stage, 431 people in the "Interest" stage, 261 people in the "Consideration" stage, 203 people in the "Intend" stage, 179 people in the "Purchase" and finally, 179 People were evaluated in the "loyalty" stage for the case of educational service company. The results show we should target 20% of our marketing efforts towards the 18-24 age group, 30% towards females, 20% towards high-income individuals, 10% towards rural areas, and 20% towards University education level in BSc. The best cost per conversion we obtain is 78.105×106 Rials. The results show that the simulated annealing algorithm can be valuable for identifying the optimum target audience in digital marketing campaigns. By considering the entire customer journey and allowing for more complex audience targeting, the algorithm can help companies optimize their marketing strategies and maximize their profits.

Nowadays, the citrus supply chain has been motivated by both industrial practitioners and researchers due to several real-world applications. This study considers a four-echelon citrus supply chain, consisting of gardeners, distribution centers, citrus storage, and fruit market. A Mixed Integer Non-Linear Programming (MINLP) model is formulated, which seeks to minimize the total cost and maximize the profit of the Citrus supply chain network. Due to the complexity of the model when considering large-scale samples, two well-known meta-heuristic algorithms such as Ant Colony Optimization (ACO) and Simulated Annealing (SA) algorithms have been utilized. Additionally, a new multi-objective ACO algorithm based on a set of non-dominated solutions form the Pareto frontier developed to solve the mathematical model. An extensive comparison based on different measurements analyzed to find a performance solution for the developed problem in the three sizes (small, medium, and large-scale). Finally, the various outcomes of numerical experiments indicate that the MOACO algorithm is more reliable than other algorithms.

In this paper, an integer linear programming formulation is developed for a novel fuzzy multi-period multi-depot vehicle routing problem. The novelty belongs to both the model and the solution methodology. In the proposed model, vehicles are not forced to return to their starting depots. The fuzzy problem is transformed into a mixed-integer programming problem by applying credibility measure whose optimal solution is an (α,β)-credibility optimal solution to the fuzzy problem. To solve the problem, a hybrid genetic-simulated annealing-auction algorithm (HGSA), empowered by a modern simulated annealing cooling schedule function, is developed. Finally, the efficiency of the algorithm is illustrated by employing a variety of test problems and benchmark examples. The obtained results showed that the algorithm provides satisfactory results in terms of different performance criteria.

In this research, we present a mathematical model for allocating people to different jobs and shifting employees between related jobs. This action will reduce the repetitive activities workload and ergonomic risks at the planned time horizon, and finally increases the organization's efficiency. In this proposed model, the devices are semi-automatic and it is possible to allocated more than one task to one person. Regarding the modeling and the case study of the constraints, it is shown that the complexity of this problem type is NP-Hard, and the result of accurate methods for solving the problem is not possible in a reasonable time. Due to this Simulated Annealing (SA) algorithm is used to study the proposed model and comparison of the results of SA algorithm with the results of precise optimization methods shows the better performance of the Simulated Annealing algorithm in terms of the time and answer quality.

Due to many damages that human activities have imposed on the environment, authorities, manufacturers, and industry owners have taken into account the development of supply chain more than ever. One of the most influential and essential human activities in the supply chain are transportation which green vehicles such as electric vehicles (EVs) are expected to generate higher economic and environmental impact. To this end, designing efficient routing scheme for the fleet of EVs is significant. A remarkable issue about EVs is their need to stations for charging their battery. Due to the existence of time limitations, more attention should be paid to time spent at the charging station, so considering the queuing system at charging stations makes more precise time calculations. Furthermore, multi-graphs are more consistent with the characteristics of the transportation network. Hence, we consider alternative paths including two criterion cost and energy consumption in the network. First, we develop a mixed integer linear programming for the electric vehicle routing problem on a multi-graph with the queue in charging stations to minimize the traveling and charging costs. Since the proposed problem is NP-hard in a strong sense, we provide a simulated annealing algorithm to search the solution space efficiently and explore a large neighborhood within short computational time. The efficiency of the model is investigated with numerical and illustrative examples. Then the sensitivity analysis is performed on the proposed model to indicate the importance of the queuing system and the impact of battery capacity on the penetration of EVs.

In this paper, a new multi-objective time-cost constrained resource availability cost problem is proposed. The mathematical model is aimed to minimize resource availability cost by considering net present value of resource prices in order to evaluate the economic aspects of project to maximize the quality of project's resources to satisfy the expectations of stakeholders and to minimize the variation of resource usage during project. Since the problem is NP-hard, to deal with the problem a simulated annealing approach is applied, also to validate our results GAMS software is used in small size test problems. Due to the dependency of SA algorithm to its initial parameters a taghuchi method is used to find the best possible SA parameters combinations to reach near optimum solutions in large size problems.

This paper presents a new robust mathematical model for the multi-product capacitated single allocation hub location problem with maximum covering radius. The objective function of the proposed model minimizes the cost of establishing hubs, the expected cost of preparing hubs for handling products, shipping and transportation in all scenarios, and the cost variations over different scenarios. In the proposed model, a single product of a single node cannot be allocated to more than one hub, but different products of one node can be allocated to different hubs. Also, a product can be allocated to a hub only if equipment related to that product is installed on that hub. Considering the NP-Hard complexity of this problem, a GA-based meta-heuristic algorithm is developed to solve the large scale variants of the problem. To evaluate the performance of the proposed algorithm, its results are compared with the results of exact method and simulated annealing algorithm. These results show the good performance of the proposed algorithm.

This paper aims at single-objective optimization of multi-product for three-echelon supply chain architecture consisting of production plants, distribution centers (DCs) and customer zones (CZs). The key design decisions considered are: the quantity of products to be shipped from plants to DCs, from DCs to CZs , cycle length, and production quantity so as to minimize the total cost .To optimize the objective, three-echelon network model is mathematically represented considering the associated constraints, production, capacityand shipment costs and solved using genetic algorithm (GA) and Simulated Annealing (SA).Some numerical illustrations are provided at the end to not only show the applicability of the proposed methodology, butalso to select the best method using a t-test along with the simple additive weighting (SAW) method.

In a supply chain, cross-docking is one of the most innovative systems for ameliorating the operational performance at distribution centers. Cross-docking is a logistics strategy in which freight is unloaded from inbound trucks and (almost) directly loaded into outbound trucks, with little or no storage in between, thus no inventory remains at the distribution center. In this study, we consider the scheduling problem of inbound and outbound trucks with multiple dock doors, aiming at the minimization of the makespan. The considered scheduling problem determines where and when the trucks must be processed; also due to the interchangeability specification of products, product assignment is done simultaneously as well. Inbound trucks enter the system according to their release times, however, there is no mandatory time constraint for outbound truck presence at a designated stack door; they should just observe their relative docking sequences. Moreover, a loading sequence is determined for each of the outbound trucks. In this research, a mathematical model is derived to find the optimal solution. Since the problem under study is NP-hard, a simulated annealing algorithm is adapted to find the (near-) optimal solution, as the mathematical model will not be applicable to solve large-scale real-world cases. Numerical examples have been done in order to specify the efficiency of the metaheuristic algorithm in comparison with the results obtained from solving the mathematical model.

The urban waste collection is one of the major municipal activities that involves large expenditures and difficult operational problems. Also, waste collection and disposal have high expenses such as investment cost (i.e. vehicles fleet) and high operational cost (i.e. fuel, maintenance). In fact, making slight improvements in this issue lead to a huge saving in municipal consumption. Some incidents such as altering the pattern of waste collection and abrupt occurrence of events can cause uncertainty in the precise amount of waste easily and consequently, data uncertainty arises. In this paper, a novel mathematical model is developed for robust capacitated arc routing problem (CARP). The objective function of the proposed model aims to minimize the traversed distance according to the demand uncertainty of the edges. To solve the problem, a hybrid metaheuristic algorithm is developed based on a simulated annealing algorithm and a heuristic algorithm. Moreover, the results obtained from the proposed algorithm are compared with the results of exact method in order to evaluate the algorithm efficiency. The results have shown that the performance of the proposed hybrid metaheuristic is acceptable.

Waste collection is a highly visible municipal service that involves large expenditures and difficult operational problems. In addition, it is expensive to operate in terms of investment costs (i.e. vehicles fleet), operational costs (i.e. fuel and maintenances) so that generating small improvements in this area can lead to huge savings in municipal expenditures. Among the issues raised in the process of decisions making by managers and associated policy makers, one can point to determining the optimal weekly policies of waste collection. In this paper, the periodic capacitated arc routing problem (PCARP)in mobile disposal sites is described and the authors seek to determine the optimal routes of required edges (streets or alleys) per week, number and location of mobile disposal sites, and the number of required vehicles. We present two simulated annealing algorithms, which are different in cooling schedule and number of iterations of each temperature. To evaluate the performance of these algorithms on small-sized problems, the solver “CPLEX"in application “GAMS” is used. The experimental results show that the presented algorithms have appropriate performance and a reasonable time range.

In today’s competitive transportation systems, passengers search to find traveling agencies that are able to serve them efficiently considering both traveling time and transportation costs. In this paper, we present a new model for the traveling salesman problem with multiple transporters (TSPMT). In the proposed model, which is more applicable than the traditional versions, each city has different transporting vehicles and the cost of travel through each city is dependent on the transporting vehicles type. The aim is to determine an optimal sequence of visited cities with minimum traveling times by available transporting vehicles within a limited budget. First, the mathematical model of TSPMT is presented. Next, since the problem is NP-hard, a new hybrid parallel simulated annealing algorithm with a new coding scheme is proposed. To analyze the performance of the proposed algorithm, 50 numerical examples with different budget types are examined and solved using the algorithm. The computational results of these comparisons show that the algorithm is an excellent approach in speed and solution quality.