مسئله مسیریابی وسیله نقلیه باز (OVRP) یکی از مسائل مورد علاقه در ریاضیات محاسباتی است که بسیار مورد توجه محققان و دانشمندان قرار می‌گیرد. در این مسئله هدف تعیین کمینه هزینه جابجایی چندین وسیله نقلیه است که به طور هم‌زمان از انبار کالا شروع به حرکت می‌کنند و تعدادی از مشتری‌ها را مورد ملاقات قرار می‌دهند. باید توجه کرد که برخلاف مسئله مسیریابی وسیله نقلیه (VRP)، در این مسئله وسائل نقلیه لازم نیست که به انبار کالا برگردند. این مقاله نوعی روش فراابتکاری که در فاز اول آن از روش اصلاحی نمونه مورچگان (EAS) برای یافتن جوا‌ب‌هایی زیر بهینه استفاده می‌کند و در فاز دوم الگوریتم‌های درج و جابجایی برای یافتن جواب‌های بهتر به کار گرفته می‌شود. این الگوریتم بر روی مجموعه‌ای از 15 مثال با 50-400 مشتری مورد آزمایش واقع گردید که معلوم شد که این الگوریتم قادر است که در 10 مثال به بهترین جواب تاکنون یافت شده دست یابد. به علاوه از نظر کیفیت جواب‌های بدست آمده، ثابت شد که الگوریتم پیشنهادی بسیار رقابت پذیر است و انحراف معیار الگوریتم در همه مثال‌ها در حدود 1 درصد قرار دارد. به طور کل می‌توان گفت که الگوریتم پیشنهادی در مقایسه با سایر روش‌های موجود برای حل مسئله OVRP از نظر کیفیت جواب‌‌ها نتایج بهتری را بدست آورده است. تفاصيل المقالة

The vehicle routing problem with simultaneous pickup and delivery (VRPSPD) is a well-known combinatorial optimization problem which addresses provided service to a set of customers using a homogeneous fleet of capacitated vehicles. The objective is to minimize the distance traveled. The VRPSPD is an NP-hard combinatorial optimization problem. Therefore, practical large-scale instances of VRPSPD cannot be solved by exact solution methodologies within acceptable computational time. Our interest was therefore focused on meta-heuristic solution approaches. For this reason, a modified tabu search (PA) is proposed for solving the VRPSPD in this paper. tComputational results on several standard instances of VRPSPD show the efficiency of the PA compared with other meta-heuristic algorithms. تفاصيل المقالة

The traveling salesman problem (TSP) is a well-known combinatorial optimization problem and holds a central place in logistics management. The TSP has received much attention because of its practical applications in industrial problems. Many exact, heuristic and metaheuristic approaches have been proposed to solve TSP in recent years. In this paper, a modified ant colony optimization (MACO) is presented which possesses a new strategy to update the increased pheromone, a candidate list and a mutation operation to solve the TSP. In addition, some local search algorithms are utilized as an effective criterion and only a global updating is used in order to increase pheromone on the edges of the best route. The proposed metaheuristic algorithm is tested on the well-known TSP instances involving 14 benchmark problems from 48 to 200 nodes. The computational results show that our algorithm is better than other meta-heuristic algorithms in terms of solution quality. Furthermore, the gap of the proposed algorithm stays on average almost 1% of the execution time and also the most best known solutions of the benchmark problems are found. تفاصيل المقالة

The capacitated clustering problem (CCP) is one of the most important combinational optimization problems that nowadays has many real applications in industrial and service problems. In the CCP, a given n nodes with known demands must be partitioned into k distinct clusters in which each cluster is detailed by a node acting as a cluster center of this cluster. The objective is to minimize the sum of distances from all cluster centers to all other nodes in their cluster, such that the sum of the corresponding node weights does not exceed a fixed capacity and every node is allocated to exactly one cluster. This paper presents a hybrid three-phase meta-heuristic algorithm (HTMA) including sweep algorithm (SA), ant colony optimization (ACO) and two local searches for the CCP. At the first step, a feasible solution of CCP is produced by the SA, and at the second step, the ACO, insert and swap moves are used to improve solutions. Extensive computational tests on standard instances from the literature confirm the effectiveness of the presented approach compared to other meta-heuristic algorithms. تفاصيل المقالة