Ismail Karaoglan

A steady-state genetic algorithm for multi-product supply chain network design

Supply chain network (SCN) design is to provide an optimal platform for efficient and effective supply chain management (SCM). The problem is often an important and strategic operations management problem in SCM. The design task involves the choice of facilities (plants and distribution centers (DCs)) to be opened and the distribution network design to satisfy the customer demand with minimum cost. This paper presents a solution procedure based on steady-state genetic algorithms (ssGA) with a new encoding structure for the design of a single-source, multi-product, multi-stage SCN. The effectiveness of the ssGA has been investigated by comparing its results with those obtained by CPLEX, Lagrangean heuristic, hyrid GA and simulated annealing on a set of SCN design problems with different sizes.

A hybrid discrete particle swarm optimization for vehicle routing problem with simultaneous pickup and delivery

Vehicle routing problem (VRP) is an important and well-known combinatorial optimization problem encountered in many transport logistics and distribution systems. The VRP has several variants depending on tasks performed and on some restrictions, such as time windows, multiple vehicles, backhauls, simultaneous delivery and pick-up, etc. In this paper, we consider vehicle routing problem with simultaneous pickup and delivery (VRPSPD). The VRPSPD deals with optimally integrating goods distribution and collection when there are no precedence restrictions on the order in which the operations must be performed. Since the VRPSPD is an NP-hard problem, we present a heuristic solution approach based on particle swarm optimization (PSO) in which a local search is performed by variable neighborhood descent algorithm (VND). Moreover, it implements an annealing-like strategy to preserve the swarm diversity. The effectiveness of the proposed PSO is investigated by an experiment conducted on benchmark problem instances available in the literature. The computational results indicate that the proposed algorithm competes with the heuristic approaches in the literature and improves several best known solutions.

A branch and cut algorithm for the location-routing problem with simultaneous pickup and delivery

This paper addresses a location-routing problem with simultaneous pickup and delivery (LRPSPD) which is a general case of the location-routing problem. The LRPSPD is defined as finding locations of the depots and designing vehicle routes in such a way that pickup and delivery demands of each customer must be performed with same vehicle and the overall cost is minimized. We propose an effective branch-and-cut algorithm for solving the LRPSPD. The proposed algorithm implements several valid inequalities adapted from the literature for the problem and a local search based on simulated annealing algorithm to obtain upper bounds. Computational results, for a large number of instances derived from the literature, show that some instances with up to 88 customers and 8 potential depots can be solved in a reasonable computation time.

hGA: Hybrid genetic algorithm in fuzzy rule-based classification systems for high-dimensional problems

The aim of this work is to propose a hybrid heuristic approach (called hGA) based on genetic algorithm (GA) and integer-programming formulation (IPF) to solve high dimensional classification problems in linguistic fuzzy rule-based classification systems. In this algorithm, each chromosome represents a rule for specified class, GA is used for producing several rules for each class, and finally IPF is used for selection of rules from a pool of rules, which are obtained by GA. The proposed algorithm is experimentally evaluated by the use of non-parametric statistical tests on seventeen classification benchmark data sets. Results of the comparative study show that hGA is able to discover accurate and concise classification rules.

The green vehicle routing problem

We develop a solution approach to solve the green vehicle routing problem.We propose a simulated annealing heuristic to improve the quality of solutions.We present a new formulation having fewer variable and constraints.We evaluate the algorithm in terms of the several performance criterions.Our algorithm is able to optimally solve 22 of 40 benchmark instances. This paper develops a simulated annealing heuristic based exact solution approach to solve the green vehicle routing problem (G-VRP) which extends the classical vehicle routing problem by considering a limited driving range of vehicles in conjunction with limited refueling infrastructure. The problem particularly arises for companies and agencies that employ a fleet of alternative energy powered vehicles on transportation systems for urban areas or for goods distribution. Exact algorithm is based on the branch-and-cut algorithm which combines several valid inequalities derived from the literature to improve lower bounds and introduces a heuristic algorithm based on simulated annealing to obtain upper bounds. Solution approach is evaluated in terms of the number of test instances solved to optimality, bound quality and computation time to reach the best solution of the various test problems. Computational results show that 22 of 40 instances with 20 customers can be solved optimally within reasonable computation time.

A memetic algorithm for the capacitated location-routing problem with mixed backhauls

The design of distribution networks is one of the most important problems in supply chain and logistics management. The main elements in designing a distribution network are location and routing decisions. As these elements are interdependent in many distribution networks, the overall system cost can decrease if location and routing decisions are simultaneously tackled. In this paper, we consider a Capacitated Location-Routing Problem with Mixed Backhauls (CLRPMB) which is a general case of the capacitated location-routing problem. CLRPMB is defined as finding locations of the depots and designing vehicle routes in such a way that pickup and delivery demands of each customer must be performed with the same vehicle and the overall cost is minimized. Since CLRPMB is an NP-hard problem, we propose a memetic algorithm to solve the problem. To evaluate the performance of the proposed approach, we conduct an experimental study and compare its results with the lower bounds obtained by the branch-and-cut algorithm on a set of instances derived from the literature. Computational results indicate that the proposed approach is able to find optimal or very good quality solutions in a reasonable computation time.

Two-stage vehicle routing problem with arc time windows: A mixed integer programming formulation and a heuristic approach

In this paper, we introduce a new variant of the Vehicle Routing Problem (VRP), namely the Two-Stage Vehicle Routing Problem with Arc Time Windows (TS_VRP_ATWs) which generally emerges from both military and civilian transportation. The TS_VRP_ATW is defined as finding the vehicle routes in such a way that each arc of the routes is available only during a predefined time interval with the objective of overall cost minimization. We propose a Mixed Integer Programming (MIP) formulation and a heuristic approach based on Memetic Algorithm (MA) to solve the TS_VRP_ATW. The qualities of both solution approaches are measured by using the test problems in the literature. Experimental results show that the proposed MIP formulation provides the optimal solutions for the test problems with 25 and 50 nodes, and some test problems with 100 nodes. Results also show that the proposed MA is promising quality solutions in a short computation time.

A hybrid genetic algorithm for the location-routing problem with simultaneous pickup and delivery

The design of distribution networks is one of the most important problems in supply chain and logistics management. The main elements in designing a distribution network are location and routing decisions. As these elements are interdependent in many distribution networks, the overall system cost can decrease if location and routing decisions are simultaneously tackled. In this paper, we consider a Location-Routing Problem with simultaneous pickup and delivery (LRPSPD) which is a general case of the location-routing problem. The LRPSPD is defined as finding locations of the depots and designing vehicle routes in such a way that pickup and delivery demands of each customer must be performed with same vehicle and the overall cost is minimized. Since the LRPSPD is an NP-hard problem, we propose a hybrid heuristic approach based on genetic algorithms (GA) and simulated annealing (SA) to solve the problem. To evaluate the performance of the proposed approach, we conduct an experimental study and compare its results with the upper bounds obtained by flow-based MIP formulation on a set of instances derived from the literature. Computational results indicate that the proposed approach is able to find optimal or very good quality solutions in a reasonable computation time.

A genetic ant colony optimization approach for concave cost transportation problems

The concave cost transportation problem (ccTP) is one of the practical distribution and logistics problems. The ccTP arises when the unit cost for transporting products decreases as the amount of products increases. Generally, these costs are modeled as nonlinear, especially concave. Since the ccTP is NP-hard, solving large-scale problems is time- consuming. In this paper, we propose a hybrid search algorithm based on genetic algorithms (GA) and ant colony optimization (ACO) to solve the ccTP. This algorithm, called hGACO, is a GA supplemented with ACO in where ACO is implemented to exploit information stored in pheromone trails during genetic operations, i.e. crossover and mutation. The effectiveness of hGACO is investigated comparing its results with those obtained by five different metaheuristic approaches given in the literature for the ccTP.

An adaptive tabu-simulated annealing for concave cost transportation problems

The transportation problem (TP) is one of the most popular network problems because of its theoretical and practical importance. If the transportation cost linearly depends on the transported amount of the product, then TP is solvable in polynomial time with linear programming methods. However, in the real world, the transportation costs are generally nonlinear, frequently concave where the unit cost for transporting products decreases as the amount of products increases. Since concave cost transportation problems (ccTPs) are NP-hard, solving large-scale problems is time consuming. In this study, we propose a hybrid algorithm based on the concepts borrowed from tabu search (TS) and simulated annealing (SA) to solve the ccTP. This algorithm, called ATSA (adaptive tabu-simulated annealing), is an SA approach supplemented with a tabu list and adaptive cooling strategy. The effectiveness of ATSA has been investigated in two stages using a set of TPs with different sizes. The first stage includes performance analysis of ATSA using SA, ASA (adaptive simulated anealing) and TS, which are basic forms of ATSA. In the second stage, ATSA has been compared with the heuristic approaches given in the literature for ccTP. Statistical analysis shows that ATSA exhibits better performance than its basic forms and heuristic approaches.