Solving the Location Routing Problem of the Central Rubber Market by Tabu Search

The central rubber market of Songkhla province is the center of the collection and distribution of rubber production from sellers to purchasers. It is considered ineffective because sellers need to deliver whole raw rubber to the market, resulting in high transport costs, especially for those who came a great distance and had little quantity. This research applied the tabu search method to solve the location selection problem of the rubber purchasing depot and manage transport routes to the market. Results found that there were 16 purchasing depots. The central rubber market had unlimited purchasing capacity while the other purchasing depots limited the quantity of rubber to 10 tons. There were five transport routes and five trucks (four ten-wheeled trucks and one ten-wheeled truck with a trailer). The total delivery costs were 53,313.89 baht per day. The answers about efficiency from the Lingo 13 program with small, medium, and large problems and real problems were not significantly statistically different at a significance level of 0.05

Ant colony system (ACS) with hybrid local search to solve vehicle routing problems

This research applied an Ant Colony System algorithm with a Hybrid Local Search to solve Vehicle Routing Problems (VRP) from a single depot when the customers’ requirements are known. VRP is an NP-hard optimization problem and has usually been successfully solved optimum by heuristics. A fleet of vehicles of a specific capacity are used to serve a number of customers at minimum cost, without violating the constraints of vehicle capacity. There are meta-heuristic approaches to solve these problems, such as Simulated Annealing, Genetic Algorithm, Tabu Search and the Ant Colony System algorithm. In this case a hybrid local search was used (Cross-Exchange, Or-Opt and 2-Opt algorithm) with an Ant Colony System algorithm. The Experimental Design was tested on 7 various problems from the data set online in the OR-Library. There are five different problems in which customers are randomly distributed with the depot in an approximately central location. The customers were grouped into clusters. The results are evaluated in terms of optimal routes using optimal distances. The experimental results are compared with those obtained from meta-heuristics and they show that the proposed method outperforms six meta-heuristics in the literature

The Differential Evolution Algorithm for Solving the Problem of Size Selection and Location of Infectious Waste Incinerator

The disposal of infectious waste remains one of the most severe medical, social, and environmental problems in almost every country. Choosing the right location and arranging the most suitable transport route is one of the main issues in managing hazardous waste. Identifying a site for the disposal of infectious waste is a complicated process because both tangible and intangible factors must be considered together, and it also depends on various rules and regulations. This research aims to solve the problem of the size selection and location of infectious waste incinerators for 109 community hospitals in the upper part of northeastern Thailand by applying a differential evolution algorithm to solve the problem with the objective of minimizing the total system cost, which consists of the cost of transporting infectious waste, the fixed costs, and the variable cost of operating the infectious waste incinerator. The developed differential evolution produces vectors that differ from the conventional differential evolution. Instead of a single set of vectors, three are created to search for the solution. In addition to solving the problem of the case study, this research conducts numerical experiments with randomly generated data to measure the performance of the differential evolution algorithm. The results show that the proposed algorithm efficiently solves the problem and can find the global optimal solution for the problem studied

An Integrated Inventory-Routing System for Multi-item Joint Replenishment with Limited Vehicle Capacity

Abstract. In this paper, we develop a mathematical programming approach for coordinating inventory and transportation decisions in an inbound commodity collection system. In particular, we consider a system that consists of a set of geographically dispersed suppliers that manufacture one or more non-identical items, and a central warehouse that stocks these items. The warehouse faces a constant and deterministic demand for the items from outside retailers. The items are collected by a fleet of vehicles that are dispatched from the central warehouse. The vehicles are capacitated, and must also satisfy a frequency constraint. Adopting a policy in which each vehicle always collects the same set of items, we formulate the inventory-routing problem of minimizing the long-run average inventory and transportation costs as a set partitioning problem. We employ a column generation approach to determine a lower bound on the total costs, and develop a branch-and-price algorithm that finds the optimal assignment of items to vehicles. We also propose greedy constructive heuristics, and develop a very largescale neighborhood (VLSN) search algorithm to find near-optimal solutions for the problem. Computational tests are performed on a set of randomly generated problem instances. Key words: Inventory-routing, Multi-item inventory replenishment, Transportation, Economic order quantity, Column generation, Very large-scale neighborhood searc