In this paper, an artificial neural network (ANN) model is developed to determine the optimum
level of finished goods inventory as a function of product demand, setup, holding, and material
costs. The model selects a feed-forward back-propagation ANN with four inputs, ten hidden
neurons and one output as the optimum network. The model is tested with a manufacturing
industry data and the results indicate that the model can be used to forecast finished goods
inventory level in response to the model parameters. Overall, the model can be applied for
optimization of finished goods inventory for any manufacturing enterprise in a competitive
business environment.

The maximal covering location problem (MCLP) seeks to locate a predefined number of
facilities in order to maximize the number of covered demand points. In a classical sense,
MCLP has three main implicit assumptions: all or nothing coverage, individual coverage, and
fixed coverage radius. By relaxing these assumptions, three classes of modelling formulations
are extended: the gradual cover models, the cooperative cover models, and the variable radius
models. In this paper, we develop a special form of MCLP which combines the characteristics
of gradual cover models, cooperative cover models, and variable radius models. The proposed
problem has many applications such as locating cell phone towers. The model is formulated as
a mixed integer non-linear programming (MINLP). In addition, a simulated annealing
algorithm is used to solve the resulted problem and the performance of the proposed method is
evaluated with a set of randomly generated problems.

This paper presents an analytical technique to determine the optimum number of cards to control material release in a CONWIP system. The work focuses on the card setting problem for a flow-shop system characterised by the presence of a batch processing machine (e.g. a kiln for long heat treatment). To control production, two different static approaches are developed: the first one is used when the bottleneck coincides with the batch processing machine and the second one is proposed when the bottleneck is another machine of the flow shop. In both contexts, by means of the appropriate model, one can optimize the performance of the flow-shop by maximizing the throughput and keeping the work in process at a minimum level. Numerical examples are also included in the paper to confirm the validity of the models and to demonstrate their practical utility.

A multiproduct pipeline provides an economic way to transport large volumes of refined petroleum products over long distances. In such a pipeline, different products are pumped back−to−back without any separation device between them. The sequence and lengths of such pumping runs must be carefully selected in order to meet market demands while minimizing pipeline operational costs and satisfying several constraints. The production planning and scheduling of the products at the refinery must also be synchronized with the transportation in order to avoid the usage of the system at some peak−hour time intervals. In this paper, we propose a multi−period mixed integer nonlinear programming (MINLP) model for an optimal planning and scheduling of the production and transportation of multiple petroleum products from a refinery plant connected to several depots through a single pipeline system. The objective of this work is to generalize the mixed integer linear programming (MILP) formulation proposed by Cafaro and Cerdá (2004, Computers and Chemical Engineering) where only a single planning period was considered and the production planning and scheduling was not part of the decision process. Numerical examples show how the use of a single period model for a given time period may lead to infeasible solutions when it is used for the upcoming periods. These examples also show how integrating production planning with the transportation and the use of a multi−period model may result in a cost saving compared to using a single−period model for each period, independently.

Solving resource constrained project scheduling problem (RCPSP) has important role in the context of project scheduling. Considering a single objective RCPSP, the goal is to find a schedule that minimizes the makespan. This is NP-hard problem (Blazewicz et al., 1983) and one may use meta-heuristics to obtain a global optimum solution or at least a near-optimal one. Recently, various meta-heuristics such as ACO, PSO, GA, SA etc have been applied on RCPSP. Bee algorithms are among most recently introduced meta-heuristics. This study aims at adapting artificial bee colony as an alternative and efficient optimization strategy for solving RCPSP and investigating its performance on the RCPSP. To evaluate the artificial bee colony, its performance is investigated against other meta-heuristics for solving case studies in the PSPLIB library. Simulation results show that the artificial bee colony presents an efficient way for solving resource constrained project scheduling problem.

The primary assumptions with many multi-period inventory lot-sizing models are fixed time horizon and uniform demand variation within each period. In some real inventory situations, however, the time horizon may be unknown, uncertain or imprecise in nature and the demand pattern may vary within a given replenishment period. This paper presents an economic order quantity model for deteriorating items where demand has different pattern with unknown time horizon. The model generates optimal replenishment schedules, order quantity and costs using a general ramp-type demand pattern that allows three-phase variation in demand. Shortages are allowed with full backlogging of demand and all possible replenishment scenarios that can be encountered when shortages and demand pattern variation occur in multi-period inventory modeling are also considered. With the aid of numerical illustrations, the advantages of allowing for variation in demand pattern within replenishment periods, whenever they occur, are explored. The numerical examples show that the length of the replenishment period generated by the model varies with the changes in demand patterns.

Meta-heuristic approaches are general algorithmic framework, often nature-inspired and designed to solve NP-complete optimization problems in cellular manufacturing systems and has been a growing research area for the past two decades. This paper discusses various meta-heuristic techniques such as evolutionary approach, Ant colony optimization, simulated annealing, Tabu search and other recent approaches, and their applications to the vicinity of group technology/cell formation (GT/CF) problem in cellular manufacturing. The nobility of this paper is to incorporate various prevailing issues, open problems of meta-heuristic approaches, its usage, comparison, hybridization and its scope of future research in the aforesaid area.

This paper presents a new vendor-buyer system where there are different objectives for both sides. The proposed method of this paper is different from the other previously published works since it considers different objectives for both sides. In this paper, the vendor’s emphasis is on the crashing of the setup cost, which not only helps him compete in the market but also provides better services to his customers; and the buyer’s aim is to reduce the lead time, which not only facilitates the buyer to fulfill the customers’ demand on time but also enables him to earn a good reputation in the market or vice versa. In the light of the above stated facts, an integrated vendor-buyer stochastic inventory model is also developed. The propsed model considers two cases for demand during lead time: Case (i) Complete demand information, Case (ii) Partial demand information. The proposed model jointly optimizes the buyer’s ordered quantity and lead time along with vendor’s setup cost and the number of shipments. The results are demonstrated with the help of numerical examples.

Location-routing problem (LRP) is established as a new research area in the context of location analysis. The primary concern of LRP is on locating facilities and routing of vehicles among established facilities and existing demand points. In this work, we address the capacitated LRP which arises in many practical applications within logistics and supply chain management. The objective is to minimize the overall system costs which include the fixed costs of opening depots and using vehicles at each depot site, and the variable costs associated with delivery activities. A novel heuristic is proposed which is based on variable neighborhood descent (VND) algorithm to solve the resulted problem. The computational study indicates that the proposed VND based heuristic is highly competitive with the existing solution algorithms in terms of solution quality.

In this paper, we address the problem of scheduling jobs in a no-wait flowshop problem with sequence-dependent setup times with the objective of minimizing makespan. This problem is well-known for being nondeterministic polynomial-time hard, and small contribution to the problem has been made. We propose a new constructive heuristic named GAPH based on a structural property. The effectiveness of the structural property is crucial given that it is responsible for 100% of the success rate of the total problems tested. The computational results demonstrate that the proposed approach is superior than three of the best-know methods in the literature such as the twos by Bianco, Dell’Olmo and Giordani (INFOR Journal: 37 (1), 3-19, 1999) and TRIPS heuristic adapted for sequence-dependent setup times objective by Brown, Mcgarvey and Ventura (Journal of the Operational Research Society, 55 (6), 614-621, 2004) in terms of the solution quality and that it requires less computational effort.