Michael Racer

Alternative Supply Chain Production-Sales Policies for New Product Diffusion: An Agent-Based Modeling and Simulation Approach

Applying agent-based modeling and simulation (ABMS) methodology, this paper analyzes the impact of alternative production–sales policies on the diffusion of a new generic product and the generated NPV of profit. The key features of the ABMS model, that captures the marketplace as a complex adaptive system, are: (i) supply chain capacity is constrained; (ii) consumers’ new product adoption decisions are influenced by marketing activities as well as positive and negative word-of-mouth (WOM) between consumers; (iii) interactions among consumers taking place in the context of their social network are captured at the individual level; and (iv) the new product adoption process is adaptive. Conducting over 1 million simulation experiments, we determined the “best” production–sales policies under various parameter combinations based on the NPV of profit generated over the diffusion process. The key findings are as follows: (1) on average, the build-up policy with delayed marketing is the preferred policy in the case of only positive WOM as well as the case of positive and negative WOM. This policy provides the highest expected NPV of profit on average and it also performs very smoothly with respect to changes in build-up periods. (2) It is critical to consider the significant impact of negative word-of-mouth in choosing production–sales policies. Neglecting the effect of negative word-of-mouth can lead to poor policy recommendations, incorrect conclusions concerning the impact of operational parameters on the policy choice, and suboptimal choice of build-up periods.

A hybrid heuristic for the generalized assignment problem

Abstract The objective of this study is to develop a hybrid heuristic (HH) for solving the generalized assignment problem (GAP) and conduct a computational comparison with the leading alternative heuristic approaches. HH is designed around the two best known heuristics: Heuristic GAP (HGAP) and Variable-Depth-Search Heuristic (VDSH). Previous performance characteristic studies have shown that HGAP dominates VDSH in terms of solution CPU time, while VDSH obtains solutions of 13% to 300% better quality within a ‘reasonable’ time. The main idea in this paper is to hybridize the two approaches, such that the inherent values of both heuristics are realized.

A robust heuristic for the Generalized Assignment Problem

The Generalized Assignment Problem, in the class of NP-hard problems, occurs in a wide range of applications — vehicle packing, computers, and logistics, to name only a few. Previous research has been concentrated on optimization methodologies for the GAP. Because the Generalized Assignment Problem is NP-hard, optimization methods tend to require larger computation times for large-scale problems. This paper presents a new heuristic,Variable-Depth-Search Heuristic (VDSH). We show that on the sets of large test problems, the quality of the solution found by VDSH exceeds that of the leading heuristic by an average of over twenty percent, while maintaining acceptable solution times. On difficult problem instances, VDSH provides solutions having costs 140% less than those found by the leading heuristic. A duality gap analysis of VDSH demonstrates the robustness of our heuristics.

Algorithms for mixed-model sequencing with due date restrictions

Abstract In a Mixed-Model Sequencing (MMS) environment, customer demands with respect to timeliness of delivery conflict with the production-centered goal of smooth component utilization. This research is designed to be an initial step in the process of dealing with both balance and lateness objectives. The results show that a slight decrease in on-time production leads to substantial gains in smoothing component utilization. The fundamental task in addressing the multiple-objective environment is to distinguish those problem models in a sequence, whose due dates cause the schedule to be unbalanced with respect to part usage. This paper also presents some insights into the behavior of the multi-objective MMS problem. Inherent characteristics of the MMS are revealed and explained, primarily with respect to demand patterns and lateness restrictions. Because of the problem complexity it is necessary to employ a heuristic to produce quality solutions for large problems. This study evaluates a set of heuristics, focusing on the tradeoffs between lateness measures, smooth component utilization, and computation time. This research involves the first application of local search heuristics to this problem. One heuristic, the Border Swap, proves to be an extremely effective mechanism for recognizing problem models, and solving the multi-objective problem.

A Single-Objective Recovery Phase Model

The Federal Emergency Management Agency FEMA has identified the four phases of disaster related planning as mitigation, preparation, response, and recovery. The recovery phase is characterized by activity to return life to normal or improved levels. Very little research considers the recovery phase, which encompasses restoring services and rebuilding disaster stricken areas of the highway transportation network. Existing recovery phase models deal primarily with travel times and do not focus on specific routes for reconstruction. This research proposes a plan for repair and restoration of bridges to restore a highway network that allows accessibility to key facilities in the area. This research differs from other recovery phase models in that actual routes are chosen for recovery based on given criteria. The single-objective optimization model developed in this paper is a flexible model that can be applied to a variety of natural disaster situations and other situations that involve damage to transportation components where decisions on recovery strategies must be made.

Heuristic sensitivity analysis in a combinatoric environment: An exposition and case study

Abstract Sensitivity orpost-optimality analysis investigates the effect of parametric changes on heuristic robustness and solution quality. This approach is relatively unexplored for combinatorial optimization problems, and yet is of considerable interest in analyzing performance characteristics of heuristic approaches. The purpose of this paper is to: (1) develop the semantics and rationale of parametric analysis within the combinatoric environment; (2) present as an example the design and implementation of sensitivity analysis procedures for a newly developed heuristic — theVariable-Depth-Search Heuristic (VDSH) — to solve the Generalized Assignment Problem (GAP). The concepts and methodology discussed in this paper may as well be applied to other heuristics, or in developing a heuristic sensitivity analysis procedure for a large-scale optimization method.

AEGIS--attribute experimentation guiding improvement searches

The development of a quality heuristic is a challenging undertaking. While some work has been done to link solution quality and problem inputs, relatively little has been done to methodically address that linkage. This research, a meta-heuristic framework called AEGIS, is an initial attempt to integrate problem characteristics into the solution process itself. As the name implies, the goal is to provide guidance to the solution process, through a well-defined learning process. By utilizing statistical techniques and concepts, this study will demonstrate how such knowledge may be used to drive the function of the algorithm.